Abstract

In cognitive radio network unlicensed users continuously scan a vast range of frequencies to detect ‘white spaces’ or ‘spectrum holes’ that are temporarily and spatially not being used for communications by licensed user and this process is known as spectrum sensing. In order to execute the cooperative spectrum sensing among cognitive radio users, data fusion schemes are superior to that of decision fusion ones in terms of the detection performance but suffer from the disadvantage of huge traffic overhead when bandwidth constraint of communication channels is taken into account. In this study, a cluster-based data and decision fusion approach is 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 performance of proposed model.

Keywords:

Cooperative sensing, data fusion, decision fusion, deflection coefficient, selective weight,

References

Competing interests

The authors have no competing interests.

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Copyright

The authors have no competing interests.