Research Article SDN Controlled mmWave Massive MIMO Hybrid ...downloads.hindawi.com/journals/misy/2016/9767065.pdf · massive MIMO systems [ , ]. Nevertheless, the development of

Research ArticleSDN Controlled mmWave Massive MIMO Hybrid Precoding for5G Heterogeneous Mobile Systems

Na Chen1 Songlin Sun1 Michel Kadoch2 and Bo Rong3

1School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing 100876 China2Department of Electrical Engineering Ecole de Technologie Superieure Universite du Quebec Montreal QC Canada H3C 1K33Communications Research Centre Canada Ottawa ON Canada K2H 8S2

Correspondence should be addressed to Songlin Sun slsunbupteducn

Received 6 November 2015 Revised 1 February 2016 Accepted 9 February 2016

Academic Editor Qilian Liang

Copyright copy 2016 Na Chen et alThis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

In 5G mobile network millimeter wave (mmWave) and heterogeneous networks (Hetnets) are significant techniques to sustaincoverage and spectral efficiency In this paper we utilize the hybrid precoding to overcome hardware constraints on the analog-only beamforming in mmWave systems Particularly we identify the complicated antenna coordination and vast spatial domaininformation as the outstanding challenges in mmWave Hetnets In our work we employ software defined network (SDN) toaccomplish radio resource management (RRM) and achieve flexible spacial coordination in mmWave Hetnets In our proposedscheme SDN controller is responsible for collecting the user channel state information (CSI) and applying hybrid precoding basedon the calculated null-space of victim users Simulation results show that our design can effectively reduce the interference to victimusers and support high quality of service

1 Introduction

In the past few years the fourth-generation (4G) mobilecommunication systems have achieved rapidly deploymentand operated successfully in many countries around theworld Nevertheless the widespread use of smart phones andtablet personal computers has brought about an increasingdemand for mobile data access Under such a conditionthe fifth-generation (5G) mobile system has attracted muchattention as a means of providing enhanced connectivity formore diversified devices with higher mobility 5G mobilesystem is expected to dominate future implementations oftelecommunication networks and its related technologieshave become popular research topics

The major challenge in the 5G era is to efficientlysupport the increasing demand of network capacity whilethe spectrum resource remains scarce [1] That is to say thefuture network is expected to guarantee quality of service(QoS) while handling the complex context of operationscharacterized by a tenfold increase in traffic [2] Moreoverservice providers are paying more attention to the operatingexpense (OPEX) and the influence on global climate change

which makes the energy efficiency an urgent issue [3] Underthis condition operators promote the Hetnet architecturewhere a cellular system consists of a large number of densifiedlow power nodes (LPNs) or small cells [4 5] In HetnetsLPNs can support high data rate to nearby mobile users andtherefore improve the system capacity by frequency reuseFurthermore LPNs possess significant reduction in energyconsumption as they can transmit signals with lower power[6]

MmWave and massive multiple-input multiple-output(MIMO) techniques are also regarded as promising solutionsfor future 5Gmobile system for they can provide tremendousincrease in both the spectrum efficiency and the availablebandwidth [7] Massive MIMO tends to scale up the con-ventional MIMO to increase network capacity and mmWaveenables a large antenna array to be arranged in a small sizeas it has short wavelength associated with high frequenciesOn the other hand by exploiting the precoding technique toconcentrate the signal in a specific direction massive MIMOcan provide sufficient antenna gains to compensate for theserious signal attenuation brought by mmWave frequencies

Hindawi Publishing CorporationMobile Information SystemsVolume 2016 Article ID 9767065 10 pageshttpdxdoiorg10115520169767065

2 Mobile Information Systems

In mmWave systems the precoding schemes are mainlyperformed in the baseband domain where a fully digitalprecoder is utilized to modify both the amplitude and phaseof transmitted signals Although the fully digital precodingscheme can achieve optimal performance it brings enormoushardware complexity and energy consumption as it requiresan expensive radio frequency (RF) chain for every antennaThus this becomes a serious problemdue to the huge numberof antennas at the base station (BS) in mmWave systemsFortunately the hybrid precoding technique combining dig-ital precoding and analog precoding has been proposedto significantly reduce the number of RF chains which isconsidered as an essential technique for realistic mmWavemassive MIMO systems [8 9]

Nevertheless the development of Hetnets and massiveMIMO induces an increasing network complexity whichtends to the virtualization of network functionality [1011] For example massive MIMO leads to the problem ofmanaging huge spatial domain resource and processing vastinformation in mmWave systems To tackle such conditionsSDN is introduced owing to its inherent advantage in inte-grating and processing information [12 13]

In our work we study how SDN benefits the RRM in5G mmWave Hetnets We proposed a network architecturewhich is consistent with 5G concepts and also compatibleto current 3GPP LTE Advanced (LTE-A) standard In 4Gnetwork the main issue for RRM is the time and frequencydomain management such as frequency reuse [14] intercellinterference coordination (ICIC) [15] and enhanced inter-cell interference coordination (eICIC) In 5G however thesystem has to reach the overwhelming densities of devicesand network infrastructure nodes as well as supporting anastonishing numbers of antennas in massive MIMO systems[16] In mmWave 5G Hetnets there are macrobase station(macro-BS) and small cells all employing multiple antennaswhich leads to huge spacial domain information Thus itbecomes a critical task formmWaveHetnets to retrieve storeand utilize the spatial information In our proposed architec-ture SDN takes the responsibility for integrating huge spacialdomain information and performing null-space calculationconcerning massive MIMO coordination By doing this thenull-space information is integrated to enable whole networkcoordination and achieves a better performance than thenetworks without SDN This novel architecture implementscoordinated RRM and eases the operation pressure of accesspoints significantly

The rest of this paper is organized as follows Section 2presents the 5G SDN architecture supporting RRM Section 3introduces the mmWave technology and Section 4 empha-sizes the mmWave precoding problem In Section 5 wepropose our two-tier interference free mmWave hybrid pre-coding scheme followed by the performance evaluation inSection 6 In the end Section 7 concludes the paper

2 SDN Controlled 5G HeterogeneousMobile System

In 5G mobile system Hetnets massive MIMO and SDNare expected to be the key technologies sustaining operation

Femtosmall cell

Femtosmall cell

MacrocellPicocell

Cell phone

Cell phone

Cell phone

Cell phone

Cell phone

Figure 1 A generalized architecture of heterogeneous network

performance In this section we will overview these frontiertechnologies and introduce our SDN assisted 5G architectureinvolving mmWave system

21 Hetnet Hetnet is regarded as the most viable solutionto the impending mobile data traffic crunch in the contextof LTE-A in the industry It builds different layers andcell sizes by deploying eNodeBs of different transmissionpowers such as macro micro pico and femto as shownin Figure 1 Hetnet becomes an inevitable trend for futuredevelopment of information networks since it makes full useof the complementary characteristics of different networktiers [17 18] Nevertheless different tiersrsquo networking accesstechnologies and various service requirements make spatialdomain management a new problem we have to solve In 5GHetnets we involve SDN controller to collect and managespacial information from a variety of access points

