Bayesian sparse channel estimation and tracking

It is recognized that wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order to exploit this sparse structure and make it more feasible for practical applications, this article investigates sparse channel estimation for OFDM from the perspective of Bayesian learning. Under the Bayesian learning framework, the large-scale compressed sensing problem, as well as large time delay for the estimation of the doubly selective channel over multiple consecutive OFDM symbols, can be avoided. In addition, the time-varying channel can be tracked naturally by iteratively updating the maximum likelihood function of the channel impulse response. Simulation studies show a significant improvement in channel estimation and promising performance for channel tracking with reduced the number of pilot tones.