An Optimal Channel Occupation Time Adjustment Method for ... · methods the problem of throughput maximization of LTE is formulated as a constrained non-linear optimization problem

0018-9545 (c) 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TVT.2019.2940123, IEEETransactions on Vehicular Technology

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. , NO. , AUGEST 2019 1

An Optimal Channel Occupation Time AdjustmentMethod for LBE in Unlicensed Spectrum

Qian Wang, Xiaojiang Du, Senior Member, IEEE, Zhipeng Gao*, Member, IEEE, Mohsen Guizani, Fellow, IEEE

Abstract—Load Based Equipment (LBE) mechanism is acategory of Listen-Before-Talk (LBT) protocol adopted by LTEto access unlicensed channels to realize fair coexistence betweenLTE and Wi-Fi networks. However, most LBE optimizationmethods fix Channel Occupation Time (COT) in LBE neglect-ing its influence on network throughput and fairness. In thepaper, considering the influence, we propose an optimal COTadjustment method for the LBE mechanism to maximize thethroughput of LTE while satisfying the LTE users’ data ratedemands and ensuring the fairness coexistence between LTEand Wi-Fi networks. To this end, we first propose a methodto separately calculate the throughput of LTE network and Wi-Fi network on the coexistence unlicensed spectrum consideringthe synchronous of LTE on licensed and unlicensed spectrum,and then utilize a virtual Wi-Fi network construction method toassure the used fairness criterion. Based on the aforementionedmethods the problem of throughput maximization of LTE isformulated as a constrained non-linear optimization problemsolved by an optimal algorithm. Simulation results prove ourproposed throughput calculation method is valid, and thendemonstrate the LBE mechanism using our proposed methodoptimizes the throughput of the SBS network and ensuresfairness.

Index Terms—Unlicensed spectrum sharing, LBT, LBE, fair-ness criterion, throughput maximization.

I. INTRODUCTION

THE penetration of smart devices has led to the existingcellular systems facing the challenge of scarce spectrum

resource [1], [2]. To solve the problem, 3rd GenerationPartnership Project (3GPP) and some leading companies suchas Qualcomm, Huawei, Verizon propose to extend the new-generation of LTE systems to the unlicensed 5GHz spectrum[3], [4]. However, the unlicensed spectrum has been usedby Wi-Fi networks, which utilize contention-based MediumAccess Control (MAC) through Carrier Sensing Multiple Ac-cess (CSMA) and Distributed Coordination Function (DCF)protocols to resolve packet collision. Extending LTE with

Manuscript received December 7, 2018; revised June 25, 2019; acceptedAugust 21, 2019. This work was supported in part by the US Nation ScienceFoundation (NSF) under grants CNS-1564128 and CNC-1824440. The reviewof this paper was coordinated by Dr. Linglong Dai. (Corresponding Author:Zhipeng Gao)

Q. Wang and Z. Gao are with the State Key Laboratory of Network-ing and Switching Technology, Beijing University of Posts and Telecom-munications, Beijing 100876, China (e-mail: [email protected];[email protected]).

X. Du is with the Department of Computer and Information Science,Temple University, Philadelphia, PA 19122, USA (e-mail: [email protected]).

M. Guizani is with the Department of Computer Science and Engineering,Qatar University, Qatar (e-mail: [email protected]).

centralized user admission and MAC protocol to the unli-censed spectrum will largely decrease the performance ofWi-Fi networks [5]–[7]. Therefore, 3GPP proposes LicensedAssisted Access (LAA) technology, in which Listen-Before-Talk (LBT) approach is adopted by each Small Base Station(SBS) to access unlicensed channels to realize fair coexistencebetween LTE and Wi-Fi networks in the 5GHz unlicensedspectrum.

LBT as a channel accessing mechanism used to realize faircoexistence, it has two categories: Frame Based Equipment(FBE) and Load Based Equipment (LBE) illustrated in Fig. 1.FBE: Frame structure is divided into periods with a fixedlength. Before transmitting on a channel the device performsa Clear Channel Assessment (CCA) check based on energydetection lasting at least 20µs at the end of the fixed period.If the channel is idle during the CCA check period, thedevice transmits immediately for a duration that equals toChannel Occupancy Time (COT) and then it should havean idle period which is more than 5% of COT; otherwise,the device defers transmission and repeats the CCA checkat the end of the next frame. LBE: before every burst oftransmission a device shall perform a CCA check where itobserves the channel for a defer duration. If the channel issensed idle and the current transmission is not immediatelyafter a successful transmission, the device starts transmission;otherwise it reverts to the extended CCA (eCCA) which is aback-off procedure (the back-off counter is decremented by 1every eCCA slot time), and starts to transmit when the back-off counter becomes 0.

CC

A

!""# ≥ %&'(

CC

A

Fixed Frame Period

FBE COT

)*( ≤COT ≤ )&*(

Idle

Idle≥ ,%COT

CC

A

Fixed Frame Period

Channel Busy

CC

ALBE Data Transmission

Channel Busy eCCA

……

CC

A Data Transmission

time

time

Fig. 1. Two categories of the LBT mechanism.

In 3GPP Rel. 13 [8] LAA is mandated to implement theLBE as a coexistence protocol, and what’s more, comparingwith FBE which will wait for a long fixed frame period ifone CCA check fails, LBE allows for more channel accessopportunities because the transmitter can continuously detectthe channel [9]. Hence, in the paper we enforce the SBS

0000-0000 ©2015 IEEE. Personal use is permitted. However, permission to use thismaterial for any other purposes must be obtained from the IEEE by sending a request to [email protected].

2 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. , NO. , AUGEST 2019

using LBE mechanism to access unlicensed spectrum. Thefinal standardized frozen Technical Specification (TS) 36.213of 3GPP release 13 and 14 fix the COT in LBE mechanismbased on priority class number [10], for example, when theLBE priority class number is 1, the allowed COT is 2ms.However, [11], [12] point out the fixed COT is inefficientunder the varying traffic load of the coexistence network.Intuitively, when the throughput of Wi-Fi network becomessmall, the efficiency of the spectrum decreases if COT is stillset too short. Therefore, how to adjust the COT of SBS inLBE to optimize the efficiency of the unlicensed spectrum isan important issue.

The fairness is an important constraint in the process ofadjusting the COT of SBS on unlicensed spectrum. How-ever, there is no standard to reach a consensus. The nowexisting work evaluates fairness based on their requirementsusing different standard. For example, [13] evaluates fairnessregarding the amount of time of each entity accessing thewireless channel, and [14] uses the throughput to quantify thefairness. Recently, a new fairness criterion based on 3GPPproposed fairness in Rel. 13 [8] is proposed in [15], [16]. Thedefinition is an LTE network does not impact the performanceof the Wi-Fi network on a carrier more than an additional Wi-Fi network operating on the same carrier and offering the samelevel of traffic load of the LTE network. None of the existingLBE optimal methods adopts the criterion to evaluate fairness.In the paper, we use the criterion.

Therefore, in this paper, we focus on adaptively adjustingCOT of every SBS user in different unlicensed channelsto maximize the throughput of the SBS on the unlicensedspectrum while ensuring fair coexistence between SBS andWi-Fi networks on every unlicensed channel and satisfyingthe SBS users’ data rate demands. The problem is formulatedas a non-linear programming problem, and we use the interiormethod to solve it. The proposed COT adjustment method isin fact based on centralized scheduling on each SBS. Ourmajor contributions are summarized as follows:

• Modeling: to the best of our knowledge, this is thefirst work using the new proposed criterion to evaluatefairness in the procedure of adjusting COT of SBS inunlicensed channels.

• Parameter calculation: we propose a method to sep-arately calculate the throughput of SBS and the Wi-Fi network on a coexistence unlicensed channel. In thecalculation, we consider the synchronization of SBS onlicensed and unlicensed spectrum. What’s more, to satisfyfairness, we use a method proposed in our former paper[17] to construct a virtual Wi-Fi network.

• Performance evaluation: we first prove our proposedthroughput calculation method for the LTE and Wi-Ficoexistence network is valid, and then simulation resultsprove the performance of LBE using our proposed COTadjustment method outperforms other two COT adjust-ment methods from the aspects of LTE users’ throughputand fairness assurance.

The remainder of this paper is organized as follows. InSection II, we describe the network model and the problem

formulation. In Section III, we present the methods of cal-culating related parameters in problem formulation. SectionIV shows the optimal algorithm, and the performance ofthe proposed method is evaluated in Section V. We describerelated work in Section VI and conclude the paper in SectionVII.

