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Performance of Limited Feedback MU-MIMO inDistributed Antenna Systems with Different
Scheduler
Mehran Behjati, Mahamod IsmailDept. of Electrical, Electronic and System Engineering
Universiti Kebangsaan Malaysia43600 UKM Bangi, Selangor Malaysia{mbehjati, mahamod}@eng.ukm.my
Muhammad@Yusoff IbrahimFaculty of Electrical Engineering
Universiti Teknologi MARA40450 Shah Alam, Selangor Malaysia
Abstract Distributed antenna system (DAS) acts as an
effective solution to mitigating interference and path-loss by
decreasing the access distance for users, and increases the cell
coverage and system capacity as well. This study evaluatesperformance of scheduling methods on the DAS where practical
system constraints are considered, such as path-loss, out-of-cell
interference and limited feedback. Zero-forcing multiuser MIMO
precoding is utilized as downlink transmission strategy, when
imperfect channel state information is available at the
transmitter. System performance is evaluated by empirical
cumulative density functions of the cell throughput, where a long-
term evolution advanced (LTE-A) standard compliant simulator
is utilized for simulation. It is demonstrated that by spreading the
transmit antennas throughout the cell more cell throughput is
achievable. Moreover, by utilizing an appropriate scheduling
algorithm, more potential of DAS can be extracted and leads to
substantial cell throughput.
Keywordsdistributed antenna system; limited feedback; zero-
forcing precoding; scheduling
I. INTRODUCTIONIn cellular networks, interference is known as a major
destructive factor to provide uniform high data-rate throughoutthe coverage area. A partial solution to this problem is reducingthe overall transmit power by utilizing distributed antennasystem (DAS). Moreover, DAS enhances the coverage area,capacity, and throughput, especially in shadowed and blindenvironments [1], [2]. To do so, remote radio units (RRUs) aredistributed over the cell area and are connected to the central
base station (BS) via low-latency and high-bandwidth
dedicated connections acting as distributed antenna arrays(DAAs).
DAS reduces the access distance for users, wherebyreduces the required uplink transmission power and achievessignificant transmission power gain, moreover increases thesum-rate capacity versus conventional cellular system [3], [1].Results in [2] show that DAS reduces inter-cell interferenceand significantly improves capacity, specially for users arelocated close to the cell edges. By applying multiuser MIMO(MU-MIMO) transmission to the DAS, more spatial multiuser
diversity can be exploited. Authors of [4] compared thethroughput of MU-MIMO zero-forcing (ZF) beamforming withand without DASs, and demonstrated advantages of MU-MIMO in DAS, and expressed that by utilizing full MU-MIMO to all RRUs best performance in term of area spectralefficiency is achievable.
Accordingly, this study applies full MU-MIMOtransmission at the central BS and each RRU, as well,according to the LTE-A specifications [5]. Furthermore,system performance evaluated under practical systemlimitations, such as limited feedback and out-of-cellinterference. Recently performance of DASs under perfect andimperfect CSI with different quantization methods investigatedin [6]. Authors of [3] showed that DAS can properly mitigateinterference, if multiple users scheduled simultaneously.Furthermore, authors of [7] shows that with scheduling
multiple best users simultaneously, the system capacitysurpasses over scheduling the best user, because inter-userinterference mitigated effectively and the spatial degree offreedom can be fully exploited. The results in [7] are onlyconsidered for three single antenna RRUs. Therefore, this studyinvestigates the cell throughput in terms of empiricalcumulative density functions (ECDF) when differentscheduling methods and different configuration of DAS areapplied to the MU-MIMO system.
In the case of frequency division duplex (FDD) system,channel state information (CSI) should be fed back via userequipments (UEs) to the BS, in order to compute the precoderfor inter-user interference cancelation and schedule users and
set a suitable modulation and coding scheme (MCS). To saveuplink resource, at the receiver side, the achieved CSI isquantized by random channel direction quantization (RCDQ)method [8] and conveyed to the BS by limited bits. Afterwards,at the transmitter side, ZF beamforming [9] as a promisingtransmission strategy is utilized to exploit multiplexing gainand mitigate interference. The simulation results demonstratethat, by utilizing proper scheduler method the potential of DAScan be extracted and with increasing the number of RRUs andtransmit antenna per RRU more cell throughput can beachieved.