22 Massive MIMO In massive MIMO systems the antennaarrays consisting of a few hundred elements simultaneouslyserve many tens of mobile users with the same time-frequency resource [19] Massive MIMO is an enabler forfuture broadband (fixed and mobile) networks which willbe energy-efficient spectrum-efficient and robust Howevermassive MIMO antenna arrays engender huge amounts ofspacial domain information in real time which significantlyincreases the computational complexity and brings aboutthe massive MIMO coordination problem The network canoperate well only when SDN processes spacial informationsuccessfully

23 SDN The concept of SDN was first introduced in the1990s and became popular in the 21st century The architec-ture of SDN was formally defined by the open networkingforum (ONF) In SDN architecture network devices consistof three layers that is application layer control layer andinfrastructure layer [20]

Currently SDN has found its best practice in data centersand has been paid increasing attention by network organiza-tions around the world [21] SDN is regarded as a promisingmethod to solve current and emerging problems for future 5G

Mobile Information Systems 3

LPN intended users

Mobility management

LPN victim users

Null space analysis

BS users

RRM

Controller 1 API Controller 2 API

Controller 1 (eg CSI acquirement)

Controller 2 (eg null-space calculation)

Network virtualization layer

LPN

SDN controllerMobile terminals

BS

LPN

API

SDN controller

Figure 2 5G SDN architecture for spatial domain management

Hetnets [22 23] Figure 2 illustrates our proposed architec-ture where an SDN controller is employed to manage spatialinformation and conduct massive MIMO coordination for5G network In this architecture user information is firstcollected by BSLPNs and then reported to SDN SDNcontroller will perform the information processing suchas CSI analysis and null-space calculation SDN controllerthen sends the processed results and instructions back toBSLPNs to perform better coordination concerning theglobal knowledge of network spatial information In thisarchitecture SDN takes over the heavy operation fromBS andLPNs to increase the efficiency of 5G networks

3 Millimeter Wave Technology

31 Millimeter Wave Introduction Millimeter wave is apromising technology for future 5G cellular networks In corenetwork the mmWave system is expected to bring high-capacity wireless access networks in the future [24] Futurenetwork equipped with mmWave can achieve super widebandwidth as the frequency of mmWave ranges from 265 to300GHz Moreover mmWave has a much narrower antennabeam size compared to the microwave therefore it can aimat the target more precisely Practically mmWave cellularsystems can not alone provide high quality service across arange of deployments Thus mmWave network is generallyheterogeneous due to the inherent limitations of mmWavepropagation As shown in Figure 3 it is quite likely that localarea networks and cellular network will blur over time

32 5G mmWave Hetnet The current 4G cellular networksare capable of providing reliable communications and seam-less coverage because of the lower frequency band they useIn order to implement smooth and cost-efficient transitionfrom 4G to 5G one of the solutions is to deploy 4G +

PrivateISP

Operatorcore

network

Multioperatorand third-party backhaul

mmWave indoor andoutdoor small cells

Directional mmWavepico- and relays

with macrooverlay

Figure 3 Future network implementing mmWave systems

mmWave system structure in 5G Hetnets as illustrated inFigure 4 This 5G Hetnet architecture consists of 4G basestations mmWave base stations and mobile devices In thisarchitecture of 4G + mmWave 4G network is responsible fortransmitting management information and low rate data liketext and voice Meanwhile the mmWave band takes over thetransmission of high-rate multimedia applications [1 25]

4 Millimeter Wave Precoding

41 mmWave Precoding Problems With a large number ofantennas linear precoding has low complexity and compa-rable performance to its counterpart nonlinear precodingHowever MIMO precoding in mmWave systems is generallydifferent than the precoding at lower frequencies To exploit


4G base station

mmWave base station

Mobile device

Figure 4 The 4G + mmWave system structure

multiple antennas the conventional precoding schemes tendto modify the amplitudes and phases of the complex symbolsat the baseband and then upconvert the processed signal toaround the carrier frequency after passing through digital-to-analog (DA) converters mixers and power amplifiers(ie RF chains) Thus each antenna element needs to besupported by a dedicated RF chain Actually this is tooexpensive to be implemented in mmWave systems due to thelarge number of antennas [1]

42 Hybrid Precoding To solve the above problem hybridprecoding structurewith active antennas is consideredwhichgenerally controls the signal phase on each antenna via anetwork of analog phase shifters As illustrated in Figure 5a hybrid precoding structure is divided into analog anddigital domains Firstly user data streams are precodedby the baseband digital processing Then the streams aretransferred by the corresponding RF chains andmapped intoeach antenna element by the analog phase processing (RFprocessing) In the process the number of RF chains is lowerthan that of antennas which reduces the complexity andpower consumption in mmWave system Notably basebanddigital processing can make both amplitude and phase mod-ifications but analog phase processing only gets the phasechanges with variable phase shifters and combiners [15]

DigitalprecodingData

streams

RFchain

Phaseshifter

RFchain

Phaseshifter

Figure 5 Base station hybrid precoding arrangement

5 Two-Tier Interference Free mmWaveHybrid Precoding Scheme

In this section we propose our scheme to implement effectiveRRM inmmWave 5G network Here we use null-space basedhybrid precoding to overcome the constraints and mitigateintertier interference inmmWave systemWe also distinguishour work by involving an SDN controller to handle massiveMIMO coordination for the whole 5G network

51 System Model Let us assume a downlink mmWavesystemwith119870 single-antenna users as shown in Figure 8TheBS andLPNs are all equippedwithmassiveMIMOcontaining119873BS and119873 transmit antennas respectively Let119870119895 denote thenumber of users of the 119895th LPN Define 119862119895 as the number ofusers interfered by the 119895th LPN and served by other nodes119870119895 and 119862119895 should meet the constraint119870119895 +119862119895 le 119873 Thus thestationary channel H119895 isin C(119870119895+119862119895)times119873 between a certain LPN 119895

and the users it serves is

H119895 =

[[[[[[[[[[[[[[

[

H1198951

H119895119870119895H119895119870119895+1

H119895119870119895+119862119895

]]]]]]]]]]]]]]

]

(1)

where the 119896th row vectorH119895119896 isin C1times119873 is the channel betweenthe 119895th LPN and the 119896th user Specially H0 denotes thestationary channel between macro-BS and the users it servesH0119896 denotes the channel between the macro-BS and the 119896thuser


Source bits

Hybridprecoding

Null-space

Poweramplifier

SDNcontroller

UsersVictimusers

s

x

n

y

CSI

V(0)

Figure 6 Block diagram of our proposed scheme

Based on the proposed SDN architecture mentionedabove Figure 6 illustrates our processing diagram emphasiz-ing the acquisition of CSI for LPNs and the generation of null-space based hybrid precoding matrix And we will expoundthe details in the following

52 CSI Acquisition Method In practice LPNs is only able tocollect the local CSI through the backward channel of theirown served users Thus they can not access to the CSI ofthe external victim users interfered by them Fortunately thehub-spoke structure of the SDNnetwork enables theCSI of allmmWave channels to be collected and disseminated throughthe backhaul link [26]

