performance comparison of lte transmission modes in high...

Performance Comparison of LTE TransmissionModes in High Speed Channels using Soft Sphere

DecoderSyed Ali Irtaza, Aamir Habib, Qamar-ul-Islam

Institute of Space Technology, Islamabad-44000, Pakistanemail: [email protected],aamir.habib @ist.edu.pk,qamar.islam @ist.edu.pk

Abstract—Recent technologies have made real-time high datarate communication a reality. Long Term Evolution (LTE) is oneof them. In this paper we discuss the performance of TransmitDiversity (TxD) and Spatial multiplexing (SM) using MultipleInput Multiple Output (MIMO) communication in comparisonwith Single Input Single Output (SISO) communication to achievehigh data rates in LTE communication. Vehicular A (VehA) andVehicular B (VehB) tapped delay channel model are used to modelthe high speed outdoor movements of the LTE user. Soft SphereDecoder (SSD) with Minimum Mean Square Error (MMSE)equalization is used. Sphere decoder (SD) gives a sub optimalMaximum Likelihood (ML) solution with reduced complexity.Complexity is reduced as compared to the ML decoder as thesolution is chosen by considering the values that are enclosed bythe radius of the sphere. Soft decisions are incorporated to havereduced error probability. The comparison is performed betweentransmission modes to select the mode of communication basedupon the SNR and throughput to achieve optimal utilization ofthe resources. It is observed that at lower values of SNR, TxDperforms better, giving higher throughput and reduced BlockError Rate (BLER). As the SNR values are increased beyond acertain threshold, the performance of Closed Loop Spatial Mul-tiplexing (CLSM) and Open Loop Spatial Multiplexing (OLSM)performs better than the TxD in terms of throughput thereforemaking TxD not suitable for higher values of SNR. The LeastSquare (LS) estimation for feedback provides no improvementat high speed to compensate the rapid change of the channelbehavior.Keywords-VehB, SSD, Transmit Diversity, Closed Loop SpatialMultiplexing, Open Loop Spatial Multiplexing, LS.

I. INTRODUCTION

In modern world, requirement of high data rate commu-nication has become inevitable. The applications like videostreaming, image transmission etc demands high speed datatransmission with mobility. In order to fulfill these datarequirement Third Generation Partnership Project (3GPP) pro-posed whole new transmission model named as Long TermEvolution (LTE) to provide high speed mobile communication.

LTE modeled as in Figure 1, is an emerging wirelesstechnology which can provide very high data rates by usingMIMO communication. The use of multiple antennas in LTEprovides spatial and multiplexing gains by exploiting thedifferent channel states [1]. High data rates can be achieved ascompared to SISO communications in the presence of fadingchannels.

Orthogonal Frequency Division Multiplexing (OFDM) be-ing a necessary part of LTE and is used to convert the fre-quency selective fading behavior of the channel to frequencyflat fading channel behavior [2]. This improves the bandwidthefficiency by closely placing the symbols without having InterSymbol Interference (ISI).

Different MIMO transmission methods are deployed toachieve data rate compliance with the LTE standards. Wefocus on the performance of Transmit Diversity (TxD), OpenLoop Spatial Multiplexing (OLSM) and Closed Loop SpatialMultiplexing (CLSM) techniques are used to compare theirperformance in a Vehicular A (VehA) and Vehicular B (VehB)channel environment.

Decoding process for LTE simulations is done with SphereDecoder (SD) supported by soft decision decoding. Soft de-cision decoding implies that all range of the data is used todecode the data symbols with an error correcting code. Thisprovides better results as compared to hard decision decoderwhere only a fixed reference is available for decoding the data.

These simulation results are achieved with the help of linklevel LTE simulator [3] compliant with the parameters speci-fied by the 3GPP working group [4]. In the following paperSection II describes the channel model and the Soft SphereDecoder (SSD) algorithm. In Section III, the TxD, OLSMand CLSM modes of transmission are explained. The SectionIV explains outcome of these simulations and observations.Conclusions are given in Section V.

II. CHANNEL MODEL

The proposed MIMO [5] system model consisting of NT

transmit antennas and MR receive antennas can be defined bythe following Equation (1).

y = Hx+ z, (1)

HMR,NT=

h1,1 h1,2 · · · h1,NT

h2,1 h2,2 · · · h2,NT

......

. . ....

hMR,1 hMR,2 · · · hMR,NT

, (2)

where y= [y1y2 · · · yMR] is the received vector, H is the chan-

nel coefficient matrix of the dimensions MR × NT defining

International Journal of Engineering & Technology IJET-IJENS Vol: 12 No: 03

128803-4747 IJET-IJENS @ June 2012 IJENS I J E N S

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Fig. 1. Block diagram of a MIMO transmission scheme.

the channel gains and z = [z1z2 · · · zMR] is the noise.H and

z are assumed to be (i.i.d) Zero Mean Circularly SymmetricComplex Gaussian (ZMCSCG).

The input is passed through the interleaver after the channelcoding is applied. The interleaver processes the input such thatthe consecutive bits are placed far apart to avoid burst errordue to fading. The modulation scheme is than applied which inthis case is 16-QAM with an effective coding rate of 0.6016.The modulated data is passed through the serial to parallelconverter. On reception data is processed with a SSD.

