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Research ArticleAdaptive Power Allocation and Splitting withImperfect Channel Estimation in Energy HarvestingBased Self-Organizing Networks
Kisong Lee1 and JeongGil Ko2
1Department of Information and Telecommunication Engineering Kunsan National University Gunsan 573-701 Republic of Korea2Department of Software and Computer Engineering Ajou University Suwon 16499 Republic of Korea
Correspondence should be addressed to JeongGil Ko jgkoajouackr
Received 16 June 2016 Accepted 12 July 2016
Academic Editor Hyun-Ho Choi
Copyright copy 2016 K Lee and J Ko This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
Asminiature-sized embedded computing platforms are ubiquitously deployed to our everyday environments the issue ofmanagingtheir power usage becomes important especially when they are used in energy harvesting based self-organizing networks One wayto provide these devices with continuous power is to utilize RF-based energy transfer Previous research in RF-based informationand energy transfer builds up on the assumption that perfect channel estimation is easily achievable However as our preliminaryexperiments and many previous literature in wireless network systems show making perfect estimations of the wireless channelis extremely challenging due to their quality fluctuations To better reflect reality in this work we introduce an adaptive powerallocation and splitting (APAS) scheme which takes imperfect channel estimations into consideration Our evaluation results showthat the proposedAPAS scheme achieves near-optimal performances for transferring energy and data over a single RF transmission
1 Introduction
As we slowly enter the era of the Internet of Things (IoT) wewill start to experience various embedded computing systemsbeing introduced to our everyday lives In particular it isimportant tomaintain a long sensing and operational lifetimein self-organizing networks (SONs) Given that SONs are typ-ically meant to tackle applications with little or no humanintervention their operational durations can determine theoverall systemrsquos self-conguration self-optimization and self-healing performance [1 2] For this a decade of researchin the wireless embedded systems domain has introduceda number of schemes for optimizing energy efficiency onresource limited computing platforms [3ndash5]
In addition to these schemes that focus on conservingthe power usage another direction of research is to gatherenergy This energy gathering can take two different formswhere in the first an explicit hardware module is attachedfor harvesting energy from external sources (eg sunlightwind and vibration) [6 7] and in the second the powergenerated from radio frequency (RF) signals can be used to
transfer energy [8ndash10] Given that the latter is only minimallyaffected by external environmental factors we believe thatit is an interesting research direction to explore Since theydo not require a large-sized energy harvesting unit applyingenergy transfer techniques to data communications caneffectively reduce the size of the the hardware used in low-power wireless networking Based on these benefits in thiswork we study the possibilities of information and energytransfer using RF signals for powering low-power embeddedcomputing platforms
Given its attractiveness a number of previous works havetried tackling interesting issues in various aspects of thisresearch field For example in [11 12] the authors investigatedthe theoretical performance limits for simultaneous wirelessinformation and power transfer (SWIPT) The works in [1314] proposed time switching and dynamic power splittingfor enabling efficient SWIPT Furthermore Nasir et al tookthese findings to a networking perspective and introducedthe relaying protocol in [15] Here the authors proposed anetwork where an energy constrained relay node harvestsenergy from the RF signals of a source node and uses this
Hindawi Publishing CorporationMobile Information SystemsVolume 2016 Article ID 8243090 7 pageshttpdxdoiorg10115520168243090
2 Mobile Information Systems
harvested energy for relaying information to the next hopIn [16] Shen et al proposed transmitter designs for sum-rate maximization with energy harvesting constraints on amultiple-input single-output (MISO) interference channel Inaddition an energy efficient resource allocation algorithmfor SWIPT was investigated [17] and multiuser schedulingschemes for improving user fairness were studied in wirelessnetworks with energy harvesting [18]
Despite these efforts from the research community inthis work we identify one important assumption that most ofthese previous works made Namely these works commonlytook the assumption that wireless channels would be continu-ously stable and the communication quality would be perfectin all cases However research from the wireless networkingsystems community showed that this observation is far frombeing true These RF signals can be severely impacted byexternal factors such as human movement environmentalchanges and even the time of day Therefore we believethat taking such real-world channel factors into considerationas we model the information and energy transfer behaviorsof RF signals is important Specifically in this work we tryto understand the performance of wireless links in realityand present a novel information and energy transfer modelthat reflects the nonperfect nature and inevitably imperfectchannel quality estimations of real-world wireless environ-ments In addition we propose an adaptive power alloca-tion and splitting (APAS) scheme with considerations forimperfect wireless channel estimations in energy harvestingbased SONs Our evaluations show that APAS outperformspreexisting schemes and performs close to the optimal
We summarize the contributions of this work threefold
(i) We present empirical results on the RF characteristicsin indoor environments to showcase the dynamicsof real-world wireless channels Our findings leadto a conclusion that perfect channel estimations aredifficult to achieve due to various unexpected externalfactors
(ii) We introduce a novel signal transfermodel with chan-nel estimation errors in consideration for analyzingthe simultaneous transfer of information and energyusing RF signals Our model reflects realistic channelenvironments and therefore provides accurate per-formance bounds
(iii) Using convex optimization techniques we proposea resource allocation strategy that finds suboptimaltransmission power allocation and power splittingratios iteratively for a given training intervalThroughsimulations we show that the proposed schemeprovides near-optimal performance as well as con-siderable performance improvement compared toconventional schemes
The rest of this paper is structured as follows We startoff the paper with an empirical study of real-world channelenvironments in Section 2Thefindings fromour preliminarystudies become the basis of our system model presentedin Section 3 and the proposed adaptive power allocationand splitting (APAS) scheme in Section 4 Using Section 5
minus90minus85minus80minus75minus70minus65
0 100 200 300 400 500 600 700 800 900
RSSI
(dBm
)
Sample number
Stable linkDynamic link
Figure 1 Empirical RSSI traces for stable and dynamic indoor envi-ronments
we evaluate the performance of our proposed scheme andsummarize our work in Section 6
2 Real Channel Environments
Beforewe explain the details of our proposed schemewe startby presenting empirical study results that show how dynamica practical wireless channel environment can be Specificallywe configure a transmitter node and a receiver node to bepositioned sim5 meters apart The transmitter node sends peri-odic packets with a packet transmission interval of 250msecat 0 dBm and we test the wireless link in two different sce-narios one with no surrounding humanmovement activities(eg night-time) and another with continuous movement(eg day-time) between the two nodes Since nodes wereinstalled in a hallway environment with consistent humanactivities the dynamic links in Figure 1 are sure to have highchannel quality variability In this experimental setting wepresent the received signal strength indicator (RSSI) valueobserved at the receiver for incoming packets Specificallyin Figure 1 we plot the RSSI over time for both the stableand dynamic links Notice from Figure 1 that with naturalhuman-generated link dynamics (eg typical human move-ment behaviors) the signal strengths of incoming packetsseverely fluctuate
These empirical results though tested for a single envi-ronment provide experimental evidence on the dynamics ofreal wireless channels Using Figure 1 we try to show thatschemes designed for real-world should not assume a stablewireless channel This leads to an observation that perfectchannel estimation can be difficult to achieve thus channelestimation analysis for information and energy transfercannot be perfect in most cases The remainder of this workbuilds up on such empirical findings to propose a modelfor analyzing the RF-based information and energy transferunder imperfect channel estimations
3 System Model
Based on our observations of real-world channel environ-ments we take into consideration imperfect channel esti-mations in an orthogonal frequency division multiplexing(OFDM) based wireless point-to-point link consisting of asingle transmitter (Tx) and receiver (Rx) pair as shown in
Mobile Information Systems 3
Figure 2 Tx and Rx nodes each are equipped with a single-antenna and the frequency band is divided into independent119873 subchannels We assume that the subchannels followa discrete time block-fading model in which the channelstate is invariant for a transmission block interval 119879 [1920] In addition each real subchannel ℎ
119899 is assumed to
be an independent identically distributed complex randomvariable such as ℎ
119899sim CN(0 1205902
ℎ) for 1 le 119899 le 119873 In a practical
system the channel estimation process obtains and exploitsthe channel state information (CSI) The transmission block119879 is divided into two periods where in the first we define119879
120591for channel training and in the second we define the
duration 119879 minus 119879120591for data transmission The minimum mean
square error (MMSE) criterion is assumed to be used for esti-mating the channel status and each estimated subchannel ℎ
119899
follows a Gaussian distribution such as ℎ119899sim CN(0 1205902
ℎ
) for1 le 119899 le 119873
Under such settings we note that the RF signals (at theRx node) can be used in two ways either for informationdecoding (ID) or for energy harvesting (EH) while thesemodes cannot take place simultaneously Here the receivedsignal is split in two portions in each subchannel the portionof 120588119899among 119879 minus 119879
120591is reserved for ID while 1 minus 120588
119899is for EH
before performing active analog or digital signal processing(We assume that the Rx node is equipped with a perfectpassive power splitting unit [17]) In addition as RF signalsare split in two streams two types of noise should be consid-ered antenna noise 119899
119860 and signal processing noise 119899
119878 119899119860is
generated at the Rx antenna while 119899119878is introduced when the
received signal is divided into ID and EH Nevertheless weneglect 119899
119860since it is much smaller than 119899
119878[14] Furthermore
we assume 119899119878
sim CN(0 1) Then the achievable sumrate using the estimated subchannels can be represented by[20]
119877 =
119873
sum
119899=1
119903
119899
=
119873
sum
119899=1
119879 minus 119879
120591
119879
log2(1 +
(1 + 119901
120591119879
120591) 120588
119899119901
119899
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
1 + 120588
119899119901
119899+ 119901
120591119879
120591
)
(1)
where 119901120591and 119901
119899denote the power for estimating channel
conditions and power allocated in subchannel 119899 for datatransmission respectively When performing EH at the Rxthe harvested energy is represented as
119873
sum
119899=1
119892
119899=
119873
sum
119899=1
119879 minus 119879
120591
119879
120578
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
(1 minus 120588
119899) 119901
119899 (2)
where 120578 is the energy conversion efficiency achieved by con-verting the received RF signals into harvestable energy at theRx We also assume that sum119873
119899=1120578|ℎ
119899|
2
le 1 according to thelaws of thermodynamics
We now look into the training interval the power alloca-tion and the power splitting ratio required to maximize the
sum rate while guaranteeing the minimum harvested energy119892min Consider
max119879120591
119873
sum
119899=1
119903
119899
st C1119873
sum
119899=1
119892
119899ge 119892min
C2119873
sum
119899=1
119901
119899le 119901max
C3 119901119899ge 0 forall119899
C4 0 le 120588119899le 1 forall119899
C5 0 le 119879120591le 119879
(3)
Here 119901 = 119901
1 119901
119873 and = 120588
1 120588
119873 Furthermore
the five constraints can be explained as follows ConstraintC1 ensures that the amount of energy harvested should belarger than the minimum amount of required energy 119892minConstraint C2 limits the available transmission power of theTx to 119901max Constraint C3 is a nonnegative constraint on thetransmission power Constraints C4 and C5 are the ranges of120588
119899and 119879
120591 respectively
4 Adaptive Power Allocation and Splitting
We now propose an adaptive power allocation and splitting(APAS) algorithm by solving the optimization problem in(3) We note that it is difficult to find a closed form solutionfor 119879lowast120591from optimization techniques given that the objective
function of (3) is not in concave form with respect to119879
120591 However considering the fact that 119879
120591lies within the
interval (0 119879) 119879lowast120591can be derived using a one-dimensional
exhaustive search For example 119879lowast120591can be found from the
probability density function (PDF) of channel distributionIf 119879lowast120591is determined at once it can be used for estimating
all unknown channels that will be generated Furthermoreusing the concavity of
