Optimal Decoders for 4G LTE Communications

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Optimal Decoders for 4G LTE Communication

Syed Ali Irtaza, Aamir Habib, and Qamar-ul-IslamInstitute of Space Technology, Islamabad-44000, PakistanEmail: {syed.ali, aamir.habib, qamar.islam}@ist.edu.pk

Abstract — Multiple Input Multiple Output (MIMO)techniques are used to realize practical high data ratesystems which laid the foundation of Long Term Evolution(LTE). Various transmission techniques like TransmitDiversity (TxD), Open Loop Spatial Multiplexing (OLSM)and Closed Loop Spatial Multiplexing (CLSM) are deployedin the realm of MIMO. The spectral efficiency is improvedwith the help Orthogonal Frequency Division Multiplexing

(OFDM). In this paper we will focus on CLSM, to evaluateits performance with the help of Zero Forcing (ZF),Minimum Mean Sqaure Error (MMSE), SoftSphereDecoder (SSD), SSD K-Best (SSDKB) and SIC receivers tofind the optimal decoder in LTE environment. The SSD,SSD-KB and SIC uses MMSE based equalizers. The channelenvironment used are Additive White Gaussian (AWGN),Vehicular A (VehA), Vehicular B (VehB) and an outdoorPedestrian (Ped B) channel model. A Least Square (LS)estimated feedback obtained by the averaging of twochannel instances is used to improve BLER in the case offading channels.

I ndex Terms — VehB, SSD, SIC, CLSM, LTE, LS.

I. I NTRODUCTION

Wireless communications continue to strive for higherdata rates and a better link reliability in order to providemore advanced services on the go. The use of multipleantennas at both the transmitter and receiver side, i.e., multiple-input multiple- output (MIMO) communications,is one of the most promising technologies to fulfill thesedemands. Indeed, MIMO systems are capable ofachieving increased data rates and an improved linkreliability compared to single-antenna systems withoutthe aid of additional bandwidth or transmit power. Theseimprovements, however, require the use of morecomputationally intensive data detection algorithms at thereceiver side. In particular, optimum data detection caneasily become complex. Conventional sub-optimumdetection techniques have a low computational cost buttheir performance is in general less significant to that ofoptimum data detection. Thus, there is a strong demandfor computationally efficient data detection algorithmsthat are able to reduce this performance gap. One of the

promising technologies to provide high data rate at highspeeds while maintaining the specified Quality of Service(Qos) is Long Term Evolution (LTE). LTE provides a

maximum downloading data rate of 299.6Mbits/s and anuploading data rate of 75.4Mbits/s constrained by the

MIMO configuration and user speed. to facilitate higherdata rate it provides a flexible spectrum management andsupports allocation of multiple bandwidth slots to sameuser on demand. IT also supports multiple antennaconfiguration schemes from 4x4 MIMO to 1x1 SISO.different transmission schemes like multiplexing andtransmit diversity to achieve high data rates nad provides

aupport for users moving upto a speed of 500Km/hr (310M/hr) depending upon the user terrain. Multiple decodingalgorithms are available in LTE communication networkto reduce the bit error probability. In this paper we mainlyfocus on Zero Forcing (ZF), Minimum Mean SqaureError (MMSE), SoftSphere Decoder (SSD), SSD K-Best(SSDKB) and SIC receivers to find the optimal decoderin LTE environment. The SSD, SSD-KB and SIC usesMMSE based equalizers. The channel environment usedare Additive White Gaussian (AWGN), Vehicular A(VehA), Vehicular B (VehB) and an outdoor Pedestrian(Ped B) channel model. The simulation results achievedmeet industrial standards with the help of link level LTE

simulator [1] compliant with the parameters specified bythe 3GPP working group. In the following paper SectionII describes the channel model and receiver algorithm. InSection III, the CLSM mode of transmission is explained.The Section IV explains outcome of these simulationsand observations. Conclusions are given in Section V.

II. CHANNEL M ODEL

The proposed MIMO [2] system model consisting of

T N transmit antennas and R M receive antennas,defined by the following Equation (1).

