cooperative cdma with blind adaptive sic

8/12/2019 Cooperative CDMA with Blind Adaptive SIC

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Improved Cooperative CDMA Using Blind

Adaptive Interference Cancellation

Indu Shakya, Falah H.Ali, Elias StipidisCommunications Research Group

Department of Engineering and Design

University of Sussex

Brighton,UK

Email:{i.l.shakya, f.h.ali, e.stipidis}@sussex.ac.uk

Abstract In this paper, we introduce blind adaptive interfer-ence cancellation for a practical uplink CDMA effected by mul-tiple access interference (MAI) in cooperative communicationssystems scenario. During the collaboration phase, each pairedusers exchange their data to communicate with the base-stationby using their own and pairing users channels to achieve thespatial diversity. The base-station performs ranking of userspower and detects the strongest using the successive interferencecancellation (SIC) principle to gradually remove MAI from thedesired signals. The proposed scheme uses an improved SICwhich performs adaptive despreading to form better estimates ofusers data and then uses blind MAI estimation and cancellationby making use of the despreader weights to minimize the residualMAI. Simulation results show that the scheme achieves betterdiversity gain and allows much higher number of users to sharethe same bandwidth compared with conventional cooperativeschemes.

I. INTRODUCTION

Recently, cooperative communication has emerged as an

interesting approach to improve the link performance of wire-

less networks by sharing the antennas and other resourcesamong the users [1], [2], [3], [4], [5], [6], [7], [8]. It becomes

more useful particularly for the mobile users, which can

not due to their size and power limitations, employ more

than one antenna to communicate with other users or base-

station. The paper by Sendonaris et. al. [1] considered user

cooperation assuming orthogonal user channels in multiuser

communications for CDMA system. However, in practice MAI

almost always exists and must be removed if the system

performance is to be improved. The performance of a single

user with relay assisted diversity in uplink CDMA under

different propagation environments is investigated in [9]. It

is shown that the conventional matched filter (MF) detection

which ignores the presence of MAI, fails to attain full diversitygain. An improved receiver that suppresses MAI before signal

combining from relays are shown to achieve performance

very close to perfect cooperation. However, the scheme only

considered a single user system with multiple relays assisting

the user for achieving the diversity. A new scheme combining

user cooperation a SIC under practical uplink CDMA is

described in [8]. Where the SIC used the correlator output

for MAI estimation and cancellation [10]. This has shown to

provide improved BER and achievable rate compared with a

SIC only and cooperative MF schemes under moderate system

loading conditions and with nearfar users.

It is well known that the conventional SIC using correlators

suffers from imperfect MAI estimation problem as system

loading increases. This causes error propagation to later users

detection stages and the SIC may perform even worse than

without cancellation [11]. This problem can be alleviatedto great extent by improved SIC design that uses constant

modulus (CM) property of user transmitted signals to blindly

suppress MAI while estimating desired users signal at the de-

spreader output. Furthermore it uses a simple gradient descent

based adaptive algorithm to update the estimates blindly within

the SIC process [12] and referred to here as BA-SIC. The

proposed scheme employs the BA-SIC within the cooperation

diversity system framework of uplink CDMA. As will be

shown later, it improves the system performance considerably

compared with conventional cooperative schemes.

The paper is organized as follows. In section II, the

system model is presented. The proposed cooperative

transmission scheme is described in section III and theoperation of the conventional Cooperative SIC is described

in section IV. The Cooperative BA-SIC scheme is described

in section V. Section VI shows the BER simulation results

and comparisons. Finally the paper is concluded in section VII.

I I . SYSTEM M ODEL ANDA SSUMPTIONS

A typical multiuser communication scenario of an up-

link synchronous CDMA with a pair of cooperating users

{1, 2},.., {k, i},.., {K 1,K} and the base-station receiver

{d

} system employing the proposed cooperative scheme is

shown in Figure 1. A common multiple access channel (MAC)with BPSK modulated user signals with fading and AWGN is

assumed. To gain clear insight into the impact of cooperation

on multiuser SIC reception under MAI conditions, there are

some assumptions made in this paper. These assumptions are

used for all the cooperative techniques (CBA-SIC, C-MF and

C-SIC) used here in this paper for comparisons and are listed

below:

1) The cooperating pair of users are chip synchronized

before they start to cooperate and transmit each others data.


