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Iterative MIMO Signal Detection with Inter-Cell

Interference Cancellation for Downlink

Transmission in Coded OFDM Cellular SystemsManabu Mikami

and Teruya Fujii

Wireless System R&D Center, Softbank Mobile Corp.

238 Aomi, Koutou-ku, Tokyo, 1358070 Japan

Email: {manabu.mikami,teruya.fujii }@mb.softbank.co.jp

AbstractMIMO/OFDM is receiving considerable attentionfor next generation mobile radio communication systems. Thesesystems require improved throughput performance to users atcell-edges. In our previous study, we introduced an enhancementof MLD (Maximum Likelihood signal Detection) algorithm, inwhich desired and interference signals are jointly detected, toimprove the cell-edge throughput in the downlink of single-cell-frequency-reuse MIMO/OFDM cellular systems. In order toachieve higher cell-edge throughput in coded OFDM cellularsystems, this paper proposes an iterative signal detection with

ICI cancellation based on an enhancement of MAP (MaximumA posteriori Probability) algorithm in which signal path searchspace includes not only desired signal space but also interferencesignal space. Its transmission characteristics are evaluated bycomputer simulation and it is confirmed that the proposediterative signal detection can achieve higher ICI cancellation andimprove BLER (BLock Error Rate) and throughput performancemore than the conventional MLD-based signal detection.

I. INTRODUCTION

OFDM (Orthogonal Frequency Division Multiplexing)

modulation technique is attracting attention as a new wireless

transmission technology for mobile communication systems

due to, among others, its high spectral efficiency in multi-

path fading channels. Various wireless access systems employan OFDM scheme, e.g. 3GPP LTE (Long Term Evolution)

[1], 3GPP2 UMB (Ultra Mobile Broadband) [2] and Mobile

WiMAX [3]. On the other hand, MIMO (Multi-Input Multi-

Output) transmission technique, which uses multiple antennas

at both transmitter and receiver, is also attracting attention due

to its throughput improvement. The combination of OFDM

and MIMO, called MIMO/OFDM, is a promising candidate

for the next generation systems.

Since, traffic demand for mobile communications is still

increasing, there is a strong and continuing need to evolve

wireless access systems in order to achieve higher spectral

efficiency. New frameworks have been established, such as

IMT-Advanced and LTE-Advanced [4], [5]. In order to im-prove the spectral efficiency without bandwidth expansion

in MIMO/OFDM cellular systems, the authors are studying

ECO-MIMO/OFDM (Enhanced COoperative MIMO/OFDM)

system [6], in which multiple BSs (Base Stations) are coordi-

nated to control the MIMO transmission scheme and the radio

resource allocation to each user.

MSs (Mobile Stations) at a cell-edge suffer from large ICI

(Inter-Cell Interference) in OFDM cellular systems with a sin-

gle cell frequency reuse, and cell-edge throughput performance

is severely degraded when there are a lot of users in adjacent

cells. In [7], we introduced a downlink transmission method

with ICI cancellation based on a joint detection of desired and

interference signals for a single-cell-frequency-reuse OFDM

cellular systems. The signal detection algorithm of MS re-

ceiver is an enhancement of MLD (Maximum Likelihood sig-

nal Detection) in which the signal path search space includes

not only desired signal space but also interference signal

space. We evaluated a throughput performance when each BS

was equipped with a single transmit antenna, and confirmed

that the proposed transmission method is effective for ICI

cancellation and cell-edge throughput improvement. Unfortu-

nately, in MIMO systems using multiple transmit antennas,

the number of interference signals is generally increased and

the signal detection accuracy is degraded compared to that

of a single-antenna transmission case. Hence, MIMO/OFDM

cellular systems require a signal detection algorithm with ICI

cancellation that offers higher signal detection accuracy. On

the other hand, in channel-coded MIMO systems that employ

SDM (Space Division Multiplexing) technique, it has been

reported that excellent transmit performance is achieved by

iterative processing based on the MAP (Maximum A posteriori

Probability) algorithm where a priori probability informationis exchanged between a soft-output MIMO signal detector and

a soft-input/soft-output channel decoder. Simulation results

in [8][10] confirmed that the MAP-based MIMO signal

detection can obtain higher signal detection accuracy in single-

cell/single-user environments than the MLD-based MIMO

signal detection.

This paper proposes an iterative signal detection scheme

with ICI cancellation based on an enhancement of MAP

algorithm for downlink transmission in coded MIMO/OFDM

cellular systems with SDM. Its transmission performances are

evaluated by computer simulations, and we confirm that the

proposed MAP-based iterative signal detection yields larger

ICI cancellation effect and better throughput performance thanthe conventional MLD-based signal detection.