A. Receiver Algorithm

SSD gives the ML solution with soft outputs. These MLsymbols are chosen from a reduced set of vectors within theradius of a given sphere rather than a complete vector length.The radius of the sphere is adjusted so that there exists onlyone ML symbol within the given radius. SSD provides suboptimal ML solution [6] with reduced complexity. MMSE isused to estimate the channel. The Soft Sphere Decoder (SSD)solution is given by the following equation.

argminx‖y −Hx‖ = argmin

x(x− x̂)THTH(x− x̂), (3)

where (·)T denotes the transpose of matrix. Equation 3 givesthe unconstrained solution of the real time system. Thismeans that the ML solution can be determined by the term(x− x̂)THTH(x− x̂). No ML value exists outside the spherebecause there ML value is greater than those which existsinside the sphere [5] hence making a unique detection.

III. TRANSMISSION MODELS

Transmission modes improve the spatial and multiplexinggains by the use of diversity and spatial multiplexing [7]. Themethods used to enhance the diversity and multiplexing gainsare TxD and SM.

A. Transmit Diversity

In TxD Figure 2, Space Time Block Codes (STBC) are usedto provide improvement against the channel deteriorating ef-fects. Alamouti STBC are considered to be the simplest spacetime block codes. It is well known that Alamouti codes [8] canachieve full diversity and full code rate simultaneously. But

for MIMO Systems having more than two transmit antennasdiversity and orthogonality can only be achieved at the costof slower date rates. Therefore we cannot achieve high datarates beyond a certain value and powerful coding schemesare required to achieve higher data rates as the SNR → ∞.Another issue with TxD is that it is single rank i.e. it does notsupport multi stream transmission [9]

Fig. 2. Block diagram of a MIMO Transmission using Transmit Diversity

B. Spatial Multiplexing

SM provides extra gain as compared to TxD [10].Indepen-dent data streams are transmitted from the NT transmit anten-nas in spatial multiplexing. Two classes of spatial multiplex-ing, open and closed loop spatial multiplexing Figures 3 and 4,are discussed. OLSM transmits the independent data streamswithout deploying any feedback algorithm. High data rate isachieved as compared to TxD as multiple independent streamsare transmitted. This endorses high BLER. To compensate thisBLER CLSM is used. In CLSM essential amount of CSI isused as feedback which enables us to achieve high throughputwith lower BLER.

Fig. 3. Block diagram of a MIMO Transmission using CLSM.

Fig. 4. Block diagram of a MIMO Transmission using OLSM



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−5 0 5 10 15 20 25 3010

−2

10−1

100

BLER, CQI 9, VehA, 800 subframes

BL

ER

SNR [dB]

CLSM MIMO 4x4OLSM MIMO 4x4TxD SUMIMO 4x4CLSM MIMO 2x2OLSM MIMO 2x2TxD MIMO 2x2SISO

Fig. 5. BLER of Different Transmission Modes for VehA MIMO Configurations.

−5 0 5 10 15 20 25 300

1

2

3

4

5

6

7

8Throughput, CQI 9 , VehA, 800 subframes

thro

ug

hp

ut [M

bp

s]

SNR [dB]

CLSM MIMO 4x4OLSM MIMO 4x4TxD SUMIMO 4x4CLSM MIMO 2x2OLSM MIMO 2x2TxD MIMO 2x2SISO

Fig. 6. Throughput of Different Transmission Modes for VehA MIMO Configurations.

IV. SIMULATION RESULTS AND DISCUSSION

In this paper, Hybrid Automatic Repeat Request (HARQ)is set to a maximum value of 03 to provide retransmissionin the case of fading i.e. block fading in this scenario. Softdecisions are made using the max log map criterion for lowerprobability of error. VehA and VehB channels are consideredfor observing the LTE link behavior. A complete detail of theparameters used in the simulations are given by the Table 1.

In case of 4x4 MIMO system Figures 7 and 8, at highSNR, in VehB channel OLSM and CLSM gives almost equal

through put and BLER because of the fact that the feedbackis also affected by the speed and cannot produce a significantimprovement in the throughput. While the STBC coded systemgives a constant throughput at high SNR. This is because thedata rate is saturated due to the coded transmission whereasindependent data streams are transmitted in the OLSM andCLSM system. Also in case of high speed outdoor communi-cation, the channel behavior is too rapid to be tracked, andfeedback does not provide sufficient information to reduceBLER. This makes CLSM behavior similar to that of OLSM.So in fast moving scenario at high SNR, OLSM is preferred



75

−5 0 5 10 15 20 25 3010

−2

10−1

100

BLER, CQI 9, Vehicular B, 800 subframes

BL

ER

SNR [dB]

SISO TxD mode MIMO 2X2 OLSM MIMO 2X2 CLSM SUMIMO 2X2 TxD MIMO 4X4 OLSM MIMO 4X4CLSM MIMO 4X4

Fig. 7. BLER of Different Transmission Modes for VehB MIMO Configurations.

