  We propose a factor-graph-based approach to joint
channel-estimation-and-decoding (JCED) 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" (GAMP) algorithm recently developed
in the context of compressed sensing, and show that it can be successfully
coupled with soft-input soft-output decoding, as well as hidden Markov
inference, through the standard sum-product framework. For N subcarriers and
any channel length L < N, the resulting JCED-GAMP scheme has a computational
complexity of only O(N log2 N + N|S|), where |S| is the constellation size.
Numerical experiments using IEEE 802.15.4a channels show that our scheme yields
BER performance within 1 dB of the known-channel bound and 3-4 dB better than
soft equalization based on LMMSE and LASSO.
