Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Time series: data analysis and theory
Time series: data analysis and theory
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Convex Optimization
On Model Selection Consistency of Lasso
The Journal of Machine Learning Research
Spectrum sensing in cognitive radio networks: the cooperation-processing tradeoff
Wireless Communications & Mobile Computing - Cognitive Radio, Software Defined Radio And Adaptive Wireless Systems
Motion-compensated techniques for enhancement of low-quality compressed videos
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Optimal multiband joint detection for spectrum sensing in cognitive radio networks
IEEE Transactions on Signal Processing
Spline-based spectrum cartography for cognitive radios
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
Spatiotemporal Sensing in Cognitive Radio Networks
IEEE Journal on Selected Areas in Communications
Spline-based spectrum cartography for cognitive radios
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Decentralized sparse signal recovery for compressive sleeping wireless sensor networks
IEEE Transactions on Signal Processing
Distributed sparse linear regression
IEEE Transactions on Signal Processing
Sparsity-aware estimation of CDMA system parameters
EURASIP Journal on Advances in Signal Processing - Special issue on advanced equalization techniques for wireless communications
Cooperative spectrum sensing in cognitive radio networks: A survey
Physical Communication
Robustness against Byzantine Failures in Distributed Spectrum Sensing
Computer Communications
Compressed sensing construction of spectrum map for routing in cognitive radio networks
Wireless Communications & Mobile Computing
ML aided context feature extraction for cognitive radio
Computer Networks: The International Journal of Computer and Telecommunications Networking
Wireless Personal Communications: An International Journal
Hi-index | 35.69 |
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.