119901 and we can find their suboptimalvalues iteratively which reflect channel estimation errors (Ina biconvex problem where the problem in (3) is convex withrespect to
119901 for a fixed or vice versa the convergence ofa partial optimum solution can be guaranteed by using theblock coordinate descent (BCD) algorithm [21])
For a given 119879120591 Tx estimates CSI on subchannels and
determines the allocated power and the power splitting ratioThe Lagrangian function of (3) can be expressed by
Λ (119901 120582 120583) =
119873
sum
119899=1
119903
119899+ 120582(
119873
sum
119899=1
119892
119899minus 119892min)
+ 120583(119901max minus119873
sum
119899=1
119901
119899)
(4)
Here 120582 and 120583 are nonnegative Lagrangian multipliers whichcorrespond to constraints C1 and C2 respectively To find asolution to this we decouple the original problem into par-allel 119873 subproblems for each subchannel By discarding the
4 Mobile Information Systems
Block interval T
Channel estimation
Tx Rx
Data transmission
Power
Energyharvester
Informationdetector
splitter
T120591
120588nnA
T minus T120591
1 minus 120588n
ℎn rarr ℎn
nS
Figure 2 System model for wireless information and energy transfer
constant terms the Lagrangian function (4) for a particularsubchannel 119899 can be represented as
Λ (119901
119899 120588
119899 120582 120583) = 119903
119899+ 120582119892
119899minus 120583119901
119899 (5)
By taking the derivative of (5) with respect to 119901119899 we can
obtain a power allocation strategy as in the following
119901
lowast
119899=
[
[
[
119867
119890
1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
sdot
radic
4120588
119899
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
) + (ln 2)1198672119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
4
120577
119899
4 (ln 2) 1205882119899120577
119899
minus
119867
119890(2 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
2120588
119899(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
]
]
]
+
(6)
Here [119909]+ = max (0 119909) 119867119890= 1 + 119901
120591119879
120591 and 120577
119899= (119879(119879 minus
119879
120591))120583 minus 120582120578|
ℎ
119899|
2
(1 minus 120588
119899) For the obtained 119901lowast
119899from (6) we
also take the derivative of (5) with respect to 120588119899 to obtain the
power splitting strategy as in the following
120588
lowast
119899=
[
[
[
[
119867
119890
1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
sdotradic
4
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
) + (ln 2)1198672119890120582120578
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
6
4 (ln 2) 1199012119899120582120578
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
minus
119867
119890(2 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
2119901
119899(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
]
]
]
]
1
0
(7)
Here [119909]10= min (max (0 119909) 1)
Then 119901lowast119899and 120588lowast
119899can be interpreted in terms of chan-
nel estimation channel condition and harvested energy asfollows We especially note that an increment in 119879
120591ensures
the exact estimation of channel conditions but decreases thedata transmission time An extremely large 119879
120591does not allow
for a dedicated data transmission time as a result 119901lowast119899and
120588
lowast
119899become zero In addition 119901lowast
119899and 120588lowast
119899are proportional to
|
ℎ
119899|
2 so they show large values on subchannels with goodchannel conditions 119901lowast
119899is proportional to 120582120578|ℎ
119899|
2 which isrelated to the amount of harvested energy but 120588lowast
119899is inversely
proportional to 120582120578|ℎ119899|
2 An amount of119901lowast119899increases on a good
subchannel where large amounts of energy harvesting arepossible while 120588lowast
119899decreases on that subchannel to meet the
constraint C1 for harvested energy tightly In short 119901lowast119899and
120588
lowast
119899are adjusted reciprocally to the maximize sum rate while
guaranteeing 119892minBased on the obtained
997888rarr
119901
lowast and997888rarr
120588
lowast Lagrangian multiplierscan be updated by a well-known bisection algorithm or agradient algorithm We detail the overall procedure of theproposed algorithm in Algorithm 1
5 Simulation Results and Discussion
For evaluations we configure a simulation environment asin the following Specifically we set 119873 = 32 119879 = 1000120578 = 09 119901
120591= 119901max = 43 dBm and 119892min = 20 dBm We
assume that subchannel ℎ119899experiences Rayleigh fading so ℎ
119899
is generated as a random variable distributed exponentiallywith 1205902
ℎ= 10
minus4 In addition it is independent from othersubchannels ℎ
119895for 119895 = 119899 We compare the performance of
APAS with three algorithms OPAS EPAS and E2PAS
(i) Optimal power allocation and splitting (OPAS) withperfect CSI at the Tx (CSIT) 119901 and are determinedoptimally
(ii) Equal Power Allocation and Adaptive Power Splitting(EPAS) power is allocated equally to all subchannelsand is determined adaptively to meet 119892min withrespect to (7)
Mobile Information Systems 5
0246
OPAS
0246
EHID
0246
EPAS
APAS
5 30252015100246
Subchannel index
5 3025201510Subchannel index
5 3025201510Subchannel index
5 3025201510Subchannel index
E2PAS
Ratio
of
pmax
()
Ratio
of
pmax
()
Ratio
of
pmax
()
Ratio
of
pmax
()
Figure 3 Power allocation and splitting of OPAS APAS EPAS andE2PAS
(1) Initialize 119901 and Lagrangian multipliers
(2) for 119879120591= 1 119879
(3) Estimate |ℎ119899|
2 for forall119899 based on the PDF of channel(4) Evaluate 119877(5) end for(6) Return 119879lowast
120591= max
119879120591
119877
(7) repeat(8) Find
997888rarr
119901
lowast according to (6)(9) Find
997888rarr
120588
lowast according to (7)(10) Update Lagrangian multipliers 120582 and 120583(11) until
997888rarr
119901
lowast and997888rarr
120588
lowast converge
Algorithm 1 Adaptive power allocation and splitting
(iii) Equal Power Allocation and Equal Power Splitting(E2PAS) power is allocated equally to all subchannelsand is determined equally for all subchannels tomeet 119892min
Figure 3 shows the power allocation and splitting ofOPAS APAS EPAS and E2PAS respectively In OPAS andAPAS a large amount of power is allocated to the subchannelwith the highest channel gain and a portion of power issplit to harvest energy on that subchannel It is best to usethe power allocated to the best subchannel for guaranteeing119892min since energy can be harvested with higher efficiency
00
02
04
06
08
10
Cor
relat
ion
coeffi
cien
t
APASEPASE2PAS
gmin
=10
dBm
gmin
=13
dBm
gmin
=16
dBm
gmin
=19
dBm
Figure 4 Correlation coefficient of APAS EPAS and E2PAS
The differences of resource allocation between OPAS andAPAS come from the errors in channel estimation buttheir resource allocation forms show similar tendency Thisindicates that APAS can achieve a performance close to theoptimal bound On the other hand in EPAS and E2PASpower is allocated equally on all subchannels and energyis harvested on several subchannels therefore a subset ofthe power can be used inefficiently In particular despiteits implementation simplicity performance can be degradedseverely in E2PAS due to the fact that resource allocation isperformed regardless of the channel conditions
Figure 4 shows the correlation coefficient of APAS EPASand E2PAS with varying 119892min This result targets for showinghow the power allocation and splitting of each scheme issimilar to OPAS In OPAS more power is allocated to thebest subchannel to ensure 119892min with a high efficiency as 119892minincreases As a result we can notice here that the correlationcoefficients of EPAS and E2PAS decrease seriously On theother hand the correlation coefficient of APAS stays highsim09 evenwith increasing119892minTherefore this result suggeststhat APAS can adapt the power allocation and splitting strat-egy to a level similar to the optimal solution under variousconditions
Figure 5 plots the data rate 119877 versus the training interval119879
120591 which shows the effects of 119879
120591on 119877 As 119879
120591increases it
is possible to estimate the channel conditions more accu-rately As a result 119877 increases gradually to a peak pointHowever additional increase in 119879
120591beyond its optimal value
causes reduction in the dedicated transmission time therebydecreasing 119877 This suggests that there is an optimal value of119879
120591for maximizing 119877 from the tradeoff relationship between
the accuracy of channel estimations and the duration of datatransmission timeThe optimal training interval119879lowast
120591increases
with a large 119892min which indicates that an exact channel
6 Mobile Information Systems
0 1008060402018
19
20
21
22
23
Dat
a rat
e R
(bits
sH
z)
OPAS (gmin = 17 dBm)APAS (gmin = 17 dBm)
OPAS (gmin = 20 dBm)APAS (gmin = 20 dBm)
Training interval T120591
Tlowast120591 = 20 Rlowast = 205
Tlowast120591 = 10 Rlowast = 218
Figure 5 Data rate 119877 versus training interval 119879120591
10 2018161412
14
16
18
20
22
24
OPASAPAS
EPAS
Dat
a rat
e R
(bits
sH
z)
E2PAS
Harvested energy gmin (dBm)
Figure 6 Data rate 119877 versus harvested energy 119892min when 119879120591 = 20
estimation is desired to satisfy a large requirement for the har-vested energy There is no significant difference in particularless than 10 between the maximum 119877 achieved at 119879lowast
120591and
the optimal bound of 119877Figure 6 shows the data rate119877 versus the harvested energy
119892min when 119879120591= 20 As 119892min increases a large portion of
power should be used for harvesting energy In consequence119877 decreases gradually for all algorithms APAS has the gainof adaptive power allocation compared with EPAS while ithas the gain of adaptive power allocation and splitting whencompared to E2PAS Therefore we can confirm the gain ofeach adaptive strategy by comparing APAS with EPAS andE2PAS respectively In EPAS and E2PAS the rates at which119877 decreases with increasing 119892min are noticeably steeper thanthose of OPAS and APAS This is mainly due to the fact thatin EPAS and E2PAS the power cannot be used efficiently
Therefore APAS outperforms EPAS and E2PAS significantlyat larger 119892min values For example APAS outperforms EPASand E2PAS in terms of 119877 by 20 and 40 at 119892min = 20 dBmrespectively On the other hand the performance differencebetween OPAS and APAS remains relatively constant despiteincreasing 119892min
6 Conclusion
RF-based information and energy transferring techniqueshold the potential to dramatically change the design of wire-less systems and their networking architecturesNeverthelessthe research community is still in the early stages of validatingtheir effectiveness In this work we target to tackle one of thestrongest assumptions that most of the works in informationand energy transfer made which is the assumption thatperfect channel estimation is possible We show throughan empirical study that the variability of wireless channelsmakes perfect estimations of the wireless environment closeto impossible For this we propose APAS which takes intoconsideration imperfect channel estimation results for eval-uating the effectiveness of information and energy transferon wireless devices for energy harvesting based SONs InAPAS the power allocation and splitting ratio is determinedadaptively with considerations for the estimated channelquality In addition our results indicate that APAS achievesnear-optimal performances under various conditions Wehope that this work can act as a catalyst in enabling futureresearch that tries to adopt RF-based information and energytransfer in realistic channel environments
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
This research was supported by Basic Science Research Pro-gram through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT amp FuturePlanning (2015R1C1A1A01051747)
References
[1] H Hu J Zhang X Zheng Y Yang and P Wu ldquoSelf-configuration and self-optimization for LTE networksrdquo IEEECommunications Magazine vol 48 no 2 pp 94ndash100 2010
[2] 3GPP ldquoSelf-organizing networks (SON) concepts and require-mentsrdquo 3GPP TS 32500 2008 V800
[3] G Y Li Z Xu C Xiong et al ldquoEnergy-efficient wireless com-munications tutorial survey and open issuesrdquo IEEE WirelessCommunications vol 18 no 6 pp 28ndash35 2011
[4] C Han T Harrold S Armour et al ldquoGreen radio radiotechniques to enable energy-efficient wireless networksrdquo IEEECommunications Magazine vol 49 no 6 pp 46ndash54 2011
[5] H Bogucka and A Conti ldquoDegrees of freedom for energysavings in practical adaptive wireless systemsrdquo IEEE Commu-nications Magazine vol 49 no 6 pp 38ndash45 2011
[6] HKulah andKNajafi ldquoEnergy scavenging from low-frequencyvibrations by using frequency up-conversion for wireless sensor
Mobile Information Systems 7
applicationsrdquo IEEE Sensors Journal vol 8 no 3 pp 261ndash2682008
[7] S Sudevalayam and P Kulkarni ldquoEnergy harvesting sensornodes survey and implicationsrdquo IEEE Communications Surveysand Tutorials vol 13 no 3 pp 443ndash461 2011
[8] T Le K Mayaram and T Fiez ldquoEfficient far-field radiofrequency energy harvesting for passively powered sensornetworksrdquo IEEE Journal of Solid-State Circuits vol 43 no 5 pp1287ndash1302 2008
[9] R J M Vullers R van Schaijk I Doms C Van Hoof and RMertens ldquoMicropower energy harvestingrdquo Solid-State Electron-ics vol 53 no 7 pp 684ndash693 2009
[10] M Pinuela P D Mitcheson and S Lucyszyn ldquoAmbient RFenergy harvesting in urban and semi-urban environmentsrdquoIEEE Transactions onMicrowaveTheory and Techniques vol 61no 7 pp 2715ndash2726 2013
[11] L R Varshney ldquoTransporting information and energy simul-taneouslyrdquo in Proceedings of the IEEE International Symposiumon Information Theory (ISIT rsquo08) pp 1612ndash1616 IEEE TorontoCanada July 2008
[12] P Grover and A Sahai ldquoShannonmeets tesla wireless informa-tion andpower transferrdquo inProceedings of the IEEE InternationalSymposium on Information Theory (ISIT rsquo10) pp 2363ndash2367IEEE Austin Tex USA June 2010
[13] L Liu R Zhang and K-C Chua ldquoWireless information trans-fer with opportunistic energy harvestingrdquo IEEE Transactions onWireless Communications vol 12 no 1 pp 288ndash300 2013