I I . SYSTEM MODEL

Figure 1 shows the system model [7]. We consider the

downlink transmission of an MS at the cell-edge in a coded

MIMO/OFDM cellular system with a single-cell frequency

reuse. Note that Fig. 1 illustrates the case where the total

number of BSs NB = 2 (i.e. the number of adjacent cells isone). In this figure, the other MSs in the adjacent cells which

are simultaneously communicating with adjacent BSs using

978-1-4244-2517-4/09/$20.00 2009 IEEE

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n(k) denotes a receiver noise vector whose elements followindependent complex Gaussian distributions. When the timing-

and the frequency-offsets can be ignored, we can see that a set

of BS antennas acts as a virtual array antenna from the MS

side. Accordingly, the channel between the BS antennas and

the MS antennas can be regarded as a virtual MIMO channel.

The received signal vector of the k-th sub-carrier at the MS,x

(k), can be expressed as

x(k) = H(k)s(k) + n(k), (2)

where the Nr Nt dimensional matrix H(k) denoting thevirtual MIMO CSI matrix whose columns are comprised of

HD(k) and each GnBejnB and the NB 1 dimensional

vector s(k) denoting the virtual transmit signal vector can berepresented as

H(k) = [HD(k) G1(k)ej1 GnB1(k)ejnB1 ] , (3)

s(k) = [sD(k) u1(k) unB1(k)]T

= [s1(k) s2(k) sNt(k)]T , (4)

Note that Nt = NB Nt0 denotes the total number of transmitantennas for all BSs. Assuming that the frequency offset fnBis small enough to be ignored, we can approximate the phase

offset nB by zero and the virtual MIMO CSI matrix canbe written as

H(k) [HD(k) G1(k) GnB1(k)] . (5)

III. ITERATIVE SIGNAL DETECTION WITH ICI CANCELLATION

A. MAP-based Signal Detection with ICI Cancellation

In order to improve the detection accuracy of the desired

signal in an ICI environment, we propose that the desired sig-

nals be detected by an enhancement version of the MAP signal

detection algorithm, in which the dimensions of the signal path

search space are expanded by adding the interference signalspace to the desired signal space.

When the CSI and AMI of both the target and the adjacent

BSs are obtained, the MAP-based MIMO signal detector

maximizes the posteriori probability P(s(k) |x(k) ), and thehard-decision output sMAP(k) can be expressed as

sMAP(k) = [ sD(k) u1(k) unB1(k) ]T

= argmaxs

(rep)m

P(s(k) = s(rep)m |x(k) ). (6)

where sD(k) and unB (k) (nB = 1, , NB 1) denote themost likely candidate of the desired signal transmitted from

the target BS and that of the interference signal transmitted

from the nB-th adjacent BS, respectively. Also s(rep)m denotes

an Nt 1 dimensional signal vector replica whose elementsare the candidates ofsD(k) and unB (k). Using Bayes rule

sMAP(k) = argmaxs

(rep)m

P(x(k)| s(k) =s(rep)m )P(s(k) =s(rep)m ). (7)

The likelihood function P(x(k)| s(k)) in (7) is written as

P(x(k)| s(k)) =1

(22)Nrexp

x(k)H(k)s(k)2

22

, (8)

where 2 is the average noise power per receive antennaper dimension (real or imaginary part). Further, by assuming

that the symbols s1(k), s2(k), , sNt(k) are independent, apriori probability P(s(k)) in (7) can be expressed as

P(s(k)) =

Ntnt=1

P(snt(k)) = exp

Ntnt=1

log P(snt(k))

. (9)

Thus, MAP-based MIMO signal detection is equivalent to the

following optimization problem

sMAP(k) = arg maxs

(rep)m

1

22x(k) H(k)s(rep)m 2

+

Ntnt=1

log P(snt(k) = s(rep)m,nt

)

, (10)

where s(rep)m,nt is the nt-th element of s(rep)m . As shown in

Fig.2, after discarding each unB (k) (nB = 1, , NB 1)from sMAP(k) in (6), the desired signals transmitted from thetarget BS, sD(k), can be accurately estimated along with ICIcancellation.

