Anti-sampling-distortion compressive wideband spectrum sensing for Cognitive Radio

Authors:
Yipeng Liu;Qun Wan
Affiliations:
Department of Electronic Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.;Department of Electronic Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
Venue:
International Journal of Mobile Communications
Year:
2011

Citing 12
Cited 0

Discrete-time signal processing

Discrete-time signal processing
Atomic Decomposition by Basis Pursuit

SIAM Journal on Scientific Computing
Convex Optimization

Convex Optimization
Uncertainty-aware Wireless Sensor Networks

International Journal of Mobile Communications
Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit

Foundations of Computational Mathematics
Peak-to-average power ratio reduction technique for MIMO/OFDM systems

International Journal of Mobile Communications
Compressive wide-band spectrum sensing

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A survey of spectrum sensing algorithms for cognitive radio applications

IEEE Communications Surveys & Tutorials
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

IEEE Transactions on Information Theory
Compressed sensing

IEEE Transactions on Information Theory
Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs

IEEE Communications Magazine
Cognitive radio: brain-empowered wireless communications

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, Compressed Sensing (CS) is introduced to wideband spectrum sensing for cognitive radio to deal with the too high sampling rate challenge. The standard sparse signal recovery of CS does not consider the distortion in the Analogue-to-Information Converter (AIC). Thus we define a new sparse signal model with bounded sampling error, and an Anti-Sampling-Distortion Constraint (ASDC) is deduced. We combine the L1-norm based sparse constraint with the ASDC to get a novel robust sparse signal recovery operator. Numerical simulations demonstrate that the proposed method outperforms standard sparse wideband spectrum sensing in accuracy, denoising ability, etc.