= Hx z y (1)

1,1 1,2 1,

2,1 1,2 2,

,

,1 ,2 ,

=

N T

N T

M N R T

M M M N R R R T

h h h

h h h

h h h

H (2)

where y = ][ 21 R M y y y is the received vector, H

is the channel coefficient matrix of the dimensions

T R N M defining the channel gain expected valuesand ][= 21 R M z z z z is the noise. z is assumed to be

(i.i.d) Zero Mean Circularly Symmetric Complex

Lecture Notes on Information Theory Vol. 1, No. 4, December 2013

©2013 Engineering and Technology Publishing 170doi: 10.12720/lnit.1.4.170-174

Manuscript received June 10, 2013; revised August 29, 2013.



Gaussian (ZMCSCG). The channel H is defined by thechannel delay profile. The input is divided into differentstreams of data with the help of spatial demultiplexer asin Fig. 1. The streams are than processed by the turbodecoder to provide communication at low values of SNR.

IFFT is used to provide computational efficiency andcyclic prefix is added to maintain synchronization.Thestreams are passed through the inter-leaver after thechannel coding is applied. The inter-leaver processes theinput such that the consecutive bits are placed far apart toavoid burst error due to fading. The modulation scheme isthan applied which in this case is 16-QAM with aneffective coding rate of 0.6016. The modulated data is

passed through the serial to parallel converter. Onreception data is processed with the decoder.

Figure 1. MIMO transmission scheme

Receiver Algorithm A brief description of the receivers is given below:

ZF ReceiverZero-Forcing (ZF) detection is the simplest and

effective technique for retrieving multiple transmitteddata streams at the receiver with very little complexity.

The probability density function (PDF) for thesignal-to noise-plus-interference ratio (SINR) at theoutput of a zero forcing (ZF) detector in a flat fadingchannel was derived in [3], [4]. The zero-forcing (ZF)technique is used to nullify the interference with the helpof following weight matrix:

1

= H H ZF W H H H (3)

where H . denotes the Hermitian transpose operation.In other words, it inverts the effect of channel as

;=~ yW x ZF ZF

= ZF x z (4)

where z H H H z W z H H ZF ZF

1==~

. Note that theZF error performance is directly proportional to the

power of ZF z ~ . (i.e.,2

2~

ZF z ). The post-detection can be

calculated using SVD as212

2)(=~ z H H H z H H

ZF (5)

212.= z U V V V H H

(6)

21.= z U V H (7)

Since 22 === x x xQxQ xQx H H H for a

unitary matrix Q , the expected value of the noise poweris given as

MMSE ReceiverMultiple antennas offer significant performance

improvements in wireless communication systems byenabling communications by minimizing the error at

higher data rates. Linear receivers likeminimum-mean-squared-error (MMSE) receiver are a practical solution to provide lower complexity and higherdata rates with the aid of multiplexing techniques whichin our work is spatial multiplexing. The MMSE receiveris particularly important as it optimally trade offstrengthening the energy of the desired signal of interestand canceling unwanted interference by using its receivedegrees of freedom (DOF) such that the signal-tointerference-and-noise ratio (SINR) is maximized. In [5],multiplexing at receiver side is used for spatial diversityto increase the desired signal power, while in [6],multiplexing at receiver side is used to cancel

interference from the strongest interferer nodes. In [7],MMSE receivers are used and the average spectralefficiency, a per-link performance measure, was obtainedin the large antenna regime. In [8 - 10], by usingsub-optimal and MMSE linear receivers, the results oftransmission capacity were shown to scale linearly withthe number of receive antennas. The post-detectionsignal-to-interference plus noise ratio (SINR) can bemaximized by using the MMSE criteria, the MMSEweight matrix is used which given as

2 1= ( ) H MMSE H z W H H I H (8)

For MMSE receiver to perform efficiently, the

statistical information of noise 2 is required. The ith

row vector MMSE iw , of the weight matrix in Equation 8

is obtained by solving the optimization equation given below:

2

,=( ,..., ) 2 21 2

=1,

= a min i x

i MMSE N T w w w N T x i z

j j i

wh E W rg

E wh w

(9)

Using the MMSE weight in Equation 8, we obtain thefollowing relationship:

yW x MMSE MMSE =~

(10)

z H I H H z

H 12 )(=


©2013 Engineering and Technology Publishing 171

22 1

2 2= .

