IEEE Global Communications Conference, Dec. 9-12, Atlanta, GA USA.

Non-parametric Mitigation of Periodic Impulsive Noise in Narrowband Powerline Communications

Jing Lin 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 - bevans@ece.utexas.edu

Paper - Slides

Smart Grid Communications Research at UT Austin

Abstract

Periodic impulsive noise synchronous to the main powerline frequency is the dominant noise component in OFDM-based narrowband (NB) powerline communications (PLC). Such noise occurs in periodic bursts, where a single burst could corrupt multiple OFDM symbols. Standardized NB PLC systems use frequency-domain interleaving (FDI) in combination with forward error correction to combat impulsive noise. Alternate designs adopt time-domain block interleaving (TDI) in which the receiver deinterleaver scatters an impulsive noise burst into short impulses over a large number OFDM symbols. In bursty impulsive noise, TDI-OFDM (FDI-OFDM) works better at high (low) SNR. In this paper, we develop non-parametric methods for periodic impulsive noise mitigation in coded TDI-OFDM systems. We exploit the sparse structure of the time-domain noise after the deinterleaver, and propose sparse Bayesian learning based algorithms that estimate and remove the noise impulses by observing the null and pilot tones of received signal and using decision feedback from the decoder. The proposed methods do not assume any statistical noise model and hence do not require any training. In simulations, the proposed methods in TDI-OFDM systems achieve up to 6 dB SNR gain over FDI-OFDM systems at typical NB PLC SNR values.

Discussion

Question 1: Does it improve the BER performance if a larger interleaver is adopted?
Answer 1: Yes. In our simulations, using a larger interleaver improves the performance, but not significantly. A larger interleavler does make the noise samples more independent. However the performance of the sparse Bayesian learning algorithm is mainly affected by the sparsity of the noise. As long as the interleaver size is multiples of the noise period, the sparsity of the deinterleaved noise is the same, regardless of the interleaver sizes.

Question 2: How did you generate the periodic impulsive noise in your simulation?
Answer 2: We used the linear periodically time-varying noise model in the IEEE P1901.2 narrowband PLC standard. We shape the noise spectrum according to the field measurements as shown in slide 2.

Question 3: In your formulation of the compressed sensing problem, is the sensing matrix random or fixed? It's not exactly compressed sensing if the sensing matrix is fixed.
Answer 3: The sensing matrix is fixed. It's the sub-DFT matrix. I think compressed sensing doesn't require the sensing matrix to be random.

One participant mentioned that a smaller interleaver could also break a long burst into several shorter bursts. Then one could exploit the block sparsity of the noise to recover it.

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Last Updated 01/03/14.