−5 0 5 10 15 20 25 300

0.5

1

1.5

2

2.5

3

3.5Throughput, CQI 9 , Vehicular B, 800 subframes

thro

ug

hp

ut [M

bp

s]

SNR [dB]

SISO TxD mode MIMO 2X2 OLSM MIMO 2X2 CLSM SUMIMO 2X2 TxD MIMO 4X4 OLSM MIMO 4X4CLSM MIMO 4X4

Fig. 8. Throughput of Different Transmission Modes for VehB MIMO Configurations.

as it gives the same performance as CLSM and requires lessenergy as the feedback is not used giving the user equipment(UE) extra life.

In CLSM, in order to get better performance, if the amountof feedback bits are increased they produce a sufficient amountof overhead. Another problem with CLSM in fast movingcommunication is that by the time as feedback is availablethe channel behavior is already changed.

At low SNR, from Figures 7 and 8, STBC system givesthe best performance in terms of BLER and throughput ascompared with that of the OLSM and CLSM.

In 2x2 MIMO system with VehB channel, from Figures 7and 8 the maximum through put achieved by the TxD, OLSMand CLSM is same but the TxD performs better in terms ofBLER. So TxD comes out to be the preferred method fortransmission.

In case of VehA channel from Figures 5 and 6, 4x4 MIMOCLSM and OLSM performs equally and again feedbackplaying no roll in achieving high data rates due to the highspeed movement. It is also observed that the 2x2 CLSMand OLSM outperforms the 4x4 MIMO TxD systems. TxDsystems are useful only in the case of low SNR or where the



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Parameters ValuesChannel Veh A, Veh BUser Speed 30 Km/H, 120 Km/HFading Type Block FadingRetransmission Algorithm HARQNo of Retransmissions 03Routing Algorithm Round RobinSoft Demapper Max Log MapModulation 16 QAM, CQI 9Feedback Estimation Least Square

TABLE ILTE SIMULATION PARAMETERS.

requirement of lower probability of bit error supersedes thedata rate requirement.

It can be observed from Figures 5 and 7, the channel speedeffects the bit error rate to a great extent. At lower speeds, wecan have much better BLER as compared to the high speedVehB channel. This allows us to achieve higher data rates asin Figure 6 and Figure 8.

It is also observed as in Figure 6, in case of VehA channel,SISO gives the same throughput as the 2X2 MIMO TxD,CLSM and OLSM at the higher SNR. This provides us withanother option to achieve optimal BLER and throughput whileutilizing the minimum number of antennas in case of high SNRtransmission.

V. CONCLUSIONS

In order to achieve higher through put in LTE, OLSM mustbe preferred against CLSM and TxD at the higher values ofSNR. Besides the throughput OLSM provides lesser complex-ity as compared with CLSM as the high speed mobility doesnot allow the LS estimated feedback to provide any gain interms of throughput and BLER. The space time block codedTxD is useful only at lower values of SNR and to providestable output against the deteriorating effects of the channel.A carefully designed mechanism is needed to select betweenthe different transmission modes and the configuration of theMIMO system to give the optimal results with respect tothroughput, BLER and SNR without increasing the complexityof the system. In this work we are not able to improvethe efficiency of the CLSM . There is a great room forimprovement in terms of throughput and BLER with the helpof CLSM, if we are able to develop a way to enhance theeffect of feedback without increasing the overhead.

REFERENCES

[1] D. Tse and P. Viswanath, Fundamentals of Wireless Communications,August 13, 2004.

[2] A. Goldsmith, Wireless Communications. Cambridge University Press,2005.

[3] C. Mehlfuhrer, M. Wrulich, J. C. Ikuno, D. Bosanska, and M. Rupp,“Simulating the long term evolution physical layer,” in Proc. of the 17thEuropean Signal Processing Conference (EUSIPCO 2009), Glasgow,Scotland, Aug. 2009.

[4] T. S. G. R. A. N. G. R. A. Network, “Evolved universal terrestrialradio access (e-utra); multiplexing and channel coding,” 3rd GenerationPartnership Project (3GPP), vol. TS 36.212, March 2009.

[5] Y. S. Cho, J. Kim, W. Y. Yang, and C. G. Kang, MIMO-OFDM WirelessCommunications With Matlab. John Wiley & Sons (Asia) Pte Ltd, 2010.

[6] M. L. Honig, Ed., Advances in Multiuser Detection. John Wiley &Sons, INC., Publications, 2009.

[7] A. Lozano and N. Jindal, “Transmit diversity vs. spatial multiplexing inmodern mimo systems,” in IEEE Transactions on Wireless Communica-tions, vol. 9, no. 1, January 2010.

[8] “Switching between open and closed loop multi-stream transmission,”Swedish Patent WO 2011/115 532 A1, 2011.

[9] J. Guan, X. Ye, and P. Tian, “A robust scheme for transmit diversityand spatial multiplexing based on channel spatial correlation,” in Inter-national Conference on MultiMedia and Information Technology, 2008.

[10] G. Wetzker, “Definition of spatial multiplexing gain,” in ElectronicLetters, 2005.



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