Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm
Abstract
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.
Keywords
Compressive sensing, LS, MMSE, Channel estimation, MIMO, OFDM
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
A. Munshi and S. Unnikrishnan, "Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm," in Journal of Communications Software and Systems, vol. 17, no. 1, pp. 13-19, February 2021, doi: 10.24138/jcomss.v17i1.1084
@article{munshi2021performanceanalysis,
author = {Ami Munshi and Srija Unnikrishnan},
title = {Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm},
journal = {Journal of Communications Software and Systems},
month = {2},
year = {2021},
volume = {17},
number = {1},
pages = {13--19},
doi = {10.24138/jcomss.v17i1.1084},
url = {https://doi.org/10.24138/jcomss.v17i1.1084}
}