II. NETWORK MODEL AND PROBLEM FORMULATION

We consider coexisting LTE and Wi-Fi networks. Theunlicensed spectrum is divided into non-overlapped channels,for example, channels 1, 6, 11 in the 2.4GHz band andchannels 36, 40, 44, . . . , 64 in the 5GHz spectrum. We assumedifferent Wi-Fi AP uses different unlicensed channel to avoidstrong co-channel interference, and every Wi-Fi network is inthe coverage range of other Wi-Fi networks to avoid hiddennode problem which is the collision produced by undetectednodes. In the coexistence network, the set of Wi-Fi networksW is in the coverage of a single LTE Small Base Station(SBS) with users U(|U | = m), as shown in Fig. 2, wherethe circle with solid line represents the coverage range of theSBS; the circle with dotted line represents the coverage rangeof the Wi-Fi network and lines with arrow represent usedunlicensed channels of Wi-Fi networks. Let C represent theset of unlicensed channels accessing by Wi-Fi networks Wand the Wi-Fi AP accessing the unlicensed channel i ∈ C isrepresented by APi, which constructs the network wi with itsserved ni stations.

SBS

AP1

w3The SBS coverage range

The Wi-Fi network coverage rangeAn unlicensed channel An unlicensed channel

An unlicensed channel

AP2

AP3

w1

w2

Fig. 2. The network model.

Comparing with the Wi-Fi AP using one channel to serveits stations, the SBS by Carrier Aggregation (CA) technologycan aggregate multiple non-contiguous unlicensed channels ascomplementary channels. tj,i represents the time duration ofthe SBS user j ∈ U occupying the channel i and its range isshown in (1). The problem of the paper is to help the SBSadaptively adjust tj,i(∀j,∀i) to maximize the throughput ofthe SBS on unlicensed spectrum while ensuring the fairnessbetween the SBS and Wi-Fi networks on every unlicensedchannel and satisfying SBS users’ downlink data rate de-mands. In this paper, we assume the unlicensed spectrum cansatisfy SBS users’ downlink data rate demands. The problemcan be formulated as:Maximize:

∑i∈C

∑j∈U TPj,i



WANG et al.: AN OPTIMAL CHANNEL OCCUPATION TIME ADJUSTMENT METHOD FOR LBE IN UNLICENSED SPECTRUM 3

Subject to:tj,i ≥ 0,∀j,∀i, (1)∑

j∈Utj,i ≤ Tcot,∀i, (2)

∑i∈C

TPj,i ≥ Rj ,∀j, (3)

TP lwi≥ TPw

′i

wi ,∀i, (4)

where TPj,i represents the throughput of the SBS user j onthe unlicensed channel i. (2) constrains the allowed maximumCOT Tcot of the unlicensed channel. (3) represents the SBSusers’ data rate requirements are guaranteed, where Rj is theminimum required downlink data rate of the SBS user j onunlicensed channels for reaching his/her data rate demand. (4)represents the fairness criterion, where TP l

wiis the throughput

of the Wi-Fi network wi under the influence of the SBS, TPw′i

wi

is the throughput of wi under the influence of w′

i offering thesame traffic of the SBS on the channel i, and the detaileddefinition is illustrated in Section III-C.

III. PARAMETERS CALCULATION

In this section, we first derive Rj , then calculate thethroughput of the SBS and the Wi-Fi network on the coexis-tence unlicensed channel, last elaborate on the expression ofthe fairness criterion.

A. The Derivation of Rj

In LAA, the downlink data rate of user j consists of twoparts. The first is on the licensed spectrum, and the secondis on the unlicensed spectrum. Therefore, user j needed datarate on unlicensed channels is

Rj = rj − rLj , (5)

where rj is the downlink data rate requirement of j, andrLj is the maximum downlink data rate of j on the licensedspectrum. In our scenario, there is only one SBS, whichuses OFDMA technique to allocate spectrum resources to itsusers and shares the unlicensed spectrum with Wi-Fi APs intime-division multiple access (TDMA) way, and Wi-Fi APsuse different unlicensed channels to transmit data. Therefore,there is no interference among SBS users, among Wi-Fi APs,between the SBS and a Wi-Fi AP. The maximum downlinkdata rate of j on the licensed spectrum can be expressed as

rLj = BLj log2(1 +

PLSBSh

Lj

BLj N0

), (6)

where BLj is the downlink bandwidth of the licensed spectrum

allocated to j, PLSBS is the transmission power of the SBS,

hLj is the channel gain between j and the SBS on the licensedspectrum, and N0 is the noise power. What’s more, themaximum data rate of j on the unlicensed channel can beexpressed as

rULj = Bclog2(1 +

PULSBSh

ULj

BcN0), (7)

where Bc is the bandwidth of the unlicensed channel, PULSBS is

the transmission power allocated to the SBS on the unlicensedspectrum, hUL

j is the channel gain between j and the SBS onan unlicensed channel.

B. The Throughput of the SBS and the Wi-Fi Network

In the section, we calculate the throughput of the SBSand the Wi-Fi network on a coexistence unlicensed channel,and we assume all SBSs and Wi-Fi networks are in thesaturated condition that means SBSs and Wi-Fi stations willimmediately have a packet available for transmission after thecompletion of each successful transmission.

The SBS: it accesses the unlicensed channel based on theLBE described in the final specification of LTE-LAA TS36.123 [10]. Before transmitting, the SBS performs CCA us-ing energy detection where it observes the channel for a deferperiod. The defer period depends on the access priority classnumber of the transmitted LBE frame. If sensed idle and thecurrent transmission does not immediately follow a successfultransmission, the SBS starts transmission; otherwise the SBSperforms an eCCA check based on the exponential back-offstrategy, i.e., it selects a back-off counter and decreases theback-off counter every slot. The process continues until thecounter reaches zero, and then the SBS has a transmissionopportunity. When a collision happens in the transmissionprocess, the SBS re-enters the back-off procedure, and theback-off number is selected randomly from a contentionwindow [0, 2cW0 − 1] where c is the time of retransmission,and W0 is the minimum contention window size. When cexceeds the maximum retransmission time m, it stays at themaximum window size for el times; then c resets to 0.

Data Transmission

Channel Busy

eCCA

…………

Reservation

LBE slot LBE slot LBE slot Boundary LBE slot

AC

K

time

Fig. 3. Synchronous LBE frame

In the process mentioned above, the SBS senses the channelbased on the Wi-Fi slot (tw = 9µs), but it begins itsdata transmission based on the LTE slot tl. To maintainsynchronization with the licensed LTE frame, the SBS willreserve the channel until the start of the next LTE slot viasending a reservation signal, when it gets a data transmissionopportunity [18] as illustrated in Fig. 3. In the paper, weassume the SBS station is in saturated condition, hence, theSBS always performs the eCCA check since there are alwaysdata pending for transmission. The Markov chain of LBEmechanism for the saturated SBS is shown in Fig. 4. Let{s(t) = r, b(t) = k} denote the possible states in the Markovchain, where s(t) is the retransmission stage, b(t) is the back-off counter. The one-step transition probabilities are:

p{r, k|r, k + 1} = 1, k ∈ (0,Wi − 2), r ∈ (0,m+ el), (8)

p{0, k|r, 0} = 1− pliW0

, k ∈ (0,W0 − 1), r ∈ (0,m+ el), (9)


p{r, k|r−1, 0} = pliWi

, k ∈ (0,Wi−1), r ∈ (0,m+el), (10)

p{0, k|m+ el, 0} =pliW0

, k ∈ (0,Wm − 1), (11)

!,#!-10, 0

��#! ��#! ��#!

0, 1

$,#$-11, 0

%&'�#$

1, 1

(,#(-1m, 0 m, 1

1 1 1

1 1 1

1 1 1

(+ $,#(-1m+1, 0 m+1, 11 1 1

m+*',#(-1m+*', 0 1 1 1m+*', 1

%&'�#$ %&'�#$

%&'�#( %&'�#( %&'�#(

%&'�#( %&'�#( %&'�#(

%&'�#( %&'�#( %&'�#(

$ − %&'%&' ��#!��#! ��#!

Fig. 4. State transmission diagram

where pli is the probability that a transmitted packet ofthe SBS encounters a collision in the unlicensed channeli, and Wi = 2cW0 is the contention window size at theretransmission stage c. (8) is the transition probability ofthe back-off counter from (k + 1) to k; (9) represents thetransition probability after successful transmission; (10) is thetransition probability after unsuccessful transmission; (11) isthe transition probability after unsuccessful transmission in(m+el)th retransmission stage. In the steady state, we derive(12) from [19], where br,k(k ∈ (0,Wi − 1), r ∈ (0,m+ el))is the stationary distribution of the Markov chain; τ li is theprobability that the SBS transmits during a genetic slot timein the unlicensed channel i.

