Work

Compressive detection of direct sequence spread spectrum signals

Year: 2018

Type: article

Abstract: In spread spectrum (SS) communications, the input signal is spread over a wider bandwidth to avoid interference or interception. One of the most common SS techniques is direct sequence SS (DSSS). In this Letter, the authors propose non‐cooperative compressive detection techniques for DSSS signals. The proposed compressive detection framework allows the use of random as well as designed measurement kernels. A technique for designing compressive measurement kernels which exploit DSSS signal structure and non‐uniform usage of spreading codes is proposed. Theoretical and simulation results are provided to compare the performance of the proposed methods with their conventional counterparts.

Source: Electronics Letters

DOI: 10.1049/el.2018.6280

Authors: Feng Liu, Michael W. Marcellin, Nathan A. Goodman, Ali Bilgin

Institutions: Nankai University, University of Arizona, University of Oklahoma

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: Defense Advanced Research Projects Agency, Advanced Research Projects Agency

Award: G2237409082