H

ZF E z E U z

1 1= ( . . ) H H E tr U zz U

1 1

= ( . . ) H H tr U E zz U

2 1 1= ( . . )

H

z tr U U

2 2= ( . ) z tr

2

2=1

= N

T z

i i



MMSE z x ~~=

where MMSE z ~ = ))(( 12 z H I H H z

H . Using

Singular Value Decomposition (SVD), the post detectionnoise power is given by the Equation 12.

12 1= . H

MMSE E z E U z

222

22

1=

= z i

i z T N

i

(11)

For a MMSE receiver it is preferable to have a highdensity of single-stream transmissions than a low densityof multi-stream transmissions. This is because in MMSEdetection, the interference powers from the strongestinterferers source remaining afterinterference-cancellation are weaker for single streamtransmission than multi-stream transmission.Soft Sphere Decoder

SSD gives the ML solution with soft outputs. TheseML symbols are chosen from a reduced set of vectorswithin the radius of a given sphere rather than a completevector length. The radius of the sphere is adjusted suchthat there exists only one ML symbol within the givenradius. SSD provides sub optimal ML solution [11] withreduced complexity provided MMSE is used to estimatethe channel. The Soft Sphere Decoder (SSD) solution isgiven by the following equation.

x x

ˆ ˆ

= ( ) ( )min min

T T arg y Hx arg x x H H x x (12)

where T )( denotes the transpose of matrix. Equation 12gives the unconstrained solution of the real time system.This means that the ML solution can be determined by

the term )ˆ

()ˆ

( x x H H x x T T . No ML value existsoutside the sphere because there ML value is greater thanthose which exists inide the sphere hence making aunique detection as in Fig. 2.

Figure 2. Illustration of the sphere in sphere decoding.

K- Best Soft Sphere DecoderThe K-Best SSD is a variant of SSD, and performs its

operation on K best selected optioins unlike the SSDwhich considers only one point.Successive Interference Canceller Decoder

SIC receiver is a collection of linear receiver bankswhich successively cancels the interference which in thiscase are MMSE receivers, as shown in the Fig. 3.

M.

III. TRANSMISSION

MODELS

MIMO improve the spatial and multiplexing gains bythe use of diversity and spatial multiplexing [12]. Themethods used to enhance the diversity and multiplexinggains is CLSClosed Loop Spatial Multiplexing

Independent data streams are transmitted from the

T N transmit antennas in CLSM Fig. 4. In CLSMessential amount of CSI is used as feedback whichenables us to achieve high throughput with lower BLER.

IV. SIMULATION R ESULTS AND DISCUSSION

TABLE I. LTE SIMULATION PARAMETERS .

Parameters Values Receivers ZF, MMSE, SSD, SSDKB, SICChannel Veh A, Veh B, PedB, AWGNUser Speed 30 Km/h, 120 Km/h and 3Km/hFading Type Block FadingRetransmission Algo. HARQ

No of Retransmissions 03Soft Demapper Max Log MapModulation 16 QAM, CQI 9Feedback Estimation Least SquareFeedback Bits 01Resource Blocks 06

In this paper, Hybrid Automatic Repeat Request(HARQ) is set to a maximum value of 03 to provide

retransmission in the case of fading i.e. block fading inthis scenario. Soft decisions are made using the max logmap criterion for lower probability of error. VehA andVehB channels are considered for observing the LTE link



Figure 3. SIC receiver.

Figure 4. Block diagram of a MIMO transmission using CLSM.





different as in the case of VehA and VehB channel. SSDand SSDKB performs almost same at the higher values ofSNR. At the lower values of SNR, the 2x2 version of SICreceiver is performing better than the 4x4 versions ofSSD, SSDKB , MMSE and ZF receivers in terms of

throughput and SNR.

Figure 12. Receivers BLER in PedB channel using CLSM.

V. CONCLUSIONS In order to achieve higher through put [13] in LTE,

SIC receiver must be used in all channel models.Considering the performance/complexity trade off SSDand SSDKB receivers provide a reasonable output interms of throughput and BLER as compared with the SICreceiver. This performance/complexity trade-off makesSSD and and its variant SSDKB as the optimal receivers.A carefully designed mechanism is needed to select theoptimal receiver according to the throughput and BLERrequirements of the user keeping in view the

performance/complexity trade-off in case of both highand low values of SNR. There is a great room forimprovement in terms of throughput and BLER with thehelp of CLSM. This can be improved by increasing thenumber of pilot channels or by increasing the number of

bits per pilot channel providing the feedback whileconserving the communication standards specified by the3GPP and ITU-T to get the advantages of CLSM.

