Exploiting Hidden Block Sparsity: Interdependent Matching Pursuit for Cyclic Feature Detection
Global Communications Conference (GLOBECOM)
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Wang, Y., Chen, W., & Wassell, I. (2013). Exploiting Hidden Block Sparsity: Interdependent Matching Pursuit for Cyclic Feature Detection. Global Communications Conference (GLOBECOM), 1119-1124. https://doi.org/10.1109/GLOCOM.2013.6831224
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.
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).
External DOI: https://doi.org/10.1109/GLOCOM.2013.6831224
This record's URL: https://www.repository.cam.ac.uk/handle/1810/251287