Performance evaluation of hard‐decision and soft‐data aided cooperative spectrum sensing over Nakagami‐m fading channel

Abstract

AbstractThe secondary users can opportunistically utilize the spectrum holes left by the licensed users (also called primary users [PUs]) with the help of cognitive radio (CR) technology. The cooperative spectrum sensing (CSS) is crucial for the implementation of CR algorithms in the next‐generation wireless networks, for the specific identification of spectral gaps of PUs under any fading/shadowing channel. Accurate spectrum sensing reveals that the CUs only provide data when the PUs report being idle. Yet, there is always a possibility that CU transmission will cause interference with PU communications, leading to inefficient spectrum use. So, this work explores the performance of a proposed CSS by considering such factors with the key objective of enhancing spectrum utilisation efficiency. This paper evaluates the role of the proposed CSS with a variety of hard decision fusion rules (HDFR) schemes as well as soft data fusion schemes (SDFS) methods. Toward this end, for the proposed system, closed‐form expressions for the evaluation of various performance metrics such as the false alarm, miss‐detection probabilities, total error probability (TEP), and throughput/energy efficiency are derived by taking into consideration the Nakagami‐m fading environment using different hard and soft algorithms and are validated by means of numerical simulations. Furthermore, by taking into account various network specifications, such as channel error probability (q), fading severity parameter (m), number of samples (M), number of CR nodes (N), average sensing (S) channel signal‐to‐noise ratio, and sensing threshold (λ), a performance evaluation between the SDFS and HDFR schemes has been carried out. Furthermore, using HDFR/SDFS schemes, analytical models are formulated for determining an optimal number of CR nodes () and optimal threshold () values for achieving the lowest TEP and highest energy efficiency.