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Extension to asynchronous case should be possible with a little

modification to the scheme.

2) The amount of interference from non paired user nodes

to the cooperating pair of users is small and can be treated as

background noise. This may be easily justified due to uniform

distribution of users within the coverage of a typical cellular

system. Therefore, the cooperating user nodes see much less

interference from other users compared to the base-station

receiver, which is usually placed in the center of a cell to be

able to transmit and receive to/from all users more efficiently.

3) The pairing users are assumed to know the phases of their

transmit channels such that during transmission, their signals

are multiplied with appropriate phase offsets for coherent

combining at the base-station receiver [1].

Fig. 1. Cooperation scenario between pairs of users

In the proposed cooperative CDMA uplink system, during

the first symbol period, the users transmit their own data using

their originally assigned spreading sequences. At the same

time, the users that are pairing with transmitting users receive

and decode the transmitted signals. During the second period,

the pairing users forward the decoded data using the spreading

sequences of their partners. Without loss of generality and for

the sake of ease in presentation, we proceed our analysis for

{k, i} pair of users. It is easy to understand that the samecooperation scenario applies to all other pairs of cooperating

users. The signals received at the cooperating pairs{k, i} atthe first period can be written as:

ri(t)=

Pkgki(t)bk(t)ck(t) +vi(t),

rk(t)=

Pigik(t)bi(t)ci(t) +vk(t),(1)

where, Pk is the signal power, g(t) = k(t)ej(t) is thecomplex fading channel between the users with amplitude

(t) and phase (t) components with variance 2, bk(t) =m=bk(m)p(tmTb) is the data signal, wherebk(m) is

a binary sequence taking values[1, +1]with equal probabil-ities, p(t) is rectangular pulse with period Tb. The spreadingsequence is denoted asck(t) =

n=ck(n)p(tnTc)with

antipodal chips ck(n) of rectangular pulse shaping functionp(t) of period Tc and normalized power over a symbol period

is equal to unityTb0 ck(t)2dt = 1. The spreading factor is

N = Tb/Tc and v(t) is the AWGN with two sided powerspectral density

N0/2.

The received composite signal at the base-station receiver

d from all users transmissions during the first period is rd(t)and from that of the partnering users in the second period is

rd(t) and can be written as:

rd(t)=

Kk=1

gkd(t)sk(t) +v(t)

rd(t)=K

i=1,i=k

gid(t)si(t) +v(t)

(2)

wheresk(t) =

Pkbk(t)ck(t) is the transmitted signal ofkth

user. The model of signal si(t) transmitted during the secondperiod is exactly the same as above but they are originated

from the i, i= k user using kth users estimated data bk(t)using their own channels gid(t) = i(t)ejid(t). The signalmodel described above applies to all pairs of cooperating users

with appropriate modifications.

The cooperative scheme performs satisfactorily when the

inter-user channel gains are higher or at least equal to that of

the respective transmit channels of the users to the destination

(base-station in this work) [13]. Assuming average noise

variance of the users and the base-station receivers are equal,

the relative signal to noise ratio (SNR) gain in dB of inter-user

channelsk, i compared to the respective transmit channelsof the users to the base-station can be shown as

k=2ki2kd

, i=2ik2id

(3)

where, 2ki, 2ik and

2kd,

2id are the variances of inter-

user channels gki, gik and the users channels to thebase-station receiver gkd, gid, respectively. Symmetry ofinter-user channels i.e. gki = gik, k assumed here isreasonable as in [1]. In the near far condition, 2kd = 2idand hence k = i with nearfar ratio being defined as = max{2jd}/2kd, j {1, 2, . ,i, . ,K}, j=k.

