Hybrid variational Bayesian channel estimation, demodulation and decoding for OFDM under sparse multipath channels

In this paper, a novel hybrid OFDM receiver based on sparse variational Bayesian (VB) learning and soft-input soft-output decoding is proposed. By noticing that a key part of the inference problem approximated by VB (message-passing) methods may be inferred exactly, an genetic interfacing structure is proposed allowing the use of virtually all existing soft-input soft-output decoding schemes. Therefore the tasks of joint channel state estimation, demodulation and decoding are iteratively solved under the proposed hybrid variational Bayesian framework. The bit-interleaved coded modulation with Turbo coding is used to demonstrate the potential performance of the proposed structure. Very promising results in performance are observed in computer simulated experiments.