Multiuser detection for DS-CDMA systems using evolutionary programming

Lim, Heng Siong; Rao, M. V. C.; Tan, Alan Wee Chiat; Chuah, H. T.

doi:10.1109/lcomm.2002.808372

Cited by 41 publications

(31 citation statements)

References 8 publications

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“…However, only few applications of these tools are found in the domains of signal processing and wireless communication. Heuristic optimization based multi-user detectors has been proposed in [17][18][19][20][21][22][23][24]. To make a maximum likelihood (ML) decision for the MUD problem, we need to solve a binary constrained minimization that is also known as a Binary Quadratic Program (BQP) in the area of optimization.…”

Section: Heuristic Algorithms In Multiuser Detectionmentioning

confidence: 99%

“…In general, local search algorithms have a number of advantages over other conventional optimization and search techniques such as the simplicity of the algorithm, the ability to handle all sorts of functional representations of problems, including very complex function relationships [22] [23]. However, these algorithms become complex if search space is large.…”

Section: Memetic Algorithmmentioning

confidence: 99%

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A Survey of Advances in Multi-User Detection in DS-CDMA

Shah¹,

Naeem²,

Sheikh³

et al. 2006

IEEE International Conference on Computer Systems and Applications, 2006.

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In Direct Sequence Code Division Multiple Access (DS-CDMA) all users transmit at the same time and at the same frequency thus causing mutual interference. In such situation when the powers of the interfering signals are large compared to the desired signal, the performance of the matched filter receiver degrades. This is due to the near-far effect. One way to combat this effect is to use stringent power control, as is done in most commercial systems. Another approach is to use multiuser detection (MUD), which are near-far resistant. MUD has the more fundamental potential of raising capacity by canceling Multiple Access Interference (MAI). MUD has now developed into an important, full-fledged field in multipleaccess communication systems. In this paper, we present advances in algorithms for MUD.

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Section: Heuristic Algorithms In Multiuser Detectionmentioning

confidence: 99%

Section: Memetic Algorithmmentioning

confidence: 99%

A Survey of Advances in Multi-User Detection in DS-CDMA

Shah¹,

Naeem²,

Sheikh³

et al. 2006

IEEE International Conference on Computer Systems and Applications, 2006.

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“…O algoritmo heurístico de programação evolucionária (EP) é o algoritmo evolucionário mais simples, pois utiliza apenas o critério de mutação, com probabilidade p m de ocorrência, como estratégia de diversificação e nenhuma estratégia de intensificação (FOGEL, 1994;LIM et al, 2003). Com isso, este algoritmo possui a menor complexidade computacional por geração dentre os evolucionários, mas a convergência é mais lenta, sendo necessárias gerações extras para atingir o desempenho desejado.…”

Section: Programação Evolucionária (Ep)unclassified

Detecção multiusuário utilizando algoritmos heurísticos evolucionários e de busca local

Neto¹,

Abrão²,

Jeszensky³

2004

Semina: Tech. Ex.

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ResumoNeste trabalho, são apresentados os principais algoritmos heurísticos baseados na teoria da evolução genética e de busca local. Esses algoritmos são aplicados ao problema da detecção multiusuário (MuD) para sistemas DS/CDMA em canais com desvanecimento Rayleigh Plano. A eficiência destes algoritmos é comparada através do compromisso desempenho versus complexidade computacional. A complexidade computacional é determinada em termos do número de operações necessárias para se alcançar o desempenho ML (ou muito próximo). Analisa-se, também, a perda de desempenho quando há ocorrência de erros na estimativa do canal. Palavras-chaves: Detecção multiusuário. Algoritmos heurísticos. Evolucionários. Busca local. Complexidade computacional. AbstractThe characteristics of the main heuristic algorithms based on genetic evolution theory and local search, applied to a DS/CDMA multi-user detection problem in Flat Rayleigh fading channel, are shown. The algorithms comparisons through the performance versus computational complexity tradeoff are carried out. The computational complexity is established in terms of the number of operations to reach the Maximum Likelihood (ML) performance. The estimation error effects on the performance are also considered.

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“…Randomized search heuristics (RSH) are effective methods for such kinds of problems, so many RSH based multiuser detectors have been studied and exhibit better performance than that of the other linear or nonlinear detectors. Earlier works on applying RSH to OMD problem can be found in [3][4][5][6][7] .The essence of optimum multiuser detection is to search for possible combinations of the users' entire transmitted bit sequence that maximizes the logarithm likelihood function (LLF) derived from the maximum likelihood sequence estimation rule [1] , which is called fitness function or objective function in the RSH multiuser detectors [3][4][5][6][7] . Comparing with so much emphasis on the implementation details and the performance analysis of these algorithms, little attention has been paid on the analysis of statistic characteristics of the OMD problem in terms of combinatorial optimization.…”

Section: Introductionmentioning

confidence: 99%

Fitness landscape analysis for optimum multiuser detection problem

Zhu

2007

Wuhan Univ. J. of Nat. Sci.

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Optimum multiuser detection (OMD) for CDMA systems is an NP-complete combinatorial optimization problem. Fitness landscape has been proven to be very useful for understanding the behavior of combinatorial optimization algorithms and can help in predicting their performance. This paper analyzes the statistic properties of the fitness landscape of the OMD problem by performing autocorrelation analysis, fitness distance correlation test and epistasis measure. The analysis results explain why some random search algorithms are effective methods for OMD problem and give hints how to design more efficient randomized search heuristic algorithms for OMD.In direct-sequence code-division multiple access (DS-CDMA) communication systems, transmitters multiply each user's signal by a distinct code waveform. Detectors receive a signal composed of the sum of all active users' signals, which overlap in time and frequency. A particular user's signal is detected by correlating the entire received signal with that user's code waveform without regard for the other users, which inevitably yields multiple access interference (MAI) at the output of matched filter. MAI is the main factor limiting performance in CDMA systems. While optimum multiuser detection (OMD) [1] , which is based on the maximum likelihood sequence estimation rule, is the most promising technique for mitigating MAI, its computational complexity increases exponentially with the number of active users, which leads to its implementation impractical.From a combinatorial optimization viewpoint, the OMD is an NP-complete problem [2] . Randomized search heuristics (RSH) are effective methods for such kinds of problems, so many RSH based multiuser detectors have been studied and exhibit better performance than that of the other linear or nonlinear detectors. Earlier works on applying RSH to OMD problem can be found in [3][4][5][6][7] .The essence of optimum multiuser detection is to search for possible combinations of the users' entire transmitted bit sequence that maximizes the logarithm likelihood function (LLF) derived from the maximum likelihood sequence estimation rule [1] , which is called fitness function or objective function in the RSH multiuser detectors [3][4][5][6][7] . Comparing with so much emphasis on the implementation details and the performance analysis of these algo-

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Multiuser detection for DS-CDMA systems using evolutionary programming

Cited by 41 publications

References 8 publications

A Survey of Advances in Multi-User Detection in DS-CDMA

A Survey of Advances in Multi-User Detection in DS-CDMA

Detecção multiusuário utilizando algoritmos heurísticos evolucionários e de busca local

Fitness landscape analysis for optimum multiuser detection problem

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