Joint data recovery and NBI mitigation in OFDM systems using CS framework

Abstract

In this paper, a novel approach is proposed to jointly recover the transmitted data and mitigate narrow-band interference (NBI) in OFDM systems using compressive sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. The main idea behind our approach is to represent the transmitted data and the NBI signal as a sparse vector and then solve a joint optimization problem. Therefore, the modulated signal using popular modulation schemes such as BPSK, QPSK, and M-PAM is represented by binary representation using some dictionaries. NBI is a sparse signal in the frequency domain, however, frequency-grid-mismatch destroys the sparsity of NBI at the receiver. We propose a structured-dictionary-mismatch formulation to estimate the frequency-grid-mismatch and recover the sparsity of the NBI in the frequency domain. The optimization problem is formulated as a combined re-weighted l 1 and l 2,1 norms. The solution aims to recover the transmitted data and NBI jointly.