Landscape properties and hybrid evolutionary algorithm for optimum multiuser detection problem

Authors:
Shaowei Wang;Qiuping Zhu;Lishan Kang
Affiliations:
School of Electronic Information, Wuhan University, Wuhan, Hubei, P.R. China;School of Electronic Information, Wuhan University, Wuhan, Hubei, P.R. China;State Key Laboratory of Software Engineering, Wuhan University, Wuhan, Hubei, P.R. China
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Year:
2006

Citing 6
Cited 2

Minimum probability of error for asynchronous Gaussian multiple-access channels

IEEE Transactions on Information Theory
Multiuser Detection

Multiuser Detection
Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming

Journal of Heuristics
Multiuser detection in CDMA - a comparison of relaxations, exact, and heuristic search methods

IEEE Transactions on Wireless Communications
A comparison of predictive measures of problem difficulty inevolutionary algorithms

IEEE Transactions on Evolutionary Computation
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem

IEEE Transactions on Evolutionary Computation

Genetic Local Search for Optimum Multiuser Detection Problem in DS-CDMA Systems

ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
New ant colony optimization for optimum multiuser detection problem in DS-CDMA systems

ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Optimum multiuser detection (OMD) for CDMA systems is an NP-complete 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, including epistasis variance, correlation length and fitness distance correlation coefficient in different instances, explain why some random search algorithms are effective methods for the OMD problem and give hints how to design more efficient randomized search heuristic algorithms for it. Based on these results, a multi-start greedy algorithm is proposed for multiuser detection and simulation results show it can provide rather good performance for cases where other suboptimum algorithms perform poorly.