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Fig. 1. MU-MIMO DAS architecture
The rest of the paper is organized as follows. Section IIoutlines the system model of limited feedback MU-MIMO,which contains downlink transmission, CSI feedback,
precoding, and scheduling. Simulation methodology isillustrated in Section III. Section IV presents the simulationresults. Finally Section V summarizes and concludes the paper.
II. LIMITED FEEDBACK MU-MIMOSYSTEM MODELA.MU-MIMO Downlink Channel
Figure 1 depicts the architecture of considered MU-MIMODAS with I cells which each cell i = {1, , I} contains onecentral BS with Mc,i transmit antennas and Ri RRUs whicheach of them equipped with Mr,i, where r{1 , , Ri}. The totalnumber of users which served in cell i is Ki and each userk{1,,Ki}is equipped with Nk,ireceive antennas. The input-output relationship on kth user in cell iis given by
yk,i= Hk,ixk,i+ Hk,i xu,iKu=1,uk + Hk,i(j)XjIj=0,ji + nk,i (1)
where, , is the perturbed received signal vector to user incell , ,(), is the channel matrix between user incell and all transmit antennas of cell (=,+ ,=1 ), for simplicity whenever =, the superscriptomitted, ,=,(). ,~(, 2. ) is additive whiteGaussian noise (AWGN) with variance 2, and,~( , )is the scheduled and precoded symbol vectoras follows
xk,i= fk,isk,i (2)
Xj= fk,jsk,j= FjSjk
,
jI\
i
where {1, , K i} is the set of selected users that areserved in parallel over a given time-frequency resource in celli, sk,i (O, PTx D ) is the selected users symbols whichindependently generated by channel encoders with statistical
power sk,i2= 1 , and fk,i is the precoder vector whichmaps the transmit symbol vector onto the Mitransmit antennas,and allocates the available transmit power P among users.
In the receiver side users apply a linear receiver filters,wk,i N,1, to quantize their respective channels.Consequently the symbol of kth user can be estimated byapplying w
kto the perturbed receive vector y
k,
i, as
sk,i= wk,iHyk,i= wk,iHHk,ifk,isk,i+ wk,iHHk,i fk,isk,iKu=1,uk +wk,iH Hk,i(j)FjSjIj=0,ji + nk, (3)
B. Channel State Information FeedbackIn the MU-MIMO FDD systems, transmitter requires CSI,
to compute the precoding matrix, schedule users and select anappropriate MCS for downlink transmission. As channelmatrix contain multi-dimensional variable, UEs should toexploit beneficial characteristics of their channels and quantize
them in order to reduce the feedback overhead. In cellularnetworks, CSI is divided into two categories:
1) Channel quality indicator (CQI): There is an unusualproblem to calculate the CQI in the receiver side, whencalculation of CQI depends to the scheduler ahead of
transmission, and scheduler computation depends to the CQIfeedback. Therefore, to solve this problem, scheduler canmake decision based on the expected SINR value (not on theexact SINR value). As a preliminary solution, it can beassumed that there is no quantization error in the CDIfeedback, therefore
SINRk,ipk,i||hk,i||2cos2k,i=k,i(1) (4)
As a better approximation, authors of [10] proposed a lower-
bound of SINR as CQI, as
SINRk,iPM|h,|
cos,1+PM|h,|
sin,=k,i(2) (5)
where ,is the angle between effective channel direction andquantized channel direction. Therefore CQI feedback provided
by
CQIk=(k) (6)where
is quantization function.
To select an appropriate MCS, a look-up table is used asdefined in [11]. Moreover, to decrease the quantization error,and reduce the feedback overhead, as well, a combination ofquantized CQI feedback with hybrid automatic repeat request(HARQ) protocol [12] is utilized at the simulator.
