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Analysis on Performance of Equal Gain Combination for Cooperative Spectrum Sensing over Nakagami Channels in Cognitive Radio

Weiguang Zhao

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Abstract

The performances of cooperative spectrum sensing with equal gain combination over Nakagami channels are analyzed in cognitive radio networks.The series form of detection probability is derived by transforming the multiple integral to a single integral and using the equivalent representation of generalized Marcum Q-function.Simulation shows that the correctness of the theoretical analysis results are validated,and a group of practical parameter settings is given.Parameter selection for designing cognitive radio networks can be provided by expressions of detection performance.

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What this paper is about

The performances of cooperative spectrum sensing with equal gain combination over Nakagami channels are analyzed in cognitive radio networks.The series form of detection probability is derived by transforming the multiple integral to a single integral and using the equivalent representation of generalized Marcum Q-function.Simulation shows that the correctness of the theoretical analysis results are validated,and a group of practical parameter settings is given.Parameter selection for designing cognitive radio networks can be provided by expressions of detection performance.

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Available abstract

The performances of cooperative spectrum sensing with equal gain combination over Nakagami channels are analyzed in cognitive radio networks.The series form of detection probability is derived by transforming the multiple integral to a single integral and using the equivalent representation of generalized Marcum Q-function.Simulation shows that the correctness of the theoretical analysis results are validated,and a group of practical parameter settings is given.Parameter selection for designing cognitive radio networks can be provided by expressions of detection performance.

Key concepts: Cognitive radio, Nakagami distribution, Correctness, Computer science, Spectrum (functional analysis), Representation (politics), Selection (genetic algorithm), Fading

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