τ li =

m+el∑r=0

br,0

=2

W0((1−pl

i)(1−(2pli)

m+1)

(1−2pli)(1−(pl

i)m+el )

+ 2m(pl

i)m+1−(pl

i)m+el+1

1−(pli)

m+el+1 ) + 1.

(12)

The Wi-Fi network: Wi-Fi stations access the unlicensedchannel based on 802.11 by the Distributed CoordinationFunction (DCF) – a distributed slotted medium access schemebased on Carrier Sensing Multiple Access Collision Avoid-ance (CSMA/CA) protocol and the corresponding request-to-send/clear-to-send (RTS/CTS) access mechanisms. Based onthe Bianchi model [20], the probability τwi that a Wi-Fi stationin wi occupies the channel i to transmit in a randomly chosenslot time is expressed in (13).

τwi =2

1 + cwwi + pwi cw

wi

∑mi

k=0(2pwi )

k, (13)

where cwwi is the minimum size of contention window of wi,

mi is the maximum retransmission stage of wi, and pwi isthe probability that a transmitted packet of a station in wi

encounters a collision.The probability τ li that SBS transmits data during a slot in

the channel i is calculated using (12). The SBS shares theunlicensed channel i with the Wi-Fi network wi composedof ni stations. In the scenario, in a random slot the collisionprobability of the SBS with at least one of Wi-Fi stations iscalculated by (14), and the collision probability of a Wi-Fistation with at least one of other remaining Wi-Fi stations orthe SBS is calculated using (15).

pli = 1− (1− τwi )ni , (14)

pwi = 1− (1− τ li )(1− τwi )ni−1. (15)

pli, pwi , τ

li , τ

wi can be calculated by jointly integrating (12)-

(15), since other used parameters such as m, el,W0, cwwi

are known. To compute the average throughput of SBS orthe Wi-Fi network on a channel, the average time durationof a transmission is needed. It should contain events ofsuccessful transmission of the SBS, successful transmissionof the Wi-Fi station, the confliction among Wi-Fi stations andthe confliction between the SBS and the Wi-Fi station. Theaverage time duration in channel i is:

tai = piitw + pwsitwsi + plsit

lsi + pwcit

wci + plcitci (16)

where

pii = (1− τ li )(1− τwi )ni ,

pwsi = niτwi (1− τwi )ni−1(1− τ li ),

twsi = RTS/C + CTS/C + (H + Ewi[p])/C+

ACK/C + 3SIFS +DIFS + 4σ,

plsi = τ li (1− τwi )ni ,

tlsi = tlri +∑j∈U

tj,i + tb,

tlri =1⌊tltw

⌋ b tltwc−1∑

k=0

ktw,

pwci = (1− (1− τwi )ni − niτwi (1− τwi )ni−1)(1− τ li ),twci = RTS/C +DIFS + σ,

plci = τ li (1− (1− τwi )ni).

pii is the probability that i is idle in a slot. tw is the durationof the Wi-Fi slot because the SBS and Wi-Fi stations competea channel based on the Wi-Fi slot. pwsi is the probability ofa successful transmission of a station of wi on i, and twsi isthe time duration of the transmission, where Ewi

[p] is thetransmitted packet size of a Wi-Fi station, H is the packetheader, C is the channel data rate, σ is the propagation delay,and RTS,CTS,ACK,SIFS,DIFS are parameters in theRTS/CTS mechanism of the Wi-Fi network.plsi is the probability of a successful transmission of the

SBS on i, and tlsi is the time duration of the transmissionthat contains the channel reservation time tlri after gettingthe transmission opportunity and the data transmission time


of SBS users on i and a boundary slot tb followed by thedata transmission. tlri is the average value of all possiblereservation time. pwci is the probability that the channel isconflicted by Wi-Fi stations, and twci is the duration of theconfliction. plci is the probability that the SBS and a Wi-Fistation conflict the channel, tci represents the conflict durationdetermined by the larger value between tlci and twci. t

lci is the

time duration of a failure data transmission of the SBS, whichis the same with the time of the successful data transmissiontlsi because the SBS gets the failure message until the end ofthe data transmission. twci is the time duration of a failure datatransmission of a Wi-Fi station. Based on 802.11 standards,RTS is 304bits and DIFS equals to 34µs. The maximum valueof twci is approximately equal to 186µs when the data rate ofthe Wi-Fi network is minimum (2Mbps) and σ = 0.1µs. Thevalue is much smaller than tlci with a constraint of at least1ms. Thus, in the paper tci = tlci = tlsi. We use a functionwith variable tj,i to express tai where bi1, bi2, bi3 are constantsshown as follows:

tai = bi1 + bi2(bi3 +∑j∈U

tj,i) (17)

where

bi1 = piitw + pwsitwsi + pwcit

wci,

bi2 = plsi + plci,

bi3 = tlri + tb.

The methods of calculating the throughput TPj,i of theSBS user j on i, the throughput TP l

i of SBS on i and thethroughput TP l

wiof wi under the influence of the SBS are

respectively illustrated as follows:

TPj,i =plsir

ULj tj,i

tai,

TP li =

∑j∈U

TPj,i,

TPwi =

pwsiEwi[p]

tai.

C. The Fairness Criterion

SBS

APiSBS coverage range

Wi-Fi AP coverage range

An unlicensed channel

Wi

Fig. 5. The wi shares the unlicensed channel i with SBS.

Some SBS users are assigned to share the unlicensedchannel i with Wi-Fi network wi, as shown in Fig. 5, where

the line with arrow represents the used unlicensed channeli; the circle with solid line represents the coverage range ofSBS; the circle with dotted line represents the coverage rangeof wi. In the paper, the adopted fairness criterion is the LTEnetwork does not impact the throughput of the Wi-Fi networkon a carrier more than an additional Wi-Fi network operatingon the same carrier and offering the same level of traffic loadof the LTE network. Therefore, the fairness criterion on thecoexistence channel i is the throughput TP l

wiof wi under

the influence of the SBS should be larger than the throughput

TPw

′i

wi of wi under the influence of another network w′

i, whichoffering the same traffic load of the SBS and occupying thesame channel of wi illustrated in Fig. 6, where the circle withdotted line represents the coverage range of the Wi-Fi network,and the line with arrow represents the two networks use thesame unlicensed channel.

!"#

Wi-Fi AP coverage range

The unlicensed channel i

!$

Fig. 6. The wi shares the unlicensed channel i with w′i .

Considering the scenario that on the channel i the Wi-Fi network offering the same traffic of SBS may not exist,a virtual network w

′

i with n′

i users is constructed. We setthe size of its contention window cww

′

i and its maximumretransmission times m

′

i are same with wi. Bianchi model[24] proposed a method to calculate the throughput of w

′

i,when w

′

i occupies a channel alone, illustrated as follows:

TPw′i=

pw′

tripw

′

si Ew′i[p]

(1− pw′

tri)tw + pw′

tripw′

si tw′

si + pw′

tri(1− pw′

si )tw′

ci(18)

where

tw′

si = RTS/C + CTS/C + (H + Ew′i[p])/C+


tw′

ci = RTS/C +DIFS + σ,

τw′

i =2

1 + cwwi + pw

′

i cwwi

∑mi

k=0(2pw′

i )k,

pw′

i = 1− (1− τw′

i )n′i−1,

pw′

tri = 1− (1− τw′

i )n′i ,

pw′

si =n

′

iτw

′

i (1− τw′

i )n′i−1

pw′

tri

.

τw′

i is the probability that a Wi-Fi station in w′

i transmitsin a slot time, where pw

′

i represents the probability that atransmitted packet of a station in w

′

i encounters a collision;pw

′

tri, pw

′

si respectively represent the probability that there is




at least one transmission in a slot time and the successfultransmission on channel i; tw is the duration of an emptyslot time; tw

′

ci is the average time that the channel is sensedbusy by each station during a collision; tw

′

si is the averagetime that the channel is sensed busy because of a successfultransmission; Ew

′i[p] is the average size of the delivery packet

in w′

i. According to (18) the specific throughput of a Wi-Fi network can also be got by changing the size of thedelivery packet besides changing the number of stations, andthe feasibility has been proved in Section V-C. Therefore, toachieve the throughput of the SBS on the channel i (TP l

i ) theaverage delivery packet size of w

′

i with n′

i stations is:

Ew′i[p] =

{ ((1−pw

′

tri)tw+pw′

tri(1−pw

′

si )tw′

ci )C

pw′

tripw

′si

+ Y C}TP li

C − TP li

(19)

where

Y = RTS/C + CTS/C +H/C +ACK/C+

3SIFS +DIFS + 4σ.