[14] L Liu R Zhang and K-C Chua ldquoWireless information andpower transfer a dynamic power splitting approachrdquo IEEETransactions on Communications vol 61 no 9 pp 3990ndash40012013
[15] A A Nasir X Zhou S Durrani and R A Kennedy ldquoRelayingprotocols for wireless energy harvesting and information pro-cessingrdquo IEEETransactions onWireless Communications vol 12no 7 pp 3622ndash3636 2013
[16] C Shen W-C Li and T-H Chang ldquoWireless information andenergy transfer in multi-antenna interference channelrdquo IEEETransactions on Signal Processing vol 62 no 23 pp 6249ndash62642014
[17] D W K Ng E S Lo and R Schober ldquoWireless informationand power transfer energy efficiency optimization in OFDMAsystemsrdquo IEEE Transactions on Wireless Communications vol12 no 12 pp 6352ndash6370 2013
[18] R Morsi D S Michalopoulos and R Schober ldquoMultiuserscheduling schemes for simultaneous wireless information andpower transfer over fading channelsrdquo IEEE Transactions onWireless Communications vol 14 no 4 pp 1967ndash1982 2015
[19] R A Berry and R G Gallager ldquoCommunication over fadingchannels with delay constraintsrdquo IEEE Transactions on Informa-tion Theory vol 48 no 5 pp 1135ndash1149 2002
[20] B Hassibi and B M Hochwald ldquoHow much training is neededin multiple-antenna wireless linksrdquo IEEE Transactions onInformation Theory vol 49 no 4 pp 951ndash963 2003
[21] J Gorski F Pfeuffer and K Klamroth ldquoBiconvex sets andoptimization with biconvex functions a survey and extensionsrdquoMathematical Methods of Operations Research vol 66 no 3 pp373ndash407 2007
Submit your manuscripts athttpwwwhindawicom
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2 Mobile Information Systems
harvested energy for relaying information to the next hopIn [16] Shen et al proposed transmitter designs for sum-rate maximization with energy harvesting constraints on amultiple-input single-output (MISO) interference channel Inaddition an energy efficient resource allocation algorithmfor SWIPT was investigated [17] and multiuser schedulingschemes for improving user fairness were studied in wirelessnetworks with energy harvesting [18]
Despite these efforts from the research community inthis work we identify one important assumption that most ofthese previous works made Namely these works commonlytook the assumption that wireless channels would be continu-ously stable and the communication quality would be perfectin all cases However research from the wireless networkingsystems community showed that this observation is far frombeing true These RF signals can be severely impacted byexternal factors such as human movement environmentalchanges and even the time of day Therefore we believethat taking such real-world channel factors into considerationas we model the information and energy transfer behaviorsof RF signals is important Specifically in this work we tryto understand the performance of wireless links in realityand present a novel information and energy transfer modelthat reflects the nonperfect nature and inevitably imperfectchannel quality estimations of real-world wireless environ-ments In addition we propose an adaptive power alloca-tion and splitting (APAS) scheme with considerations forimperfect wireless channel estimations in energy harvestingbased SONs Our evaluations show that APAS outperformspreexisting schemes and performs close to the optimal
We summarize the contributions of this work threefold
(i) We present empirical results on the RF characteristicsin indoor environments to showcase the dynamicsof real-world wireless channels Our findings leadto a conclusion that perfect channel estimations aredifficult to achieve due to various unexpected externalfactors
(ii) We introduce a novel signal transfermodel with chan-nel estimation errors in consideration for analyzingthe simultaneous transfer of information and energyusing RF signals Our model reflects realistic channelenvironments and therefore provides accurate per-formance bounds
(iii) Using convex optimization techniques we proposea resource allocation strategy that finds suboptimaltransmission power allocation and power splittingratios iteratively for a given training intervalThroughsimulations we show that the proposed schemeprovides near-optimal performance as well as con-siderable performance improvement compared toconventional schemes
The rest of this paper is structured as follows We startoff the paper with an empirical study of real-world channelenvironments in Section 2Thefindings fromour preliminarystudies become the basis of our system model presentedin Section 3 and the proposed adaptive power allocationand splitting (APAS) scheme in Section 4 Using Section 5
minus90minus85minus80minus75minus70minus65
0 100 200 300 400 500 600 700 800 900
RSSI
(dBm
)
Sample number
Stable linkDynamic link
Figure 1 Empirical RSSI traces for stable and dynamic indoor envi-ronments
we evaluate the performance of our proposed scheme andsummarize our work in Section 6
2 Real Channel Environments
Beforewe explain the details of our proposed schemewe startby presenting empirical study results that show how dynamica practical wireless channel environment can be Specificallywe configure a transmitter node and a receiver node to bepositioned sim5 meters apart The transmitter node sends peri-odic packets with a packet transmission interval of 250msecat 0 dBm and we test the wireless link in two different sce-narios one with no surrounding humanmovement activities(eg night-time) and another with continuous movement(eg day-time) between the two nodes Since nodes wereinstalled in a hallway environment with consistent humanactivities the dynamic links in Figure 1 are sure to have highchannel quality variability In this experimental setting wepresent the received signal strength indicator (RSSI) valueobserved at the receiver for incoming packets Specificallyin Figure 1 we plot the RSSI over time for both the stableand dynamic links Notice from Figure 1 that with naturalhuman-generated link dynamics (eg typical human move-ment behaviors) the signal strengths of incoming packetsseverely fluctuate
These empirical results though tested for a single envi-ronment provide experimental evidence on the dynamics ofreal wireless channels Using Figure 1 we try to show thatschemes designed for real-world should not assume a stablewireless channel This leads to an observation that perfectchannel estimation can be difficult to achieve thus channelestimation analysis for information and energy transfercannot be perfect in most cases The remainder of this workbuilds up on such empirical findings to propose a modelfor analyzing the RF-based information and energy transferunder imperfect channel estimations
3 System Model
Based on our observations of real-world channel environ-ments we take into consideration imperfect channel esti-mations in an orthogonal frequency division multiplexing(OFDM) based wireless point-to-point link consisting of asingle transmitter (Tx) and receiver (Rx) pair as shown in
Mobile Information Systems 3
Figure 2 Tx and Rx nodes each are equipped with a single-antenna and the frequency band is divided into independent119873 subchannels We assume that the subchannels followa discrete time block-fading model in which the channelstate is invariant for a transmission block interval 119879 [1920] In addition each real subchannel ℎ
119899 is assumed to
be an independent identically distributed complex randomvariable such as ℎ
119899sim CN(0 1205902
ℎ) for 1 le 119899 le 119873 In a practical
system the channel estimation process obtains and exploitsthe channel state information (CSI) The transmission block119879 is divided into two periods where in the first we define119879
120591for channel training and in the second we define the
duration 119879 minus 119879120591for data transmission The minimum mean
square error (MMSE) criterion is assumed to be used for esti-mating the channel status and each estimated subchannel ℎ
119899
follows a Gaussian distribution such as ℎ119899sim CN(0 1205902
ℎ
) for1 le 119899 le 119873
Under such settings we note that the RF signals (at theRx node) can be used in two ways either for informationdecoding (ID) or for energy harvesting (EH) while thesemodes cannot take place simultaneously Here the receivedsignal is split in two portions in each subchannel the portionof 120588119899among 119879 minus 119879
120591is reserved for ID while 1 minus 120588
119899is for EH
before performing active analog or digital signal processing(We assume that the Rx node is equipped with a perfectpassive power splitting unit [17]) In addition as RF signalsare split in two streams two types of noise should be consid-ered antenna noise 119899
119860 and signal processing noise 119899
119878 119899119860is
generated at the Rx antenna while 119899119878is introduced when the
received signal is divided into ID and EH Nevertheless weneglect 119899
119860since it is much smaller than 119899
119878[14] Furthermore
we assume 119899119878
sim CN(0 1) Then the achievable sumrate using the estimated subchannels can be represented by[20]
119877 =
119873
sum
119899=1
119903
119899
=
119873
sum
119899=1
119879 minus 119879
120591
119879
log2(1 +
(1 + 119901
120591119879
120591) 120588
119899119901
119899
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
1 + 120588
119899119901
119899+ 119901
120591119879
120591
)
(1)
where 119901120591and 119901
119899denote the power for estimating channel
conditions and power allocated in subchannel 119899 for datatransmission respectively When performing EH at the Rxthe harvested energy is represented as
119873
sum
119899=1
119892
119899=
119873
sum
119899=1
119879 minus 119879
120591
119879
120578
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
(1 minus 120588
119899) 119901
119899 (2)
where 120578 is the energy conversion efficiency achieved by con-verting the received RF signals into harvestable energy at theRx We also assume that sum119873
119899=1120578|ℎ
119899|
2
le 1 according to thelaws of thermodynamics
We now look into the training interval the power alloca-tion and the power splitting ratio required to maximize the
sum rate while guaranteeing the minimum harvested energy119892min Consider
max119879120591
119873
sum
119899=1
119903
119899
st C1119873
sum
119899=1
119892
119899ge 119892min
C2119873
sum
119899=1
119901
119899le 119901max
C3 119901119899ge 0 forall119899
C4 0 le 120588119899le 1 forall119899
C5 0 le 119879120591le 119879
(3)
Here 119901 = 119901
1 119901
119873 and = 120588
1 120588
119873 Furthermore
the five constraints can be explained as follows ConstraintC1 ensures that the amount of energy harvested should belarger than the minimum amount of required energy 119892minConstraint C2 limits the available transmission power of theTx to 119901max Constraint C3 is a nonnegative constraint on thetransmission power Constraints C4 and C5 are the ranges of120588
119899and 119879
120591 respectively
4 Adaptive Power Allocation and Splitting
We now propose an adaptive power allocation and splitting(APAS) algorithm by solving the optimization problem in(3) We note that it is difficult to find a closed form solutionfor 119879lowast120591from optimization techniques given that the objective
function of (3) is not in concave form with respect to119879
120591 However considering the fact that 119879
120591lies within the
interval (0 119879) 119879lowast120591can be derived using a one-dimensional
exhaustive search For example 119879lowast120591can be found from the
probability density function (PDF) of channel distributionIf 119879lowast120591is determined at once it can be used for estimating
all unknown channels that will be generated Furthermoreusing the concavity of
119901 and we can find their suboptimalvalues iteratively which reflect channel estimation errors (Ina biconvex problem where the problem in (3) is convex withrespect to
119901 for a fixed or vice versa the convergence ofa partial optimum solution can be guaranteed by using theblock coordinate descent (BCD) algorithm [21])
For a given 119879120591 Tx estimates CSI on subchannels and
determines the allocated power and the power splitting ratioThe Lagrangian function of (3) can be expressed by
Λ (119901 120582 120583) =
119873
sum
119899=1
119903
119899+ 120582(
119873
sum
119899=1
119892
119899minus 119892min)
+ 120583(119901max minus119873
sum
119899=1
119901
119899)
(4)
Here 120582 and 120583 are nonnegative Lagrangian multipliers whichcorrespond to constraints C1 and C2 respectively To find asolution to this we decouple the original problem into par-allel 119873 subproblems for each subchannel By discarding the
4 Mobile Information Systems
Block interval T
Channel estimation
Tx Rx
Data transmission
Power
Energyharvester
Informationdetector
splitter
T120591
120588nnA
T minus T120591
1 minus 120588n
ℎn rarr ℎn
nS
Figure 2 System model for wireless information and energy transfer
constant terms the Lagrangian function (4) for a particularsubchannel 119899 can be represented as
Λ (119901
119899 120588
119899 120582 120583) = 119903
119899+ 120582119892
119899minus 120583119901
119899 (5)
By taking the derivative of (5) with respect to 119901119899 we can
obtain a power allocation strategy as in the following
119901
lowast
119899=
[
[
[
119867
119890
1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
sdot
radic
4120588
119899
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
) + (ln 2)1198672119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
4
120577
119899
4 (ln 2) 1205882119899120577
119899
minus
119867
119890(2 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
2120588
119899(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
]
]
]
+
(6)
Here [119909]+ = max (0 119909) 119867119890= 1 + 119901
120591119879
120591 and 120577
119899= (119879(119879 minus
119879
120591))120583 minus 120582120578|
ℎ
119899|
2
(1 minus 120588
119899) For the obtained 119901lowast
119899from (6) we
also take the derivative of (5) with respect to 120588119899 to obtain the
power splitting strategy as in the following
120588
lowast
119899=
[
[
[
[
119867
119890
