A message-passing receiver for BICM-OFDM over unknown clustered-sparse channels

We propose a factor-graph-based approach to joint channel-estimation and-decoding of bit-interleaved coded orthogonal frequency division multiplexing (BICM-OFDM). In contrast to existing designs, ours is capable of exploiting not only sparsity in sampled channel taps but also clustering among the large taps, behaviors which are known to manifest at larger communication bandwidths. In order to exploit these channel-tap structures, we adopt a two-state Gaussian mixture prior in conjunction with a Markov model on the hidden state. For loopy belief propagation, we exploit a "generalized approximate message passing" algorithm recently developed in the context of compressed sensing, and show that it can be success fully coupled with soft-input soft-output decoding, as well as hidden Markov inference. For N subcarriers and M bits per subcarrier (and any channel length L <; N), our scheme has a computational complexity of only O(N log₂ N + N2^M). Numerical experiments using IEEE 802.15.4a channels show that our scheme yields BER performance within 1 dB of the known-channel bound and 4 dB better than decoupled channel-estimation-and-decoding via LASSO.