When w′

i occupies the same channel of wi, the throughputof the hybrid network is similar to a Wi-Fi network with (ni+n

′

i) stations occupied the channel alone. The throughput of thehybrid network is:

TPwi+w′i=

phwtriphwsi Ehw[p]

(1− phwtri )tw + phwtriphwsi t

hwsi + phwtri (1− phwsi )thwci

(20)where

Ehw[p] =niEwi

[p] + n′

iEw′i[p]

ni + n′i

,

τhwi =2

1 + cwwi + phwi cww

i

∑mi

k=0(2phwi )k

,

phwi = 1− (1− τhwi )ni+n′i−1,

phwtri = 1− (1− τhwi )n+n′i ,

phwsi =ni + n

′

iτhwi (1− τhwi )ni+n

′i−1

phwtri,

thwsi = RTS/C + CTS/C + (H + Ehw[p])/C+


thwci = RTS/C +DIFS + σ.

Therefore, the throughput TPw′i

wi of the Wi-Fi AP wi underthe influence of w

′

i is transformed to (21) by integrating(19), (20), in which αi1, αi2, αi3, αi4 are constants, sincethe some parameters used to calculate them are knownsuch as C, ni, n

′

i, Ewi[p], tw, Y, t

hwci , and other parameters

can be calculated based on above-listed equations such aspw

′

tri, pw

′

si , phwtri , p

hwsi .

TPw

′i

wi =niEwi

[p]

niEwi[p] + n

′iEw

′i[p]TPwi+w

′i=αi1 + αi2TP

li

αi3 + αi4TP li

(21)

where

αi1 = Cniphwtrip

hwsi p

w′

tripw

′

si Ewi [p],

αi2 = −niphwtriphwsi pw′

tripw

′

si Ewi [p],

αi3 = Cpw′

tripw

′

si (ni + n′

i)((1− phwtri )tw + phwtri (1− phwsi )∗

thwci + phwtriphwsi Y ) + Cnip

hwtrip

hwsi p

w′

tripw

′

si Ewi[p],

αi4 =Cn′

iphwtrip

hwsi ((1− pw

′

tri)tw + Cpw′

tri(1− pw′

si )twci+

Cpw′

tripw

′

si Y )− pw′

tripw

′

si {(ni + n′

i)((1− phwtri )tw+phwtri (1− phwsi )thwci + phwtrip

hwsi Y ) + nip

hwtrip

hwsi Ewi

[p]}.

IV. THE OPTIMAL ALGORITHM

According to the calculation of parameters, the problemformulation can be concretely written as:

Maximize:∑

i∈C∑

j∈Uplsir

ULj tj,i

bi1+bi2(bi3+∑

j∈U tj,i)

Subject to:tj,i ≥ 0,∀j,∀i,∑

j∈Utj,i ≤ Tcot,∀i,

∑i∈C

plsirULj tj,i

bi1 + bi2(bi3 +∑

j∈U tj,i)≥ Rj ,∀j,

pwsiEwi[p]

bi1 + bi2(bi3 +∑

j∈U tj,i)≥

αi1(bi1 + bi2(bi3 +∑

j∈U tj,i)) + αi2

∑j∈U p

lsir

ULj tj,i

αi3(bi1 + bi2(bi3 +∑

j∈U tj,i)) + αi4

∑j∈U p

lsir

ULj tj,i

,∀i,

where all parameters such as plsi, rULj , pwsi are constants except

that tj,i is variable. The formulated problem is a non-linearprogramming problem. In this paper, we directly use theinterior point method to solve the problem. The methodproposes to construct a barrier function (22) to transform theproblem into an optimization problem with no constraint.

f(t) = −∑i∈C

∑j∈U

plsirULj tj,i

bi1 + bi2(bi3 +∑

j∈U tj,i)+ µ∗

(∑i∈C

∑j∈U

logtj,i +∑j∈U

loggi(t) +∑i∈C

log(−ki(t)− li(t)))

(22)

where

gi(t) =∑i∈C

plsirULj tj,i

bi1 + bi2(bi3 +∑

j∈U tj,i)−Rj ,

ki(t) =∑j∈U

tj,i − Tcot,

li(t) = pwsiEwi [p]{αi3(bi1 + bi2(bi3 +∑j∈U

tj,i))+

αi4

∑j∈U

plsirULj tj,i} − (bi1 + bi2(bi3 +

∑j∈U

tj,i))∗

{αi3(bi1 + bi2(bi3 +∑j∈U

tj,i)) + αi4

∑j∈U

plsirULj tj,i}.




µ is a predetermined constant. Since the linear function,quadratic and log functions are all convex, the constructedbarrier function (22) is convex either and the local minimumvalue is the minimized value. Therefore, the value of t∗, whichis the solution that the gradient of the objective function equalsto zero showed in (23) (j ∈ U, i ∈ C), is minimum.

5f(t) = (5f(tj,i)) = 0 (23)

where5f(tj,i) =

∂f

∂tj,i.

The Newton iteration method illustrated in the (24) can beused to effectively solve the transformed problem (22), whereH is the derivative of 5f(t). In the iteration, the parameter µis constant, and the stop condition is 5f(t∗) is smaller than apredefined error value ε. The detailed process is illustrated inAlgorithm 1. In this paper, considering the integer constraintof COT of SBS, the value of t∗ is rounded down.

tn+1 = tn −H−1 5 f(t) (24)

whereH =

[∀i,∀j ∂ 5 f

∂tj,i

].

Algorithm 1 The interior algorithmInput: the objective function, constraints, µ, ε, t(0)Output: t∗

1: t∗ = t(0); //the initial value2: n = 1;3: while (5f(t∗) ≥ ε) do4: tn+1 = tn −H−1 5 f(t);5: t∗ = tn+1;6: n = n+ 1;7: end while8: return t∗;

V. THE PERFORMANCE EVALUATION

In this section, we evaluate our proposed method from threeaspects. The first is to demonstrate the influence of the COTof LTE on the throughput and fairness of the network. Then,we prove our proposed throughput calculation method forthe LTE and Wi-Fi coexistence network and the used Wi-Fi network construction method is valid. At last we evaluatethe performance of our proposed method from aspects of LTEusers’ throughput and fairness.

A. The Influence of the COT of LTE

To demonstrate the influence of the COT of LTE on thethroughput and fairness of the network, we construct the Wi-Fi and LTE coexistence network based on the LTE/Wi-Ficoexistence model [21] in an event-driven network simulator(NS3). The constructed network has one SBS, and three Wi-Fi networks. In the Wi-Fi network, Wi-Fi stations transmituplink traffic to the AP based on IEEE 802.11n protocol

TABLE ITHE WI-FI NETWORK PARAMETERS

Wi-Finetwork

The numberof stations

Data PacketSize/Bytes

A data itemtransmissionduration/ms

Throughput/Mbps

1 5 1500 0.22222 30.8532 10 1200 0.17778 27.6993 15 1000 0.14815 25.016

TABLE IINS3 SIMULATION PARAMETERS

parameters values commentsCWw

min 15 The minimum size of Wi-Fi contention windowCWw

max 1023 The maximum size of Wi-Fi contention windowm 6 The maximum back-off times of Wi-Fi CW

ACK 364bits The size of ACK frameCTS 352bits The size of CTS frameRTS 304bits The size of RTS frameSIFS 16µs Short interframe spaceDIFS 34µs Distributed interframe space

H 416bits The header size of transferred Wi-Fi packetsσ 0 The propagation delayTw 9µs The Wi-Fi slot timeTl 1ms The LTE slot timeTb 0.5ms The LTE boundary slot time

CW lmin 15 The minimum size of LTE contention window

CW lmax 1023 The maximum size of LTE contention windowel 5 The time of staying the maximum CWTcot 8ms The COT of LTE

operating at an unlicensed channel with RTS/CTS mechanism,whereas the LTE SBS accesses the same channel schedulingdownlink transmissions to its associated user based on thesynchronized LBE and it can access multiple unlicensedchannels by carrier aggregation. The data rate of the channel is54Mbps. Every Wi-Fi station generates a packet at an intervalof 200 microseconds to make sure that saturation conditionsexist at 54Mbps data rate and below.

