Population Based Pilot Allocation for MIMO Relay Compressed Channel Estimation

Relay communication networks increase the spectral efficiency and remote availability with the support of Multiple-Input Multiple-Output (MIMO). Channel estimation in the MIMO relay communication networks is mandatory to enhance the represented capabilities. Hence, developing channel estimation approaches in MIMO relay communication networks is one of the noteworthy aspects of recent researches. Besides, compressed sensing (CS) as a spectral efficient signal processing approach is attractive for estimators. Hence, in this paper, we have used CS based channel estimation method to improve the accuracy and spectral efficiency of channel estimation for MIMO-OFDM relay network. As represented, having a proper pilot allocation will increase the efficiency, and to this end, we are encountered with a combinatorial optimization which is dealt with Geometrical Differential Evolution (GDE) and Population-Based Incremental Learning (PBIL) in this paper. These are population-based evolutionary optimization. GDE is utilized for its speed and accuracy of convergence. Besides, PBIL is used for its probability nature which makes it capable of escaping from local minimum trapping. The illustrated result of simulations represents the superiority of the proposed methods in defining the pilot allocation sequences.