Cooperative Spectrum Sensing Performance-overhead Tradeoff in Cognitive Radio Network

What is it about?

In this study, a cluster-based data and decision fusion approach are implemented to jointly exploit the advantages of both and a selective optimal weight setting algorithm is proposed by utilizing normal and modified deflection coefficients maximization under Neyman-Pearson criterion in order to obtain a final decision about the presence of primary users. The simulations show promising results as the novel hybridization process visibly reduces the network traffic overhead while exhibiting a highly satisfactory detection performance in CRN. Impairments in wireless network environment like shadowing, fading and noise uncertainty are also taken into consideration while optimizing the performance of the proposed model.

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Why is it important?

promising results as the novel hybridization process visibly reduces the network traffic overhead while exhibiting a highly satisfactory detection performance in CRN

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