1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
sdotradic
4
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
) + (ln 2)1198672119890120582120578
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
6
4 (ln 2) 1199012119899120582120578
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
minus
119867
119890(2 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
2119901
119899(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
]
]
]
]
1
0
(7)
Here [119909]10= min (max (0 119909) 1)
Then 119901lowast119899and 120588lowast
119899can be interpreted in terms of chan-
nel estimation channel condition and harvested energy asfollows We especially note that an increment in 119879
120591ensures
the exact estimation of channel conditions but decreases thedata transmission time An extremely large 119879
120591does not allow
for a dedicated data transmission time as a result 119901lowast119899and
120588
lowast
119899become zero In addition 119901lowast
119899and 120588lowast
119899are proportional to
|
ℎ
119899|
2 so they show large values on subchannels with goodchannel conditions 119901lowast
119899is proportional to 120582120578|ℎ
119899|
2 which isrelated to the amount of harvested energy but 120588lowast
119899is inversely
proportional to 120582120578|ℎ119899|
2 An amount of119901lowast119899increases on a good
subchannel where large amounts of energy harvesting arepossible while 120588lowast
119899decreases on that subchannel to meet the
constraint C1 for harvested energy tightly In short 119901lowast119899and
120588
lowast
119899are adjusted reciprocally to the maximize sum rate while
guaranteeing 119892minBased on the obtained
997888rarr
119901
lowast and997888rarr
120588
lowast Lagrangian multiplierscan be updated by a well-known bisection algorithm or agradient algorithm We detail the overall procedure of theproposed algorithm in Algorithm 1
5 Simulation Results and Discussion
For evaluations we configure a simulation environment asin the following Specifically we set 119873 = 32 119879 = 1000120578 = 09 119901
120591= 119901max = 43 dBm and 119892min = 20 dBm We
assume that subchannel ℎ119899experiences Rayleigh fading so ℎ
119899
is generated as a random variable distributed exponentiallywith 1205902
ℎ= 10
minus4 In addition it is independent from othersubchannels ℎ
119895for 119895 = 119899 We compare the performance of
APAS with three algorithms OPAS EPAS and E2PAS
(i) Optimal power allocation and splitting (OPAS) withperfect CSI at the Tx (CSIT) 119901 and are determinedoptimally
(ii) Equal Power Allocation and Adaptive Power Splitting(EPAS) power is allocated equally to all subchannelsand is determined adaptively to meet 119892min withrespect to (7)
Mobile Information Systems 5
0246
OPAS
0246
EHID
0246
EPAS
APAS
5 30252015100246
Subchannel index
5 3025201510Subchannel index
5 3025201510Subchannel index
5 3025201510Subchannel index
E2PAS
Ratio
of
pmax
()
Ratio
of
pmax
()
Ratio
of
pmax
()
Ratio
of
pmax
()
Figure 3 Power allocation and splitting of OPAS APAS EPAS andE2PAS
(1) Initialize 119901 and Lagrangian multipliers
(2) for 119879120591= 1 119879
(3) Estimate |ℎ119899|
2 for forall119899 based on the PDF of channel(4) Evaluate 119877(5) end for(6) Return 119879lowast
120591= max
119879120591
119877
(7) repeat(8) Find
997888rarr
119901
lowast according to (6)(9) Find
997888rarr
120588
lowast according to (7)(10) Update Lagrangian multipliers 120582 and 120583(11) until
997888rarr
119901
lowast and997888rarr
120588
lowast converge
Algorithm 1 Adaptive power allocation and splitting
(iii) Equal Power Allocation and Equal Power Splitting(E2PAS) power is allocated equally to all subchannelsand is determined equally for all subchannels tomeet 119892min
Figure 3 shows the power allocation and splitting ofOPAS APAS EPAS and E2PAS respectively In OPAS andAPAS a large amount of power is allocated to the subchannelwith the highest channel gain and a portion of power issplit to harvest energy on that subchannel It is best to usethe power allocated to the best subchannel for guaranteeing119892min since energy can be harvested with higher efficiency
00
02
04
06
08
10
Cor
relat
ion
coeffi
cien
t
APASEPASE2PAS
gmin
=10
dBm
gmin
=13
dBm
gmin
=16
dBm
gmin
=19
dBm
Figure 4 Correlation coefficient of APAS EPAS and E2PAS
The differences of resource allocation between OPAS andAPAS come from the errors in channel estimation buttheir resource allocation forms show similar tendency Thisindicates that APAS can achieve a performance close to theoptimal bound On the other hand in EPAS and E2PASpower is allocated equally on all subchannels and energyis harvested on several subchannels therefore a subset ofthe power can be used inefficiently In particular despiteits implementation simplicity performance can be degradedseverely in E2PAS due to the fact that resource allocation isperformed regardless of the channel conditions
Figure 4 shows the correlation coefficient of APAS EPASand E2PAS with varying 119892min This result targets for showinghow the power allocation and splitting of each scheme issimilar to OPAS In OPAS more power is allocated to thebest subchannel to ensure 119892min with a high efficiency as 119892minincreases As a result we can notice here that the correlationcoefficients of EPAS and E2PAS decrease seriously On theother hand the correlation coefficient of APAS stays highsim09 evenwith increasing119892minTherefore this result suggeststhat APAS can adapt the power allocation and splitting strat-egy to a level similar to the optimal solution under variousconditions
Figure 5 plots the data rate 119877 versus the training interval119879
120591 which shows the effects of 119879
120591on 119877 As 119879
120591increases it
is possible to estimate the channel conditions more accu-rately As a result 119877 increases gradually to a peak pointHowever additional increase in 119879
120591beyond its optimal value
causes reduction in the dedicated transmission time therebydecreasing 119877 This suggests that there is an optimal value of119879
120591for maximizing 119877 from the tradeoff relationship between
the accuracy of channel estimations and the duration of datatransmission timeThe optimal training interval119879lowast
120591increases
with a large 119892min which indicates that an exact channel
6 Mobile Information Systems
0 1008060402018
19
20
21
22
23
Dat
a rat
e R
(bits
sH
z)
OPAS (gmin = 17 dBm)APAS (gmin = 17 dBm)
OPAS (gmin = 20 dBm)APAS (gmin = 20 dBm)
Training interval T120591
Tlowast120591 = 20 Rlowast = 205
Tlowast120591 = 10 Rlowast = 218
Figure 5 Data rate 119877 versus training interval 119879120591
10 2018161412
14
16
18
20
22
24
OPASAPAS
EPAS
Dat
a rat
e R
(bits
sH
z)
E2PAS
Harvested energy gmin (dBm)
Figure 6 Data rate 119877 versus harvested energy 119892min when 119879120591 = 20
estimation is desired to satisfy a large requirement for the har-vested energy There is no significant difference in particularless than 10 between the maximum 119877 achieved at 119879lowast
120591and
the optimal bound of 119877Figure 6 shows the data rate119877 versus the harvested energy
119892min when 119879120591= 20 As 119892min increases a large portion of
power should be used for harvesting energy In consequence119877 decreases gradually for all algorithms APAS has the gainof adaptive power allocation compared with EPAS while ithas the gain of adaptive power allocation and splitting whencompared to E2PAS Therefore we can confirm the gain ofeach adaptive strategy by comparing APAS with EPAS andE2PAS respectively In EPAS and E2PAS the rates at which119877 decreases with increasing 119892min are noticeably steeper thanthose of OPAS and APAS This is mainly due to the fact thatin EPAS and E2PAS the power cannot be used efficiently
Therefore APAS outperforms EPAS and E2PAS significantlyat larger 119892min values For example APAS outperforms EPASand E2PAS in terms of 119877 by 20 and 40 at 119892min = 20 dBmrespectively On the other hand the performance differencebetween OPAS and APAS remains relatively constant despiteincreasing 119892min
6 Conclusion
RF-based information and energy transferring techniqueshold the potential to dramatically change the design of wire-less systems and their networking architecturesNeverthelessthe research community is still in the early stages of validatingtheir effectiveness In this work we target to tackle one of thestrongest assumptions that most of the works in informationand energy transfer made which is the assumption thatperfect channel estimation is possible We show throughan empirical study that the variability of wireless channelsmakes perfect estimations of the wireless environment closeto impossible For this we propose APAS which takes intoconsideration imperfect channel estimation results for eval-uating the effectiveness of information and energy transferon wireless devices for energy harvesting based SONs InAPAS the power allocation and splitting ratio is determinedadaptively with considerations for the estimated channelquality In addition our results indicate that APAS achievesnear-optimal performances under various conditions Wehope that this work can act as a catalyst in enabling futureresearch that tries to adopt RF-based information and energytransfer in realistic channel environments
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
This research was supported by Basic Science Research Pro-gram through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT amp FuturePlanning (2015R1C1A1A01051747)
References
[1] H Hu J Zhang X Zheng Y Yang and P Wu ldquoSelf-configuration and self-optimization for LTE networksrdquo IEEECommunications Magazine vol 48 no 2 pp 94ndash100 2010
[2] 3GPP ldquoSelf-organizing networks (SON) concepts and require-mentsrdquo 3GPP TS 32500 2008 V800
[3] G Y Li Z Xu C Xiong et al ldquoEnergy-efficient wireless com-munications tutorial survey and open issuesrdquo IEEE WirelessCommunications vol 18 no 6 pp 28ndash35 2011
[4] C Han T Harrold S Armour et al ldquoGreen radio radiotechniques to enable energy-efficient wireless networksrdquo IEEECommunications Magazine vol 49 no 6 pp 46ndash54 2011
[5] H Bogucka and A Conti ldquoDegrees of freedom for energysavings in practical adaptive wireless systemsrdquo IEEE Commu-nications Magazine vol 49 no 6 pp 38ndash45 2011
[6] HKulah andKNajafi ldquoEnergy scavenging from low-frequencyvibrations by using frequency up-conversion for wireless sensor
Mobile Information Systems 7
applicationsrdquo IEEE Sensors Journal vol 8 no 3 pp 261ndash2682008
[7] S Sudevalayam and P Kulkarni ldquoEnergy harvesting sensornodes survey and implicationsrdquo IEEE Communications Surveysand Tutorials vol 13 no 3 pp 443ndash461 2011
[8] T Le K Mayaram and T Fiez ldquoEfficient far-field radiofrequency energy harvesting for passively powered sensornetworksrdquo IEEE Journal of Solid-State Circuits vol 43 no 5 pp1287ndash1302 2008
[9] R J M Vullers R van Schaijk I Doms C Van Hoof and RMertens ldquoMicropower energy harvestingrdquo Solid-State Electron-ics vol 53 no 7 pp 684ndash693 2009
[10] M Pinuela P D Mitcheson and S Lucyszyn ldquoAmbient RFenergy harvesting in urban and semi-urban environmentsrdquoIEEE Transactions onMicrowaveTheory and Techniques vol 61no 7 pp 2715ndash2726 2013
[11] L R Varshney ldquoTransporting information and energy simul-taneouslyrdquo in Proceedings of the IEEE International Symposiumon Information Theory (ISIT rsquo08) pp 1612ndash1616 IEEE TorontoCanada July 2008
[12] P Grover and A Sahai ldquoShannonmeets tesla wireless informa-tion andpower transferrdquo inProceedings of the IEEE InternationalSymposium on Information Theory (ISIT rsquo10) pp 2363ndash2367IEEE Austin Tex USA June 2010
[13] L Liu R Zhang and K-C Chua ldquoWireless information trans-fer with opportunistic energy harvestingrdquo IEEE Transactions onWireless Communications vol 12 no 1 pp 288ndash300 2013
[14] L Liu R Zhang and K-C Chua ldquoWireless information andpower transfer a dynamic power splitting approachrdquo IEEETransactions on Communications vol 61 no 9 pp 3990ndash40012013
[15] A A Nasir X Zhou S Durrani and R A Kennedy ldquoRelayingprotocols for wireless energy harvesting and information pro-cessingrdquo IEEETransactions onWireless Communications vol 12no 7 pp 3622ndash3636 2013
[16] C Shen W-C Li and T-H Chang ldquoWireless information andenergy transfer in multi-antenna interference channelrdquo IEEETransactions on Signal Processing vol 62 no 23 pp 6249ndash62642014
[17] D W K Ng E S Lo and R Schober ldquoWireless informationand power transfer energy efficiency optimization in OFDMAsystemsrdquo IEEE Transactions on Wireless Communications vol12 no 12 pp 6352ndash6370 2013
[18] R Morsi D S Michalopoulos and R Schober ldquoMultiuserscheduling schemes for simultaneous wireless information andpower transfer over fading channelsrdquo IEEE Transactions onWireless Communications vol 14 no 4 pp 1967ndash1982 2015
[19] R A Berry and R G Gallager ldquoCommunication over fadingchannels with delay constraintsrdquo IEEE Transactions on Informa-tion Theory vol 48 no 5 pp 1135ndash1149 2002
[20] B Hassibi and B M Hochwald ldquoHow much training is neededin multiple-antenna wireless linksrdquo IEEE Transactions onInformation Theory vol 49 no 4 pp 951ndash963 2003
[21] J Gorski F Pfeuffer and K Klamroth ldquoBiconvex sets andoptimization with biconvex functions a survey and extensionsrdquoMathematical Methods of Operations Research vol 66 no 3 pp373ndash407 2007
Submit your manuscripts athttpwwwhindawicom
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Electrical and Computer Engineering
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Computer Networks and Communications
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Hindawi Publishing Corporation
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ArtificialNeural Systems
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Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
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Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
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Mobile Information Systems 3