R EFERENCES [1] C. Mehlfuhrer, M. Wrulich, J. C. Ikuno, D. Bosanska, and M.

Rupp, “Simulating the long term evolution physical layer,” in Proc. 17th European Signal Processing Conference , GlasgowScotland, Aug 2009.

[2] Y. S. Cho, J. Kim, W. Y. Yang, and C. G. Kang, MIMO-OFDM

Wireless Communications with Matlab , John Wiley & Sons (Asia)Pte Ltd, 2010.

[3] D. Gore, R. W. Heath, and A. Paulraj, “On the performance of thezero forcing receiver in presence of transmit correlation,” in Proc .

IEEE Int. Symp. Inform . Theory, 2002, pp. 159.[4] P. Li, D. Paul, R. Narasimhan, and J. Cioffi, “On the distribution

of sinr of the mmse mimo receiv er and performance analysis,” IEEE Trans. Inform. Theory , vol. 52, no. 1, pp. 271 – 286, Jan2006.

[5] A. M. Hunter, J. G. Andrews, and S. P. Webe r, “Transmissioncapacity of ad hoc networks with spat ial diversity,” IEEE Trans.Wireless Commun. , vol. 7, no. 12, pp. 5058 – 5071, July 2008.

[6] K. Huang, J. G. Andrews, R. W. H. Jr., D. Guo, and R. A. Berry,“S¸ spatial interference cancellation for multi-antenna mobilead-hoc networks,” IEEE Trans . Inform. Theory , 2008.

[7] S. Govindasamy, D. W. Bliss, and D. H. Staelin, “Spectraleficiency in single-hop ad-hoc wireless networks with interferenceusing adapt ive antenna arrays,” IEEE J. Select. Areas Commun ,

vol. 25, no. 7, pp. 1358 – 1369, September 2007.[8] N. Jindal, J. G. Andrews, and S. P. Weber, “Rethinking mimo forwireless networks: Linear throughput increases with multiplereceive antennas,” in Proc. IEEE Int. Conf. on Commun , Dresden,Germany, June 2009 , pp. 1 – 5.

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[13] S. A. Irtaza, A. Habib, and Q. ul Islam, “Performanc e comparisonof lte transmission modes in high speed channels using soft spheredecoder, ” International Journal of Engineering & Technology , vol.12, no. 03, 2012, pp. 73 – 77.

Ali Irtaza was born in Rawalpindi, Pakistan. He iswith the Institute of Space Technology for theMasters degree in Wireless Communications and nowserving as a Lecturer since May 2013. He did hisBachelors from COMSATS University of Scienceand Technology in 2009. He has worked with ZONGin Network Operations Department. His area ofinterests include Software Defined Radio and

Advanced Wireless Communications.

Aamir Habib was born in Rawalpindi, Pakistan. He

did his Masters in Mobile and SatelliteCommunications from the University of Surrey UKin 2004 and another from Center for AdvancedStudies in Engineering, Islamabad in ComputerEngineering in 2006. Finished his Doctoral thesissponsored by Higher Education Commission,Pakistan in collaboration with Austrian Exchange

service (OeAD) Austria in Electrical Engineering. He is working at theInstitute of Space Technology, Islamabad since June 2000. ResearchInterests include Advanced Mobile Communications, Space TimeProcessing Algorithms, Antenna Selection, Algorithms for MIMOCommunications, WiMAX Technologies and Performance, AntennaSelection Methods for LTE.

Dr. Qamar ul Islam

is Head of Department,Electrical Engineering, at the Institute of SpaceTechnology. He has over twenty years ofinternational experience in the UK, Middle East and

North America. He is currently leading wireless andsatellite research groups at IST. Dr. Qamar is ChiefEditor of Journal of Space Technology and ProjectDirector ICUBE-1 Satellite Project. His research

interests are in the area of Terrestrial Wireless Communication, SatelliteCommunication and Satellite Engineering

Optimal Decoders for 4G LTE Communications

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