Genetic Algorithm Based RNN Structure for Rayleigh Fading MIMO Channel Estimation

Abstract

Multi-Input Multi-Output (MIMO) wireless communication system is an emerging area which offers substantial advantages for achieving high data rate with cost effective design. The spectral efficiency and reliability of MIMO systems greatly depends on the assumption that the transmitter and/or the receiver have perfect knowledge of channel state information (CSI). Hence, it is required to predict the CSI at the receiver and send the updated CSI back to the transmitter. In this paper particularly the Frequency-Flat Rayleigh i.i.d MIMO channel is considered, where as the Rayleigh fading channel provides complex and random coefficients. These complex channel parameters are estimated by using split complex real-time recurrent learning (SCRTRL) and fully complex real-time recurrent learning (FCRTRL) algorithm based recurrent neural network (RNN) structure with complex weights. The RNN with SCRTRL and RNN with FCRTRL algorithm produced a premature convergence which is overcome by the proposed genetic algorithm (GA) based learning process.