Direct sequence spread spectrum communication narrowband interference cancelation in compressed domain

Abstract

AbstractDue to the broad bandwidth nature of the direct sequence spread spectrum (DSSS) signal, the application of narrowband interference (NBI) cancelation algorithms based on the Nyquist‐Shannon sampling theory are limited by the high sampling rate. To solve the sampling and processing difficulty in the traditional DSSS communication interference cancelation method, we used the compressive sensing (CS) to reduce the sampling rate of the DSSS communication system, and further proposed a compressed domain NBI elimination method in this study. We proved that NBI with a certain bandwidth and DSSS signal are both sparse in specific dictionaries and proposed the corresponding sparse dictionary construction method. Detailed analysis of the compressed domain features of NBI and the DSSS signal demonstrated that it is possible to extract the strong NBI components from the compressed measurements. Two compressed domain NBI cancelation algorithms were proposed based on this idea. The extracted NBI components were filtered out from the compressed measurements directly in the first algorithm, and then we achieved the DSSS signal demodulation in compressed domain using the separated DSSS signal components with the orthogonal matching pursuit (OMP) algorithm. A proposed extraction filter block sparse Bayesian learning (EFBSBL) framework algorithm was utilized to estimate the NBI from the compressed measurements in the second algorithm. The estimated NBI was eliminated from the received signal, and then we recompressed the DSSS signal after NBI cancelation and realized the DSSS signal demodulation in compressed domain with the OMP algorithm. Simulation results support our theoretical analysis results and reveal that the two proposed algorithms are both effective for NBI cancelation and significant interference cancelation performance are achieved.