An introduction to genetic algorithms
An introduction to genetic algorithms
Multiuser Detection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Spread Spectrum CDMA Systems for Wireless Communications
Spread Spectrum CDMA Systems for Wireless Communications
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A New Modified Genetic Algorithm for Multiuser Detection in DS/CDMA Systems
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Robust joint channel and noise estimation in Bayesian blind equalizers
Signal Processing
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
IEEE Journal on Selected Areas in Communications
Journal of VLSI Signal Processing Systems
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This paper presents a low-complexity genetic algorithm (µ-GA) for multiuser detection. The probabilities of mutation and crossover of the algorithm are on-line tuned up based on the analysis of the individuals' fitness entropy, constituting, this way, a brand new method to control and adjust the diversity of the population. This detector has an extremely low computational load and offers an interesting alternative to previous suboptimal algorithms whose performance is frequently subject to the near-far problem and multiple access interference degradations. Its performance is compared with that of standard GA-based detectors, as well as traditional multiuser detectors, such as the matched filter, the decorrelator and the MMSE detectors.