BLIND CHANNEL ESTIMATION FOR OFDM SYSTEMS USING SPARSE ADAPTIVE SIGNAL PROCESSING ALGORITHMS

In the equalization process by eliminating channel interference the transmitted information can be restored effectively. In blind and semi-blind equalization only the transmitter statistics are known but not the channel or receiver. The performance of least mean-square (LMS) and least mean-fourth (LMF) algorithms for adaptive blind channel estimation and equalization is very impressive. Mostly the wireless channel vectors are sparse in nature. Two different types of sparse LMF algorithms with different sparse penalties to obtain improved results in the scenario of blind equalization as compared to variants of LMS algorithm are proposed in this paper. Multi-antenna systems like multiple input and multiple output (MIMO), massive MIMO, and millimeter wave are being widely used in most wireless applications and all cellular systems but still have the shortcomings of high path loss. Sparse techniques have very vast and efficient use in case of above systems as they take the advantage of channel sparsity and show better results when the channel statistics are not known or partially known. Orthogonal frequency division multiple access (OFDMA) along with channel estimation is utilized comprehensively in the above-mentioned communication systems and its sensitivity to frequency selective fading is less, improving the system performance and bit error rate. In this paper it is also reported how sparse technique can be useful in reducing the bit error rate in OFDM. © 2022 by Begell House, Inc.