Performance Enhancement of MC-CDMA system ... - IEEE Xplore

2012 International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, 2012, Coimbatore, INDIA

Performance Enhancement of MC-CDMA system through Turbo Multi-user Detection K. Rasadurai &N. Kumaratharan Department of Information Technology Sri Venkateswara College of Engineering, Sriperumbudur-602105 India. Email: [email protected] Email: [email protected] Abstract—In this paper turbo multi-user detection (MUD) technique is proposed for multi carrier code division multiple access (MC-CDMA) system to overcome the affects of multiple access interference (MAI) caused by the inter-cell and intra-cell interference and near far effect. The proposed technique estimates the interference from the unknown users and the interference estimate is subtracted from the received signal. Simulation results show that the turbo multi user detector significantly outperforms the conventional multi user detector for moderate and high signal-to-noise ratios. Keywords— MC-CDMA; MAI; MUD; SOVA; MMSE; MAP

I. INTRODUCTION In mobile radio communication code division multiple access (CDMA) has been a widely accepted multiple access technique. In most cases, it is implemented as direct-sequence code division multiple access (DSCDMA) in single-carrier (SC) systems. Alternatively, multi-carrier code division multiple access (MC-CDMA) offers a lot of advantages over SC systems [1]. MCCDMA system is a modulation scheme that combines the advantages of orthogonal frequency division multiplexing (OFDM) and CDMA to provide robustness against frequency selectivity in wireless channels and which suits high data rate application with multiplexing technique. MC-CDMA is robust to multi-path fading, inheriting the advantages of conventional CDMA where frequency diversity can be achieved in a broadband channel. When OFDM is employed, these sub-channels are orthogonal and do not mutually interfere. A single chip now occupies only a small -fraction of the whole bandwidth. Therefore, it is affected by flat fading and a one-tapequalizer suffices for eliminating channel distortion. Due to frequency selective fading and asynchronously transmitting subscribers, the orthogonality of scrambling codes cannot be maintained. Hence, pseudorandom sequences are employed causing severe multiple access interference (MAI) that degrades the system performance and limits system capacity. Several possibilities for combating this degradation are discussed. The interference is treated as white Gaussian noise and the

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overall system performance is improved by strong error control coding that shows a good performance especially at low signal-to-noise ratios. Due to the inherent spreading in CDMA systems, each user occupies a very large bandwidth and low rate channel coding with potentially high coding gains can be employed [2]. It is shown that strong low rate error control codes, e.g. a parallel concatenation of interleaved convolutional codes, are able to ensure a reliable transmission for low and medium system loads [10]. Multi-user detection (MUD) techniques can be applied to eliminate multiple access interference (MAI) [3-5]. These methods exploit the deterministic structure of the interference but require higher computational costs especially for asynchronous transmission. The application of OFDM reduces these costs because each chip is only affected by flat fading. For high loads, i.e. the number of active users reaches or even exceeds the CDMA spreading factor, MUD is necessary to combat MAI efficiently by using turbo MUD algorithms.

II. MUD ALGORITHMS MUD Conventional single-user detector treats MAI as white Gaussian noise. However, the interference caused by simultaneously transmitting users in a CDMA-system is highly structured and can be modeled by a (possibly time-varying) cyclostationary process [4]. This fact can be exploited resulting in considerable performance gains. Here MUD algorithms mainly focus on the case where all signature sequences are known at the receiver site, i.e. the base station for uplink transmission. The basic idea of MUD techniques is to cancel the interference caused by the other users by exploiting the available side information of the interfering users, rather than ignoring the presence of other users like in single user detection (SUD) techniques. Basically there are four types of algorithms to be deployed for the cancellation of interference namely 1. Optimum MUD 2. Linear MUD 3. Iterative Approximations of Linear MUD 4. Nonlinear Interference cancellation Most of the early work (above said algorithms) on MUD for MC-CDMA focuses on uncoded systems. The previously developed multi-user detectors typically


require knowledge of the signature waveforms of all the users in the system and ignore users whose signatures are unknown. e.g., users outside the cell. For that the receiver should have the knowledge of the signature waveforms of only K ≤ K users, where K∪ is unknown users. Subspace techniques [6] are used to estimate the interference from the unknown users and the interference estimate is subtracted from the received signal.

the MMSE filter output. The detector performs three sub functions as follows: 1.

