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
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
Smart Grid Communications Research at UT Austin
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
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
- we utilize the noise projection onto null and pilot tones to estimate and
subtract the noise impulses;
- we add the information in the date tones to perform joint noise estimation
and OFDM detection;
- we embed our algorithm into a decision feedback structure to further enhance
the performance of coded systems.
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