MMSE Version Compressed Sensing Algorithms for Sparse Channel Estimation in MIMO-ACO-OFDM System

Vishwaraj B Manur and Dr. Layak Ali

In this paper, minimum mean squared optimized underdetermined system equations based compressed sensing (CS) algorithms are proposed for multiple-input multiple-output (MIMO) asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) system. This enhanced method to solve an underdetermined linear equation is an essential approach for channel estimation technique for a digital communication system. The significant advantage of this technique is considering background noise. Compressed Sensing algorithms like Sparsity adaptive matching pursuit (SaMP), Adaptive step-size-SaMP (AS-SaMP) and Dynamic Step-size-SaMP (DSS-SaMP) are implemented using the MMSE technique on MIMO-ACO-OFDM system. The paper is carried with simulation outcomes that presents the performance of the proposed method in terms of Bit Error Rate (BER), Symbol Error Rate (SER), Mean Square Error (MSE) and Computational cost. It is shown that the MMSE solution CS algorithm shows better than the widely used least square (LS) based CS algorithms.

Volume 11 | Issue 10

Pages: 156-166

DOI: 10.5373/JARDCS/V11I10/20193019