The parameters of Wi-Fi networks are illustrated in TABLEI, and other NS3 simulation parameters are listed in TABLE II.First, we evaluate the throughput of SBS, the Wi-Fi networkand the total throughput of SBS and Wi-Fi on an unlicensedchannel with the change of COT of SBS. As shown in Fig. 7a-c, the throughput of SBS increases with the increase of itsCOT and at the same time the throughput of the Wi-Fi networkoccupied the same channel decreases, since the increase ofthe occupation time of a network will lead to the decreaseof another network when the total time is fixed. The totalthroughput of the channel increases, because the increasedthroughput of LTE is larger than the reduced throughput ofthe Wi-Fi network.

Then, we evaluate whether fairness is satisfied with thechange of the COT of SBS. The measured fairness criterion isthe throughput of a Wi-Fi network wi under the influence ofSBS (TP l

wi) is larger than the throughput of the Wi-Fi network

under the influence of the constructed virtual Wi-Fi networkw

′

i with 5 stations offering the same traffic of the SBS (TPw′i

wi ),

and it can be expressed by TP lwi> TP

w′i

wi (i ∈ {1, 2, 3}).In Fig. 7 d, the solid line represents TP l

wi, and the dotted

line with same marker represents TPw′i

wi . The solid line showsthat with the increase of the COT of SBS, the throughput




3 3.5 4 4.5 5 5.5 6 6.5 7COT of the SBS network/ms

5

10

15

20

25

30

35

40T

he th

roug

hput

/Mbp

s

Wi-Fi 1SBS on channel 1Wi-Fi 1+ SBS on channel 1


5

10

15

20

25

30

35

40

The

thro

ughp

ut/M

bps


(a) Wi-Fi network 1 (b) Wi-Fi network 2


5

10

15

20

25

30

35

The

thro

ughp

ut/M

bps


(c) Wi-Fi network 3


10

15

20

25

30

35

The

thro

ughp

ut/M

bps

Wi-Fi 1 under SBSWi-Fi 1 under Wi-Fi 1'Wi-Fi 2 under SBSWi-Fi 2 under Wi-Fi 2'Wi-Fi 3 under SBSWi-Fi 3 under Wi-Fi 3'

(d) Fairness satisfaction

Fig. 7. The throughput of networks with the change of COT.

TP lwi

decreases. The dotted line show the throughput TPw′i

wi

also reduces with the increase of the COT of SBS since thetransmitted packet size of the constructed Wi-Fi network w

′

i

increases to satisfy the increased SBS throughput. However,

the reduced value of TPw

′i

wi is small comparing with thereduced value of TP l

wi. Therefore, as the increase of the COT

of SBS, the fairness criterion will not be satisfied gradually.As shown in Fig. 7 d, the fairness of Wi-Fi network 1 is notsatisfied when COT of SBS increases to 7ms. Although thefairness is satisfied for Wi-Fi network 2 and 3, the gap between

TP lwi

and TPw′i

wi continuously decreases with the increase ofCOT of SBS. In conclusion, the COT of LTE will influencethe throughput and the fairness of networks.

B. The Coexistence Network Throughput Calculation Method

500 1000 1500 2000 2500The Wi-Fi Packet Size/Bytes

5

10

15

20

Thr

ough

put o

f Wi-F

i Net

wor

k/M

bps

The proposed methodNS-3 simulation

500 1000 1500 2000 2500The Wi-Fi Packet Size/Bytes

12

14

16

18

20

Thr

ough

put o

f SB

S ne

twor

k/M

bps


(a) Throughput of Wi-Fi vs Wi-Fi packet size (b) Throughput of SBS vs Wi-Fi packet size


8

9

10

11

12

13

14

15

Thr

ough

put o

f Wi-F

i net

wor

k/M

bps


(c) Throughput of Wi-Fi vs COT of the SBS


14

16

18

20

22

Thr

ough

put o

f SB

S ne

twor

k/M

bps


(d) Throughput of SBS vs COT of the SBS

Fig. 8. Comparing the proposed throughput calculation method with NS3simulation.

To prove the validity of our proposed throughput calculationmethod, we set up a single hop Wi-Fi network with one APand 5 Wi-Fi stations, co-existed with an SBS with one userusing NS3. Wi-Fi stations transmit uplink traffic to the APby the unlicensed channel 36 (5.180 GHz), and SBS usesthe same channel to download data to its user. The datarate of the channel is 54Mbps. Every Wi-Fi station generatesa packet at an interval of 200 microseconds to make sure

TABLE IIITESTIFY THE USED WI-FI NETWORK CONSTRUCTION METHOD

Throughput/Mbps 10 12 14 16 18Packet size/Bytes 256 322 395 475 564Throughput/Mbps 10.648 12.933 14.218 15.551 16.998

the saturation condition. Other simulation parameters are setbased on TABLE II.

We first compare the Wi-Fi and SBS throughput of ourproposed method with simulation results with the change ofthe Wi-Fi packet size. The COT of SBS is set to 5ms. Inthe proposed method, all parameters are set based on NS3simulation parameters. As illustrated in Fig. 8 a and b, thecalculated throughputs of the Wi-Fi network and SBS networkthroughput are similar to the NS3 simulation results.

Then, we compare the Wi-Fi and SBS throughput of theproposed method with simulation results with the change ofthe COT of SBS. The transmission packet size of the Wi-Finetwork is set to 1500 bytes. As illustrated in Fig. 8 c and d,the calculated throughput of the Wi-Fi and SBS network aresimilar to the NS3 simulation results. Therefore, our proposedcoexistence network throughput calculation method is accurateenough to calculate the throughput of the coexistence network.

C. The Wi-Fi Network Construction Method

The Wi-Fi network construction method aims to get the sizeof the delivery package of a Wi-Fi network to achieve thespecific throughput. It is based on our former paper [17]. Totestify the feasibility of the method in the paper, we comparethe throughput of a Wi-Fi network in NS3, whose packet sizeis got through our used method, with the specified throughput.

In the NS3 simulation environment, we set the Wi-Finetwork with one AP and five stations. Every Wi-Fi stationgenerates a packet with an interval of 200 microseconds, andthe data rate of the channel is 54Mbps. Other parameters aresame with TABLE II. The simulation results are illustrated inTABLE III.

In TABLE III, the first line is the specific throughput. Thesecond line is the calculated delivery packet size by the usedWi-Fi construction method to reach the throughput of the firstline, and the third line is the throughput of the Wi-Fi networkusing the calculated packet size in NS3 simulation. The resultsshow the throughput of the Wi-Fi network in NS3 is similar




TABLE IVTHE SBS USERS

The number of SBS users The contained SBS users1 u12 u1, u23 u1, u2, u34 u1, u2, u3, u45 u1, u2, u3, u4, u5

to the specific throughput. It proves the specific throughputcan be achieved by our used Wi-Fi construction method.

D. The Performance of the Proposed Method

In this section, we compare the performance (throughput) ofLBE mechanism using our proposed COT adjustment methodwith other two mechanisms: the LBE mechanism based oncontinuous COT and the standard LBT mechanism in TS36.213 [10] in two simulation scenarios. All parameters suchas CWw

min, CWwmax are the same in these three channel access

mechanisms except for the COT. The first scenario is changingthe number of SBS users, and the second scenario is changingthe number of Wi-Fi networks.

LBE with Continuous COT (CCOT): The SBS networkaccesses the channel using LBE, and continuously occupiesunlicensed channels to only satisfy its needed COT withoutconsidering the fairness. However, the COT of SBS shouldnot exceed the allowed maximum COT. The method serves asthe lower bound. In 3GPP Release 13 [8], when the counterreaches zero, the transmitter can occupy the channel for amaximum amount of time of q∗(13/23)ms, where q is definedby the manufacturer in the range of [4, 32]. Therefore, in thepaper, we set the maximum COT is 13ms.

The standard LBT: the COT of the mechanism is basedon the priority class of the transmitted data, for example, thedata transmission time is 2ms when the priority class numberof the data equals to 1; and the transmission time is 8/10msfor the data with priority class 4. In our simulation, we set thepriority number of transmitted data is 4, and the correspondingtransmission time of the data is 10ms.

The first simulation environment: It has one SBS, andthree Wi-Fi networks. As the number of Wi-Fi networksis small, the Wi-Fi networks can use different unlicensedchannels. Wi-Fi network 1, 2, 3 occupied unlicensed channelsare channel 1, 2, 3, respectively. The SBS accesses these threechannels. The data rates of Wi-Fi networks are 54Mbps, andother parameters of the Wi-Fi networks are same with TABLEI. We evaluate the performance of methods with the changeof the number of SBS users. The contained SBS users andrelated parameters are illustrated in TABLE IV and V.

