Comparative Analysis of Sparse and Conventional Channel Estimation Algorithms in Wireless MIMO Channel Models

In the present scenario, Multiple Input Multiple Output (MIMO) systems are in use because of the high data capacity and efficiency of the system. There is a special importance on correct channel estimation to in getting the highest performance out of these systems. Seeking efficient solutions to this problem, various practitioners have resorted to the application of sparse signal processing that makes use of the sparsity character of wireless channels. This paper focuses on the scope of sparse signal processing in the field of wireless communications with special focus on this study on the BP and OMP algorithms while comparing them to LS and ZF estimation. The simulations are conducted in several surroundings including urban and rural as well as in free space. A clear impact of sparse signal processing in alleviating the training requirements and the computing cost has been achieved with BP considered to be the most effective tool for compressed sensing. In this regard, the effectiveness of sparse approaches in MIMO channel estimation is anticipated.