Genetic Algorithm-Assisted Data Detection for OFDM Systems under Rapidly Time-Varying Channels

In this paper, the challenging problem of data detection for orthogonal frequency division multiplexing (OFDM) systems under rapidly time-varying channels is considered. Time-varying channels within a multicarrier symbol will lead to a loss of sub-channel orthogonality, and result in inter-channel interference (ICI) and an irreducible error floor in traditional receivers. The genetic algorithm (GA) assisted data detection for the single input single output (SISO) and single input multiple output (SIMO) OFDM systems are presented, respectively. Theoretical analysis and simulations show that the proposed algorithms are valid compared with minimum mean square error (MMSE) and minimum mean square error successive interference cancellation (MMSE-SIC). The GA-assisted data detection for OFDM systems under rapidly time-varying channels is flexible to provide tradeoff between performance and complexity. To accelerate the convergence of GA for SIMO OFDM, the individuals of GA are selected based on the concept of Pareto optimality.