Performance of Compressive Sensing Algorithms over Time Varying Frequency Selective Channel

Mobility environment leads to time varying frequency selective channel. Orthogonal Frequency Division Multiplexing (OFDM) be combined with Multiple Input Multiple Output (MIMO) system to increases the system capacity on time varying channel. Time varying frequency selective MIMO channel estimation demands huge number of training signals since the system has huge number of channel coefficients. In practical, most of the channels are composed of a few dominant taps and large part of taps is zero or approximately zero. They are often called sparse multi-path channels. By exploiting the coherent sparsity of the multipath fading channels, Compressive Sensing (CS) based channel estimation method provides better estimation of sparse channel than the conventional estimation methods which are suitable for rich channels and also greatly decrease the pilot overhead burden. This paper evaluates the performance of CS based channel estimation methods for MIMO-OFDM systems over time varying frequency selective channel.