III . MULTIUSERC OOPERATIVE T RANSMISSION S CHEME

Based on the system model, a signalling structure of the

proposed scheme with two users spanned over two consecutive

symbol periods is shown in (4). The same signalling structure

applies to all other consecutive periods. When appropriate,

the signals are presented in vector form and indices denoting

time dependance are dropped. A single cycle of cooperative


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transmission scheme can also be written as

sk =

Pkgkbkck ,Pkgkbici first period second period

si =

Pigibici

,

Pigibkck)

first period second period(4)

In the first period, the users transmit their signals as shownin equation (4). Due to the broadcast nature of the channels,

the signals are simultaneously received both at the cooperating

users and at the base-station receiver. At the same time, the

received signals are independently processed at the users

receivers. For handling these operations, it is assumed that

full duplex capabilities or echo cancellation technique [13] is

available. The received signals at the cooperating pairs at this

period are given in (1). The detection of signals at each other

user node is performed by first obtaining the soft estimates of

the signals by despreading the received signal with the known

spreading sequence. For example the kth user this is given by

zk = 1Tb

Tb0

rkck,k (5)

Then, by performing channel phase correction and taking the

sign of the real part of the signal zk, the estimate of the kth

users transmitted signal b k is obtained

bk = sgnzkgik (6)

where, sgn{.},{.} and{.} denote sign, real and complexconjugation operation, respectively.

During the second period, the cooperating users simply

forward the detected data of the partners bk to the base-station receiver using the their partners spreading sequences

ck. It should be noted that the estimated data may notbe identical to the transmitted of the originating users

due to the detection errors in (5) and (6). The accuracy

of detection and thus error performance improvement of

the system due to cooperation depends on the relative

SNR gains of the inter-user channels gki and gik to theirrespective transmit channels to the base-station receiver

gkd and gid. The processes (5) and (6) are performed at allpairs of mobile nodes each acting both as a user and a partner.

IV. COOPERATIVE MF (C-MF) A ND SIC (C-SIC)

SCHEMES

During both the first and second period of the cooperationscheme, the base-station receiver processes the received sig-

nals to perform detection of users data. The C-SIC receiver

performs the detection of user signals based on order of their

estimated strength using the principle of SIC. The C-MF is

obtained when there is only despreading and data detection is

used i.e. no cancellation is performed. In the first period the

signal estimation of the strongest among the users is carried

out, followed by the cancellation of its MAI contribution

from the remaining composite received signal. The relative

power estimates of the users are generated at the output of

the corresponding users matched filters and the one with the

maximum is selected, given by

zmax= max

1

Tb

Tb0

rdck

, 1 k K (7)

In the second period the estimate of the strongest user obtained

from the first period denoted by index k {max} isgenerated from the output of bank of MF as follows:

zmax= 1

Tb

Tb0

rdcmax, (8)

The estimated signals of the user from the two periods is

maximum ratio (MRC) combined to form a final decision

statistic Zmax. Note that other combining methods such asequal gain combining (EGC), minimum mean square error

combining (MMSEC) [14] are equally applicable for the

diversity combining and may have significant effect the system

performance. For the MRC, the combined signal can be shown

as follows:

Zmax= zmaxmax+zmax{}max (9)

where,maxand {}maxare amplitudes of estimated strongestuser in first period and belong to the users and its partners

channel, respectively. Finally, the hard decision of data of the

user with index k = max is performed from the SIC stage asfollows:

bk = sgn{Zmax}

(10)

The C-MF scheme uses the processes above (7)- (10) only

and performed for all K users. Note that power orderingshown in (7) does not effect the detection performance of

MF based receivers. The C-SIC reuses the estimated signal

of the strongest user in a given stage zmax, zmax to removeits MAI. For this purpose the cancellation process now has

to be applied to both the received signals from the first and

second periods for improving the estimation of weaker users

signal that follows the same processes (7) - (10). The estimates

of the users signal in the first and second period zmax andzmax are separately spread using the spreading sequence ofthe detected user with index k = max and subtracted fromthe respective received signals rd(t) and rd(t) to obtain lessinterfered received signals as follows

rd= rd zmaxckrd= r

d zmaxck

(11)

The processes (7) - (11) are repeated until all users data

signals are detected.

