Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
IEEE Spectrum
Convex Optimization
Optimal multiband joint detection for spectrum sensing in cognitive radio networks
IEEE Transactions on Signal Processing
Maximum channel throughput via cooperative spectrum sensing in cognitive radio networks
IEEE Transactions on Wireless Communications
Optimal multi-channel cooperative sensing in cognitive radio networks
IEEE Transactions on Wireless Communications
Cooperative spectrum sensing in cognitive radio networks with weighted decision fusion schemes
IEEE Transactions on Wireless Communications
Cooperative Spectrum Sensing in Cognitive Radio, Part I: Two User Networks
IEEE Transactions on Wireless Communications
Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks
IEEE Transactions on Wireless Communications
Sensing-Throughput Tradeoff for Cognitive Radio Networks
IEEE Transactions on Wireless Communications
Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks
IEEE Transactions on Wireless Communications
Optimal non-identical sensing setting for multi channels in cognitive radio networks
Computer Communications
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In this paper, joint optimization of throughput and error rate via cooperative spectrum sensing in cognitive radio networks is investigated. An optimization problem is formulated, which aims to maximize the average achievable throughput of cooperating cognitive users while keeping the error rate at a lower level. This is a multi-variable nonconvex optimization problem. Instead of solving it directly, we propose an iterative algorithm which jointly optimizes the threshold and sensing time together to decrease the effect of the error and to increase the achievable throughput. We first prove that the local error rate of the cognitive user is a convex function of energy threshold and determine a closed-form for the optimal threshold which minimizes the error rate. Then we show that the AND rule is the optimal fusion rule to maximize the achievable throughput. Furthermore we determine the least number of cooperating cognitive users that can guarantee a minimum target error rate. This initial nonconvex problem is converted into a single variable convex optimization problem which can be successfully solved by common methods e.g. Newton's method. Simulation results illustrate the fast convergence and effectiveness of the joint iterative algorithm.