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As evermore applications and services are developed for wireless devices, the dramatic growth in user data traffic has led to the legacy channels becoming congested with the corresponding imperative of requiring more spectra. This has motivated both regulatory bodies and commercial companies to investigate strategies to increase the efficiency of the existing spectrum. With the emergence of cognitive radio technology, and the transference of TV channels from analogue to digital platforms, a unique opportunity to exploit spectrum by mobile digital service providers has emerged, commonly referred to as TV White Space (TVWS). One of the challenges in utilising TVWS spectrum is reliable primary user (PU) detection which is essential as any unlicensed secondary user has no knowledge of the PU and thereby can generate interference. This paper addresses the issue of PU detection by introducing a new dynamic spectrum access algorithm that exploits the unique properties of how digital TV (DTV) frequencies are deployed. A fuzzy logic inference model based on an enhanced detection algorithm (EDA) is used to resolve the inherent uncertain nature of DTV signals. Simulation results confirm EDA significantly improves the detection probability of a TVWS channel compared to existing PU detection techniques, while providing consistently low false positive detections. The paper also analyses the impact of the hidden node problem on EDA by modelling representative buildings and proposes a novel solution.

A New Cross-Layer Dynamic Spectrum Access Architecture for TV White Space Cognitive Radio Applications