Non-parametric mitigation of periodic impulsive noise in narrowband powerline communications
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.