First, since our proposed method aims to maximize thethroughput of the SBS network with the constraint of fairnesscriterion, and COT of the SBS of accessing the unlicensedchannel is fixed in the standard LBT method (10ms), thesetwo methods don’t consider the number of SBS users inthe process of accessing the unlicensed channel. Hence, thethroughput of SBS, Wi-Fi networks, the total throughput ofSBS and Wi-Fi networks, and the reduced throughput of Wi-

TABLE VTHE PARAMETERS OF SBS USERS

The SBS user The demands of COT/ms The data rate/Mbpsu1 2.5000 54u2 2.0000 54u3 4.4000 54u4 1.5000 54u5 5.0000 54

Fi networks will not be changed with the increase of SBSusers in these two methods as illustrated in Fig. 9.

1 2 3 4 5The number of SBS users

10

20

30

40

50

60

The

tota

l thr

ough

put o

f SBS

/Mbp

s

LBE with porposed methodLBE with CCOTStandard LBT

(a) The throughput of SBS network


10

20

30

40

50

60

70

Thro

ughp

ut o

f Wi-F

i net

wor

ks/M

bps

LBE with proposed methodLBE with CCOTStandard LBT

(b) The throughput of all Wi-Fi networks


76

78

80

82

84

86

Tota

l thr

ough

put o

f SBS

+Wi-F

i/Mbp

s


(c) The total throughput of SBS and Wi-Fi networks


0

10

20

30

40

50

60

Red

uced

thro

ughp

ut o

f Wi-F

is/M

bps


(d) The reduced throughput of Wi-Fi networks

Fig. 9. The throughput of networks in the first simulation environment.

In comparison to the standard LBT method, the throughputof SBS using LBE with our proposed method is smaller shownin Fig. 9 a, since our proposed method considers the fairnessin the process of adjusting the COT of SBS. It directly leadsto larger throughput of all Wi-Fi networks shown in Fig. 9 band smaller reduced throughput of all Wi-Fi networks of thanthe standard LBT method shown in Fig. 9 d. What’s more, theinfluence of varied COT of SBS on the throughput of SBS islarger than its influence on the throughput of Wi-Fi networks.Hence, the total throughput of SBS and Wi-Fi networks ofour proposed method is smaller than the standard LBT shownin Fig. 9 c.

The COT of our proposed method is the maximizedvalue satisfying the fairness constraint. The values are10ms, 5ms, 6ms on channels 3, 2, 1 respectively. For thestandard LBT method, since its COT is set to 10ms, thefairness is only satisfied on channel 3, and the throughputof Wi-Fi 3 is the same with our proposed method illustratedin Fig. 10 a. The solid line represents the throughput of Wi-Finetwork w3 under the influence of SBS (TP l

w3), and the dotted

line with the same marker represents the throughput of Wi-Finetwork w3 under the influence of w

′

3 offering the same traffic

of SBS accessing the channel 3 (TPw′3

w3 ). In Wi-Fi network 2and 1, since the COT of SBS in the standard LBT methodis larger than our proposed method, the fairness criterion isnot satisfied and the throughputs of the Wi-Fi networks aresmaller than our proposed method shown in Fig. 10 b, c.





10

15

20

25

30T

hrou

ghpu

t of W

i-Fi n

etw

ork3

/Mbp

s

The proposed method under SBSThe proposed method under Wi-FiLBE with CCOT under SBSLBE with CCOT under Wi-FiStandard LBT under SBSStandard LBT under Wi-Fi

(a) The fairness of Wi-Fi network 3


15

20

25

30

35

40

Thro

ughp

ut o

f Wi-F

i net

wor

k2/M

bps


(b) The fairness of Wi-Fi network 2


10

15

20

25

30

35

40

45

Thr

ough

put o

f Wi-F

i net

wor

k1/M

bps


(c) The fairness of Wi-Fi network 1

Fig. 10. The fairness of Wi-Fi networks in the first simulation environment.

TABLE VITHE CONTAINED WI-FI NETWORKS

The number of Wi-Fi networks The contained Wi-Fi networks3 Wi-Fi network 1, 2, 34 Wi-Fi network 1, 2, 3, 45 Wi-Fi network 1, 2, 3,4,56 Wi-Fi network 1, 2, 3, 4, 5, 67 Wi-Fi network 1, 2, 3, 4, 5, 6, 7

Second, since the LBE with CCOT method aims to satisfyCOT demands of SBS users, the throughput of SBS andthe total throughput of SBS and Wi-Fi are smaller thanthe LBE with our proposed method and the standard LBTmethod, and the throughput of the network will be changedwith the increase of SBS users. According to the accessmechanism the SBS first chooses to occupy the channel of Wi-Fi network whose throughput is minimum. In this scenario,it first occupies channel 3. If the allowed maximum COT ofchannel 3 still can not satisfy the SBS users demands, then theSBS occupies channel 2, at last occupies channel 1. When thenumber of SBS users is smaller than 5, COT demands can besatisfied by occupying channel 3. Therefore, the throughputsof Wi-Fi network 2 and 1 do not change with the increase ofSBS users shown in Fig. 10 b, c. The SBS needed COT is8ms to satisfy the COT demands of 4 users, it is smaller thanfairness constraint 10ms on channel 3. When the number ofSBS user is up to 5, its data rate demands can be satisfied byoccupying channels 3 and 2. The assigned COTs are 13mson channel 3 and 3ms on channel 2. Therefore, for the LBEwith CCOT method, the fairness cannot be satisfied on Wi-Finetwork 3 shown in Fig. 10 a and can be satisfied on Wi-Finetwork 2 and 1 shown in Fig. 10 b, c.

The second simulation environment: it has one SBS andfive SBS users. The SBS users’ COT demands and theirmaximum data rates on unlicensed channels are same withTABLE V. We evaluate the performance of methods with thechange of the number of Wi-Fi networks. The contained Wi-Finetworks are illustrated in TABLE VI, and the correspondingparameters of the Wi-Fi networks are separately listed inTABLE VII. Wi-Fi network 1, 2, 3, 4, 5, 6, 7 occupiedunlicensed channels are channel 1, 2, 3, 4, 5, 6, 7 respectively.

For the CCOT method, since it aims to satisfy SBS users’data rate demands and they are satisfied when the number of

TABLE VIITHE PARAMETERS OF WI-FI NETWORKS

The number Data packet size Throughputof stations /Bytes /Mbps

Wi-Fi network 1 5 1500 30.853Wi-Fi network 2 10 1200 27.699Wi-Fi network 3 15 1000 25.016Wi-Fi network 4 20 800 21.873Wi-Fi network 5 25 500 15.976Wi-Fi network 6 30 500 15.848Wi-Fi network 7 35 200 7.6240

(a) The throughput of SBS network (b) The throughput of all Wi-Fi networks

(c) The total throughput of SBS and Wi-Fi networks (d) The reduced throughput of Wi-Fi networks

3 3.5 4 4.5 5 5.5 6 6.5 7The number of Wi-Fi networks

20

30

40

50

60

70

80

Red

uced

thro

ughp

ut o

f Wi-F

is/M

bps

LBE with Proposed MethodLBE with CCOTStandard LBT


60

80

100

120

140

160

180

Tota

l thr

ough

put o

f LTE

+Wi-F

is/M

bps



20

30

40

50

60

70

80R

educ

ed th

roug

hput

of W

i-Fis

/Mbp

sLBE with Proposed MethodLBE with CCOTStandard LBT


60

80

100

120

140

160

180

Tot

al th

roug

hput

of L

TE

+Wi-F

is/M

bps



20

30

40

50

60

70

80

Red

uced

thro

ughp

ut o

f Wi-F

is/M

bps



0

20

40

60

80

100

Thr

ough

put o

f the

SB

S/M

bps LBE with Proposed Method

LBE with CCOTStandard LBT


20

30

40

50

60

70

80

Red

uced

thro

ughp

ut o

f Wi-F

is/M

bps


3 3.5 4 4.5 5 5.5 6 6.5 7The numbr of Wi-Fi networks

40

60

80

100

120

140

Thr

ough

put o

f all

Wi-F

is/M

bps LBE with Proposed Method

LBE with CCOTStandard LBT

Fig. 11. The throughput of networks in the second simulation environment.

Wi-Fi networks is 2, the throughput of SBS and the reducedthroughput of Wi-Fi networks will not change with the numberof Wi-Fi networks shown in Fig. 11 a, d. However, thethroughput of all Wi-Fi networks and the total throughput ofSBS and Wi-Fi networks will increase with the increase in thenumber of Wi-Fi networks. What’s more, since the throughputof SBS of the continuous COT method is minimized, itsthroughput of Wi-Fi networks is the largest shown in Fig.9 b.

