  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.
