A Message-Passing Receiver for BICM-OFDM over Unknown Clustered-Sparse Channels

TL;DR

This paper introduces a novel message-passing receiver for BICM-OFDM that exploits clustered-sparse channel structures using a Gaussian mixture prior and GAMP algorithm, achieving near-optimal performance with low complexity.

Contribution

It presents a new joint channel-estimation-and-decoding scheme that leverages channel clustering and sparsity, integrating GAMP with Markov models for improved efficiency and accuracy.

Findings

Achieves BER within 1 dB of known-channel bounds.

Outperforms LMMSE and LASSO-based soft equalization by 3-4 dB.

Computational complexity is O(N log2 N + N|S|).

Abstract

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.