Genetic Algorithm Parameter Requirements for Detection in MIMO Fading Channels

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Abstract

For multiple-input/multiple-output (MIMO) wireless communications systems employing spatial multiplexing transmission, we evaluate by simulation the parameter (i.e., population size, generation number) value requirements for detection based on genetic algorithm (GA) at the receiver. We assume transmit-correlated Rayleigh or Rician fading with realistic Laplacian power azimuth spectrum as well as azimuth spread (AS) and Rician K-factor selected according to the measurement-based WINNER II channel model, for several relevant scenarios. We first confirm that a GA whose parameters are suitably set converges to maximum-likelihood (ML)-like performance. Then, we study the effects of the number of antennas, modulation constellation size, scenario (i.e., AS and K values), and rank of the deterministic component of the channel matrix on parameter values required for GA in order to converge to ML-like performance. We find that, for poorer channel fading conditions, i.e., poorer achievable MIMO detection performance, the GA converges faster and for smaller population size. Therefore, selecting the GA parameter values according to the channel features may help achieve ML-like performance for lower complexity.

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