A Sparse Based Adaptive Channel Estimator For Wireless Channel

These days with the growth of wireless communication strategies focus on enhanced data rates, system capacity and service quality have also gained a lot of interest. To encounter these issues, proper channel modeling and precise assessment of state information of the channel are imperative for the design of the communication system. Again, factors like channel fading as well as Doppler shifts arising due to user mobility increase the complexity in the channel parameter estimation problem. Hence this paper presents an efficient channel estimator for a MIMO-OFDM system based on sparse modeling for estimation of wireless channel parameters. It uses a sigmoid-based least-mean mixed norm approach to estimate Jake's outdoor channel model. The proposed channel estimator performs better as compared to other algorithms that prove its efficacy. The performance of the system is evaluated by the Mean Square Error (MSE) and Bit Error Rate (BER) level.