In our proposed CSI acquisition method as illustratedin Figure 7 BS collects and reports the CSI information tothe SDN controller More exactly BS will send SDN thechannel matrix on victim users of each LPN which meanssending SDN every [H119867119895119870119895+1 H

119867119895119870119895+119862119895

]119867 corresponding to

the 119895th LPNThen the SDNcontrollerwill perform the block-diagonal (BD) algorithm and generate null-space vector V(0)119895Vfor LPN 119895 After that the SDN controller computes on thenull-space vector V(0)119895V to gain precoding matrix by the hybridprecoding algorithmproposed in the next subsection FinallySDN controller sends the precoding matrix to each LPNthrough downlink information In this way the SDN con-troller takes over all the matrix decomposition computation

and precoding This framework is more helpful when theLPNs are too simple to handle the computational work

Evidently when the precoding vectors span a subspace ofthe null-space of the channel vectors from victim users theinterferences from the intendedusers cannot affect the victimusers According to this the hybrid precoding matrix can beconstructed by processing the mmWave channel matrix

53Hybrid Precoding Based onNull-Space Hybrid precodingis divided among baseband and RF processing denoted byD isin C119870times119870 and A isin C119873times119870 as illustrated in Figure 8

Each entry of A is normalised to satisfy |A119886119887| = 1radic119873where |A119886119887| denotes the magnitude of the (119886 119887)th element ofA To clarify denote A119886119887 as the (119886 119887)th element of A and weperform the RF precoding according to

A119886119887 =1

radic119873

119890119887120593119886119887 (2)

where 120593119886119887 is the phase of the (119886 119887)th element of the conjugatetranspose of the stationary channel that is H119867 At thebaseband we observe an equivalent channel Heq = HA of alow dimension119870times119870 whereH is the stationary channel Weuse a ZF precoding as the digital precoding algorithm thusbaseband precoding is performed as

D = H119867eq (HeqH119867eq)minus1Λ (3)

where Λ is a diagonal matrix introducing for column powernormalization

The received signal of the 119896th user can be written as

119910119895119896 = H119895119896A119895D119895s119895119896 +H0119896A0D01199040 + 119899119895119896 (4)

where s119895 isin C119870119895times1 is the transmitted signal vector for a total of119870119895 users And s0 is the transmitted signal vector at BS 119899119895119896 isthe additive white Gaussian noise of zero mean and variance1205902For the 119895th LPN the complementary space concerning

the victim users is given as

H119895V = [H119867119895119870119895+1 sdot sdot sdotH119867119895119870119895+119862119895

]

119867

(5)

where H119895V contains the channel matrix of all the victim usersinterfered by the 119895th LPN To avoid the interference to thevictim users the hybrid precoding matrix should satisfy thefollowing condition

H119895VA119895D119895 = 0119862119895times119870119895 (6)

By performing SVD H119895V can be further written as

H119895V = U119895VΛ119895VV119867

119895V

= U119895V [[

sum

119895V0]]119862119895times119873

[k119895V1k119895V2 sdot sdot sdot k119895V119873]119867

(7)


SDN controller

CSIacquisition

BS

SVD

Hybridprecoding

DownlinkinformationReport

Projection

lfloorlceil

rfloorrceilHjK119895+1

HjK119895+C119895

V(0)

j

AjDj FjDj FjDj

LPN j

Hj1

HjK119895lfloorlceil

rfloorrceil

middot middot middot

Figure 7 Proposed CSI acquirement methods

Basebandprecoder

D

Data streams

RFchain 1

RFprecoder

A

UEK

UE

UE1

UE

N

RFchain K K + C

K + 1

Figure 8 Hybrid mmWave precoding structure

Define the null-space vector V(0)119895V = [k119895V119862119895+1 sdot sdot sdot k119895V119873]

Since the column vectors belonging to V(0)119895V locate in the null-

space of all victim users we will have H119895VV(0)

119895V = 0For the 119895th LPN define the projection matrix M119895 based

on the null-space of victim users by

M119895 = V(0)119895V (V(0)

119895V)119867

(8)

The new analog precoding matrix after projection can becalculated as

F119895 = M119895A119895 (9)

Define 119875119895 as the transmit power at the 119895th LPN satisfying119864[s119895s119867119895 ] = (119875119895119870119895)I119870119895 where s119895 denotes the signal vector for119870119895 users in totalWe further normalizeF119895 to satisfy F119895D1198952119865 =119870119895 for the total transmit power constraint

Thus the SINR of the 119896th user served by LPN 119895 is givenas

SINR119895119896 =(119875119895119870119895)

10038161003816100381610038161003816H119895119896F119895d119895119896

10038161003816100381610038161003816

2

1205902119895119896

+ sum119870119895

119903=1119903 =119896(119875119895119870119895)

10038161003816100381610038161003816H119895119896F119895d119895119903

10038161003816100381610038161003816

2 (10)

where d119895119903 denotes the 119903th column ofD119895To support each userrsquos QoS requirement the SINR should

meet

119861 log (1 + SINR119895119896) ge 119877119895119896 (11)

for 119895 = 1 2 119869 and 119896 = 1 2 119870119895 where 119877119895119896 is thedata rate demanded by user 119896 and 119861 is the total bandwidthThen the minimum F119895119896 can be solved by the following linearequation system

119861 log (1 + SINR119895119896) = 119877119895119896 (12)


minus300 minus200 minus100 0 100 200 300minus250

minus200

minus150

minus100

minus50

0

50

100

150

200

250

X (m)

Y (m

)

eNBLPN

BS userExtra LPN user

LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

1

2

3

45

6

7

8

9

10

11

12

13

14

15

1617

18

1920

21

22

23

24

25

26

27

2829

30 3132

3334

3536

3738

3940

4142

4344

4546

4748

4950

5152

5354

(a) The topology of the macrocell

minus250 minus200 minus150 minus100 minus50 0 50 100 150 200 250minus250

minus200

minus150

minus100

minus50

0

50

100

150

200

250

LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

4948 50

47

52

Associated userVictim user

X (m)

Y (m

)

eNBLPN

(b) The topology of the LPN 1

Figure 9 The overall topology

54 Improvements of Our Proposed Algorithm Comparingwith the conventional linear precoding schemes the pro-posed null-space based hybrid precoding algorithm has thefollowing improvements

541 SDN Aided CSI Acquisition In conventional Hetnetscommunication system the served user equipment reports itsCSI to the connected access point but not to the other nodesinterferingwith it In this case LPNs can not detect the victimusersrsquo CSI However by performing our proposed algorithmthe SDN controller can get enough information to form theprecoding vector that avoids the interference with the victimusers Furthermore the computational burden onLPNcan berelaxed by letting SDN perform the null-space construction[27]

542 Reduced Complexity Conventional linear precodingschemes typically require119873 RF chains which means tremen-dous hardware complexity By involving hybrid precodingthe number of RF chains in mmWave system is reducedfrom antennas scale to users scale Therefore the proposedhybrid precoding scheme owns a much lower complexitythan conventional linear precoding schemes In practice ourproposed scheme can significantly reduce the complexity andpower consumption in 5G mmWave Hetnets

