Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning

TL;DR

This paper introduces sparse Bayesian learning-based algorithms for impulsive noise mitigation in powerline communication OFDM systems, achieving significant SNR improvements without training overhead.

Contribution

It models impulsive noise as a sparse vector and develops three iterative algorithms for noise estimation and mitigation without prior training.

Findings

Up to 9 dB SNR gain in coded systems

Up to 10 dB SNR gain in uncoded systems

Effective noise mitigation without training overhead

Abstract

Additive asynchronous and cyclostationary impulsive noise limits communication performance in OFDM powerline communication (PLC) systems. Conventional OFDM receivers assume additive white Gaussian noise and hence experience degradation in communication performance in impulsive noise. Alternate designs assume a parametric statistical model of impulsive noise and use the model parameters in mitigating impulsive noise. These receivers require overhead in training and parameter estimation, and degrade due to model and parameter mismatch, especially in highly dynamic environments. In this paper, we model impulsive noise as a sparse vector in the time domain without any other assumptions, and apply sparse Bayesian learning methods for estimation and mitigation without training. We propose three iterative algorithms with different complexity vs. performance trade-offs: (1) we utilize the noise projection onto null and pilot tones to estimate and subtract the noise impulses; (2) we add the information in the data tones to perform joint noise estimation and OFDM detection; (3) we embed our algorithm into a decision feedback structure to further enhance the performance of coded systems. When compared to conventional OFDM PLC receivers, the proposed receivers achieve SNR gains of up to 9 dB in coded and 10 dB in uncoded systems in the presence of impulsive noise.