Minimum probability of error for asynchronous Gaussian multiple-access channels
IEEE Transactions on Information Theory
Multiuser Detection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Immune-Endocrine Genetic Algorithm for Multi-user Detector Problem
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
Hybrid intelligence techniques for multiuser detection in DS-CDMA UWB systems
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Simulated Annealing-Genetic Algorithm and Its Application in CDMA Multi-user Detection
ICINIS '10 Proceedings of the 2010 Third International Conference on Intelligent Networks and Intelligent Systems
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
A robust variable step-size LMS-type algorithm: analysis andsimulations
IEEE Transactions on Signal Processing
Probability of error in MMSE multiuser detection
IEEE Transactions on Information Theory
Linear multiuser detectors for synchronous code-division multiple-access channels
IEEE Transactions on Information Theory
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In this paper, we present an efficient evolutionary algorithm for the multi-user detection (MUD) problem in direct sequence-code division multiple access (DS-CDMA) communication systems. The optimum detector for MUD is the maximum likelihood (ML) detector, but its complexity is very high and involves an exhaustive search to reach the best fitness of transmitted and received data. Thus, there has been considerable interest in suboptimal multiuser detectors with less complexity and reasonable performance. The proposed algorithm is a combination of adaptive LMS Algorithm and modified genetic algorithm (GA). Indeed the LMS algorithm provides a good initial response for GA, and GA will be applied for this response to reach the best answer. The proposed GA reduces the dimension of the search space and provides a suitable framework for future extension to other optimization algorithms. Our algorithm is compared to ML detector, Matched Filter (MF) detector, conventional detector with GA; and Adaptive LMS detector which have been used for MUD in DS-CDMA. Simulation results show that the performance of this algorithm is close to the optimal detector with very low complexity, and it works better in comparison to other algorithms.