Evolutionary programming with cloning and adaptive cost function applied to multi-user DS-CDMA systems

Abrão, Taufik; Neto, Fernando Ciriaco Dias; Jeszensky, Paul Jean Etienne

doi:10.1109/isssta.2004.1371684

Cited by 12 publications

(8 citation statements)

References 2 publications

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“…Note that, an increase in the number of users causes only a little BER degradation, but the convergence becomes slower, since the initial solution (CD) is worst. 2 It is worth noticing that, in all convergence figures, 1-LS, STTS, RTS and SA have the same convergence speed, which is faster than GA and PSO. …”

Section: Convergence and Ber Performancementioning

confidence: 83%

“…where Z p m is the mutation operator over b i individual and N (0, σ 2 ) represents a Gaussian distribution with zero mean and standard deviation σ , which is related with the mutation probability p m [2,7]. The GA-MuD uses a replacement strategy called global elitism [23], where only the best P individuals from the joint population of parents and offspring are maintained for the next generation.…”

Section: Genetic Mud For S/mimo Mc-cdmamentioning

confidence: 99%

“…Moreover, mainly for E b /N 0 > 9 dB, an increase in the number of users from K = 20 to K = 32 causes a BER performance degradation. The performance of Heur-MuDs as a function of K ∈ [2,32], for E b /N 0 = 3, 6 and 9 dB, Q = 1 and 2 antennas, is shown in Fig. 4.…”

Section: Convergence and Ber Performancementioning

confidence: 99%

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S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

Abrão

Oliveira

Ciriaco

et al. 2009

Wireless Pers Commun

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This paper analyzes the complexity-performance trade-off of several heuristic near-optimum multiuser detection (MuD) approaches applied to the uplink of synchronous single/multiple-input multiple-output multicarrier code division multiple access (S/MIMO MC-CDMA) systems. Genetic algorithm (GA), short term tabu search (STTS) and reactive tabu search (RTS), simulated annealing (SA), particle swarm optimization (PSO), and 1-opt local search (1-LS) heuristic multiuser detection algorithms (Heur-MuDs) are analyzed in details, using a single-objective antenna-diversity-aided optimization approach. MonteCarlo simulations show that, after convergence, the performances reached by all nearoptimum Heur-MuDs are similar. However, the computational complexities may differ substantially, depending on the system operation conditions. Their complexities are carefully analyzed in order to obtain a general complexity-performance framework comparison and to show that unitary Hamming distance search MuD (uH-ds) approaches (1-LS, SA, RTS and STTS) reach the best convergence rates, and among them, the 1-LS-MuD provides the best trade-off between implementation complexity and bit error rate (BER) performance.

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Section: Convergence and Ber Performancementioning

confidence: 83%

Section: Genetic Mud For S/mimo Mc-cdmamentioning

confidence: 99%

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S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

Abrão

Oliveira

Ciriaco

et al. 2009

Wireless Pers Commun

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show abstract

“…In the last decade, the literature has been collecting sub-optimal solutions proposals based on iterative algorithms and heuristics, particularly evolutionary and local search, applied to inherent multiple access communication systems problems, among which we could cite the following heuristic solutions: optimal multiuser detection [12][13][14][15][16][17][18][19]; spreading sequences selection [20,21]; parameter estimation, particularly the channel coefficients estimation, delay and power users [19,22,23]; power control problem [24,25]; and resource allocation optimization [26][27][28].…”

Section: Heuristics Applied To Communication Systemsmentioning

confidence: 99%

Multidimensional Optimization-Based Heuristics Applied to Wireless Communication Systems

Ciriaco¹,

Abrão²,

Jeszensky³

2013

Search Algorithms for Engineering Optimization

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“…Outra estratégia utilizada consiste na tentativa de quantificar o número necessário de mutações que um indivíduo deve sofrer para que este resulte em um melhor conjunto de genes (ABRÃO; CIRIACO;JESZENSKY, 2004). Esta estratégia visa adaptar a matriz de desvio padrão do algoritmo EP através dos parâmetros Eb/No, efeito near-far (NFR) e função custo.…”

Section: Paisunclassified

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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Evolutionary programming with cloning and adaptive cost function applied to multi-user DS-CDMA systems

Cited by 12 publications

References 2 publications

S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

Multidimensional Optimization-Based Heuristics Applied to Wireless Communication Systems

Detecção multiusuário utilizando algoritmos heurísticos evolucionários e de busca local

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