2) Channel direction indicator (CDI): The effectivechannel vector,which is a concatenated of channel matrixand the receive filter (,=,,) contains essentialchannel statistics for interference mitigation. Therefore, userscomputes their effective channel directions as,=
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TABLE I. SIMULATION PARAMETERS
Parameters Value
Terminal power 43 dBmNoise power spectral density -174 dBm/HzShadowing standard deviation 8 dBCarrier frequency 2.1 GHzChannel bandwidth 1.4 MHzSubcarrier bandwidth 15 kHzChannel model Correlated Flat RayleighNumber of transmit antennas 8Number of UEs receive antennas 1Cell radius 500 mUE speed 3 Km/h
Available MCSs
QPSK (code rate=0.076, 0.117,0.188, 0.301, 0.439, 0.588)16QAM (code rate=0.369, 0.479,0.602)64QAM (code rate=0.455, 0.553,0.650, 0.754, 0.853, 0.926)
equalizer filter MMSE
, ||,|| and in order to save the uplink bandwidth,quantize the, to a unit norm vector with utilizingquantization codebook. The simplest way to design acodebook is randomly generate the2quantization codewordsfrom an isotropic distribution on the M-dimensional unitsphere, which results to a lower complexity. Therefore, theCDI is given by
CDI: hk=hk (7)where is the effective channel quantization function. In thisstudy, the RCDQ method defined in [8] is used for quantization
purpose.
C.Zero Forcing PrecodingIn this study ZF beamforming is utilized as multi-user
transmission strategy, when BS selects a large number of users( ) and sends one data stream to each of them.Precoding matrix is computed based on the quantized effectivechannel direction , to exploit multiplexing gain and mitigateinterference. Therefore, precoding vector
,
should be
selected in a way to be orthogonal to the quantized effectivechannel vectors of other users , (\ {}), thus ,,=0. Therefore, the ZF beamforming matrix can be computed as
F = H jH(H jH jH)1diag(pj)1 2 (8)
D. SchedulingCQI feedback provides information such as channel
amplitude and quantization error for transmitter, whereby BSselects a subset of users and assigns the available resource tothem for downlink transmission. In ZF MU-MIMO precodingtransmission when the total number of receive antennas
exceeds the total number of transmit antennas, a schedulingalgorithm is vital to select a subset of orthogonal users to servein parallel over a given time-frequency resource.
Authors of [10] proposed a greedy user selection algorithm,where transmitter uses the available SINR value and searchesthrough the unscheduled users to select users which canmaximize sum-rate, where sum-rate defined as
()= log2(1 +k)k (9)
Therefore, set of selected users is according to
= arg maxK TkK (10)
where k is the current achievable sum-rate and Tk is theaverage past sum-rate of this user. (for original definition seealgorithm 2 in [10]).
As an alternative method, per user unitary and rate control(PU2RC) scheme has been proposed to 3GPP [13], where userscheduling and beamforming are jointly and practicallydesigned. PU2RC supports limited feedback MU-MIMOsystem and it capable to exploit multiuser diversity. [14]showed that in presence of large enough number of users,
PU2RC outperforms the zero-forcing beamforming scheme,and it is more robust against CSI quantization errors.
III. METHODOLOGYIn this study, to simulate the results the Vienna LTE-A
link-level simulator [15] is used which is compliant to theLTE-A specifications. In order to simulate the results of DAS,all users are randomly distributed in one central cell which issurrounded with two interfering base stations tiers. Central cellis equipped with one central BS and some uniformlydistributed antenna arrays which located equiangularly on aring with radius of (2 3 ) . The simulation
parameters are listed in Table I.
System-level simulation of MU-MIMO requires detailedknowledge of physical layer and results to massivecomputational complexity. Therefore, Vienna simulator usesthe hybrid link/system level simulations to consider the
physical details of one cell and out-of-cell interference as well(see [6] for out-of-cell interference model). The system
performance evaluated under limited feedback methods whichdescribed in Section II, where eight bits assigned for CSIfeedback. The performance of system evaluated under differentscheduling method as illustrated in table II. Finally, simulationresults are presented as empirical CDFs of the average cellthroughput.