Figure 2 Tx and Rx nodes each are equipped with a single-antenna and the frequency band is divided into independent119873 subchannels We assume that the subchannels followa discrete time block-fading model in which the channelstate is invariant for a transmission block interval 119879 [1920] In addition each real subchannel ℎ
119899 is assumed to
be an independent identically distributed complex randomvariable such as ℎ
119899sim CN(0 1205902
ℎ) for 1 le 119899 le 119873 In a practical
system the channel estimation process obtains and exploitsthe channel state information (CSI) The transmission block119879 is divided into two periods where in the first we define119879
120591for channel training and in the second we define the
duration 119879 minus 119879120591for data transmission The minimum mean
square error (MMSE) criterion is assumed to be used for esti-mating the channel status and each estimated subchannel ℎ
119899
follows a Gaussian distribution such as ℎ119899sim CN(0 1205902
ℎ
) for1 le 119899 le 119873
Under such settings we note that the RF signals (at theRx node) can be used in two ways either for informationdecoding (ID) or for energy harvesting (EH) while thesemodes cannot take place simultaneously Here the receivedsignal is split in two portions in each subchannel the portionof 120588119899among 119879 minus 119879
120591is reserved for ID while 1 minus 120588
119899is for EH
before performing active analog or digital signal processing(We assume that the Rx node is equipped with a perfectpassive power splitting unit [17]) In addition as RF signalsare split in two streams two types of noise should be consid-ered antenna noise 119899
119860 and signal processing noise 119899
119878 119899119860is
generated at the Rx antenna while 119899119878is introduced when the
received signal is divided into ID and EH Nevertheless weneglect 119899
119860since it is much smaller than 119899
119878[14] Furthermore
we assume 119899119878
sim CN(0 1) Then the achievable sumrate using the estimated subchannels can be represented by[20]
119877 =
119873
sum
119899=1
119903
119899
=
119873
sum
119899=1
119879 minus 119879
120591
119879
log2(1 +
(1 + 119901
120591119879
120591) 120588
119899119901
119899
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
1 + 120588
119899119901
119899+ 119901
120591119879
120591
)
(1)
where 119901120591and 119901
119899denote the power for estimating channel
conditions and power allocated in subchannel 119899 for datatransmission respectively When performing EH at the Rxthe harvested energy is represented as
119873
sum
119899=1
119892
119899=
119873
sum
119899=1
119879 minus 119879
120591
119879
120578
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
(1 minus 120588
119899) 119901
119899 (2)
where 120578 is the energy conversion efficiency achieved by con-verting the received RF signals into harvestable energy at theRx We also assume that sum119873
119899=1120578|ℎ
119899|
2
le 1 according to thelaws of thermodynamics
We now look into the training interval the power alloca-tion and the power splitting ratio required to maximize the
sum rate while guaranteeing the minimum harvested energy119892min Consider
max119879120591
119873
sum
119899=1
119903
119899
st C1119873
sum
119899=1
119892
119899ge 119892min
C2119873
sum
119899=1
119901
119899le 119901max
C3 119901119899ge 0 forall119899
C4 0 le 120588119899le 1 forall119899
C5 0 le 119879120591le 119879
(3)
Here 119901 = 119901
1 119901
119873 and = 120588
1 120588
119873 Furthermore
the five constraints can be explained as follows ConstraintC1 ensures that the amount of energy harvested should belarger than the minimum amount of required energy 119892minConstraint C2 limits the available transmission power of theTx to 119901max Constraint C3 is a nonnegative constraint on thetransmission power Constraints C4 and C5 are the ranges of120588
119899and 119879
120591 respectively
4 Adaptive Power Allocation and Splitting
We now propose an adaptive power allocation and splitting(APAS) algorithm by solving the optimization problem in(3) We note that it is difficult to find a closed form solutionfor 119879lowast120591from optimization techniques given that the objective
function of (3) is not in concave form with respect to119879
120591 However considering the fact that 119879
120591lies within the
interval (0 119879) 119879lowast120591can be derived using a one-dimensional
exhaustive search For example 119879lowast120591can be found from the
probability density function (PDF) of channel distributionIf 119879lowast120591is determined at once it can be used for estimating
all unknown channels that will be generated Furthermoreusing the concavity of
119901 and we can find their suboptimalvalues iteratively which reflect channel estimation errors (Ina biconvex problem where the problem in (3) is convex withrespect to
119901 for a fixed or vice versa the convergence ofa partial optimum solution can be guaranteed by using theblock coordinate descent (BCD) algorithm [21])
For a given 119879120591 Tx estimates CSI on subchannels and
determines the allocated power and the power splitting ratioThe Lagrangian function of (3) can be expressed by
Λ (119901 120582 120583) =
119873
sum
119899=1
119903
119899+ 120582(
119873
sum
119899=1
119892
119899minus 119892min)
+ 120583(119901max minus119873
sum
119899=1
119901
119899)
(4)
Here 120582 and 120583 are nonnegative Lagrangian multipliers whichcorrespond to constraints C1 and C2 respectively To find asolution to this we decouple the original problem into par-allel 119873 subproblems for each subchannel By discarding the
4 Mobile Information Systems
Block interval T
Channel estimation
Tx Rx
Data transmission
Power
Energyharvester
Informationdetector
splitter
T120591
120588nnA
T minus T120591
1 minus 120588n
ℎn rarr ℎn
nS
Figure 2 System model for wireless information and energy transfer
constant terms the Lagrangian function (4) for a particularsubchannel 119899 can be represented as
Λ (119901
119899 120588
119899 120582 120583) = 119903
119899+ 120582119892
119899minus 120583119901
119899 (5)
By taking the derivative of (5) with respect to 119901119899 we can
obtain a power allocation strategy as in the following
119901
lowast
119899=
[
[
[
119867
119890
1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
sdot
radic
4120588
119899
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
) + (ln 2)1198672119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
4
120577
119899
4 (ln 2) 1205882119899120577
119899
minus
119867
119890(2 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
2120588
119899(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
]
]
]
+
(6)
Here [119909]+ = max (0 119909) 119867119890= 1 + 119901
120591119879
120591 and 120577
119899= (119879(119879 minus
119879
120591))120583 minus 120582120578|
ℎ
119899|
2
(1 minus 120588
119899) For the obtained 119901lowast
119899from (6) we
also take the derivative of (5) with respect to 120588119899 to obtain the
power splitting strategy as in the following
120588
lowast
119899=
[
[
[
[
119867
119890
1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
sdotradic
4
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
) + (ln 2)1198672119890120582120578
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
6
4 (ln 2) 1199012119899120582120578
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
minus
119867
119890(2 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
2119901
119899(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
]
]
]
]
1
0
(7)
Here [119909]10= min (max (0 119909) 1)
Then 119901lowast119899and 120588lowast
119899can be interpreted in terms of chan-
nel estimation channel condition and harvested energy asfollows We especially note that an increment in 119879
120591ensures
the exact estimation of channel conditions but decreases thedata transmission time An extremely large 119879
120591does not allow
for a dedicated data transmission time as a result 119901lowast119899and
120588
lowast
119899become zero In addition 119901lowast
119899and 120588lowast
119899are proportional to
|
ℎ
119899|
2 so they show large values on subchannels with goodchannel conditions 119901lowast
119899is proportional to 120582120578|ℎ
119899|
2 which isrelated to the amount of harvested energy but 120588lowast
119899is inversely
proportional to 120582120578|ℎ119899|
2 An amount of119901lowast119899increases on a good
subchannel where large amounts of energy harvesting arepossible while 120588lowast
119899decreases on that subchannel to meet the
constraint C1 for harvested energy tightly In short 119901lowast119899and
120588
lowast
119899are adjusted reciprocally to the maximize sum rate while
guaranteeing 119892minBased on the obtained
997888rarr
119901
lowast and997888rarr
120588
lowast Lagrangian multiplierscan be updated by a well-known bisection algorithm or agradient algorithm We detail the overall procedure of theproposed algorithm in Algorithm 1
5 Simulation Results and Discussion
For evaluations we configure a simulation environment asin the following Specifically we set 119873 = 32 119879 = 1000120578 = 09 119901
120591= 119901max = 43 dBm and 119892min = 20 dBm We
assume that subchannel ℎ119899experiences Rayleigh fading so ℎ
119899
is generated as a random variable distributed exponentiallywith 1205902
ℎ= 10
minus4 In addition it is independent from othersubchannels ℎ
119895for 119895 = 119899 We compare the performance of
APAS with three algorithms OPAS EPAS and E2PAS
(i) Optimal power allocation and splitting (OPAS) withperfect CSI at the Tx (CSIT) 119901 and are determinedoptimally
(ii) Equal Power Allocation and Adaptive Power Splitting(EPAS) power is allocated equally to all subchannelsand is determined adaptively to meet 119892min withrespect to (7)
Mobile Information Systems 5
0246
OPAS
0246
EHID
0246
EPAS
APAS
5 30252015100246
Subchannel index
5 3025201510Subchannel index
5 3025201510Subchannel index
5 3025201510Subchannel index
E2PAS
Ratio
of
pmax
()
Ratio
of
pmax
()
Ratio
of
pmax
()
Ratio
of
pmax
()
Figure 3 Power allocation and splitting of OPAS APAS EPAS andE2PAS
(1) Initialize 119901 and Lagrangian multipliers
(2) for 119879120591= 1 119879
(3) Estimate |ℎ119899|
2 for forall119899 based on the PDF of channel(4) Evaluate 119877(5) end for(6) Return 119879lowast
120591= max
119879120591
119877
(7) repeat(8) Find
997888rarr
119901
lowast according to (6)(9) Find
997888rarr
120588
lowast according to (7)(10) Update Lagrangian multipliers 120582 and 120583(11) until
997888rarr
119901
lowast and997888rarr
120588
lowast converge
Algorithm 1 Adaptive power allocation and splitting
(iii) Equal Power Allocation and Equal Power Splitting(E2PAS) power is allocated equally to all subchannelsand is determined equally for all subchannels tomeet 119892min
Figure 3 shows the power allocation and splitting ofOPAS APAS EPAS and E2PAS respectively In OPAS andAPAS a large amount of power is allocated to the subchannelwith the highest channel gain and a portion of power issplit to harvest energy on that subchannel It is best to usethe power allocated to the best subchannel for guaranteeing119892min since energy can be harvested with higher efficiency
00
02
04
06
08
10
Cor
relat
ion
coeffi
cien
t
APASEPASE2PAS
gmin
=10
dBm
gmin
=13
dBm
gmin
=16
dBm
gmin
=19
dBm
Figure 4 Correlation coefficient of APAS EPAS and E2PAS
The differences of resource allocation between OPAS andAPAS come from the errors in channel estimation buttheir resource allocation forms show similar tendency Thisindicates that APAS can achieve a performance close to theoptimal bound On the other hand in EPAS and E2PASpower is allocated equally on all subchannels and energyis harvested on several subchannels therefore a subset ofthe power can be used inefficiently In particular despiteits implementation simplicity performance can be degradedseverely in E2PAS due to the fact that resource allocation isperformed regardless of the channel conditions
Figure 4 shows the correlation coefficient of APAS EPASand E2PAS with varying 119892min This result targets for showinghow the power allocation and splitting of each scheme issimilar to OPAS In OPAS more power is allocated to thebest subchannel to ensure 119892min with a high efficiency as 119892minincreases As a result we can notice here that the correlationcoefficients of EPAS and E2PAS decrease seriously On theother hand the correlation coefficient of APAS stays highsim09 evenwith increasing119892minTherefore this result suggeststhat APAS can adapt the power allocation and splitting strat-egy to a level similar to the optimal solution under variousconditions
Figure 5 plots the data rate 119877 versus the training interval119879
120591 which shows the effects of 119879
120591on 119877 As 119879
120591increases it
is possible to estimate the channel conditions more accu-rately As a result 119877 increases gradually to a peak pointHowever additional increase in 119879
120591beyond its optimal value
causes reduction in the dedicated transmission time therebydecreasing 119877 This suggests that there is an optimal value of119879
120591for maximizing 119877 from the tradeoff relationship between
the accuracy of channel estimations and the duration of datatransmission timeThe optimal training interval119879lowast
120591increases
with a large 119892min which indicates that an exact channel
6 Mobile Information Systems
0 1008060402018
19
20
21
22
23
Dat
a rat
e R
(bits
sH
z)
OPAS (gmin = 17 dBm)APAS (gmin = 17 dBm)
OPAS (gmin = 20 dBm)APAS (gmin = 20 dBm)
Training interval T120591
Tlowast120591 = 20 Rlowast = 205
Tlowast120591 = 10 Rlowast = 218
Figure 5 Data rate 119877 versus training interval 119879120591
10 2018161412
14
16
18
20
22
24
OPASAPAS
EPAS
Dat
a rat
e R
(bits
sH
z)
E2PAS
Harvested energy gmin (dBm)
Figure 6 Data rate 119877 versus harvested energy 119892min when 119879120591 = 20
estimation is desired to satisfy a large requirement for the har-vested energy There is no significant difference in particularless than 10 between the maximum 119877 achieved at 119879lowast
120591and
the optimal bound of 119877Figure 6 shows the data rate119877 versus the harvested energy