543 Improved QoS By performing hybrid precoding basedon null-space of the victim users the interference with thevictim users can be totally avoided for the channel matrixof victim users and the generated precoding vectors areorthogonal to each other Meanwhile the SINR calculationis adjusted to further conform to the practice Thus it caneffectively mitigate the interference to achieve improved QoS

for users with satisfying power consumption cooperatingwith hybrid precoding algorithm

6 Simulation Results

To evaluate the performance of our proposed hybrid pre-coding scheme this section presents the numerical resultsbased on simulations in 5G Hetnets scenario We apply thenetwork topology that combines mmWave system and smallcells and implement our proposed algorithm to achieve targetQoS with low complexity We deploy the hybrid precodingto reduce hardware complexity in mmWave systems and takethe property of null-space to mitigate the interference withthe victim users

Figure 9 illustrates the simulation scenario where theintended users are marked in red and victim users in greenMeanwhile we analyze the system performance over differ-ent simulation parameters and channel realizations Table 1displays the main simulation parameters which characterizethe macrocell and the LPNs

The channels model is similar to that for heterogeneousdeployments suggested by the 3GPP LTE standard but thesmall-scale fading is assumed to be Rayleigh according torecent works on massive MIMO The correlation matrixbetween the BS and each user is modeled according to thephysical channel model where the main characteristics areantenna correlation and reduces rank channels

In the following we will present the simulation results inthe the scenario as described in Figure 9 in which each userhas the QoS demand of 20Mbps In the proposed precodingscheme SDN performs the null-space construction andtransmits the corresponding part of the precoding vector tothe LPNs as described in Section 4 For comparison pur-poses we also simulated two conventional linear precoding


Table 1 Simulation parameters

Parameters ValuesNumber of the macrocell users 119873BS isin 20 28 36 60 42Antennas of the macrocell 119873BS isin 52 56 60 112 50Antennas of the LPNs 119873LPN isin 4 5 6 8

Macrocell radius 500mLPNs radius 50mTotal bandwidth 1GhzNumber of per LPN users 4

30 40 50 60 70 80 900

5

10

15

20

25

Antennas at the BS

Syste

m th

roug

hput

(Mbp

s)

Null-space based hybrid precodingNull-space based ZF precodingNull-space based MRT precoding

Figure 10 System throughput of the different number of antennas

schemes max ratio transmission (MRT) and ZF which arenull-space based aswellTheoretically ZF precodingwill havethe optimal performanceThe simulation results contain boththe conventional linear precoding schemes and the proposedhybrid precoding scheme

61 System Throughput by Different Number of AntennasFigure 10 shows the system throughput effected by the num-ber of massive MIMO antennas at access points We comparethe proposed hybrid precoding scheme to the linear ZF andMRT precoding schemes which all combine the null-spacemethod to mitigate inner-tier interference Massive MIMOshowed a performance improvement in system throughputand the increased antennas can eliminate the interferencewith the victim users We can see that the null-space basedhybrid precoding scheme gains considerable throughputcomparing to the optimal ZF precoding and overwhelms theMRT precoding when antennas increaseThismeans that ourproposed precoding scheme can offer a complexity reductionat a comparable performance [28]

62 Spectral Efficiency by Different SNR Figure 11 comparesthe spectral efficiency of the conventional linear precoding

SNR (dB)

Spec

tral

effici

ency

(bps

Hz)

0

5

10

15

20

25

30


minus20 minus15 minus10 minus5 0 5 10 15 20 25 30

Figure 11 Spectral efficiency of different SNR of users

2 3 4 5 6 7 8 90

20

40

60

80

100

120

140

Rate requirement of the single user (Mbps)

Tota

l pow

er (W

)

Null-space based MRT precodingNull-space based hybrid precodingNull-space based ZF precoding

Figure 12 Impact of the different QoS requirements

schemes and the proposed hybrid one under different SNRconditions We can see that the hybrid precoding schemehas a higher spectral efficiency than the MRT precodingIt means that this hybrid precoding scheme is suitable forrealistic mmWave system since it is physically practical andgains comparable spectral efficiency

63 Utility of Different Algorithms Impacted by Different QoSRequirements Figure 12 compares the different algorithmsunder different QoS requirements As we can see the null-space based hybrid precoding scheme is still comparableto the ZF precoding and superior to the MRT precodingin the power consumption aspect Figure 12 also shows


1 2 3 4 5 6 7 80

2

4

6

8

10

12

14

16

18

20

Extra antennas number

SIN

R of

vic

tim u

sers

(dB)

Conventional hybrid precodingNull-space based hybrid precoding

Figure 13 Effect of the extra antennas

the relationship between the user QoS and the correspondingpower consumption By using the null-space based precodingschemes the interference of the other BSLPNs with thevictim users can be totally eliminated which consumes lesspower than the non-null-space based ones [29]

64 SINR Effect by Different Number of Extra AntennasFigure 13 illustrates the SINR distribution of the conventionalhybrid precoding scheme [29] and the proposed null-spacebased one The hybrid precoding scheme named phased-ZF (PZF) essentially applies phase-only control at the RFdomain and then performs a low-dimensional basebandzero-forcing (ZF) precoding based on the effective channelseen from baseband As we can see the proposed schemehas a better SINR which indicates an improved systemperformanceThe difference between these two schemesrsquo per-formances is due to the fact that the victim users encounterstrong interference from other nodes in the conventionalscheme while the proposed hybrid precoding scheme utilisesthe extra antennas to indicate victim users and then projectits users to the corresponding null-space Hence it elim-inates the strong interference to the victim users and theSINR of the victim users will increase In Figure 13 theaccess points are not equipped with sufficient antennas andthe interference can only be partially removed The extraantennas can indicate more victim users and therefore thenew system can eliminate more interference When there aresufficient antennas it can totally eliminate the interferencefrom other BSLPNs This means that having more extraantennas contributes to achieving higher SINR significantly

In general the proposed mmWave precoding schemecan drastically alleviate the interference and maintain adesirable system performance for next-generation HetnetsThe projection to the null-space protects the victim users andthe combined hybrid precoding algorithms reduce hardware

complexity in mmWave systems Comparing to the conven-tional design the new scheme is practical and has a satisfyingsystem throughput with low complexity by taking advantageof the SDN aided CSI acquisition

7 Conclusion

In this paper we focus on the spatial domain resourcemanagement and interference mitigation in mmWave Het-nets We have explored a centralized 5G architecture usingSDN technology to provide coordinated RRM for the wholenetwork Moreover we have proposed a lightweight CSIacquisition method and a null-space based hybrid precodingscheme Our work can significantly oppress the impacton neighboring victim users in LPN-covered small cellsSimulation results show that our proposed design can supportsufficient network capacity and improve SINR of the smallcell users This design also has an advantage in practice as itovercomes the physical constraints in mmWave system