V. COOPERATIVE B LINDA DAPTIVESIC (CBA-SIC)

The performance of conventional SIC degrades significantly

under high system loading due to biased estimates of linear

correlators used. In view of the performance limitations of

the conventional SIC and the problem of inaccurate MAI

estimation and cancellation, it is desirable to generate some


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adaptive despreader weights that do not allow a decision

statisticzk to revert its sign when the presence of MAI tendsto do so. The CM algorithm (CMA) is a simple algorithm

that attempts to maintain constant modulus of the signals at

the output [15], [16], [17] and its complexity is only O(N)computation per symbol per user, whereN is the length ofthe despreader weight vector. An adaptive despreader using

the CM criterion is shown in Figure 2. Provided that the CM

algorithm is fast enough to track the changes in MAI power

variations and the corresponding weights are selected, the

decision error due the MAI effects can be eliminated. Practical

CM algorithms however may not perform perfectly and there

are bound to be some inevitable misconvergence problems.

However, the useful properties of the CMA is exploited within

the adaptive despreaders and suitably implemented within SIC

in [12] also referred to here as Blind Adaptive SIC (BA-SIC).

In the sequel, the BA-SIC algorithm embedded within the

cooperative diversity system i.e. CBA-SIC is proposed and

evaluated.

Fig. 2. CMA-aided despreading process of BA-SIC

At the first symbol period, the weights of the despreaders

are initialized with users spreading sequence wk(1) = ckand wk(1) =ck, respectively. Without loss of generality, it isassumed that the first user (strongest among K users) to be

detected is user1. Similarly next strongest user is assigned anindex as user 2 and so on. At the first stage, the received signal

can be expressed as rd(m) = r1(m) and rd(m) = r1(m),respectively, where r1 = [r1(1), r1(2), . . ,r1(N)]

T. The re-

maining composite signal after cancellation at kth stage forthe detection of the users and its cooperating pair signals are

expressed as rk+1(m) and rk+1(m), respectively. Below, thediversity combining, detection and interference cancellation

procedures for k th stage is described.

At kth stage, the decision statistic zk(m) is obtainedby multiplying chips of rk(m) with the vector of weightswk(m) = [wk(mN + 1), wk(mN + 2), . . ,wk(mN +N)]

T

and summed over the symbol period given by

zk(m) = {rk(m)}Twk(m)zk(m) = {rk(m)}Twk(m)

(12)

The CM criterionJCMcan be written as minimization of thefollowing cost function

JCM=Ezk(m)2 2

(13)

where, E{.} is the expectation operator, is the dispersionconstant, which is equal to unity for binary phase shift keying

(BPSK) signals. The instantaneous error signal ek(m) iscalculated as

ek(m) = zk(m){zk(m)2 }ek(m) = zk(m){zk(m)2 }

(14)

The estimated gradient vector of the error signal is then

calculated by

k(m) = rk(m)ek(m)

k(m) = rk(m)ek(m)

(15)

Using the gradient of (15), the weight vector at next symbol

wk(m+ 1) is updated as follows

wk(m+ 1) =wk(m) k(m)wk(m+ 1) =w

k(m) k(m)

(16)

where, is the step-size usually chosen as a small number[18] is used to adapt elements of the weight vector to minimize

the cost function (13). The output signals zk(m) and zk(m)are combined with MRC using amplitude estimates k(m)and i(m). The combined signal is delivered to the decisionmaking process to perform hard decision

bk(m) = sgnzk(m)k(m) +zk(m)i(m) (17)The cancellation process also requires amplitude estimation

of the detected user signal and spreading. For this purpose,

scaling factors k(m) are first obtained using the despreaderweights and the known spreading sequence as follows

k(m) = ck(m)

wk(m)

k(m) = ck(m)

wk(m)

(18)

where,

ck(m) = 1N

Nn=1

|ck(mN+ n)|

wk(m) = 1

N

Nn=1

|wk(mN+ n)|

wk(m) = 1

N

Nn=1

|wk(mN+ n)|

(19)

are the mean amplitude of users spreading sequence chips and

the mean of the weight vector updated by the CM algorithm,


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respectively. The estimated symbol is then scaled with its

new amplitude estimate k(m) and spread to generate thecancellation term as follows

xk(m) = k(m)zk(m)ck(m)

xk(m) = k(m)zk(m)ck(m)

(20)

The remaining composite signal after the interference cancel-

lation is

rk+1(m) = rk(m) xk(m)rk+1(m) = rk(m) xk(m)

(21)

The processes (12)-(21) are repeated for each stage until the

weakest user is detected.

