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NBI mitigation in DSSS communications via block sparse Bayesian learning

2019; Elsevier BV; Volume: 158; Linguagem: Inglês

10.1016/j.sigpro.2018.12.020

1872-7557

Yongshun Zhang, Xin Jia, Canbin Yin, Hongfeng Pang,

Distributed Sensor Networks and Detection Algorithms

Narrowband interference (NBI) is a common interference pattern in direct-sequence spread-spectrum (DSSS) communications. NBI mitigation is a very important issue to ensure the effectiveness of DSSS communications. However, the existing NBI-mitigation algorithms for DSSS communications are confined to a high sampling rate. In order to solve this problem, compressive sensing (CS) is applied to NBI mitigation in DSSS communications, the impact of NBI on the reconstruction of the DSSS signal after compressed sampling is analyzed, and a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate NBI in DSSS communications. An NBI-mitigation method in DSSS communications based on BSBL is proposed that uses the BSBL framework algorithm to reconstruct the NBI from the compressed signal and then cancels the interference in the time domain. After the NBI mitigation, the conventional CS algorithm is used to demodulate the DSSS signal in the compressed domain. In order to further improve the performance of the method, a filtering BSBL (FBSBL) framework is proposed using the block-sparsity feature of NBI and noise-like feature of the DSSS signal in the frequency domain. A DSSS communications NBI mitigation algorithm based on FBSBL_EM is designed. An equivalent measurement matrix is constructed use the constructed filtering matrix, which could reduce the noise and DSSS signal components in the procedure of compressed sampling. This leads to a highly accurate NBI reconstruction and further improves the NBI mitigation performance. The reported simulation results demonstrate that the proposed method is effective in cancelling the NBI in DSSS communications and significantly outperforms other conventional methods. A comprehensive analysis and comparison of the algorithm is presented and discussed. The results obtained in this study may be extended to other spread-spectrum communication systems, like the code-division multiple-access (CDMA) system.