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work is supported in part by National Natural ScienceFoundation of China (NSFC) under Grant no 61471066 andthe scholarship fromChina ScholarshipCouncil (CSC) underGrant CSC no 201506470023

References

[1] R Q Hu and Y Qian ldquoAn energy efficient and spectrumefficient wireless heterogeneous network framework for 5Gsystemsrdquo IEEECommunicationsMagazine vol 52 no 5 pp 94ndash101 2014

[2] M Palkovic P Raghavan M Li A Dejonghe L Van der Perreand F Catthoor ldquoFuture software-defined radio platforms andmapping flowsrdquo IEEE Signal Processing Magazine vol 27 no 2pp 22ndash33 2010

[3] I Chih-Lin C Rowell S Han Z Xu G Li and Z Pan ldquoTowardgreen and soft a 5G perspectiverdquo IEEE Communications Maga-zine vol 52 no 2 pp 66ndash73 2014

[4] R Q Hu Y Qian S Kota and G Giambene ldquoHetnetsmdasha newparadigm for increasing cellular capacity and coveragerdquo IEEEWireless Communications vol 18 no 3 pp 8ndash9 2011

[5] R Q Hu and Y Qian Heterogeneous Cellular Networks JohnWiley amp Sons Hoboken NJ USA 2013

[6] X Duan and X Wang ldquoAuthentication handover and privacyprotection in 5G hetnets using software-defined networkingrdquoIEEE Communications Magazine vol 53 no 4 pp 28ndash35 2015

[7] E G Larsson O Edfors F Tufvesson and T L MarzettaldquoMassive MIMO for next generation wireless systemsrdquo IEEECommunications Magazine vol 52 no 2 pp 186ndash195 2014

[8] A Alkhateeb O El Ayach G Leus and R W Heath ldquoChannelestimation and hybrid precoding for millimeter wave cellularsystemsrdquo IEEE Journal on Selected Topics in Signal Processingvol 8 no 5 pp 831ndash846 2014


[9] A Alkhateeb J Mo N Gonzalez-Prelcic and R W Heath JrldquoMIMO precoding and combining solutions for millimeter-wave systemsrdquo IEEE Communications Magazine vol 52 no 12pp 122ndash131 2014

[10] V Jungnickel K Manolakis W Zirwas et al ldquoThe role of smallcells coordinated multipoint and massive MIMO in 5Grdquo IEEECommunications Magazine vol 52 no 5 pp 44ndash51 2014

[11] B Panzner W Zirwas S Dierks et al ldquoDeployment and imple-mentation strategies for massive MIMO in 5Grdquo in Proceedingsof the IEEE GlobecomWorkshops (GCWkshps rsquo14) pp 346ndash351Austin Tex USA December 2014

[12] C-F Lai R-H Hwang H-C Chao M M Hassan and AAlamri ldquoA buffer-aware HTTP live streaming approach forSDN-enabled 5G wireless networksrdquo IEEE Network vol 29 no1 pp 49ndash55 2015

[13] P Ameigeiras J J Ramos-Munoz L Schumacher J Prados-Garzon J Navarro-Ortiz and J M Lopez-Soler ldquoLink-levelaccess cloud architecture design based on SDN for 5G net-worksrdquo IEEE Network vol 29 no 2 pp 24ndash31 2015

[14] S Sun B Rong and Y Qian ldquoArtificial frequency selectivechannel for covert cyclic delay diversity orthogonal frequencydivision multiplexing transmissionrdquo Security and Communica-tion Networks vol 8 no 9 pp 1707ndash1716 2015

[15] Q Li R Q Hu Y Qian and G Wu ldquoCooperative communica-tions for wireless networks techniques and applications in LTE-advanced systemsrdquo IEEE Wireless Communications vol 19 no2 pp 22ndash29 2012

[16] M Boucadair and C Jacquenet ldquoSoftware-defined networkinga perspective fromwithin a service provider environmentrdquo RFC7149 Internet Engineering Task Force 2014

[17] X Chen R Q Hu and Y Qian ldquoDistributed resource andpower allocation for device-to-device communications under-laying cellular networkrdquo in Proceedings of the IEEE GlobalCommunications Conference (GLOBECOM rsquo14) pp 4947ndash4952Austin Tex USA December 2014

[18] Z Zhang R Q Hu Y Qian A Papathanassiou and G WuldquoD2D communication underlay uplink cellular network withfractional frequency reuserdquo in Proceedings of the 11th Inter-national Conference on the Design of Reliable CommunicationNetworks (DRCN rsquo15) pp 247ndash250 Kansas City Mo USAMarch 2015

[19] L L Wei R Q Hu T He and Y Qian ldquoDevice-to-device(d2d)communications underlaying MU-MIMO cellular networksrdquoin Proceedings of the IEEE Global Communications Conference(GLOBECOM rsquo13) pp 4902ndash4907 IEEE Atlanta Ga USADecember 2013

[20] S Sun M Kadoch L Gong and B Rong ldquoIntegrating net-work function virtualization with SDR and SDN for 4G5Gnetworksrdquo IEEE Network vol 29 no 3 pp 54ndash59 2015

[21] B A A Nunes M Mendonca X-N Nguyen K Obraczkaand T Turletti ldquoA survey of software-defined networkingpast present and future of programmable networksrdquo IEEECommunications Surveys amp Tutorials vol 16 no 3 pp 1617ndash1634 2014

[22] R Guerzoni I Vaishnavi A Frimpong and R TrivisonnoldquoVirtual linkmapping for delay critical services in SDN-enabled5G networksrdquo in Proceedings of the 1st IEEE Conference onNetwork Softwarization (NetSoft rsquo15) pp 1ndash9 IEEE LondonUK April 2015

[23] H-H Cho C-F Lai T K Shih andH-C Chao ldquoIntegration ofSDR and SDN for 5Grdquo IEEE Access vol 2 pp 1196ndash1204 2014

[24] J J Vegas Olmos and I Tafur Monroy ldquoMillimeter-wavewireless links for 5G mobile networksrdquo in Proceedings of the17th International Conference on Transparent Optical Networks(ICTON rsquo15) p 1 IEEE Budapest Hungary July 2015

[25] P WangW Song D Niyato and Y Xiao ldquoQoS-aware cell asso-ciation in 5G heterogeneous networks with massive MIMOrdquoIEEE Network vol 29 no 6 pp 76ndash82 2015

[26] Y Yan YQianH Sharif andD Tipper ldquoA survey on smart gridcommunication infrastructures motivations requirements andchallengesrdquo IEEE Communications Surveys and Tutorials vol15 no 1 pp 5ndash20 2013

[27] Y Yan Y Qian H Sharif and D Tipper ldquoA survey on cybersecurity for smart grid communicationsrdquo IEEE Communica-tions Surveys and Tutorials vol 14 no 4 pp 998ndash1010 2012

[28] K Lu Y QianM Guizani andH-H Chen ldquoA framework for adistributed key management scheme in heterogeneous wirelesssensor networksrdquo IEEE Transactions on Wireless Communica-tions vol 7 no 2 pp 639ndash647 2008