Soft Interference Cancellation

∪

In the group-build MUD, we have knowledge of the first ( K ≤ K ) user’s spreading sequences (and received amplitudes), whereas the rest of the users are unknown to the receiver. Denotes S as the matrix consisting of the first columns of S. Denote the K remaining K = K − K columns of S by s% . These signature sequences are unknown to the receiver. Then the received signal is the superposition of the K users transmitted signal plus the ambient noise, given by ∪

∪

K

∪

III. TURBO MUD ALGORITHM FOR MC-CDMA Introduction The concept of turbo MUD is to merge the conventional multi-user receiver structure with group-blind multi-user detection [6]. Algorithm used in the turbo MUD is soft input soft output (SISO) algorithm. The SISO can be implemented using one of two algorithms, the soft output viterbi algorithm (SOVA) or the maximum a posteriori (MAP) algorithm (also called the bahl, cocke, jelinek and raviv algorithm in deference to its inventors). Both of these algorithms are related to the Viterbi algorithm, which is commonly used to decode conventional convolutional codes. The key distinction is that while the Viterbi algorithm outputs hard bit decisions, the SOVA and MAP algorithms output soft decisions that can be cast in the form of a log likelihood ratio (LLR). SISO Group-Blind Detector for Synchronous MCCDMA A SISO algorithm is defined as one that accepts a-priori information at its input and produces a-posteriori information at its output. Suppose the probability of a bit being a 1 is p(1)=0.4 and that of 0 is p(0)=0.6. Now, these probabilities are called soft-values. If, using these probabilities, the decision is made that the bit is 0 (since p(0)>p(1), then the decision is called hard decision of the information bit. The information is no longer “soft”. The turbo group blind receiver is the SISO group-blind multiuser detector. The SISO MAP channel detector accepts, as inputs, the priori LLR’s for the code bits of the known users. It delivers, as outputs, updated LLR’s for the codes bits and at the last iteration for LLR’s of the information bits. Soft interference cancellation (SIC) and minimum mean squared error (MMSE) filtering accomplish this. Specifically, using the a priori LLR’s and knowledge of the signature sequences and received amplitudes of the known users, the detector performs a soft interference cancellation for each user, in which estimates of the MAI from the other known users and an estimate for the interference caused by the unknown users are subtracted from the received signal. This is in contrast to previously developed multi-user detectors, which ignore the interference from unknown users. Residual interference is suppressed by passing the resulting signal through an MMSE filter. The a posteriori LLR can be computed from

∪

∪

K r (i ) = ∑ A b (i ) s + n(i ) , i= 0,..,M-1 i k k k =1

(1)

where K is the number of users, where Ai amplitude of K user, bk(i) denoted ith symbol of the K user of coded interleaver bits, where sk is the normalized spreading waveform of the ith user, Where n(i) is zero mean i.i.d gaussian noise vector with variance that is independent of the symbol sequences, where M is number of data symbol per user per frame. The detector first forms soft estimates of the user code bits as

(2) b k (i) E{bk (i)} = tanh(1/ 2λ 2[bk (i )]) where λ 2[bk (i )] is the priori LLR of the kth bit during the ith time slot delivered by the MAP decoder. The estimate of the unknown interferers is given by the equation γ (i )

% % ˆ(i ) r ( i ) − SAb

(3)

The interference estimate is given by the equation % % ( i ) + SAb % % ˆ (i ) + v (i ) Iˆ ( i ) = SAd

(4) where d (i ) [ d 1(i ) d (i )....dk (i )] T. Now subtracting the interference estimate from the received signal forms a new vector and the equation is written as 2

ζ (i)

∧

r (i ) − I (i) ˆ %% %% (i) + w(i) = SAb(i) − SAd

(5) (6)

where w(i ) n(i) − v(i ) . For each known user a SIC on ζ (i) to obtain rk (i )

(

(( % % d ( i ),1 ≤ k ≤ k% ζ ( i ) − SAb ( i ) + SA k

(7)

2. MMSE Filtering

MMSE Filtering takes the background noise in to account and utilizes the knowledge of the received signal powers. It minimizes the mean squared error between the actual data and the soft outputs of the conventional detectors. An instantaneous linear MMSE filter is then applied to rk (i ) to obtain z k (i )

x k (i )T rk (i )


