NBI cancellation for smart grid communications: A block sparse Bayesian learning perspective

Abstract

A block sparse Bayesian learning (BSBL) based approach of narrowband interference (NBI) cancellation for cyclic prefixed orthogonal frequency division multiplexing based smart grid communications is proposed in this paper. The BSBL theory is firstly introduced to recover the practical block sparse NBI with a frequency offset compared with the sub-carriers. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach. A BSBL based method, estimated partitioned BSBL, is proposed for NBI recovery. The intra-block correlation is firstly considered to facilitate the recovery of block sparse NBI. Reported simulation results demonstrate that the proposed methods are effective and significantly outperform conventional counterparts.