[29] K Lu Y Qian and H-H Chen ldquoWireless broadband accesswimax and beyondmdasha secure and service-oriented networkcontrol framework for wimax networksrdquo IEEECommunicationsMagazine vol 45 no 5 pp 124ndash130 2007

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Distributed Sensor Networks


Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014


ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014


Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications


Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia


Biomedical Imaging


ArtificialNeural Systems

Advances in


RoboticsJournal of



Computational Intelligence and Neuroscience

Industrial EngineeringJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in



In mmWave systems the precoding schemes are mainlyperformed in the baseband domain where a fully digitalprecoder is utilized to modify both the amplitude and phaseof transmitted signals Although the fully digital precodingscheme can achieve optimal performance it brings enormoushardware complexity and energy consumption as it requiresan expensive radio frequency (RF) chain for every antennaThus this becomes a serious problemdue to the huge numberof antennas at the base station (BS) in mmWave systemsFortunately the hybrid precoding technique combining dig-ital precoding and analog precoding has been proposedto significantly reduce the number of RF chains which isconsidered as an essential technique for realistic mmWavemassive MIMO systems [8 9]

Nevertheless the development of Hetnets and massiveMIMO induces an increasing network complexity whichtends to the virtualization of network functionality [1011] For example massive MIMO leads to the problem ofmanaging huge spatial domain resource and processing vastinformation in mmWave systems To tackle such conditionsSDN is introduced owing to its inherent advantage in inte-grating and processing information [12 13]

In our work we study how SDN benefits the RRM in5G mmWave Hetnets We proposed a network architecturewhich is consistent with 5G concepts and also compatibleto current 3GPP LTE Advanced (LTE-A) standard In 4Gnetwork the main issue for RRM is the time and frequencydomain management such as frequency reuse [14] intercellinterference coordination (ICIC) [15] and enhanced inter-cell interference coordination (eICIC) In 5G however thesystem has to reach the overwhelming densities of devicesand network infrastructure nodes as well as supporting anastonishing numbers of antennas in massive MIMO systems[16] In mmWave 5G Hetnets there are macrobase station(macro-BS) and small cells all employing multiple antennaswhich leads to huge spacial domain information Thus itbecomes a critical task formmWaveHetnets to retrieve storeand utilize the spatial information In our proposed architec-ture SDN takes the responsibility for integrating huge spacialdomain information and performing null-space calculationconcerning massive MIMO coordination By doing this thenull-space information is integrated to enable whole networkcoordination and achieves a better performance than thenetworks without SDN This novel architecture implementscoordinated RRM and eases the operation pressure of accesspoints significantly

The rest of this paper is organized as follows Section 2presents the 5G SDN architecture supporting RRM Section 3introduces the mmWave technology and Section 4 empha-sizes the mmWave precoding problem In Section 5 wepropose our two-tier interference free mmWave hybrid pre-coding scheme followed by the performance evaluation inSection 6 In the end Section 7 concludes the paper

2 SDN Controlled 5G HeterogeneousMobile System

In 5G mobile system Hetnets massive MIMO and SDNare expected to be the key technologies sustaining operation

Femtosmall cell

Femtosmall cell

MacrocellPicocell

Cell phone

Cell phone

Cell phone

Cell phone

Cell phone

Figure 1 A generalized architecture of heterogeneous network

performance In this section we will overview these frontiertechnologies and introduce our SDN assisted 5G architectureinvolving mmWave system

21 Hetnet Hetnet is regarded as the most viable solutionto the impending mobile data traffic crunch in the contextof LTE-A in the industry It builds different layers andcell sizes by deploying eNodeBs of different transmissionpowers such as macro micro pico and femto as shownin Figure 1 Hetnet becomes an inevitable trend for futuredevelopment of information networks since it makes full useof the complementary characteristics of different networktiers [17 18] Nevertheless different tiersrsquo networking accesstechnologies and various service requirements make spatialdomain management a new problem we have to solve In 5GHetnets we involve SDN controller to collect and managespacial information from a variety of access points

22 Massive MIMO In massive MIMO systems the antennaarrays consisting of a few hundred elements simultaneouslyserve many tens of mobile users with the same time-frequency resource [19] Massive MIMO is an enabler forfuture broadband (fixed and mobile) networks which willbe energy-efficient spectrum-efficient and robust Howevermassive MIMO antenna arrays engender huge amounts ofspacial domain information in real time which significantlyincreases the computational complexity and brings aboutthe massive MIMO coordination problem The network canoperate well only when SDN processes spacial informationsuccessfully

23 SDN The concept of SDN was first introduced in the1990s and became popular in the 21st century The architec-ture of SDN was formally defined by the open networkingforum (ONF) In SDN architecture network devices consistof three layers that is application layer control layer andinfrastructure layer [20]

Currently SDN has found its best practice in data centersand has been paid increasing attention by network organiza-tions around the world [21] SDN is regarded as a promisingmethod to solve current and emerging problems for future 5G


LPN intended users

Mobility management

LPN victim users

Null space analysis

BS users

RRM





LPN


BS

LPN

API

SDN controller






PrivateISP

Operatorcore

network




with macrooverlay






4G base station

mmWave base station

Mobile device





streams

RFchain

Phaseshifter

RFchain

Phaseshifter






H119895 =

[[[[[[[[[[[[[[

[

H1198951

H119895119870119895H119895119870119895+1

H119895119870119895+119862119895

]]]]]]]]]]]]]]

]

(1)



Source bits

Hybridprecoding

Null-space

Poweramplifier

SDNcontroller

UsersVictimusers

s

x

n

y

CSI

V(0)





119867119895119870119895+119862119895







A119886119887 =1

radic119873

119890119887120593119886119887 (2)





119910119895119896 = H119895119896A119895D119895s119895119896 +H0119896A0D01199040 + 119899119895119896 (4)




]

119867

(5)


H119895VA119895D119895 = 0119862119895times119870119895 (6)


H119895V = U119895VΛ119895VV119867

119895V

= U119895V [[

sum

119895V0]]119862119895times119873


(7)


SDN controller

CSIacquisition

BS

SVD

Hybridprecoding


Projection

lfloorlceil


HjK119895+C119895

V(0)

j

AjDj FjDj FjDj

LPN j

Hj1


rfloorrceil



Basebandprecoder

D

Data streams

RFchain 1

RFprecoder

A

UEK

UE

UE1

UE

N

RFchain K K + C

K + 1







M119895 = V(0)119895V (V(0)

119895V)119867

(8)


F119895 = M119895A119895 (9)



SINR119895119896 =(119875119895119870119895)

10038161003816100381610038161003816H119895119896F119895d119895119896

10038161003816100381610038161003816

2

1205902119895119896

+ sum119870119895

119903=1119903 =119896(119875119895119870119895)

10038161003816100381610038161003816H119895119896F119895d119895119903

10038161003816100381610038161003816

2 (10)


meet

119861 log (1 + SINR119895119896) ge 119877119895119896 (11)