= µ k (i )bk (i ) + µk (i) (8) where µk (i ) = E{zk (i )bk (i )} , is the equivalent amplitude of the kth user’s signal at the filter output. The filter N xk (i ) ∈ IR is chosen to MMSE between the code bit bk(i)

xk (i)

arg min x ∈ IR N E{[bk (i) − xT rk (i)]2 }

(9) where the expectation is with respect to the ambient noise and the interfering users. The solution to (9) is xk = E{rk (i) rk (i)T }−1 E{bk (i )rk (i )} (10)

and the filter output z (i) , i.e. k

Figure 1. Log-Likelihood Calculation Finally, the extrinsic information delivered by the SISO MUD is λ1 bk ( i )  log  p[ zk (i) | bk (i) = +1] / p[ zk (i) | bk (i) = −1 2

/ 2vk 2 (i ) + [ zk (i) + µk (i)]2 / 2vk 2 (i) = 2 zk (i ) / 1 − µ k (i)

= − [ zk (i) − µk (i) ]

(11) This estimate, the LLR can be seen as a form of confidence or a reliability factor. The larger this number, the better the estimate. The first decoder passes its estimate of the reliability factor to the next decoder, which does the same thing and passes its estimation of confidence to the first decoder. The two decoders continue this way iteratively until they have reached some preset value of confidence or reliability. Then they pass the final data set and start on the next batch. The extrinsic information delivered by the SISO MUD is given to deinterleavers and then to MAP channel decoding and again is interleaved. Finally the MAP channel decoders give the decoded information bits. C.

Proposed MC-CDMA Error Correction Algorithm

The proposed algorithms mainly focus on the use of codes in the system. Here turbo codes are used at the encoder. Turbo codes are a new class of error correction codes that were introduced along with a practical decoding algorithm in MC-CDMA. The importance of turbo codes is that they enable reliable communications with power efficiencies close to the theoretical limit predicted by

Claude Shannon. The proposed MC-CDMA transmitter with turbo encoding is shown in the Fig.1. A turbo code is the parallel concatenation of two RSC codes separated by an interleaver. The upper encoder receives the data directly, while the lower encoder received the data after it has been interleaved by a permutation function α . The interleaver α is called a pseudo-random interleaver that is it maps bits in position i to position α (i) according to a prescribed, but random generated rule. The interleaver operates in a block wise fashion, interleaving L bits at a time, and thus turbo codes are actually block codes. Since both encoders are systematic and receive the same set of data (although in permuted order), only one of the systematic outputs needs to be sent. By convention, the systematic output of the top encoder is sent while the systematic output of the lower encoder is not transmitted. However, the parity outputs of both encoders are transmitted. The overall code rate of a turbo code composed from the parallel concatenation of two rates 1/2 systematic codes is 1/3. This code rate can be made higher by puncturing. The code rate of a turbo code is typically increased to r = 1/2 by only transmitting the odd indexed parity bits from the upper encoder and the even indexed parity bits from the lower encoder (along with all the systematic bits from the top encoder). The output is given to interleavers and then it is mapped and the usual MCCDMA transmitting process is done at the transmitter. The proposed turbo multi-user detector is shown in Fig.2.


In a “conventional” receiver, the interface between subsystems involves the passing of bits, or hard decisions, down the stages of the chain. Whenever hard-decisions are made, information is lost and becomes unavailable to subsequent stages. Additionally, stages at the beginning of the processing chain do not benefit from the information derived by the stages further down the chain. The interface between the stages can be greatly improved by using the same strategy used to decode turbo codes. The term “turbo processing” was coined to describe the general strategy of iterative feedback decoding or

iteration. With turbo processing, each subsystem is implemented with a SISO algorithm, such as Log MAP and SOVA. Soft decision values, typically in the form of LLR’s are passed to the chain and refined by subsequent stages. The soft-output of the final stage is then fed back to the first stage and a second iteration of processing is initiated. Several iterations of turbo processing can be executed, although as with turbo codes, a law of diminishing returns limits the maximum processing gains.

