Comparison of Compressed Sensing algorithms for MIMO-OFDM Systems

Abstract

Estimation of the channel accurately in a MIMO-OFDM system is crucial to guarantee the performance of the system. In this paper the Subspace Pursuit (SP), Orthogonal Matching Pursuit (OMP), Compressed Sampling Matching Pursuit(CoSaMP) and Distributed Compressed Sensing(DCS) algorithms combined with Minimum Mean Square Error(MMSE) and Least Mean Square (LMS) tools are used to estimate the channel coefficients for MIMO-OFDM system. These algorithms are used for the channel estimation in MIMO-OFDM system to develop the joint sparsity of the MIMO channel. Simulation results shows that SP, OMP, CoSaMP and DCS algorithms combined with MMSE and LMS tools provides significant reduction in Normalized Mean Square Error (NMSE) when compared to SP ,CoSaMP, DCS algorithms with Least Square (LS) tool and also the conventional channel estimation methods such as LS, MMSE and LMS. Moreover DCS combined with LMS tool performs better than SP and OMP techniques with LMS tool with less computational time complexity.