IEEE Journal on Selected Areas in Communications, vol. 31, no. 7, Jul. 2013, pp. 1172-1183.

Impulsive Noise Mitigation in Powerline Communications using Sparse Bayesian Learning

Jing Lin. Marcel Nassar and Brian L. Evans

Department of Electrical and Computer Engineering, Wireless Networking and Communications Group, The University of Texas at Austin, Austin, TX 78712 USA
jing.lin08@gmail.com - nassar.marcel@mail.utexas.edu - bevans@ece.utexas.edu

Paper Draft

Smart Grid Communications Research at UT Austin

Abstract

Additive asynchronous and cyclostationary impulsive noise limit 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 date 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.

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Last Updated 01/03/14.