VI . SIMULATION R ESULTS ANDC OMPARISONS

Fig. 3. Performance of Cooperative BA-SIC in flat Rayleigh fading channelwith K=20, Gold sequence, N=31

A baseband model of K user synchronous uplink DS-CDMA system with BPSK modulation and short binary Gold

sequences of length N = 31 is used. All the uplink and inter-

user channels are assumed to be Rayleigh flat fading with

normalized Doppler shift fdTb = 0.003 and a small step sizeof = 0.0001 is assumed in the adaptive algorithm. The

system using different cooperative schemes is simulated inMATLAB using 20000 Rayleigh faded symbols for each user

for obtaining the average BER of users and 40000 symbols

for each user in the case of BER calculation for the weakest

user.

In Figure 3, BER simulation results of the proposed Coop-

erative BA-SIC (CBA-SIC) system is plotted under system

loading of K = 20 users. The BER of Non cooperativeSIC and C-SIC under the same system conditions are also

shown. The CBA-SIC that uses blind adaptive approach for

suppressing and canceling MAI, improves the amplitude and

data estimates of users and that of cooperative signals, much

improved BER at high system load is expected. As expected,

the proposed technique shows noticeable improvement in the

error performance compared to the Cooperative SIC (C-SIC)

receivers under the same system settings as the k increases:for example, a SNR gains of 2 dB for a BER of103 canbe seen here.

The BER performance of the proposed CBA-SIC is shown

in Figure 4 and compared with C-SIC for system loading of

K= 6 24 users under fixedEb/N0 = 20 dB. The degree ofcooperation is quantified by the relative SNR gains of inter-

user channels to the transmit channels of the respective users

i.e. k. The CBA-SIC shows a superior BER performanceunder high system loading conditions, which is expected due

to improved estimation of users data signals and amplitudes

of the blind adaptive approach to MAI cancellation.

Fig. 4. BER performance vs. number of users of Cooperative BA-SIC inflat Rayleigh fading channel with Eb/N0 = 20dB, Gold Sequence, N=31

The impact of nearfar conditions on the BER of CBA-SIC

is shown in Figure 5. System loading ofK = 6 24 isconsidered, where the desired weakest user has unity power

while all other users signals are received at power level

uniformly distributed between 010 i.e. = 10 dB. TheEb/N0 of the desired weakest user is assumed to be 20 dB.

The step-size of adaptive algorithm is set as = 0.0001 . TheBER of the user is plotted against different system loading

i.e. the number of users and also under different ratio of

inter-user channel SNR gains k. It can be observed that theperformance of CBA-SIC is robust in nearfar conditions and

high system loading of up to 18 users. The performance of

C-SIC shows similar gain, however the degradation in BER

starts with much lower system loading of 12 users compared

with CBA-SIC. Also it is noted that C-SIC does not benefit

much from strong channel SNR of the partners compared with


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CBA-SIC. The reason for this is the conventional SIC while

removes part of MAI also suffers from unreliable estimation of

weaker users signals and hence only the fraction of diversity

gain achieved. The results for CBA-SIC and C-SIC on the

other hand are in strong contrast with Cooperative MF (C-MF)

detection technique, where the BER of weakest user degrades

rapidly even under very small number of users.

Fig. 5. BER performance vs. number of users of Cooperative BA-SIC inflat Rayleigh fading channel with nearfar condition (10dB), Eb/N0 of theweakest user=20dB, Gold Sequence, N=31

VII. CONCLUSIONWe proposed a new Cooperative BA-SIC scheme to

improve the performance of uplink CDMA. It is noted that

the improved MAI estimation and cancellation performance

of the BA-SIC provides higher number of users to enjoy the

cooperative diversity gains than that with a conventional SIC.

Also it is noted that the nearfar effects can provide more

improved BER under low system loading conditions. Future

work will constitute theoretical analysis of achievable rates

and evaluation under frequency selective fading channels.

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cooperative cdma with blind adaptive sic

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