Note that soft-input channel decoders require that the

MIMO detectors output not only a hard decision but also its

reliability information. The MIMO signal detectors process thesoft reliability information provided by the channel decoders,

and the channel decoders process the soft information pro-

vided by the MIMO signal detectors. In the proposed signal

detection, the soft reliability information of each bit of not

only desired signals but also interference signals is exchanged

between the soft-output MIMO signal detectors and the soft-

input/soft-output channel decoders in an iterative process until

the desired performance is achieved.

B. Soft Reliability Information Calculation

Mc = log2 Mary represents the number of bits per con-stellation symbol of each OFDM sub-carrier. Let c(k) =

{c1(k), , cNtMc(k)} be a coded bit sequence belongingto the virtual transmitted signal vector s(k). In general,by assuming that the coded bits c1(k), , cNtMc(k) areindependent, the soft reliability information for the i-th (i = 1, , NtMc) bit in s(k) at the MIMO detector output can beexpressed as a posteriori LLR (Log Likelihood Ratio),

LD1(ci(k) |x(k) ) = logP(ci(k) = +1)

P(ci(k) = 1) LA1

(ci(k))

+log

m{c

(rep)

m,i=+1}

P(x(k)

c(k) =c(rep)m )j,j=i

P(cj(k) =c(rep)

m,j)

m{c

(rep)m,i

=1}

P(x(k) c(k) =c(rep)m )j,j=i

P(cj(k) =c

(rep)

m,j) LE1

(ci(k)|x(k) )

, (11)

where P(ci(k) =1 |x(k) ) represents probabilities for ci(k)= 1 conditioned on the received signal vector x(k). Notethat the logical zero for a bit is represented by ci(k) =+1 and the logical one by ci(k) = 1 in (11). c

(rep)m =

{c(rep)m,1, , c(rep)m,NtMc

} denotes a bit sequence belonging to

transmitted signal vector replica s(rep)m . Also LA1(ci(k)) and

LE1(ci(k) |x(k) ) denote a priori LLR and extrinsic LLR at

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the soft-output MIMO signal detectors, respectively. Since the

transmitted signal vector at the k-th sub-carrier, s(k), is ob-tained by modulating the coded bit sequence block c(k) ontoan Mary-QAM constellation using the gray mapping functions(k) = map{c(k)}, the extrinsic LLR LE1(ci(k) |x(k) ) in(11) can be rewritten as [8]

LE1(ci(k) |x(k) )

= log

m{c

(rep)

m,i=+1}

P(x(k)| s(k) = s(rep)m ) exp 12c(rep)Tm,[i] LA1,[i]

m{c(rep)

m,i=1}

P(x(k)| s(k) = s(rep)m ) exp

1

2c

(rep)T

m,[i]LA1,[i]

, (12)

where c(rep)

m,[i] denotes the coded bit sequence block omitting

the i-th bit of c(rep)m , and LA1,[i] denotes the (NtMc 1) 1dimensional vector whose elements are comprised of all a

priori LLR LA1(cj(k)) except for j = i. With the Max-logapproximation, the extrinsic LLR LE1(ci(k) |x(k) ) becomes

LE1(ci(k) |x(k) )

12

maxm{c

(rep)

m,i=+1}

12x(k) H(k)s(rep)m 2 + c(rep)Tm,[i] LA1,[i]

1

2max

m{c(rep)m,i

=1}

1

2

x(k) H(k)s(rep)m 2 + c(rep)Tm,[i] LA1,[i] ,(13)

where s(rep)m = map{c

(rep)m } [8].

As shown in Fig.2, the soft-output MIMO signal detectors

take channel observations x(k) and a priori LLR LA1(ci(k))(i = 1, , NtMc) (i = 1, , NtMc) computes theextrinsic LLR LE1(ci(k) |x(k) ) for each of the NtMc codedbits per the received signal vector x(k) according to (13). Theextrinsic LLR LE1(ci(k) |x(k) ) is deinterleaved to become a

priori input LA2 to the soft-input/soft-output channel decoderswhich calculate extrinsic information LE2 on the outer codedbits. Then LE2 is reinterleaved and fed back as a prioriLLR LA1(ci(k)) to the soft-output MIMO detectors, thuscompleting a cycle or iteration. Each iteration reduces the

bit error rate by this exchange of information. Meanwhile,

it is well known that implementations of MAP-based iterative

MIMO signal detection algorithms are generally difficult with-

out computational complexity reduction in the case of high

modulation level or the large number of transmit antennas

as well as MLD without computational complexity reduction,

and the complexity-reduced algorithms using sphere decoding

have been proposed [8][10]. In this paper, however, such

complexity-reduced algorithms are not used, because thispaper is intended to evaluate the ideal performance of the

proposed iterative signal detection with ICI cancellation based

on the MAP algorithm.