119861 log (1 + SINR119895119896) = 119877119895119896 (12)



minus200

minus150

minus100

minus50

0

50

100

150

200

250

X (m)

Y (m

)

eNBLPN


LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

1

2

3

45

6

7

8

9

10

11

12

13

14

15

1617

18

1920

21

22

23

24

25

26

27

2829

30 3132

3334

3536

3738

3940

4142

4344

4546

4748

4950

5152

5354



minus200

minus150

minus100

minus50

0

50

100

150

200

250

LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

4948 50

47

52


X (m)

Y (m

)

eNBLPN

















30 40 50 60 70 80 900

5

10

15

20

25

Antennas at the BS

Syste

m th

roug

hput

(Mbp

s)






SNR (dB)

Spec

tral

effici

ency

(bps

Hz)

0

5

10

15

20

25

30




2 3 4 5 6 7 8 90

20

40

60

80

100

120

140


Tota

l pow

er (W

)






1 2 3 4 5 6 7 80

2

4

6

8

10

12

14

16

18

20


SIN

R of

vic

tim u

sers

(dB)







7 Conclusion




Acknowledgment


References






































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




LPN intended users

Mobility management

LPN victim users

Null space analysis

BS users

RRM





LPN


BS

LPN

API

SDN controller






PrivateISP

Operatorcore

network




with macrooverlay






4G base station

mmWave base station

Mobile device





streams

RFchain

Phaseshifter

RFchain

Phaseshifter






H119895 =

[[[[[[[[[[[[[[

[

H1198951

H119895119870119895H119895119870119895+1

H119895119870119895+119862119895

]]]]]]]]]]]]]]

]

(1)



Source bits

Hybridprecoding

Null-space

Poweramplifier

SDNcontroller

UsersVictimusers

s

x

n

y

CSI

V(0)





119867119895119870119895+119862119895







A119886119887 =1

radic119873

119890119887120593119886119887 (2)





119910119895119896 = H119895119896A119895D119895s119895119896 +H0119896A0D01199040 + 119899119895119896 (4)




]

119867

(5)


H119895VA119895D119895 = 0119862119895times119870119895 (6)


H119895V = U119895VΛ119895VV119867

119895V

= U119895V [[

sum

119895V0]]119862119895times119873


(7)


SDN controller

CSIacquisition

BS

SVD

Hybridprecoding


Projection

lfloorlceil


HjK119895+C119895

V(0)

j

AjDj FjDj FjDj

LPN j

Hj1


rfloorrceil



Basebandprecoder

D

Data streams

RFchain 1

RFprecoder

A

UEK

UE

UE1

UE

N

RFchain K K + C

K + 1







M119895 = V(0)119895V (V(0)

119895V)119867

(8)


F119895 = M119895A119895 (9)



SINR119895119896 =(119875119895119870119895)

10038161003816100381610038161003816H119895119896F119895d119895119896

10038161003816100381610038161003816

2

1205902119895119896

+ sum119870119895

119903=1119903 =119896(119875119895119870119895)

10038161003816100381610038161003816H119895119896F119895d119895119903

10038161003816100381610038161003816

2 (10)


meet

119861 log (1 + SINR119895119896) ge 119877119895119896 (11)


119861 log (1 + SINR119895119896) = 119877119895119896 (12)



minus200

minus150

minus100

minus50

0

50

100

150

200

250

X (m)

Y (m

)

eNBLPN


LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

1

2

3

45

6

7

8

9

10

11

12

13

14

15

1617

18

1920

21

22

23

24

25

26

27

2829

30 3132

3334

3536

3738

3940

4142

4344

4546

4748

4950

5152

5354



minus200

minus150

minus100

minus50

0

50

100

150

200

250

LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

4948 50

47

52


X (m)

Y (m

)

eNBLPN

















30 40 50 60 70 80 900

5

10

15

20

25

Antennas at the BS

Syste

m th

roug

hput

(Mbp

s)






SNR (dB)

Spec

tral

effici

ency

(bps

Hz)

0

5

10

15

20

25

30




2 3 4 5 6 7 8 90

20

40

60

80

100

120

140


Tota

l pow

er (W

)






1 2 3 4 5 6 7 80

2

4

6

8

10

12

14

16

18

20


SIN

R of

vic

tim u

sers

(dB)







7 Conclusion




Acknowledgment


References






































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




4G base station

mmWave base station

Mobile device





streams

RFchain

Phaseshifter

RFchain

Phaseshifter






H119895 =

[[[[[[[[[[[[[[

[

H1198951

H119895119870119895H119895119870119895+1

H119895119870119895+119862119895

]]]]]]]]]]]]]]

]

(1)



Source bits

Hybridprecoding

Null-space

Poweramplifier

SDNcontroller

UsersVictimusers

s

x

n

y

CSI

V(0)





119867119895119870119895+119862119895







A119886119887 =1

radic119873

119890119887120593119886119887 (2)





119910119895119896 = H119895119896A119895D119895s119895119896 +H0119896A0D01199040 + 119899119895119896 (4)




]

119867

(5)


H119895VA119895D119895 = 0119862119895times119870119895 (6)


H119895V = U119895VΛ119895VV119867

119895V

= U119895V [[

sum

119895V0]]119862119895times119873


(7)


SDN controller

CSIacquisition

BS

SVD

Hybridprecoding


Projection

lfloorlceil


HjK119895+C119895

V(0)

j

AjDj FjDj FjDj

LPN j

Hj1


rfloorrceil



Basebandprecoder

D

Data streams

RFchain 1

RFprecoder

A

UEK

UE

UE1

UE

N

RFchain K K + C

K + 1







M119895 = V(0)119895V (V(0)

119895V)119867

(8)


F119895 = M119895A119895 (9)



SINR119895119896 =(119875119895119870119895)

10038161003816100381610038161003816H119895119896F119895d119895119896

10038161003816100381610038161003816

2

1205902119895119896

+ sum119870119895

119903=1119903 =119896(119875119895119870119895)

10038161003816100381610038161003816H119895119896F119895d119895119903

10038161003816100381610038161003816

2 (10)


meet

119861 log (1 + SINR119895119896) ge 119877119895119896 (11)


119861 log (1 + SINR119895119896) = 119877119895119896 (12)



minus200

minus150

minus100

minus50

0

50

100

150

200

250

X (m)

Y (m

)

eNBLPN


LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

1

2

3

45

6

7

8

9

10

11

12

13

14

15

1617

18

1920

21

22

23

24

25

26

27

2829

30 3132

3334

3536

3738

3940

4142

4344

4546

4748

4950

5152

5354



minus200

minus150

minus100

minus50

0

50

100

150

200

250

LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

4948 50

47

52


X (m)

Y (m

)

eNBLPN

















30 40 50 60 70 80 900

5

10

15

20

25

Antennas at the BS

Syste

m th

roug

hput

(Mbp

s)






SNR (dB)

Spec

tral

effici

ency

(bps

Hz)

0

5

10

15

20

25

30




2 3 4 5 6 7 8 90

20

40

60

80

100

120

140


Tota

l pow

er (W

)