The COT values of our proposed method on channels 1-7are 5ms, 6ms, 10ms, 13ms, 13ms, 13ms, 13ms respectively.These values are the maximum values of satisfying the fairnesscriterion. Comparing with the standard LBT method, which




sets COT of all channels to 10ms, the throughput of the SBSof our proposed method is smaller than it and the throughputof all Wi-Fi networks of our proposed methods is larger thanit when the number of Wi-Fi networks is 3, but the gapgradually decreases with the increase of the number of Wi-Fi networks illustrated in Fig. 11 a and b. In a channel, theincrease of COT of the SBS will increase the throughputof the SBS, at the same time the throughput of the Wi-Finetwork using the channel to transmit data will be reduced.However, the increased throughput of SBS is larger than thereduced throughput of Wi-Fi networks. Therefore, in Fig. 11c, at first the total throughput of the standard LBT method islarger than our proposed method, but the throughput of ourproposed method is gradually larger than the standard LBTmethod with the increase of the number of Wi-Fi networks.The total throughput of these two methods is larger than theCCOT method.

We use Wi-Fi network 2 to evaluate the influence of thenumber of Wi-Fi networks on the fairness illustrated in Fig.12. For our proposed method, the COT of channel 2 is 6ms. Itis the maximum value of satisfying the fairness criterion andwill not change with the number of Wi-Fi networks. In thestandard LBT method the COT is fixed to 10ms, therefore, thefairness is not satisfied in channel 2 and at the same time it willnot change with the number of Wi-Fi networks. For the CCOTmethod, it occupies channel 2 2ms to satisfy the SBS users’COT demands, hence, the fairness is satisfied. What’s more,since SBS users’ demands do not change, the throughputof Wi-Fi network 2 will not be influenced with the changeof the number of Wi-Fi networks in these mechanisms. Inconclusion, the LBE mechanism using our proposed methodoptimizes the throughput of SBS while ensuring the fairness.

3 4 5 6 7The number of Wi-Fi networks

12

13

14

15

16

Thro

ughp

ut o

f Wi-F

i net

wor

k2/M

bps


Fig. 12. The fairness evaluation of Wi-Fi network 2 in the second simulationenvironment.

VI. RELATED WORK

Nowadays there are lots of work studying the LBE an-alytical framework for the coexistence of Wi-Fi and LTEnetworks. [19] characterizes when the 3GPP notion of fairnessin the coexistence is achievable. [22] explores the impact ofenergy detection threshold on Wi-Fi and LTE coexistence.[23] establishes different Markov chain models for Cat. 4LBT scheme, Cat. 3 LBT scheme classified by 3GPP [8],Wi-Fi DCF, and evaluates the performance of LTE, Wi-Fi interms of throughput and transmission delay. All these works

aim to analyze the performance of the coexistence networkwithout considering performance optimization. To improvethe performance of the coexistence LTE and Wi-Fi networks,most existing papers propose to adjust access parameters (thecontention window size of the back-off procedure, energydetection threshold of the CCA check) of LBE.

Adjusting the contention window: [24] proposes a mathe-matical framework to find the optimal size of cellular base sta-tions, which maximizes the total throughput of both networkswhile satisfying the required throughput of each network. In[25] LTE adjusts the CW size to an appropriate value usingsensing, calculating and comparing the slot utilization in aperiod of time to gain higher throughput. [26] proposes aFair Downlink Traffic Management (FDTM) scheme to tunethe minimum CW value of LTE which is fixed in the back-off procedure of the Cat. 3 LBT scheme and assign feasibleweights for LTE with different traffic loads to ensure thethroughput fairness for Wi-Fi networks. In [27], the SBSadaptively adjusts the back-off window size according to theavailable licensed spectrum bandwidth and the Wi-Fi trafficload to satisfy the quality-of-service requirements of small cellusers and minimize the collision probability of Wi-Fi users.

Optimizing the energy detection threshold: [28] appliesthe optimal stopping theory that LBE should stop listening andtransmitting once the channel quality exceeds an optimizedenergy detection threshold to study the throughput optimaltransmission strategy. [29] enhances the overall system capac-ity and satisfies the 3GPP fairness criterion from the aspectof spectrum reuse. It combines the advantages of transmissionstart time alignment and energy detection threshold adaptioninto a unified access framework, in which the alignmentreference interval is adaptively adjusted to control the channel-access probability for LAA and Wi-Fi systems.

The aforementioned papers ignore the influence of theparameter COT in LBT on the throughput of network. [11]points out that the conventional LBT mechanism fixing theChannel Occupation Time is inefficient under the varyingtraffic load in Wi-Fi and LTE systems. It proposes an adaptiveLBE-LBT mechanism composed of on-off adaptation for theCOT of LTE and short-long adaptation for idle time. [30]proposes a hybrid MAC protocol that combines the bestfeatures of LBE MAC and FBE MAC. By jointly optimizingthe sleep period and the CW size of LTE, the best co-existingperformance regarding the total network throughput and thethroughput fairness of LTE and Wi-Fi can be achieved, withminimal reservation overhead. Besides fairness, [31] proposesa model for the distribution of the MAC delays experiencedby the Wi-Fi packets and LTE frames, which can be used todynamically adjust the LBE parameters including the durationof LTE frames, the average initial LTE contention window notonly to achieve channel-time fairness but also to guaranteeMAC-delay bounds with a specific probability. However, sincethere is not a fairness standard reached a consensus, thesepapers use different standards to evaluate fairness. None ofthem considers the fairness criterion used in our paper. Thefairness is the LTE network does not impact the performanceof the Wi-Fi network on a carrier more than an additionalWi-Fi network operating on the same carrier and offering the




same level of traffic load of the LTE network.

VII. CONCLUSION

In this paper, we proposed a channel occupation timeadjustment method for the LBE mechanism to maximize thethroughput of LTE while ensuring the fairness coexistence andsatisfying the LTE users’ data rate demands. In the processof calculating the throughput of LTE and Wi-Fi networkson a coexistence unlicensed channel, we propose a newmethod considering the synchronization of SBS on licensedand unlicensed spectrum. In addition, to satisfy fairness, wepropose a virtual Wi-Fi network construction method to dealwith the situation that the Wi-Fi network offering the samelevel of traffic load of the LTE network does not existon the carrier. The NS3 simulation results first prove ourproposed throughput calculation method for the LTE and Wi-Fi coexistence network is valid, and then, it demonstrates thatthe proposed method optimizes the throughput of the SBSnetwork and ensures fairness. In the future, we will studyhow to adjust the size of contention window or jointly adjustthe size of contention window and COT to maximize thethroughput of LTE network while satisfying the used fairnesscriterion.

REFERENCES

[1] C. V. N. Index, “Global mobile data traffic forecast update, 2015–2020,”Cisco white paper, 2016.

[2] L. Zhu, C. Zhang, C. Xu, X. Du, R. Xu, K. Sharif, and M. Guizani,“Prif: A privacy-preserving interest-based forwarding scheme for socialinternet of vehicles,” IEEE Internet of Things Journal, vol. 5, no. 4, pp.2457–2466, 2018.

[3] 3GPP, “Radio frequency system scenarios,” Technical SpecificationGroup Radio Access Networks, TR 25.942, 2011, version 10.0.0.

[4] HUAWEI, “U-lte: unlicensed spectrum utilization of lte,” May, 2017.[5] N. Rupasinghe and I. Guvenc, “Licensed-assisted access for wifi-lte

coexistence in the unlicensed spectrum,” in 2014 IEEE GlobecomWorkshops (GC Wkshps). IEEE, 2014, pp. 894–899.

[6] H. Zhang, S. Chen, X. Li, H. Ji, and X. Du, “Interference managementfor heterogeneous networks with spectral efficiency improvement,”IEEE Wireless Communications, vol. 22, no. 2, pp. 101–107, 2015.

[7] Y. Jian, C.-F. Shih, B. Krishnaswamy, and R. Sivakumar, “Coexistenceof wi-fi and laa-lte: Experimental evaluation, analysis and insights,”in 2015 IEEE international conference on communication workshop(ICCW). IEEE, 2015, pp. 2325–2331.

[8] 3GPP, “Study on licensed-assisted access to unlicensed spectrum,”Technical Specification Group Radio Access Networks, TR 36.889,2015, version 13.0.0.

[9] S. Zinno, G. Di Stasi, S. Avallone, and G. Ventre, “On a fair coexistenceof lte and wi-fi in the unlicensed spectrum: A survey,” ComputerCommunications, vol. 115, pp. 35–50, 2018.

