Reduction of Computational Time for Cooperative Sensing Using Reinforcement Learning Algorithm

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Published by IEEE Nigeria, Computer Science Section


Cooperative spectrum sensing in cognitive radio systems is characterized by high computational time for decision making due to the fusing of individual decisions of cognitive radios involved in the cooperative scheme. This increases the communication overhead of the network. In this paper, an adaptive cooperative spectrum sensing algorithm is developed with improved detection algorithm. Reinforcement learning is thenincorporated to improve the decision making efficiency of the cooperative spectrum sensing such that less time is required to make a decision at the fusion centre. Three temporal difference learning techniques were compared in order to select the most efficient to reduce sensing and decision delays. Appropriate learning rate was utilized in the sensing and decision making algorithm to enhance the performance ofthe adaptive cooperative spectrum sensing. Results reveal significant reduction in the computation time required in cooperative spectrum sensing and decisions. This permits greater efficiency in dynamic spectrum management as the limited electromagnetic spectrum is being utilized for telecommunication services.



Cognitive Radio, Cooperative Spectrum Sensing, Computational Time, Reinforcement Learning