Sensor Selection and Optimal Energy Detection Threshold for Efficient Cooperative Spectrum Sensing

• Published in: IEEE transactions on vehicular technology Vol. 64; no. 4; pp. 1565 - 1577
• Main Authors: Ebrahimzadeh, Ataollah, Najimi, Maryam, Andargoli, Seyed Mehdi Hosseini, Fallahi, Afshin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cognitive radio; Detection; Detectors; Energy conservation; Energy consumption; Energy efficiency; Energy management; Fading; Mathematical analysis; Networks; Optimization; Sensors; Signal to noise ratio; Thresholds; Cognitive wireless sensor networks; detection threshold; detection probability and false alarm probability; cooperative spectrum sensing
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2014.2331681

In this paper, an energy-efficient scheme is proposed for cooperative spectrum sensing in cognitive sensor networks. In our scheme, we introduce a technique to select the sensing nodes and to set energy detection threshold so that energy saving can be accomplished in the nodes. Our objective is to minimize the energy consumed in distributed sensing subject to constraints on global probability of detection and probability of false alarm by determining the detection threshold and selection of the sensing nodes. The energy detector is applied to detect the primary-user activity for the sake of simplicity. At first, it is assumed that the instantaneous signal-to-noise ratio (SNR) for each node is known. Then, the optimal conditions are obtained, and a closed-form equation is expressed to determine the priority of nodes for spectrum sensing, as well as the optimum detection threshold. This problem is also solved when the average SNRs of sensors are available according to real situations. To achieve more energy savings, the problem of joint sensing node selection, detection threshold, and decision node selection is analyzed, and an efficient solution is extracted based on the convex optimization framework. Simulation results show that the proposed algorithms lead to significant energy savings in cognitive sensor networks.