119892min when 119879120591= 20 As 119892min increases a large portion of
power should be used for harvesting energy In consequence119877 decreases gradually for all algorithms APAS has the gainof adaptive power allocation compared with EPAS while ithas the gain of adaptive power allocation and splitting whencompared to E2PAS Therefore we can confirm the gain ofeach adaptive strategy by comparing APAS with EPAS andE2PAS respectively In EPAS and E2PAS the rates at which119877 decreases with increasing 119892min are noticeably steeper thanthose of OPAS and APAS This is mainly due to the fact thatin EPAS and E2PAS the power cannot be used efficiently
Therefore APAS outperforms EPAS and E2PAS significantlyat larger 119892min values For example APAS outperforms EPASand E2PAS in terms of 119877 by 20 and 40 at 119892min = 20 dBmrespectively On the other hand the performance differencebetween OPAS and APAS remains relatively constant despiteincreasing 119892min
6 Conclusion
RF-based information and energy transferring techniqueshold the potential to dramatically change the design of wire-less systems and their networking architecturesNeverthelessthe research community is still in the early stages of validatingtheir effectiveness In this work we target to tackle one of thestrongest assumptions that most of the works in informationand energy transfer made which is the assumption thatperfect channel estimation is possible We show throughan empirical study that the variability of wireless channelsmakes perfect estimations of the wireless environment closeto impossible For this we propose APAS which takes intoconsideration imperfect channel estimation results for eval-uating the effectiveness of information and energy transferon wireless devices for energy harvesting based SONs InAPAS the power allocation and splitting ratio is determinedadaptively with considerations for the estimated channelquality In addition our results indicate that APAS achievesnear-optimal performances under various conditions Wehope that this work can act as a catalyst in enabling futureresearch that tries to adopt RF-based information and energytransfer in realistic channel environments
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
This research was supported by Basic Science Research Pro-gram through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT amp FuturePlanning (2015R1C1A1A01051747)
References
[1] H Hu J Zhang X Zheng Y Yang and P Wu ldquoSelf-configuration and self-optimization for LTE networksrdquo IEEECommunications Magazine vol 48 no 2 pp 94ndash100 2010
[2] 3GPP ldquoSelf-organizing networks (SON) concepts and require-mentsrdquo 3GPP TS 32500 2008 V800
[3] G Y Li Z Xu C Xiong et al ldquoEnergy-efficient wireless com-munications tutorial survey and open issuesrdquo IEEE WirelessCommunications vol 18 no 6 pp 28ndash35 2011
[4] C Han T Harrold S Armour et al ldquoGreen radio radiotechniques to enable energy-efficient wireless networksrdquo IEEECommunications Magazine vol 49 no 6 pp 46ndash54 2011
[5] H Bogucka and A Conti ldquoDegrees of freedom for energysavings in practical adaptive wireless systemsrdquo IEEE Commu-nications Magazine vol 49 no 6 pp 38ndash45 2011
[6] HKulah andKNajafi ldquoEnergy scavenging from low-frequencyvibrations by using frequency up-conversion for wireless sensor
Mobile Information Systems 7
applicationsrdquo IEEE Sensors Journal vol 8 no 3 pp 261ndash2682008
[7] S Sudevalayam and P Kulkarni ldquoEnergy harvesting sensornodes survey and implicationsrdquo IEEE Communications Surveysand Tutorials vol 13 no 3 pp 443ndash461 2011
[8] T Le K Mayaram and T Fiez ldquoEfficient far-field radiofrequency energy harvesting for passively powered sensornetworksrdquo IEEE Journal of Solid-State Circuits vol 43 no 5 pp1287ndash1302 2008
[9] R J M Vullers R van Schaijk I Doms C Van Hoof and RMertens ldquoMicropower energy harvestingrdquo Solid-State Electron-ics vol 53 no 7 pp 684ndash693 2009
[10] M Pinuela P D Mitcheson and S Lucyszyn ldquoAmbient RFenergy harvesting in urban and semi-urban environmentsrdquoIEEE Transactions onMicrowaveTheory and Techniques vol 61no 7 pp 2715ndash2726 2013
[11] L R Varshney ldquoTransporting information and energy simul-taneouslyrdquo in Proceedings of the IEEE International Symposiumon Information Theory (ISIT rsquo08) pp 1612ndash1616 IEEE TorontoCanada July 2008
[12] P Grover and A Sahai ldquoShannonmeets tesla wireless informa-tion andpower transferrdquo inProceedings of the IEEE InternationalSymposium on Information Theory (ISIT rsquo10) pp 2363ndash2367IEEE Austin Tex USA June 2010
[13] L Liu R Zhang and K-C Chua ldquoWireless information trans-fer with opportunistic energy harvestingrdquo IEEE Transactions onWireless Communications vol 12 no 1 pp 288ndash300 2013
[14] L Liu R Zhang and K-C Chua ldquoWireless information andpower transfer a dynamic power splitting approachrdquo IEEETransactions on Communications vol 61 no 9 pp 3990ndash40012013
[15] A A Nasir X Zhou S Durrani and R A Kennedy ldquoRelayingprotocols for wireless energy harvesting and information pro-cessingrdquo IEEETransactions onWireless Communications vol 12no 7 pp 3622ndash3636 2013
[16] C Shen W-C Li and T-H Chang ldquoWireless information andenergy transfer in multi-antenna interference channelrdquo IEEETransactions on Signal Processing vol 62 no 23 pp 6249ndash62642014
[17] D W K Ng E S Lo and R Schober ldquoWireless informationand power transfer energy efficiency optimization in OFDMAsystemsrdquo IEEE Transactions on Wireless Communications vol12 no 12 pp 6352ndash6370 2013
[18] R Morsi D S Michalopoulos and R Schober ldquoMultiuserscheduling schemes for simultaneous wireless information andpower transfer over fading channelsrdquo IEEE Transactions onWireless Communications vol 14 no 4 pp 1967ndash1982 2015
[19] R A Berry and R G Gallager ldquoCommunication over fadingchannels with delay constraintsrdquo IEEE Transactions on Informa-tion Theory vol 48 no 5 pp 1135ndash1149 2002
[20] B Hassibi and B M Hochwald ldquoHow much training is neededin multiple-antenna wireless linksrdquo IEEE Transactions onInformation Theory vol 49 no 4 pp 951ndash963 2003
[21] J Gorski F Pfeuffer and K Klamroth ldquoBiconvex sets andoptimization with biconvex functions a survey and extensionsrdquoMathematical Methods of Operations Research vol 66 no 3 pp373ndash407 2007
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
4 Mobile Information Systems
Block interval T
Channel estimation
Tx Rx
Data transmission
Power
Energyharvester
Informationdetector
splitter
T120591
120588nnA
T minus T120591
1 minus 120588n
ℎn rarr ℎn
nS
Figure 2 System model for wireless information and energy transfer
constant terms the Lagrangian function (4) for a particularsubchannel 119899 can be represented as
Λ (119901
119899 120588
119899 120582 120583) = 119903
119899+ 120582119892
119899minus 120583119901
119899 (5)
By taking the derivative of (5) with respect to 119901119899 we can
obtain a power allocation strategy as in the following
119901
lowast
119899=
[
[
[
119867
119890
1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
sdot
radic
4120588
119899
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
) + (ln 2)1198672119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
4
120577
119899
4 (ln 2) 1205882119899120577
119899
minus
119867
119890(2 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
2120588
119899(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
]
]
]
+
(6)
Here [119909]+ = max (0 119909) 119867119890= 1 + 119901
120591119879
120591 and 120577
119899= (119879(119879 minus
119879
120591))120583 minus 120582120578|
ℎ
119899|
2
(1 minus 120588
119899) For the obtained 119901lowast
119899from (6) we
also take the derivative of (5) with respect to 120588119899 to obtain the
power splitting strategy as in the following
120588
lowast
119899=
[
[
[
[
119867
119890
1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
sdotradic
4
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
) + (ln 2)1198672119890120582120578
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
6
4 (ln 2) 1199012119899120582120578
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
minus
119867
119890(2 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
2119901
119899(1 + 119867
119890
1003816
1003816
1003816
1003816
1003816
ℎ
119899
1003816
1003816
1003816
1003816
1003816
2
)
]
]
]
]
1
0
(7)
Here [119909]10= min (max (0 119909) 1)
Then 119901lowast119899and 120588lowast
119899can be interpreted in terms of chan-
nel estimation channel condition and harvested energy asfollows We especially note that an increment in 119879
120591ensures
the exact estimation of channel conditions but decreases thedata transmission time An extremely large 119879
120591does not allow
for a dedicated data transmission time as a result 119901lowast119899and
120588
lowast
119899become zero In addition 119901lowast
119899and 120588lowast
119899are proportional to
|
ℎ
119899|
2 so they show large values on subchannels with goodchannel conditions 119901lowast
119899is proportional to 120582120578|ℎ
119899|
2 which isrelated to the amount of harvested energy but 120588lowast
119899is inversely
proportional to 120582120578|ℎ119899|
2 An amount of119901lowast119899increases on a good
subchannel where large amounts of energy harvesting arepossible while 120588lowast
119899decreases on that subchannel to meet the
constraint C1 for harvested energy tightly In short 119901lowast119899and
120588
lowast
119899are adjusted reciprocally to the maximize sum rate while
guaranteeing 119892minBased on the obtained
997888rarr
119901
lowast and997888rarr
120588
lowast Lagrangian multiplierscan be updated by a well-known bisection algorithm or agradient algorithm We detail the overall procedure of theproposed algorithm in Algorithm 1
5 Simulation Results and Discussion
For evaluations we configure a simulation environment asin the following Specifically we set 119873 = 32 119879 = 1000120578 = 09 119901
120591= 119901max = 43 dBm and 119892min = 20 dBm We
assume that subchannel ℎ119899experiences Rayleigh fading so ℎ
119899
is generated as a random variable distributed exponentiallywith 1205902
ℎ= 10
minus4 In addition it is independent from othersubchannels ℎ
119895for 119895 = 119899 We compare the performance of
APAS with three algorithms OPAS EPAS and E2PAS
(i) Optimal power allocation and splitting (OPAS) withperfect CSI at the Tx (CSIT) 119901 and are determinedoptimally
(ii) Equal Power Allocation and Adaptive Power Splitting(EPAS) power is allocated equally to all subchannelsand is determined adaptively to meet 119892min withrespect to (7)
Mobile Information Systems 5
0246
OPAS
0246
EHID
0246
EPAS
APAS
5 30252015100246
Subchannel index
5 3025201510Subchannel index
5 3025201510Subchannel index
5 3025201510Subchannel index
E2PAS
Ratio
of
pmax
()
Ratio
of
pmax
()
Ratio
of
pmax
()
Ratio
of
pmax
()
Figure 3 Power allocation and splitting of OPAS APAS EPAS andE2PAS
(1) Initialize 119901 and Lagrangian multipliers
(2) for 119879120591= 1 119879
(3) Estimate |ℎ119899|
2 for forall119899 based on the PDF of channel(4) Evaluate 119877(5) end for(6) Return 119879lowast
120591= max
119879120591
119877
(7) repeat(8) Find
997888rarr
119901
lowast according to (6)(9) Find
997888rarr
120588
lowast according to (7)(10) Update Lagrangian multipliers 120582 and 120583(11) until
997888rarr
119901
lowast and997888rarr
120588
lowast converge
Algorithm 1 Adaptive power allocation and splitting
(iii) Equal Power Allocation and Equal Power Splitting(E2PAS) power is allocated equally to all subchannelsand is determined equally for all subchannels tomeet 119892min
Figure 3 shows the power allocation and splitting ofOPAS APAS EPAS and E2PAS respectively In OPAS andAPAS a large amount of power is allocated to the subchannelwith the highest channel gain and a portion of power issplit to harvest energy on that subchannel It is best to usethe power allocated to the best subchannel for guaranteeing119892min since energy can be harvested with higher efficiency
00
02
04
06
08
10
Cor
relat
ion
coeffi
cien
t
APASEPASE2PAS
gmin
=10
dBm
gmin
=13
dBm
gmin
=16
dBm
gmin
=19
dBm
Figure 4 Correlation coefficient of APAS EPAS and E2PAS
The differences of resource allocation between OPAS andAPAS come from the errors in channel estimation buttheir resource allocation forms show similar tendency Thisindicates that APAS can achieve a performance close to theoptimal bound On the other hand in EPAS and E2PASpower is allocated equally on all subchannels and energyis harvested on several subchannels therefore a subset ofthe power can be used inefficiently In particular despiteits implementation simplicity performance can be degradedseverely in E2PAS due to the fact that resource allocation isperformed regardless of the channel conditions
Figure 4 shows the correlation coefficient of APAS EPASand E2PAS with varying 119892min This result targets for showinghow the power allocation and splitting of each scheme issimilar to OPAS In OPAS more power is allocated to thebest subchannel to ensure 119892min with a high efficiency as 119892minincreases As a result we can notice here that the correlationcoefficients of EPAS and E2PAS decrease seriously On theother hand the correlation coefficient of APAS stays highsim09 evenwith increasing119892minTherefore this result suggeststhat APAS can adapt the power allocation and splitting strat-egy to a level similar to the optimal solution under variousconditions
Figure 5 plots the data rate 119877 versus the training interval119879
120591 which shows the effects of 119879
120591on 119877 As 119879
120591increases it
is possible to estimate the channel conditions more accu-rately As a result 119877 increases gradually to a peak pointHowever additional increase in 119879
120591beyond its optimal value
causes reduction in the dedicated transmission time therebydecreasing 119877 This suggests that there is an optimal value of119879
120591for maximizing 119877 from the tradeoff relationship between
the accuracy of channel estimations and the duration of datatransmission timeThe optimal training interval119879lowast
120591increases
with a large 119892min which indicates that an exact channel
