Comparison of Compressed Sensing MMSE Channel estimation with conventional LS and MMSE

Multiple-input multiple-output (MIMO) Orthogonal frequency division multiplexing (OFDM) technology is becoming mature in wireless communication systems. It has led to the third and fourth generation wireless systems which have been providing good range, reliability and higher data rates. To extract the benefits provided by the MIMO systems, it is necessary to estimate the noisy channel through which the information is transmitted. To acquire the channel parameters, we propose compressed sensing (CS) method based on priority which considers the channel with few dominant taps, i.e., sparse nature of the channel is exploited. In this method, priority is taken into consideration, where all user equipments (UE) are not of equivalent significance and more inclination is given to the user with high need. When the number of UEs is more than the available channels, rating is given to UEs based on some heuristics. With this proposed method, we can obtain better performance in-terms of BER and SNR when compared to the conventional methods such as least square (LS) and minimum mean square error (MMSE) techniques. Simulation results show the comparison between the proposed method and the conventional method which proves that the compressed sensing technique outperforms the LS and MMSE techniques.