Probability-based pilot allocation for MIMO relay Distributed compressed sensing channel estimation

Multiple-Input Multiple-Output (MIMO) relay communication systems are used as an efficient system in spectral efficiency and power allocation view point. In these systems, some of the facilities need channel state information (CSI). Besides, new estimation methods based on compressed sensing (CS) are well known for their spectral efficiency and accuracy. In this paper, we have used a Distributed CS-based channel estimation method to improve the accuracy and spectral efficiency of channel estimation for MIMO-Orthogonal Frequency Division Multiplexing relay network. Specifically, using Least Squares (LS) estimation increases the accuracy of well-known Compressive Sampling Matching Pursuit (CoSaMP) algorithm and proposes Block-verified CoSaMP (B-vCoSaMP). To improve the accuracy of estimation, we are encountered with a combinatorial optimization which is dealt with probability-based approaches in this paper. More particularly, three probability-based optimization methods have been proposed to optimize the mutual coherence of measurement matrix called Sequential Cross-Entropy (SCE), Extended Estimating of Distribution Algorithm (EEDA), and Parallel Cross-Entropy (PCE). All these methods are based on sampling from a Probability Density Function (PDF) which is updated in each iteration using elite samples of the population. The simulation results represent the accuracy and speed of the proposed methods, and the comparison is expressed as well.