[10] 3GPP, “Evolved universal terrestrial radio access physical layer proce-dures,” TS 36.213, 2017, version 13.0.0.

[11] C. K. Kim, C. S. Yang, and C. G. Kang, “Adaptive listen-before-talk(lbt) scheme for lte and wi-fi systems coexisting in unlicensed band,”in 2016 13th IEEE Annual Consumer Communications & NetworkingConference (CCNC). IEEE, 2016, pp. 589–594.

[12] H. He, H. Shan, A. Huang, L. X. Cai, and T. Q. Quek, “Proportionalfairness-based resource allocation for lte-u coexisting with wi-fi,” IEEEAccess, vol. 5, pp. 4720–4731, 2016.

[13] C. Cano and D. J. Leith, “Coexistence of wifi and lte in unlicensedbands: A proportional fair allocation scheme,” in 2015 IEEE interna-tional conference on communication workshop (ICCW). IEEE, 2015,pp. 2288–2293.

[14] M. Khabazian, O. Kubbar, and H. Hassanein, “A fairness-based preemp-tion algorithm for lte-advanced,” in 2012 GLOBECOM. IEEE, 2012,pp. 5320–5325.

[15] Z. Guan and T. Melodia, “Cu-lte: Spectrally-efficient and fair coexis-tence between lte and wi-fi in unlicensed bands,” in IEEE INFOCOM.IEEE, 2016, pp. 1–9.

[16] Y. Huang, Y. Chen, Y. T. Hou, W. Lou, and J. H. Reed, “Recent advancesof lte/wifi coexistence in unlicensed spectrum,” IEEE Network, vol. 32,no. 2, pp. 107–113, 2017.

[17] Q. Wang, Z. Gao, X. Du, and L. Zhu, “An optimal lte-u access methodfor throughput maximization and fairness assurance,” in 2018 IEEE 37thInternational Performance Computing and Communications Conference(IPCCC). IEEE, 2018, pp. 1–8.

[18] A. Garcia-Saavedra, P. Patras, V. Valls, X. Costa-Perez, and D. J.Leith, “Orla/olaa: Orthogonal coexistence of laa and wifi in unlicensedspectrum,” IEEE/ACM Transactions on Networking, vol. 26, no. 6, pp.2665–2678, 2018.

[19] M. Mehrnoush, S. Roy, V. Sathya, and M. Ghosh, “On the fairnessof wi-fi and lte-laa coexistence,” IEEE Transactions on CognitiveCommunications and Networking, vol. 4, no. 4, pp. 735–748, 2018.

[20] G. Bianchi, “Performance analysis of the ieee 802.11 distributed coor-dination function,” IEEE Journal on selected areas in communications,vol. 18, no. 3, pp. 535–547, 2000.

[21] L. Giupponi, T. Henderson, B. Bojovic, and M. Miozzo, “Simulating lteand wi-fi coexistence in unlicensed spectrum with ns-3,” arXiv preprintarXiv:1604.06826, 2016.

[22] M. Mehrnoush, V. Sathya, S. Roy, and M. Ghosh, “Analytical modelingof wi-fi and lte-laa coexistence: Throughput and impact of energydetection threshold,” IEEE/ACM Transactions on Networking (TON),vol. 26, no. 4, pp. 1990–2003, 2018.

[23] Y. Gao, X. Chu, and J. Zhang, “Performance analysis of laa andwifi coexistence in unlicensed spectrum based on markov chain,” inGLOBECOM. IEEE, 2016, pp. 1–6.

[24] Y. Song, K. W. Sung, and Y. Han, “Coexistence of wi-fi and cellularwith listen-before-talk in unlicensed spectrum,” IEEE CommunicationsLetters, vol. 20, no. 1, pp. 161–164, 2015.

[25] F. Hao, C. Yongyu, H. Li, J. Zhang, and W. Quan, “Contention windowsize adaptation algorithm for laa-lte in unlicensed band,” in 2016International Symposium on Wireless Communication Systems (ISWCS).IEEE, 2016, pp. 476–480.

[26] Y. Li, T. Zhou, Y. Yang, H. Hu, and M. Hamalainen, “Fair downlinktraffic management for hybrid laa-lte/wi-fi networks,” IEEE Access,vol. 5, pp. 7031–7041, 2016.

[27] R. Yin, G. Yu, A. Maaref, and G. Y. Li, “Lbt-based adaptive channelaccess for lte-u systems,” IEEE Transactions on Wireless Communica-tions, vol. 15, no. 10, pp. 6585–6597, 2016.

[28] N. Wei, X. Lin, W. Li, Y. Xiong, and Z. Zhang, “Throughput optimallisten-before-talk for cellular in unlicensed spectrum,” in ICC. IEEE,2017, pp. 1–6.

[29] C. S. Yang, C. K. Kim, J.-M. Moon, S.-H. Park, and C. G. Kang,“Channel access scheme with alignment reference interval adaptation(aria) for frequency reuse in unlicensed band lte: Fuzzy q-learningapproach,” IEEE Access, vol. 6, pp. 26 438–26 451, 2018.

[30] S. Khairy, L. X. Cai, Y. Cheng, Z. Han, and H. Shan, “A hybrid-lbtmac with adaptive sleep for lte laa coexisting with wi-fi over unlicensedband,” in GLOBECOM. IEEE, 2017, pp. 1–6.

[31] G. J. Sutton, R. P. Liu, and Y. J. Guo, “Delay and reliability of load-based listen-before-talk in laa,” IEEE Access, vol. 6, pp. 6171–6182,2017.

Qian Wang is working toward the Ph.D. degreeat the Beijing University of Posts and Telecom-munications (BUPT), Beijing, China. Her currentresearch interests include wireless networks andopportunistic communications.




Xiaojiang Du is currently a Tenured Professor withthe Department of Computer and Information Sci-ences, Temple University, Philadelphia, PA, USA.His research interests are wireless communications,wireless networks, security and systems. He hasauthored more than 350 journal and conferencepapers in these areas, and a book. He has beenawarded over 6 million U.S. dollars research grantsfrom the U.S. National Science Foundation, ArmyResearch Office, Air Force Research Laboratory,NASA, the State of Pennsylvania, and Amazon. He

is a Life Member ACM. He is on the editorial boards of three internationaljournals.

Zhipeng Gao received the Ph.D. degree from theBeijing University of Posts and Telecommunications(BUPT), Beijing, China, in 2007. He is currentlya Professor with the State Key Laboratory of Net-working and Switching Technology, BUPT, Beijing,China. His research interests are wireless networks,edge computing and blockchain. He presides over aseries of key research projects on network and ser-vice management, including the projects supportedby the National Natural Science Foundation andthe National High-Tech Research and Development

Program of China. He received eight provincial scientific and technicalawards.

Mohsen Guizani (S’85-M’89-SM’99-F’09) re-ceived the B.S. (with distinction) and the M.S.degrees in electrical engineering, the M.S. and Ph.D.degrees in computer engineering from SyracuseUniversity, Syracuse, NY, USA, in 1984, 1986,1987, and 1990, respectively. He is currently aProfessor with the Computer Science and Engineer-ing Department, Qatar University, Qatar. Previously,he served in different academic and administra-tive positions at the University of Idaho, WesternMichigan University, University of West Florida,

University of Missouri-Kansas City, University of Colorado-Boulder, andSyracuse University. He is the author of nine books and more than 500publications in refereed journals and conferences. He guest edited a numberof Special Issues in IEEE journals and magazines. His research interestsinclude wireless communications and mobile computing, computer networks,mobile cloud computing, security, and smart grids. He is currently theEditor-in-chief of the IEEE NETWORK magazine, on the editorial boardsof several international technical journals, and the Founder and Editor-in-chief of Wireless Communications and Mobile Computing journal (Wiley).He was also a member, Chair, and General Chair of a number of internationalconferences. Throughout his career, he received three teaching awards andfour research awards. He also received the 2017 IEEE CommunicationsSociety WTC Recognition Award as well as the 2018 AdHoc TechnicalCommittee Recognition Award for his contribution to outstanding researchin wireless communications and ad-hoc sensor networks. He was the Chairof the IEEE Communications Society Wireless Technical Committee and theChair of the TAOS Technical Committee. He was the IEEE Computer SocietyDistinguished Speaker and is currently the IEEE ComSoc DistinguishedLecturer. He is a Senior Member of ACM.

Documents

An Optimal Channel Occupation Time Adjustment Method for ... · methods the problem of throughput maximization of LTE is formulated as a constrained non-linear optimization problem