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Task 1836.063, Prof. Brian L. Evans, Univ. of Texas at Austin

Task 1836.063, Prof. Brian L. Evans, Univ. of Texas at Austin. Powerline Communications for Enabling Smart Grid Applications. Task Description: Increase powerline communication (PLC) data rate for better monitoring/controlling applications for residential and commercial energy uses.

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Task 1836.063, Prof. Brian L. Evans, Univ. of Texas at Austin

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  1. Task 1836.063, Prof. Brian L. Evans, Univ. of Texas at Austin Powerline Communications for Enabling Smart Grid Applications Task Description: Increase powerline communication (PLC) data rate for better monitoring/controlling applications for residential and commercial energy uses. Anticipated Results: Adaptive methods and real-time prototypes to increase bit-rates in PLC networks Co-PIs/Collaborators: Prof. Brian, L. Evans, The University of Texas at Austin Current Students/Post-docsCurrent Status Mr. Marcel Nassar Ph. D (expected graduation in May 2012) Mr. YousofMortazavi Ph. D (expected graduation in May 2013) Ms. Jing Lin Ph. D (expected graduation in May 2014) Funding for three years: $300k Industrial Liaisons: Dr. AnandDabak(Texas Instruments) , Mr. Leo Dehner (Freescale), Mr. Michael Dow (Freescale) and Mr. Frank Liu (IBM)

  2. Task 1836.063, Prof. Brian L. Evans, Univ. of Texas at Austin Powerline Communications for Enabling Smart Grid Applications Accomplishments • A 1x1 PLC testbed: implemented an analog front-end interface and a software package. • Noise modeling: developed a physical-statistical model of asynchronous impulsive noise in PLC networks. • Noise mitigation: proposed two non-parametric algorithms for impulsive noise mitigation in OFDM PLC systems. Fig. 2: Symbol error rate (SER) vs. SNR for the conventional OFDM receiver (no cancellation), our sparse Bayesian learning (SBL) methods, and a reference algorithm using compressive sensing and least squares estimation (CS+LS). Fig. 1: A1x1 OFDM PLC prototype, which includes software running transceiver algorithms on National Instruments (NI) embedded computers, and an analog front-end (AFE) interface connecting the NI hardware with Texas Instruments PLC AFE. Table 1: Impulsive noise models in different PLC networks. Publications [1] M. Nassar, K. Gulati, Y. Mortazaviand B. L. Evans, “Statistical Modeling of Asynchronous Impulsive Noise in Powerline Communication Networks'', Proc.IEEE Int. Global Communications Conf., 2011. [2] J. Lin, M. Nassar and B. L. Evans, “Non-Parametric Impulsive Noise Mitigation in OFDM Systems Using Sparse Bayesian Learning'', Proc.IEEE Int. Global Communications Conf., 2011.

  3. Task 1836.063, Prof. Brian L. Evans, Univ. of Texas at Austin Powerline Communications for Enabling Smart Grid Applications • State of Art: • Multicarrier single-channel communication over medium-voltage (MV) and low-voltage (LV) power lines provides low data rates. • Objectives: • Enable higher data rates in the “last mile” of powerline communications (PLC). Implement a PLC testbedto quantify design tradeoffs. • Novelty: • Enhance data rate and robustness in PLC by interference modeling and mitigation, and multichannel (MIMO) transmission. 1x1 PowerlineCommunication Testbed. National Instruments embedded computers process transceiver algorithms. Texas Instruments PLC analog front-end enables half-duplex operation • Accomplishments: • Implemented a 1x1 PLC testbed. • Developed a canonical physical-statistical model of asynchronous impulsive noise in PLC networks. • Proposed two non-parametric algorithms for impulsive noise mitigation in OFDM PLC systems. • Important Publications: • [1] M. Nassaret al., IEEE Globecom2011. • [2] J. Lin et al., IEEE Globecom2011. Simulation shows that the proposed impulsive noise mitigation algorithms based on sparse Bayesian learning (SBL) achieve 5-10 dB SNR gain over conventional OFDM receivers (No cancellation).

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