We propose an iterative algorithm for OFDM receivers operating over fast time-varying channels. The design relies on the assumptions that the channel response can be characterized by a few non-negligible separable multipath components, and the temporal variation of each component gain can be well characterized with a basis expansion model 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. Using variational Bayesian inference, we embed the channel estimator in a receiver structure that performs iterative channel and noise precision estimation, intercarrier interference cancellation, detection and decoding. Simulation results illustrate the superior performance of the proposed receiver over state-of-art receivers.
Barbu, O-E., Manchón, C. N., Rom, C., Balercia, T., & Fleury, B. H. (2016). OFDM receiver for fast time-varying channels using block-sparse Bayesian learning. I E E E Transactions on Vehicular Technology, 65(12), 10053-10057. https://doi.org/10.1109/TVT.2016.2554611