This dissertation was presented to the Faculty of the Graduate School of The University of Texas at Austin in partial fulfillment of the requirements for the degree of

Ph.D. in Electrical Engineering


Robust Transceivers for Combating Impulsive Noise in Powerline Communications  


Jing Lin, Ph.D.E.E.

The University of Texas at Austin, May 2014


Prof. Brian L. Evans


Dissertation - Defense Slides

Smart Grid Communications Project - Interference Mitigation Project

Embedded Signal Processing Laboratory - Wireless Networking and Communications Group


Future smart grid systems will intelligently monitor and control energy flows in order to improve the efficiency and reliability of power delivery. This monitoring and control requires low-power, low-cost and highly reliable two-way communications between customers and utilities. To enable these two-way communication links, powerline communication (PLC) systems are attractive because they can be deployed over existing outdoor and indoor power lines. Power lines, however, have traditionally been designed for one-directional power delivery and remain hostile environments for communication signal propagation. In particular, non-Gaussian noise that is dominated by asynchronous impulsive noise and periodic impulsive noise, is one of the primary factors that limit the communication performance of PLC systems.

For my PhD dissertation, I propose transmitter and receiver methods to mitigate the impact of asynchronous impulsive noise and periodic impulsive noise, respectively, on PLC systems. The methods exploit sparsity and/or cyclostationarity of the noise in both time and frequency domains, and require no or minor training overhead prior to data transmission. Compared to conventional PLC systems, the proposed transceivers achieve dramatic improvement (up to 1000x) in coded bit error rates in simulations, while maintaining similar throughput.

Keywords: orthogonal frequency division multiplexing (OFDM), interference modeling and mitigation, smart grid communications, denoising, sparse Bayesian learning, time-frequency modulation diversity

For more information contact: Jing Lin <>