Figure 2. Proposed Multi User Detector

IV. SIMULATION RESULTS The MC-CDMA receiver model has been developed and simulated in Matlab version 7. The simulation parameters for the design and implementation of the system is given in Table I TABLE I SIMULATION PARAMETERS Parameters Description System MC-CDMA MIMO Structure 5 users with 2 unknown users Encoder Decoder

Turbo Coder Log MAP, SOVA

Frame Size

1024, 3072

Code Rate Iterations Channel

1/2, 1/3 4, 6, 8,10 Rayleigh

Modulation

BPSK

The MC-CDMA system with the above parameters is simulated. The newly developed MC-CDMA system is

simulated with different frame sizes as 1024, 3072. The system is simulated with three users with one unknown user and with different frame sizes using Log MAP and SOVA algorithms. The code rate is chosen as 1/2 or 1/3. For each simulation, a curve showing the bit-error rate (BER) versus the per-bit signal-to-noise ratio (SNR) was computed. Also if the number of iterations is increased, the detector performs well that is, the BER is reduced considerably. The simulated results of the proposed turbo group-blind MUD for MC-CDMA system is demonstrated. The spreading sequences are random and the same sequences are used for all simulations. Each user uses different random interleaver and the same interleavers are used in all simulations. All users use the same transmitted power and the chip pulse waveform is a raised cosine with roll-off factor 5. All users employ the same constraint length 3, convolutional code (with generators g1 [110] and g2 [111] ). For the performance of log MAP and SOVA for MC–CDMA system is simulated and the results are plotted.


Performance evaluation using Log MAP

10

-1

1 0 Ite ra tio n

10

-1

4 Ite ra tio n

BER ---->

BER---->

10

10

-2

10

10

10

-2

-3

-4

-3

10

-5

1

10

1

1 .5

2

2 .5 E b / N o in d B ---->

3

3 .5

3

4 5 6 E b / N 0 in d B ---->

7

8

Fig. 5 Performance of the system for Log MAP, 1024, 1/2, g[111,101],10 iteration 10

-1

-1

4 Ite ra tio n

10 Iteration 10

10

BER ---->

10

-3

-3

10

10

-2

-2

BER---->

10

9

4

Fig. 3 Performance of the system for Log MAP, 1024, 1/2,g[111,101],4 iteration 10

2

-4

-4

1

1 .5

2 Eb / N0

2.5 in dB ---->

3

3.5

4

10

-4

-5

1

2

3

4

5 6 E b / N 0 in dB ---->

7

8

9

Fig. 4 Performance of the system for Log MAP, 1024, 1/3, g[111,101],4 iteration

Fig. 6 Performance of the system for Log MAP, 1024, 1/3, g[111,101],10 iteration

Fig. 3 and 4 illustrates the performance of MC-CDMA system using Log MAP algorithm with frame size of 1024, 4 iterations and code rates of 1/2 and 1/3. The performance improves for lower code rate in terms of reduced BER. It is noticed, that the lower code rate expands the bandwidth, which provides better suppression of interference due to MAI and achieves significant coding gain at lower code rate.

Fig. 5 and 6 shows the performance of MC-CDMA system using Log MAP algorithm with frame size 1024, 10 iterations and code rates 1/2 and 1/3. It is observed from the plots that the MC-CDMA system with 10 iterations provides the better performance, when compared to the system with 4 iterations. Fig. 3 to 6 reveals that the BER performance improves significantly with the group blind turbo MUD when the iteration number is increased. Also it is noticed that the gain of turbo code improves significantly for lower code rate.

10


Performance evaluation using SOVA

10

-1

1 0 Ite ra tio n

-1

10

4 I te r a ti o n

-2

10

10 BER --->

-2

BER ---->

10

-3

-3

10

10

-4

1

2

3

4

5 6 E b / N 0 in d B ---->

7

8

9

10

-4

10

1

1 .5

2

2 .5 E b / N 0 i n d B -- - - >

3

3 .5

4

Fig. 9 Performance of the system for SOVA, 1024, 1/2, g[111,101],10 iteration

Fig. 7 Performance of the system for SOVA, 1024,1/2,g[111,101],4 iteration 10

10

1 0 Ite r a tio n

-1

10

BER---->

4 Ite ra tio n

-2

BER ---->

10

10

10

10

-1

-2

-3

-4

-3

10

-5

1

10

2

3

4

5 6 E b / N 0 in d B --->

7

8

9

-4

1

1 .5

2

2 .5 3 3 .5 E b / N 0 in d B --- ->

4

4 .5

Fig. 8 Performance of the system for SOVA, 1024,1/3,g[111,101],4 iteration. Fig. 7 and 8 illustrates the performance of MC-CDMA system using SOVA algorithm with frame size of 1024, 4 iterations and code rate of 1/2 and 1/3. The performance improves for lower code rate in terms of reduced BER. It is noticed, that the lower code rate expands the bandwidth, which provides better suppression of interference due to MAI and achieves significant coding gain at lower code rate.

