Distributed Spectrum Sensing for Cognitive Radio Networks by Exploiting Sparsity

• Published in: IEEE transactions on signal processing Vol. 58; no. 3; pp. 1847 - 1862
• Main Authors: Bazerque, J.A., Giannakis, G.B.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-03-2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive systems; Algorithms; Applied sciences; Chromium; Cognitive radio; Cognitive radios; Collaboration; compressive sampling; Computer simulation; cooperative systems; Density; Detection; Detection, estimation, filtering, equalization, prediction; distributed estimation; Exact sciences and technology; Frequency estimation; Government; Information, signal and communications theory; Interference; Mathematical models; Miscellaneous; Narrowband; Networks; parallel network processing; Radio; Radio networks; Radiofrequency identification; Sampling methods; Sampling, quantization; sensing; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; sparse models; Sparsity; spectral analysis; Studies; Telecommunications and information theory; Wireless sensor networks; Parameter estimation; parallel network processing; Wireless telecommunication; Iterative method; spectral analysis; Implementation; Cognitive radios; Model matching; Frequency band; Least squares method; Frequency spectrum; System identification; cooperative systems; Sampling; Localization; distributed estimation; sensing; compressive sampling; Power spectral density; On-line systems; Regression analysis; Narrow band; Frequency reuse; Joint detection; Spatial frequency; Simulation; Signal processing; Signal analysis; sparse models; Radio communication; Software radio
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2009.2038417

A cooperative approach to the sensing task of wireless cognitive radio (CR) networks is introduced based on a basis expansion model of the power spectral density (PSD) map in space and frequency. Joint estimation of the model parameters enables identification of the (un)used frequency bands at arbitrary locations, and thus facilitates spatial frequency reuse. The novel scheme capitalizes on two forms of sparsity: the first one introduced by the narrow-band nature of transmit-PSDs relative to the broad swaths of usable spectrum; and the second one emerging from sparsely located active radios in the operational space. An estimator of the model coefficients is developed based on the Lasso algorithm to exploit these forms of sparsity and reveal the unknown positions of transmitting CRs. The resultant scheme can be implemented via distributed online iterations, which solve quadratic programs locally (one per radio), and are adaptive to changes in the system. Simulations corroborate that exploiting sparsity in CR sensing reduces spatial and frequency spectrum leakage by 15 dB relative to least-squares (LS) alternatives.