6 Mobile Information Systems
0 1008060402018
19
20
21
22
23
Dat
a rat
e R
(bits
sH
z)
OPAS (gmin = 17 dBm)APAS (gmin = 17 dBm)
OPAS (gmin = 20 dBm)APAS (gmin = 20 dBm)
Training interval T120591
Tlowast120591 = 20 Rlowast = 205
Tlowast120591 = 10 Rlowast = 218
Figure 5 Data rate 119877 versus training interval 119879120591
10 2018161412
14
16
18
20
22
24
OPASAPAS
EPAS
Dat
a rat
e R
(bits
sH
z)
E2PAS
Harvested energy gmin (dBm)
Figure 6 Data rate 119877 versus harvested energy 119892min when 119879120591 = 20
estimation is desired to satisfy a large requirement for the har-vested energy There is no significant difference in particularless than 10 between the maximum 119877 achieved at 119879lowast
120591and
the optimal bound of 119877Figure 6 shows the data rate119877 versus the harvested energy
119892min when 119879120591= 20 As 119892min increases a large portion of
power should be used for harvesting energy In consequence119877 decreases gradually for all algorithms APAS has the gainof adaptive power allocation compared with EPAS while ithas the gain of adaptive power allocation and splitting whencompared to E2PAS Therefore we can confirm the gain ofeach adaptive strategy by comparing APAS with EPAS andE2PAS respectively In EPAS and E2PAS the rates at which119877 decreases with increasing 119892min are noticeably steeper thanthose of OPAS and APAS This is mainly due to the fact thatin EPAS and E2PAS the power cannot be used efficiently
Therefore APAS outperforms EPAS and E2PAS significantlyat larger 119892min values For example APAS outperforms EPASand E2PAS in terms of 119877 by 20 and 40 at 119892min = 20 dBmrespectively On the other hand the performance differencebetween OPAS and APAS remains relatively constant despiteincreasing 119892min
6 Conclusion
RF-based information and energy transferring techniqueshold the potential to dramatically change the design of wire-less systems and their networking architecturesNeverthelessthe research community is still in the early stages of validatingtheir effectiveness In this work we target to tackle one of thestrongest assumptions that most of the works in informationand energy transfer made which is the assumption thatperfect channel estimation is possible We show throughan empirical study that the variability of wireless channelsmakes perfect estimations of the wireless environment closeto impossible For this we propose APAS which takes intoconsideration imperfect channel estimation results for eval-uating the effectiveness of information and energy transferon wireless devices for energy harvesting based SONs InAPAS the power allocation and splitting ratio is determinedadaptively with considerations for the estimated channelquality In addition our results indicate that APAS achievesnear-optimal performances under various conditions Wehope that this work can act as a catalyst in enabling futureresearch that tries to adopt RF-based information and energytransfer in realistic channel environments
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
This research was supported by Basic Science Research Pro-gram through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT amp FuturePlanning (2015R1C1A1A01051747)
References
[1] H Hu J Zhang X Zheng Y Yang and P Wu ldquoSelf-configuration and self-optimization for LTE networksrdquo IEEECommunications Magazine vol 48 no 2 pp 94ndash100 2010
[2] 3GPP ldquoSelf-organizing networks (SON) concepts and require-mentsrdquo 3GPP TS 32500 2008 V800
[3] G Y Li Z Xu C Xiong et al ldquoEnergy-efficient wireless com-munications tutorial survey and open issuesrdquo IEEE WirelessCommunications vol 18 no 6 pp 28ndash35 2011
[4] C Han T Harrold S Armour et al ldquoGreen radio radiotechniques to enable energy-efficient wireless networksrdquo IEEECommunications Magazine vol 49 no 6 pp 46ndash54 2011
[5] H Bogucka and A Conti ldquoDegrees of freedom for energysavings in practical adaptive wireless systemsrdquo IEEE Commu-nications Magazine vol 49 no 6 pp 38ndash45 2011
[6] HKulah andKNajafi ldquoEnergy scavenging from low-frequencyvibrations by using frequency up-conversion for wireless sensor
Mobile Information Systems 7
applicationsrdquo IEEE Sensors Journal vol 8 no 3 pp 261ndash2682008
[7] S Sudevalayam and P Kulkarni ldquoEnergy harvesting sensornodes survey and implicationsrdquo IEEE Communications Surveysand Tutorials vol 13 no 3 pp 443ndash461 2011
[8] T Le K Mayaram and T Fiez ldquoEfficient far-field radiofrequency energy harvesting for passively powered sensornetworksrdquo IEEE Journal of Solid-State Circuits vol 43 no 5 pp1287ndash1302 2008
[9] R J M Vullers R van Schaijk I Doms C Van Hoof and RMertens ldquoMicropower energy harvestingrdquo Solid-State Electron-ics vol 53 no 7 pp 684ndash693 2009
[10] M Pinuela P D Mitcheson and S Lucyszyn ldquoAmbient RFenergy harvesting in urban and semi-urban environmentsrdquoIEEE Transactions onMicrowaveTheory and Techniques vol 61no 7 pp 2715ndash2726 2013
[11] L R Varshney ldquoTransporting information and energy simul-taneouslyrdquo in Proceedings of the IEEE International Symposiumon Information Theory (ISIT rsquo08) pp 1612ndash1616 IEEE TorontoCanada July 2008
[12] P Grover and A Sahai ldquoShannonmeets tesla wireless informa-tion andpower transferrdquo inProceedings of the IEEE InternationalSymposium on Information Theory (ISIT rsquo10) pp 2363ndash2367IEEE Austin Tex USA June 2010
[13] L Liu R Zhang and K-C Chua ldquoWireless information trans-fer with opportunistic energy harvestingrdquo IEEE Transactions onWireless Communications vol 12 no 1 pp 288ndash300 2013
[14] L Liu R Zhang and K-C Chua ldquoWireless information andpower transfer a dynamic power splitting approachrdquo IEEETransactions on Communications vol 61 no 9 pp 3990ndash40012013
[15] A A Nasir X Zhou S Durrani and R A Kennedy ldquoRelayingprotocols for wireless energy harvesting and information pro-cessingrdquo IEEETransactions onWireless Communications vol 12no 7 pp 3622ndash3636 2013
[16] C Shen W-C Li and T-H Chang ldquoWireless information andenergy transfer in multi-antenna interference channelrdquo IEEETransactions on Signal Processing vol 62 no 23 pp 6249ndash62642014
[17] D W K Ng E S Lo and R Schober ldquoWireless informationand power transfer energy efficiency optimization in OFDMAsystemsrdquo IEEE Transactions on Wireless Communications vol12 no 12 pp 6352ndash6370 2013
[18] R Morsi D S Michalopoulos and R Schober ldquoMultiuserscheduling schemes for simultaneous wireless information andpower transfer over fading channelsrdquo IEEE Transactions onWireless Communications vol 14 no 4 pp 1967ndash1982 2015
[19] R A Berry and R G Gallager ldquoCommunication over fadingchannels with delay constraintsrdquo IEEE Transactions on Informa-tion Theory vol 48 no 5 pp 1135ndash1149 2002
[20] B Hassibi and B M Hochwald ldquoHow much training is neededin multiple-antenna wireless linksrdquo IEEE Transactions onInformation Theory vol 49 no 4 pp 951ndash963 2003
[21] J Gorski F Pfeuffer and K Klamroth ldquoBiconvex sets andoptimization with biconvex functions a survey and extensionsrdquoMathematical Methods of Operations Research vol 66 no 3 pp373ndash407 2007
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mobile Information Systems 5
0246
OPAS
0246
EHID
0246
EPAS
APAS
5 30252015100246
Subchannel index
5 3025201510Subchannel index
5 3025201510Subchannel index
5 3025201510Subchannel index
E2PAS
Ratio
of
pmax
()
Ratio
of
pmax
()
Ratio
of
pmax
()
Ratio
of
pmax
()
Figure 3 Power allocation and splitting of OPAS APAS EPAS andE2PAS
(1) Initialize 119901 and Lagrangian multipliers
(2) for 119879120591= 1 119879
(3) Estimate |ℎ119899|
2 for forall119899 based on the PDF of channel(4) Evaluate 119877(5) end for(6) Return 119879lowast
120591= max
119879120591
119877
(7) repeat(8) Find
997888rarr
119901
lowast according to (6)(9) Find
997888rarr
120588
lowast according to (7)(10) Update Lagrangian multipliers 120582 and 120583(11) until
997888rarr
119901
lowast and997888rarr
120588
lowast converge
Algorithm 1 Adaptive power allocation and splitting
(iii) Equal Power Allocation and Equal Power Splitting(E2PAS) power is allocated equally to all subchannelsand is determined equally for all subchannels tomeet 119892min
Figure 3 shows the power allocation and splitting ofOPAS APAS EPAS and E2PAS respectively In OPAS andAPAS a large amount of power is allocated to the subchannelwith the highest channel gain and a portion of power issplit to harvest energy on that subchannel It is best to usethe power allocated to the best subchannel for guaranteeing119892min since energy can be harvested with higher efficiency
00
02
04
06
08
10
Cor
relat
ion
coeffi
cien
t
APASEPASE2PAS
gmin
=10
dBm
gmin
=13
dBm
gmin
=16
dBm
gmin
=19
dBm
Figure 4 Correlation coefficient of APAS EPAS and E2PAS
The differences of resource allocation between OPAS andAPAS come from the errors in channel estimation buttheir resource allocation forms show similar tendency Thisindicates that APAS can achieve a performance close to theoptimal bound On the other hand in EPAS and E2PASpower is allocated equally on all subchannels and energyis harvested on several subchannels therefore a subset ofthe power can be used inefficiently In particular despiteits implementation simplicity performance can be degradedseverely in E2PAS due to the fact that resource allocation isperformed regardless of the channel conditions
Figure 4 shows the correlation coefficient of APAS EPASand E2PAS with varying 119892min This result targets for showinghow the power allocation and splitting of each scheme issimilar to OPAS In OPAS more power is allocated to thebest subchannel to ensure 119892min with a high efficiency as 119892minincreases As a result we can notice here that the correlationcoefficients of EPAS and E2PAS decrease seriously On theother hand the correlation coefficient of APAS stays highsim09 evenwith increasing119892minTherefore this result suggeststhat APAS can adapt the power allocation and splitting strat-egy to a level similar to the optimal solution under variousconditions
Figure 5 plots the data rate 119877 versus the training interval119879
120591 which shows the effects of 119879
120591on 119877 As 119879
120591increases it
is possible to estimate the channel conditions more accu-rately As a result 119877 increases gradually to a peak pointHowever additional increase in 119879
120591beyond its optimal value
causes reduction in the dedicated transmission time therebydecreasing 119877 This suggests that there is an optimal value of119879
120591for maximizing 119877 from the tradeoff relationship between
the accuracy of channel estimations and the duration of datatransmission timeThe optimal training interval119879lowast
120591increases
with a large 119892min which indicates that an exact channel
6 Mobile Information Systems
0 1008060402018
19
20
21
22
23
Dat
a rat
e R
(bits
sH
z)
OPAS (gmin = 17 dBm)APAS (gmin = 17 dBm)
OPAS (gmin = 20 dBm)APAS (gmin = 20 dBm)
Training interval T120591
Tlowast120591 = 20 Rlowast = 205
Tlowast120591 = 10 Rlowast = 218
Figure 5 Data rate 119877 versus training interval 119879120591
10 2018161412
14
16
18
20
22
24
OPASAPAS
EPAS
Dat
a rat
e R
(bits
sH
z)
E2PAS
Harvested energy gmin (dBm)
Figure 6 Data rate 119877 versus harvested energy 119892min when 119879120591 = 20
estimation is desired to satisfy a large requirement for the har-vested energy There is no significant difference in particularless than 10 between the maximum 119877 achieved at 119879lowast
120591and
the optimal bound of 119877Figure 6 shows the data rate119877 versus the harvested energy
119892min when 119879120591= 20 As 119892min increases a large portion of
power should be used for harvesting energy In consequence119877 decreases gradually for all algorithms APAS has the gainof adaptive power allocation compared with EPAS while ithas the gain of adaptive power allocation and splitting whencompared to E2PAS Therefore we can confirm the gain ofeach adaptive strategy by comparing APAS with EPAS andE2PAS respectively In EPAS and E2PAS the rates at which119877 decreases with increasing 119892min are noticeably steeper thanthose of OPAS and APAS This is mainly due to the fact thatin EPAS and E2PAS the power cannot be used efficiently
Therefore APAS outperforms EPAS and E2PAS significantlyat larger 119892min values For example APAS outperforms EPASand E2PAS in terms of 119877 by 20 and 40 at 119892min = 20 dBmrespectively On the other hand the performance differencebetween OPAS and APAS remains relatively constant despiteincreasing 119892min
6 Conclusion
RF-based information and energy transferring techniqueshold the potential to dramatically change the design of wire-less systems and their networking architecturesNeverthelessthe research community is still in the early stages of validatingtheir effectiveness In this work we target to tackle one of thestrongest assumptions that most of the works in informationand energy transfer made which is the assumption thatperfect channel estimation is possible We show throughan empirical study that the variability of wireless channelsmakes perfect estimations of the wireless environment closeto impossible For this we propose APAS which takes intoconsideration imperfect channel estimation results for eval-uating the effectiveness of information and energy transferon wireless devices for energy harvesting based SONs InAPAS the power allocation and splitting ratio is determinedadaptively with considerations for the estimated channelquality In addition our results indicate that APAS achievesnear-optimal performances under various conditions Wehope that this work can act as a catalyst in enabling futureresearch that tries to adopt RF-based information and energytransfer in realistic channel environments
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
This research was supported by Basic Science Research Pro-gram through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT amp FuturePlanning (2015R1C1A1A01051747)
References
[1] H Hu J Zhang X Zheng Y Yang and P Wu ldquoSelf-configuration and self-optimization for LTE networksrdquo IEEECommunications Magazine vol 48 no 2 pp 94ndash100 2010