1 2 3 4 5 6 7 80

2

4

6

8

10

12

14

16

18

20


SIN

R of

vic

tim u

sers

(dB)







7 Conclusion




Acknowledgment


References






































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




Source bits

Hybridprecoding

Null-space

Poweramplifier

SDNcontroller

UsersVictimusers

s

x

n

y

CSI

V(0)





119867119895119870119895+119862119895







A119886119887 =1

radic119873

119890119887120593119886119887 (2)





119910119895119896 = H119895119896A119895D119895s119895119896 +H0119896A0D01199040 + 119899119895119896 (4)




]

119867

(5)


H119895VA119895D119895 = 0119862119895times119870119895 (6)


H119895V = U119895VΛ119895VV119867

119895V

= U119895V [[

sum

119895V0]]119862119895times119873


(7)


SDN controller

CSIacquisition

BS

SVD

Hybridprecoding


Projection

lfloorlceil


HjK119895+C119895

V(0)

j

AjDj FjDj FjDj

LPN j

Hj1


rfloorrceil



Basebandprecoder

D

Data streams

RFchain 1

RFprecoder

A

UEK

UE

UE1

UE

N

RFchain K K + C

K + 1







M119895 = V(0)119895V (V(0)

119895V)119867

(8)


F119895 = M119895A119895 (9)



SINR119895119896 =(119875119895119870119895)

10038161003816100381610038161003816H119895119896F119895d119895119896

10038161003816100381610038161003816

2

1205902119895119896

+ sum119870119895

119903=1119903 =119896(119875119895119870119895)

10038161003816100381610038161003816H119895119896F119895d119895119903

10038161003816100381610038161003816

2 (10)


meet

119861 log (1 + SINR119895119896) ge 119877119895119896 (11)


119861 log (1 + SINR119895119896) = 119877119895119896 (12)



minus200

minus150

minus100

minus50

0

50

100

150

200

250

X (m)

Y (m

)

eNBLPN


LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

1

2

3

45

6

7

8

9

10

11

12

13

14

15

1617

18

1920

21

22

23

24

25

26

27

2829

30 3132

3334

3536

3738

3940

4142

4344

4546

4748

4950

5152

5354



minus200

minus150

minus100

minus50

0

50

100

150

200

250

LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

4948 50

47

52


X (m)

Y (m

)

eNBLPN

















30 40 50 60 70 80 900

5

10

15

20

25

Antennas at the BS

Syste

m th

roug

hput

(Mbp

s)






SNR (dB)

Spec

tral

effici

ency

(bps

Hz)

0

5

10

15

20

25

30




2 3 4 5 6 7 8 90

20

40

60

80

100

120

140


Tota

l pow

er (W

)






1 2 3 4 5 6 7 80

2

4

6

8

10

12

14

16

18

20


SIN

R of

vic

tim u

sers

(dB)







7 Conclusion




Acknowledgment


References






































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




SDN controller

CSIacquisition

BS

SVD

Hybridprecoding


Projection

lfloorlceil


HjK119895+C119895

V(0)

j

AjDj FjDj FjDj

LPN j

Hj1


rfloorrceil



Basebandprecoder

D

Data streams

RFchain 1

RFprecoder

A

UEK

UE

UE1

UE

N

RFchain K K + C

K + 1







M119895 = V(0)119895V (V(0)

119895V)119867

(8)


F119895 = M119895A119895 (9)



SINR119895119896 =(119875119895119870119895)

10038161003816100381610038161003816H119895119896F119895d119895119896

10038161003816100381610038161003816

2

1205902119895119896

+ sum119870119895

119903=1119903 =119896(119875119895119870119895)

10038161003816100381610038161003816H119895119896F119895d119895119903

10038161003816100381610038161003816

2 (10)


meet

119861 log (1 + SINR119895119896) ge 119877119895119896 (11)


119861 log (1 + SINR119895119896) = 119877119895119896 (12)



minus200

minus150

minus100

minus50

0

50

100

150

200

250

X (m)

Y (m

)

eNBLPN


LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

1

2

3

45

6

7

8

9

10

11

12

13

14

15

1617

18

1920

21

22

23

24

25

26

27

2829

30 3132

3334

3536

3738

3940

4142

4344

4546

4748

4950

5152

5354



minus200

minus150

minus100

minus50

0

50

100

150

200

250

LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

4948 50

47

52


X (m)

Y (m

)

eNBLPN

















30 40 50 60 70 80 900

5

10

15

20

25

Antennas at the BS

Syste

m th

roug

hput

(Mbp

s)






SNR (dB)

Spec

tral

effici

ency

(bps

Hz)

0

5

10

15

20

25

30




2 3 4 5 6 7 8 90

20

40

60

80

100

120

140


Tota

l pow

er (W

)






1 2 3 4 5 6 7 80

2

4

6

8

10

12

14

16

18

20


SIN

R of

vic

tim u

sers

(dB)







7 Conclusion




Acknowledgment


References






































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





minus200

minus150

minus100

minus50

0

50

100

150

200

250

X (m)

Y (m

)

eNBLPN


LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

1

2

3

45

6

7

8

9

10

11

12

13

14

15

1617

18

1920

21

22

23

24

25

26

27

2829

30 3132

3334

3536

3738

3940

4142

4344

4546

4748

4950

5152

5354



minus200

minus150

minus100

minus50

0

50

100

150

200

250

LPN 1LPN 2

LPN 3

LPN 4 LPN 5

LPN 6

BS

4948 50

47

52


X (m)

Y (m

)

eNBLPN

















30 40 50 60 70 80 900

5

10

15

20

25

Antennas at the BS

Syste

m th

roug

hput

(Mbp

s)






SNR (dB)

Spec

tral

effici

ency

(bps

Hz)

0

5

10

15

20

25

30




2 3 4 5 6 7 8 90

20

40

60

80

100

120

140


Tota

l pow

er (W

)






1 2 3 4 5 6 7 80

2

4

6

8

10

12

14

16

18

20


SIN

R of

vic

tim u

sers

(dB)







7 Conclusion




Acknowledgment


References






































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in







30 40 50 60 70 80 900

5

10

15

20

25

Antennas at the BS

Syste

m th

roug

hput

(Mbp

s)






SNR (dB)

Spec

tral

effici

ency

(bps

Hz)

0

5

10

15

20

25

30




2 3 4 5 6 7 8 90

20

40

60

80

100

120

140


Tota

l pow

er (W

)






1 2 3 4 5 6 7 80

2

4

6

8

10

12

14

16

18

20


SIN

R of

vic

tim u

sers

(dB)







7 Conclusion




Acknowledgment


References






































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




1 2 3 4 5 6 7 80

2

4

6

8

10

12

14

16

18

20


SIN

R of

vic

tim u

sers

(dB)







7 Conclusion




Acknowledgment


References






































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in
































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in










Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



Documents

Research Article SDN Controlled mmWave Massive MIMO Hybrid ...downloads.hindawi.com/journals/misy/2016/9767065.pdf · massive MIMO systems [ , ]. Nevertheless, the development of