5

Fig. 10 Performance of the system for SOVA, 1024, 1/3, g[111,101],10 iteration Fig. 7 to 10 shows the performance of MC-CDMA system using SOVA algorithm with frame size 1024, 10 iterations and code rates 1/2 and 1/3. It is observed from the plots that the MC-CDMA system with 10 iterations provides better performance, when compared to the system with 4 iterations. Fig. 3 to 10 reveals that the BER performance improves significantly with the group blind turbo MUD when the iteration number is increased. Also, it is noticed that the gain of turbo code improves significantly for lower code rate. Further, comparing Log MAP with SOVA algorithm, it is clear that the Log MAP has better performance than SOVA.

V. CONCLUSION In future wireless communication system, the main aim is to integrate all the multimedia services (voice, data and video services) in a single unit. Hence, systems that incorporate high data rate services are required to provide the users with all the facilities at a better performance. Also the channel capacity of the system should be improved in order to accommodate more number of users. The receiver is detected by the efficient SISO algorithm, which provides a better performance when compared to

10


the previous developed multi user detectors. The simulated MC-CDMA system is capable of suppressing interference not only from known users, but also from users whose signature sequences and received amplitudes are unknown. The proposed group-blind turbo multi-user receiver for MC-CDMA system provides a significant performance improvement over a noniterative receiver, whose performance is illustrated in the simulated results.

REFERENCES [1] Yee, N., Linnartz, J.P., Fettweis, G., “Multi-Carrier CDMA in

[2]

[3] [4]

Indoor Wireless Radio Networks”, Proceedings of IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp.109-113, Yokahana, Japan, September 1993. Kuhn, V., Dekorsy, A., Kammeyer, K.D., “Low Rate Channel Coding for CDMA Systems”, International Journal of Electronics and Communications AEU, vol. 54, No. 6, pp. 353-363, August 2000. Verdu, S., “Multiuser Detection”, Cambridge University , New York, May 1998. Honig,M., Tsatsanis, M.K., “Adaptive technique for Multiuser CDMA Receivers”, IEEE Signal Processing Magazine, vol.17, No. 3 pp 49-61, May 2000.

[5]

Honig, M., Madhow, U., Verdu, S., Blinde “Adaptive Multiuser Detection”, IEEE Transactions on Information Theory, vol. 41, No. 4 pp. 944-960, July 1995. [6] Daryl Reynolds and Xiaodong Wang, “Turbo Multiuser Detection With Unknown Interferers”, IEEE Transactions on Communications, vol.50, No.4, pp. 616-622, April 2002. [7] X.Wang and H. V. Poor, “Iterative (Turbo) soft interference cancellation and decoding for coded CDMA,” IEEE Transactions on Communication, vol. 47, No. 7 pp.1046–1061, July 1999. [8] X.Wang and A. Host-Madsen, “Group-blind multiuser detection for uplink CDMA”, IEEE Journal on Selected Areas of Communication, vol.17, No. 11, pp. 1971–1984, November 1999. [9] Y. Zhang and R. S. Blum, “Multistage multi-user detection for CDMA with space-time coding,” Proceedings 2000 IEEE Workshop on Statistical and Array Processing, August 2000. [10] Zhenning Shi and Christian Schlegel, “Iterative Multiuser Detection and Error Control Code Decoding in Random CDMA”, IEEE Transactions on Signal Processing, vol.54, No.5, pp. 18861895, May 2006. [11] Besma Smida, Lajos Hanzo and Sofiene Affes, “Exact BER performance of Asynchronous MC-DC-CDMA over Fading Channels”, IEEE Transactions on Wireless Communications, vol.9, No.4, pp.1249-1254, April 2010.