A Factor Graph Approach to Joint OFDM Channel Estimation and Decoding in Impulsive Noise Environments

TL;DR

This paper introduces a factor graph-based receiver for OFDM systems that jointly estimates channel, noise impulses, symbols, and bits in impulsive noise environments, significantly improving performance over existing methods.

Contribution

It presents a novel joint estimation approach using loopy belief propagation combined with GAMP and turbo decoding, achieving near-optimal performance with low complexity.

Findings

Outperforms existing receivers under impulsive noise conditions

Achieves within 1 dB of the matched-filter bound

Complexity scales as O(N log N) and is parallelizable

Abstract

We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments. Impulsive noise arises in many modern wireless and wireline communication systems, such as Wi-Fi and powerline communications, due to uncoordinated interference that is much stronger than thermal noise. We first show that the bit-error-rate optimal receiver jointly estimates the propagation channel coefficients, the noise impulses, the finite-alphabet symbols, and the unknown bits. We then propose a near-optimal yet computationally tractable approach to this joint estimation problem using loopy belief propagation. In particular, we merge the recently proposed "generalized approximate message passing" (GAMP) algorithm with the forward-backward algorithm and soft-input soft-output decoding using a "turbo" approach. Numerical results indicate that the proposed receiver drastically outperforms existing receivers under impulsive noise and comes within 1 dB of the matched-filter bound. Meanwhile, with N tones, the proposed factor-graph-based receiver has only O(N log N) complexity, and it can be parallelized.