Efficient algorithm with lognormal distributions for overloaded MIMO wireless system

Due to outstanding search strength and well organized steps, genetic algorithm (GA) has gained high interest in the field of overloaded multiple-input/multiple-output (MIMO) wireless communications system. For overloaded MIMO system employing spatial multiplexing transmission we evaluate the performance and complexity of genetic algorithm (GA)-based detection, against the maximum-likelihood (ML) approach. We consider transmit-correlated fading channels with realistic Laplacian power azimuth spectrum. The values of the azimuth spread (AS) and Rician K-factor are set by the means of the lognormal distributions obtained from WINNER II channel models. First, we confirm that for constant complexity, GA performance is same for different combinations of GA parameters. Then, we compare the GA performance with ML in several WINNER II scenarios and channel matrix means. Finally, we compare the complexity of GA with ML. We find that GA perform similarly with ML throughout the SNR points for different scenarios and different deterministic rank. We also find that for achieving performance, GA complexity is much less than ML and thus, is an advantage in field programmable gate array (FPGA) design.