Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm

Published online: Feb 8, 2021 Full Text: PDF (1.06 MiB) DOI: 10.24138/jcomss.v17i1.1084
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Ami Munshi, Srija Unnikrishnan


In this paper, we have developed and implemented Minimum Mean Square Channel Estimation with Compressive Sensing (MMSE-CS) algorithm in MIMO-OFDM systems. The performance of this algorithm is analyzed by comparing it with Least Square channel estimation with compressive sensing (LS-CS), Least Square (LS) and Minimum Mean Square Estimation (MMSE) algorithms. It is observed that the performance of MMSE-CS in terms of Bit Error Rate (BER) metric is definitely better than LS-CS and LS algorithms and it is at par with MMSE algorithm. Moreover the role of compressive sensing theory in channel estimation is accentuated by the fact that in MMSE-CS algorithm only a very small number of channel coefficients are sensed to recreate the transmitted data faithfully as compared to MMSE algorithm.


Compressive sensing, LS, MMSE, Channel estimation, MIMO, OFDM
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