IV. PERFORMANCE EVALUATION

A. Simulation Conditions

We evaluated the performance of the proposed iterative sig-

nal detection with ICI cancellation in a coded MIMO/OFDM

cellular system by computer simulation. TABLE I shows

the simulation parameters. We assumed a two-cell model as

TABLE I Simulation conditions.

Number of BSs NB = 2Number of antennas Nt0 = 2 (per BS), Nr = 2 (MS)

OFDM parameters

Number of sub-carriers: Nsub = 64 ,Sub-carrier spacing: f0 = 15 kHz,Effective OFDM symbol duration: Ts = 1/f0,Guard interval duration: Tg = Ts/4=16.67 s

Frame duration T=12(Ts + Tg) = 1 ms (12 OFDM symbols)

Channel encodingConvolutional code (G1, G2) = (5, 7)(Constraint length K= 3, Coding rate R = 1/2,Random interleaver, Max Log-MAP decoding)

Modulation QPSK

MIMO spatial divisionmultiplexing

MCW-MIMO (Multiple codeword MIMO),Number of codeword per BS: NCW = Nt0= 2

Channelmodel

Path model

Number of paths: 5-path, Decay factor: 3 dB,Equal path interval among each path,RMS delay spread: 1.1 s

Fading modelQuasi-static Rayleigh fading (Uncorrelatedfading among each path and each antenna)

Timing offset difference 1/8 OFDM symbol lengths (fixed)

Channel est imation Ideal

Control symboldemodulation

Ideal

Desired and interferencesignal demultiplexing

Iterative MIMO signal detection based onan enhancement of MAP algorithm

shown in Fig. 1 (i.e., NB = 2). The number of transmitantennas at each BS was Nt0 = 2, and the number ofreceive antennas at the MS was Nr = 2. Consequently, inthis conditions, the number of received antenna, Nr(= 2),is less than the total number of transmit antennas for all BSs,

Nt = NB Nt0(= 4). The GI length Tg was set to a quarter ofthe effective OFDM symbol length and the frame length was

set to 1ms corresponding to 12 OFDM symbol lengths. We

used a convolutional code with a constraint length ofK = 3 asthe channel code. For simplicity, the signals transmitted from

both the target and adjacent BSs use the same modulation level

of QPSK and the same coding rate of R = 1/2. The timingoffset between the target BS and adjacent BS was fixed and set

to 1/8 effective OFDM symbol lengths. FFT window detection,channel estimation, and control symbol demodulation were

assumed to be ideal. We also assumed that the control symbols

transmitted from both the target BS and the adjacent BS were

perfectly recovered at the MS. At the MS receiver, the desired

signals and the interference signals were demultiplexed based

on the iterative MIMO signal detection algorithm described

in Section III, and the desired and interference signals were

soft-input/soft-output channel-decoded based on the Max Log-

MAP algorithm in the iterative process. After the iterative

process is completed, the user data sequences are estimated

by hard decision of a posteriori LLR value of each user data

bit at the channel decoder output.

B. Evaluation Results

We evaluated the performance of the proposed iterative

signal detection compared to that of the conventional signal

detection with ICI cancellation based on the MLD algorithm

[7]. Note that the soft-input/hard-output Viterbi channel de-

coders for channel decoding are used in the conventional signal

detection case since it is unnecessary for the channel decoders

to feed back the reliability information of each bit to the MLD-

based MIMO signal detectors.

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103

102

101

100

0 5 10 15

Conventional (MLD + Viterbi)Proposed (Niter = 0)

Proposed (Niter = 1)




AverageBLER

Average Es/N0 per receive antenna [dB]

Fig. 3. BLER performance (Average CIR = 0 dB).

Figure 3 shows the average BLER (BLock Error Rate)

performance; the parameter is the number of iterations Niter.In this figure, an average CIR (Carrier-to-Interference-power-

Ratio) is set to 0 dB, and the solid and dotted lines show

the performance of the proposed signal detection and the

conventional signal detection, respectively. When Niter = 0,the proposed signal detection has almost the same performance

as the conventional signal detection. When Niter 1 theproposed signal detection offers significantly higher BLER

performance than the conventional signal detection and the

superiority strengthens as Niter increases. For example, theproposed signal detection can reduce the required average

Es/N0 at an average BLER of 102 by about 3.5 dB and 4 dB

for the cases ofNiter = 1 and Niter = 4, respectively, comparedto the conventional signal detection. Thus, the proposed signal

detection can obtain excellent transmission performance even

though the number of receive antennas, Nr, is less than the

total number of transmit antennas for all BSs, Nt. Note that theBLER performance improvement associated with increasingNiter is small when Niter 3.

