Minimum probability of error for asynchronous Gaussian multiple-access channels
IEEE Transactions on Information Theory
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Multiuser Detection
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Local optima properties and iterated local search algorithm for optimum multiuser detection problem
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Landscape properties and hybrid evolutionary algorithm for optimum multiuser detection problem
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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This paper presents a new ant colony optimization (ACO) method to solve the optimum multiuser detection (OMD) problem in direct-sequence code-division multiple-access (DS-CDMA) systems. The idea is to use artificial ants to keep track of promising areas of the search space by laying trails of pheromone which guides the search of the ACO, as a heuristic for choosing values to be assigned to variables. An effective local search is performed after each generation of the ACO to improve the quality of solutions. Simulation results show the proposed ACO multiuser detection scheme combined with local search can converge very rapidly to the (near) optimum solutions. The bit error rate (BER) performance of the proposed algorithm is close to the OMD bound for large scale DS-CDMA systems and the computational complexity is polynomial in the number of active users.