OFDM Receiver for Fast Time-Varying Channels Using Block-Sparse Bayesian Learning

Abstract

We propose an iterative algorithm for orthogonal frequency-division multiplexing (OFDM) receivers operating over fast time-varying channels. The design relies on the assumptions that the channel response can be characterized by a few nonnegligible separable multipath components and that the temporal variation of each component gain can be well characterized with a basis expansion model (BEM) using a small number of terms. As a result, the channel estimation problem is posed as that of estimating a vector of complex coefficients that exhibits a block-sparse structure, which we solve with tools from block-sparse Bayesian learning (BSBL). Using variational Bayesian inference, we embed the channel estimator in a receiver structure that performs iterative channel and noise precision estimation, intercarrier interference (ICI) cancelation, detection, and decoding. Simulation results illustrate the superior performance of the proposed receiver over state-of-the-art receivers.