Figure 4 shows the throughput performance of the proposed

signal detection and compares its performance to that of the

conventional signal detection. In this figure, the average CIR

was set to 1 (CIR = 0 dB), and the performance in the case ofno ICI (i.e., CIR = ) is plotted for reference. For example,consider the required average Es/N0 at the average throughputof 1.8 bits/s/Hz. From Fig. 4, when CIR = 0 dB, the proposedsignal detection in the case ofNiter = 4 can reduce the requiredaverage Es/N0 by about 5 dB. Moreover, the proposed signaldetection in the case ofNiter = 4 can approximately double the

average throughput at the average Es/N0 of10 dB in CIR = 0dB compared to the conventional signal detection. This result

confirms that the proposed MAP-based signal detection can

drastically improve the average throughput performance of the

MS at the cell-edge in coded MIMO/OFDM cellular systems

compared to the conventional MLD-based signal detection.

V. CONCLUSIONS

This paper proposed an iterative signal detection with ICI

(Inter-Cell Interference) cancellation for downlink transmis-

sion in coded MIMO/OFDM cellular systems. The signal

0

1

2

3

0 5 10 15

Conventional (MLD + Viterbi)



Averagenormalizedthroughput[bits/s/Hz]

Average Es/N0 per receive antenna [dB]

No ICI(CIR = )

CIR = 0 [dB]8

Fig. 4. Throughput performance.

detection is based on an enhancement of the MAP (Maximum

A posteriori Probability) algorithm, in which the signal path

search space includes not only the desired signal space but also

the interference signal space. The performance of the proposed

iterative signal detection scheme was evaluated by computer

simulations in a comparison to that of a conventional signaldetection scheme with ICI cancellation based on MLD (Max-

imum Likelihood signal Detection) algorithm. The simulation

results confirmed that the proposed iterative signal detection

drastically improved the transmission characteristics compared

to the conventional signal detection.

ACKNOWLEDGMENT

The authors appreciate many useful comments from Dr.

Hitoshi Yoshino of Softbank Mobile on this work. Part of

this work was funded by the Ministry of Internal Affairs and

Communications of Japan, under the grant, R&D on the

cooperative control technologies for multiple base stations in

an environment consisting of various cell sizes.

REFERENCES

[1] 3GPP TR 25.814, Physical layer aspects for evolved Universal TerrestrialRadio Access (UTRA), V7.1.0, Sept. 2006.

[2] 3GPP2 C.S0084-001-0, Physical layer for Ultra Mobile Broadband(UMB) air interface Specification, Version 1.0, Apr. 2007.

[3] WiMAX Form White Paper, Mobile WiMAX Part I: A technicaloverview and performance evaluation, Aug. 2006.

[4] ITU-R Recommendation M.1645, Framework and overall objectives ofthe future development of IMT-2000 and systems beyond IMT-2000,2003.

[5] 3GPP TR 36.913, Requirements for Further Advancements for E-UTRA(LTE-Advanced), V8.0.0, June 2008.

[6] M. Mikami, A. Nagate, K. Hoshino, and T. Fujii, A study on cell-edge throughput improvement based on MIMO coordinated base stations

for downlink transmission in OFDM Systems, IEICE Technical Report,RCS2008-179, Jan. 2009.

[7] M. Mikami and T. Fujii, A downlink transmission method for OFDMcellular systems with inter-cell interference cancellation using simplifiedMLD based MMSE QRD-M algorithm, Proc. IEEE VTC2008-Spring,pp.20112015, Marina Bay, Singapore, May. 2008.

[8] B.M. Hochwald and S. ten Brink, Achieving near-capacity on a multiple-antenna channel, IEEE Trans. Commun., vol.51, no.3, pp.389399.March 2003.

[9] H. Vikalo, B. Hassibi, and, T. Kailath, Iterative decoding for MIMOchannels via modified sphere decoding, IEEE Trans. Wireless Commun.,vol.3, no.6, pp.22992311. Nov. 2006.

[10] J. Choi, Y. Hong, and J. Yuan, An approximate MAP-based iterativereceiver for MIMO channels using modified sphere detection, IEEETrans. Wireless Commun., vol.5, no.8, pp.21192126. Aug. 2006.

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