[2] 3GPP ldquoSelf-organizing networks (SON) concepts and require-mentsrdquo 3GPP TS 32500 2008 V800
[3] G Y Li Z Xu C Xiong et al ldquoEnergy-efficient wireless com-munications tutorial survey and open issuesrdquo IEEE WirelessCommunications vol 18 no 6 pp 28ndash35 2011
[4] C Han T Harrold S Armour et al ldquoGreen radio radiotechniques to enable energy-efficient wireless networksrdquo IEEECommunications Magazine vol 49 no 6 pp 46ndash54 2011
[5] H Bogucka and A Conti ldquoDegrees of freedom for energysavings in practical adaptive wireless systemsrdquo IEEE Commu-nications Magazine vol 49 no 6 pp 38ndash45 2011
[6] HKulah andKNajafi ldquoEnergy scavenging from low-frequencyvibrations by using frequency up-conversion for wireless sensor
Mobile Information Systems 7
applicationsrdquo IEEE Sensors Journal vol 8 no 3 pp 261ndash2682008
[7] S Sudevalayam and P Kulkarni ldquoEnergy harvesting sensornodes survey and implicationsrdquo IEEE Communications Surveysand Tutorials vol 13 no 3 pp 443ndash461 2011
[8] T Le K Mayaram and T Fiez ldquoEfficient far-field radiofrequency energy harvesting for passively powered sensornetworksrdquo IEEE Journal of Solid-State Circuits vol 43 no 5 pp1287ndash1302 2008
[9] R J M Vullers R van Schaijk I Doms C Van Hoof and RMertens ldquoMicropower energy harvestingrdquo Solid-State Electron-ics vol 53 no 7 pp 684ndash693 2009
[10] M Pinuela P D Mitcheson and S Lucyszyn ldquoAmbient RFenergy harvesting in urban and semi-urban environmentsrdquoIEEE Transactions onMicrowaveTheory and Techniques vol 61no 7 pp 2715ndash2726 2013
[11] L R Varshney ldquoTransporting information and energy simul-taneouslyrdquo in Proceedings of the IEEE International Symposiumon Information Theory (ISIT rsquo08) pp 1612ndash1616 IEEE TorontoCanada July 2008
[12] P Grover and A Sahai ldquoShannonmeets tesla wireless informa-tion andpower transferrdquo inProceedings of the IEEE InternationalSymposium on Information Theory (ISIT rsquo10) pp 2363ndash2367IEEE Austin Tex USA June 2010
[13] L Liu R Zhang and K-C Chua ldquoWireless information trans-fer with opportunistic energy harvestingrdquo IEEE Transactions onWireless Communications vol 12 no 1 pp 288ndash300 2013
[14] L Liu R Zhang and K-C Chua ldquoWireless information andpower transfer a dynamic power splitting approachrdquo IEEETransactions on Communications vol 61 no 9 pp 3990ndash40012013
[15] A A Nasir X Zhou S Durrani and R A Kennedy ldquoRelayingprotocols for wireless energy harvesting and information pro-cessingrdquo IEEETransactions onWireless Communications vol 12no 7 pp 3622ndash3636 2013
[16] C Shen W-C Li and T-H Chang ldquoWireless information andenergy transfer in multi-antenna interference channelrdquo IEEETransactions on Signal Processing vol 62 no 23 pp 6249ndash62642014
[17] D W K Ng E S Lo and R Schober ldquoWireless informationand power transfer energy efficiency optimization in OFDMAsystemsrdquo IEEE Transactions on Wireless Communications vol12 no 12 pp 6352ndash6370 2013
[18] R Morsi D S Michalopoulos and R Schober ldquoMultiuserscheduling schemes for simultaneous wireless information andpower transfer over fading channelsrdquo IEEE Transactions onWireless Communications vol 14 no 4 pp 1967ndash1982 2015
[19] R A Berry and R G Gallager ldquoCommunication over fadingchannels with delay constraintsrdquo IEEE Transactions on Informa-tion Theory vol 48 no 5 pp 1135ndash1149 2002
[20] B Hassibi and B M Hochwald ldquoHow much training is neededin multiple-antenna wireless linksrdquo IEEE Transactions onInformation Theory vol 49 no 4 pp 951ndash963 2003
[21] J Gorski F Pfeuffer and K Klamroth ldquoBiconvex sets andoptimization with biconvex functions a survey and extensionsrdquoMathematical Methods of Operations Research vol 66 no 3 pp373ndash407 2007
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
6 Mobile Information Systems
0 1008060402018
19
20
21
22
23
Dat
a rat
e R
(bits
sH
z)
OPAS (gmin = 17 dBm)APAS (gmin = 17 dBm)
OPAS (gmin = 20 dBm)APAS (gmin = 20 dBm)
Training interval T120591
Tlowast120591 = 20 Rlowast = 205
Tlowast120591 = 10 Rlowast = 218
Figure 5 Data rate 119877 versus training interval 119879120591
10 2018161412
14
16
18
20
22
24
OPASAPAS
EPAS
Dat
a rat
e R
(bits
sH
z)
E2PAS
Harvested energy gmin (dBm)
Figure 6 Data rate 119877 versus harvested energy 119892min when 119879120591 = 20
estimation is desired to satisfy a large requirement for the har-vested energy There is no significant difference in particularless than 10 between the maximum 119877 achieved at 119879lowast
120591and
the optimal bound of 119877Figure 6 shows the data rate119877 versus the harvested energy
119892min when 119879120591= 20 As 119892min increases a large portion of
power should be used for harvesting energy In consequence119877 decreases gradually for all algorithms APAS has the gainof adaptive power allocation compared with EPAS while ithas the gain of adaptive power allocation and splitting whencompared to E2PAS Therefore we can confirm the gain ofeach adaptive strategy by comparing APAS with EPAS andE2PAS respectively In EPAS and E2PAS the rates at which119877 decreases with increasing 119892min are noticeably steeper thanthose of OPAS and APAS This is mainly due to the fact thatin EPAS and E2PAS the power cannot be used efficiently
Therefore APAS outperforms EPAS and E2PAS significantlyat larger 119892min values For example APAS outperforms EPASand E2PAS in terms of 119877 by 20 and 40 at 119892min = 20 dBmrespectively On the other hand the performance differencebetween OPAS and APAS remains relatively constant despiteincreasing 119892min
6 Conclusion
RF-based information and energy transferring techniqueshold the potential to dramatically change the design of wire-less systems and their networking architecturesNeverthelessthe research community is still in the early stages of validatingtheir effectiveness In this work we target to tackle one of thestrongest assumptions that most of the works in informationand energy transfer made which is the assumption thatperfect channel estimation is possible We show throughan empirical study that the variability of wireless channelsmakes perfect estimations of the wireless environment closeto impossible For this we propose APAS which takes intoconsideration imperfect channel estimation results for eval-uating the effectiveness of information and energy transferon wireless devices for energy harvesting based SONs InAPAS the power allocation and splitting ratio is determinedadaptively with considerations for the estimated channelquality In addition our results indicate that APAS achievesnear-optimal performances under various conditions Wehope that this work can act as a catalyst in enabling futureresearch that tries to adopt RF-based information and energytransfer in realistic channel environments
Competing Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
This research was supported by Basic Science Research Pro-gram through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT amp FuturePlanning (2015R1C1A1A01051747)
References
[1] H Hu J Zhang X Zheng Y Yang and P Wu ldquoSelf-configuration and self-optimization for LTE networksrdquo IEEECommunications Magazine vol 48 no 2 pp 94ndash100 2010
[2] 3GPP ldquoSelf-organizing networks (SON) concepts and require-mentsrdquo 3GPP TS 32500 2008 V800
[3] G Y Li Z Xu C Xiong et al ldquoEnergy-efficient wireless com-munications tutorial survey and open issuesrdquo IEEE WirelessCommunications vol 18 no 6 pp 28ndash35 2011
[4] C Han T Harrold S Armour et al ldquoGreen radio radiotechniques to enable energy-efficient wireless networksrdquo IEEECommunications Magazine vol 49 no 6 pp 46ndash54 2011
[5] H Bogucka and A Conti ldquoDegrees of freedom for energysavings in practical adaptive wireless systemsrdquo IEEE Commu-nications Magazine vol 49 no 6 pp 38ndash45 2011
[6] HKulah andKNajafi ldquoEnergy scavenging from low-frequencyvibrations by using frequency up-conversion for wireless sensor
Mobile Information Systems 7
applicationsrdquo IEEE Sensors Journal vol 8 no 3 pp 261ndash2682008
[7] S Sudevalayam and P Kulkarni ldquoEnergy harvesting sensornodes survey and implicationsrdquo IEEE Communications Surveysand Tutorials vol 13 no 3 pp 443ndash461 2011
[8] T Le K Mayaram and T Fiez ldquoEfficient far-field radiofrequency energy harvesting for passively powered sensornetworksrdquo IEEE Journal of Solid-State Circuits vol 43 no 5 pp1287ndash1302 2008
[9] R J M Vullers R van Schaijk I Doms C Van Hoof and RMertens ldquoMicropower energy harvestingrdquo Solid-State Electron-ics vol 53 no 7 pp 684ndash693 2009
[10] M Pinuela P D Mitcheson and S Lucyszyn ldquoAmbient RFenergy harvesting in urban and semi-urban environmentsrdquoIEEE Transactions onMicrowaveTheory and Techniques vol 61no 7 pp 2715ndash2726 2013
[11] L R Varshney ldquoTransporting information and energy simul-taneouslyrdquo in Proceedings of the IEEE International Symposiumon Information Theory (ISIT rsquo08) pp 1612ndash1616 IEEE TorontoCanada July 2008
[12] P Grover and A Sahai ldquoShannonmeets tesla wireless informa-tion andpower transferrdquo inProceedings of the IEEE InternationalSymposium on Information Theory (ISIT rsquo10) pp 2363ndash2367IEEE Austin Tex USA June 2010
[13] L Liu R Zhang and K-C Chua ldquoWireless information trans-fer with opportunistic energy harvestingrdquo IEEE Transactions onWireless Communications vol 12 no 1 pp 288ndash300 2013
[14] L Liu R Zhang and K-C Chua ldquoWireless information andpower transfer a dynamic power splitting approachrdquo IEEETransactions on Communications vol 61 no 9 pp 3990ndash40012013
[15] A A Nasir X Zhou S Durrani and R A Kennedy ldquoRelayingprotocols for wireless energy harvesting and information pro-cessingrdquo IEEETransactions onWireless Communications vol 12no 7 pp 3622ndash3636 2013
[16] C Shen W-C Li and T-H Chang ldquoWireless information andenergy transfer in multi-antenna interference channelrdquo IEEETransactions on Signal Processing vol 62 no 23 pp 6249ndash62642014
[17] D W K Ng E S Lo and R Schober ldquoWireless informationand power transfer energy efficiency optimization in OFDMAsystemsrdquo IEEE Transactions on Wireless Communications vol12 no 12 pp 6352ndash6370 2013
[18] R Morsi D S Michalopoulos and R Schober ldquoMultiuserscheduling schemes for simultaneous wireless information andpower transfer over fading channelsrdquo IEEE Transactions onWireless Communications vol 14 no 4 pp 1967ndash1982 2015
[19] R A Berry and R G Gallager ldquoCommunication over fadingchannels with delay constraintsrdquo IEEE Transactions on Informa-tion Theory vol 48 no 5 pp 1135ndash1149 2002
[20] B Hassibi and B M Hochwald ldquoHow much training is neededin multiple-antenna wireless linksrdquo IEEE Transactions onInformation Theory vol 49 no 4 pp 951ndash963 2003
[21] J Gorski F Pfeuffer and K Klamroth ldquoBiconvex sets andoptimization with biconvex functions a survey and extensionsrdquoMathematical Methods of Operations Research vol 66 no 3 pp373ndash407 2007
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mobile Information Systems 7
applicationsrdquo IEEE Sensors Journal vol 8 no 3 pp 261ndash2682008
[7] S Sudevalayam and P Kulkarni ldquoEnergy harvesting sensornodes survey and implicationsrdquo IEEE Communications Surveysand Tutorials vol 13 no 3 pp 443ndash461 2011
[8] T Le K Mayaram and T Fiez ldquoEfficient far-field radiofrequency energy harvesting for passively powered sensornetworksrdquo IEEE Journal of Solid-State Circuits vol 43 no 5 pp1287ndash1302 2008
[9] R J M Vullers R van Schaijk I Doms C Van Hoof and RMertens ldquoMicropower energy harvestingrdquo Solid-State Electron-ics vol 53 no 7 pp 684ndash693 2009
[10] M Pinuela P D Mitcheson and S Lucyszyn ldquoAmbient RFenergy harvesting in urban and semi-urban environmentsrdquoIEEE Transactions onMicrowaveTheory and Techniques vol 61no 7 pp 2715ndash2726 2013
[11] L R Varshney ldquoTransporting information and energy simul-taneouslyrdquo in Proceedings of the IEEE International Symposiumon Information Theory (ISIT rsquo08) pp 1612ndash1616 IEEE TorontoCanada July 2008
[12] P Grover and A Sahai ldquoShannonmeets tesla wireless informa-tion andpower transferrdquo inProceedings of the IEEE InternationalSymposium on Information Theory (ISIT rsquo10) pp 2363ndash2367IEEE Austin Tex USA June 2010
[13] L Liu R Zhang and K-C Chua ldquoWireless information trans-fer with opportunistic energy harvestingrdquo IEEE Transactions onWireless Communications vol 12 no 1 pp 288ndash300 2013
[14] L Liu R Zhang and K-C Chua ldquoWireless information andpower transfer a dynamic power splitting approachrdquo IEEETransactions on Communications vol 61 no 9 pp 3990ndash40012013
[15] A A Nasir X Zhou S Durrani and R A Kennedy ldquoRelayingprotocols for wireless energy harvesting and information pro-cessingrdquo IEEETransactions onWireless Communications vol 12no 7 pp 3622ndash3636 2013
[16] C Shen W-C Li and T-H Chang ldquoWireless information andenergy transfer in multi-antenna interference channelrdquo IEEETransactions on Signal Processing vol 62 no 23 pp 6249ndash62642014
[17] D W K Ng E S Lo and R Schober ldquoWireless informationand power transfer energy efficiency optimization in OFDMAsystemsrdquo IEEE Transactions on Wireless Communications vol12 no 12 pp 6352ndash6370 2013
[18] R Morsi D S Michalopoulos and R Schober ldquoMultiuserscheduling schemes for simultaneous wireless information andpower transfer over fading channelsrdquo IEEE Transactions onWireless Communications vol 14 no 4 pp 1967ndash1982 2015
[19] R A Berry and R G Gallager ldquoCommunication over fadingchannels with delay constraintsrdquo IEEE Transactions on Informa-tion Theory vol 48 no 5 pp 1135ndash1149 2002
[20] B Hassibi and B M Hochwald ldquoHow much training is neededin multiple-antenna wireless linksrdquo IEEE Transactions onInformation Theory vol 49 no 4 pp 951ndash963 2003
[21] J Gorski F Pfeuffer and K Klamroth ldquoBiconvex sets andoptimization with biconvex functions a survey and extensionsrdquoMathematical Methods of Operations Research vol 66 no 3 pp373ndash407 2007
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
