Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Optimal multiband joint detection for spectrum sensing in cognitive radio networks
IEEE Transactions on Signal Processing
Sequential and cooperative sensing for multi-channel cognitive radios
IEEE Transactions on Signal Processing
Spectrum sensing measurements of pilot, energy, and collaborative detection
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Sensing-Throughput Tradeoff for Cognitive Radio Networks
IEEE Transactions on Wireless Communications
Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency
IEEE Communications Magazine
On the extraction of the channel allocation information in spectrum pooling systems
IEEE Journal on Selected Areas in Communications
HC-MAC: A Hardware-Constrained Cognitive MAC for Efficient Spectrum Management
IEEE Journal on Selected Areas in Communications
Regression methods for pricing complex American-style options
IEEE Transactions on Neural Networks
Sequential and cooperative sensing for multi-channel cognitive radios
IEEE Transactions on Signal Processing
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Sequential sensing algorithms are developed for OFDM-based hierarchical cognitive radio (CR) systems. Secondary users sense multiple subbands simultaneously for possible spectrum availabilities under hard misdetection constraints to prevent interference to the primary users. Accounting for the fact that the sensing time overhead can often be significant, a novel performance metric is introduced based on the effective achievable data rate. An optimization problem is formulated in the framework of optimal stopping problems to maximize the average effective data rate by determining the best time to stop taking samples and proceed to data transmission. A basis expansion-based suboptimal algorithm is developed to reduce the prohibitive complexity of the optimal solution. The numerical results presented verify the efficacy of the proposed approach.