IV. RESULTS AND DISCUSSIONThe simulation results obtained for DASs with different
DAS configurations and different scheduling methods. DASconfigurations are denoted as Mc,i Ri/Mr,i. To evaluate theperformance of scheduling methods in DASs, simulations areperformed in two scenarios: with and without DAAs, meansthat Ri{2,4,6}, and Ri= 0, respectively. Figure 2 presents theempirical CDFs of cell throughput under considerations. Theresults of PU2RC scheduler are showed in Figure 2 (a), andillustrate that by increasing the number of RRU, the cellthroughput increases as well. Figure 2 (b) presents the results
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(a)
(b)
(c)
1 1.5 2 2.5 3 3.50
0.2
0.4
0.6
0.8
1
Cell throughput [Mbit/s]
ECDFofcellthroughput
8-0/0
4-2/2
4-4/1
2-6/1
0 2 4 6 8 100
0.2
0.4
0.6
0.8
1
Cell throughput [Mbit/s]
ECDFofcellthroughput
8-0/0
4-2/2
4-4/1
2-6/1
0 2 4 6 8 10 12 140
0.2
0.4
0.6
0.8
1
Cell throughput [Mbit/s]
ECDFofcellthroughput
8-0/0
4-2/2
4-4/1
2-6/1
TABLE II. SCHEDULING METHODS
Model NotesPU2RC Proposed by [13] to the 3GPP-LTE standards
ZF-MUMIMO Proposed by [10],k,i(1)placements in (9)ZF-MUMIMO-flat Proposed by [10],k,i(2)placements in (9)
Fig. 3. Comparision of performance of schedulers in different DASconfigurations, in term of 0.95 of ECDF
0
24
6
8
10
12
14
8-0/0 4-2/2 4-4/1 2-6/1Cellthrou
ghputwith0.9
5ECDF
DAS configuration
PU2RC
ZF-MUMIMO
ZF-MUMIMO-flat
of ZF-MUMIMO scheduler, and shows when DAS utilized, thecell throughput considerably improved. Moreover, result of 4-2/2 configuration shows that by equipping RRU with multipletransmit antennas more throughput is achievable, wheremultiplexing gain of MU-MIMO can be exploited. Results of
Figure 2(c) belong to ZF-MUMIMO-flat scheduler and reveal aremarkable dependency to the DASs. The performance ofschedulers under different DAS configurations is compared in
Figure 3. As results show, when all transmit antennas areallocated on the central BS (8-0/0 configuration), the lowestcell throughput is achieved. It that case, ZF-MUMIMO-flat
provides better performance, because more accurate andsufficient knowledge of SINR fed back to the BS compared toZF-MIMO scheme, and under small number of users,
performance of PU2RC is not sufficient (see [14]). PU2RCperforms a weak dependency to the DAS configuration, wherecell throughput slowly improves with RRU increment. While,ZF-MUMIMO-flat scheduler goodly extracts the potential ofDASs, where for 2-6/1 configuration approximately achievesfour folds more cell throughput compared to 8-0/0configuration.
V. CONCLUSIONThis study investigates the performance of different
scheduling methods in distributed antenna systems with limitedfeedback zero-forcing multiuser MIMO transmission.Simulation results show that by spreading the transmit antennasthroughout the cell more cell throughput is achievable,moreover by assigning more transmit antenna per RRU, moremultiplexing gain can be extracted. Furthermore, it isdemonstrated that PU2RC scheme as a well-known precodingand scheduling method has a poor dependency to the DASs.The comparison of results shows that by utilizing anappropriate user selection strategy substantial cell throughputcan be achieved.
ACKNOWLEDGMENT
The study was funded by Universiti Kebangsaan Malaysia andand Universiti Teknologi MARA under grants number DPP-2013-006 and 600-RMI/Dana 5/3/RIF(285/2012) respectively.
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Fig. 2. Cell throughput performance in therm of ECDF, with differentDAS configuration and scheduling methods: (a) PU2RC, (b) ZF-MUMIMO, and (c) ZF-MUMIMO-flat
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