Abstract
These nature-inspired techniques testified to be efficient maximum likelihood (ML) function optimizers. Their simple and less complex architectures make them worthy for non-deterministic polynomial (NP)-hard multi-input multi-output (MIMO) detection problem. Genetic algorithm, particle swarm optimization and ant colony optimization methods approach near optimal performance with significantly reduced computational complexity, particularly in the case of higher constellation systems and alphabet sizes with multiple transmitting antennas as compared to traditional ML detector that is computationally expensive and nonpractical to utilize. Swarm intelligence (SI) based mechanisms show their efficacy for solving MIMO detection problem as well as a promise for these heuristic algorithms to be applied in complex modulation mechanisms. One of the main contributions of the work in this chapter is to prove that SI is a useful optimization technique for classical communications issues for which these approaches were not considered very effective in the past.
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