Exploiting hidden block sparsity: Interdependent matching pursuit for cyclic feature detection
Type
Conference Object
Change log
Authors
Wang, Y
Chen, W
Wassell, IJ
Abstract
In this paper, we propose a novel Compressive Sensing (CS)-enhanced spectrum sensing approach for Cognitive Radio (CR) systems. The new framework enables cyclic feature detection with a significantly reduced sampling rate. We associate the new framework with a novel model-based greedy reconstruction algorithm: interdependent matching pursuit (IMP). For IMP, the hidden block sparsity owing to the symmetry present in the cyclic spectrum is exploited which effectively reduces the degree of freedom of problem. Compared with conventional CS with independent support selection, a remarkable spectrum reconstruction improvement is achieved by IMP.
Description
Keywords
46 Information and Computing Sciences, 4006 Communications Engineering, 40 Engineering, 4603 Computer Vision and Multimedia Computation
Journal Title
Proceedings - IEEE Global Communications Conference, GLOBECOM
Conference Name
2013 IEEE Global Communications Conference (GLOBECOM 2013)
Journal ISSN
2334-0983
2576-6813
2576-6813
Volume Title
Publisher
IEEE
Publisher DOI
Sponsorship
Engineering and Physical Sciences Research Council (EP/K033700/1)
The work of Wei Chen is supported by the State Key Laboratory of Rail Traffic Control and Safety (No. RCS2012ZT014), Beijing Jiaotong University, and the Key grant Project of Chinese Ministry of Education (No.313006).