Cooperative Spectrum Sensing Scheme Using Fuzzy Logic Technique

Abstract

In communication system spectrum has a crucial role and wireless technologies are increasing rapidly it is required to make efficient use of spectrum to satisfy the spectrum scarcity problem. Using spectrum efficiently can be done by cognitive radio because of its ability to sense surrounding environment. Cognitive radio sense unoccupied spectrum by detecting the primary users' presence or absence in the spectrum. Using more than one cognitive radio in the detection process will increase the efficiency of spectrum usage and prevent interference between signals. Using machine learning techniques for the implementation of intelligent cognitive radio increase efficiency and detection performance and detect signal at low SNR condition. Fuzzy logic machine learning technique is implemented which is based on fuzzy membership functions and fusion centre. Energy detection is used to classify signal and noise at each cognitive radio after that each cognitive radio output information convert into membership function and apply fuzzy rules such as algebraic sum, the algebraic product give a final decision about the signal presence or absence. Simulation results show that the proposed system gives much better results compared to the conventional energy detection system and improves the performance of the system.

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

“…Spectrum sensing and allocation approaches stand as common methods employed to tackle the issue of interference within TVWS networks (Mwaimu et al, 2022;Bani and Kulkarni, 2022). Certainly, spectrum sensing involves scanning TV bands to identify available channels suitable for use by cognitive users, aiming to prevent interference between licensed and unlicensed users (Kantikar and Yelalwar, 2018;Koçkaya and Develi, 2020). On the other hand, spectrum allocation approaches are employed to reduce the potential for interference among users in the secondary role, thus enhancing their network capacities within the network (Politis et al, 2018;Mwaimu et al, 2022;Bani and Kulkarni, 2022).…”

“…On the other hand, spectrum allocation approaches are employed to reduce the potential for interference among users in the secondary role, thus enhancing their network capacities within the network (Politis et al, 2018;Mwaimu et al, 2022;Bani and Kulkarni, 2022). Indeed, existing literature often segregates the use of these approaches, focusing on implementing spectrum sensing and spectrum allocation as separate methods rather than integrating them comprehensively (Ujam et al, 2018;Orumwense and Abo-Al-Ez, 2020;Politis et al, 2018;Mwaimu et al, 2022;Bani and Kulkarni, 2022;Kantikar and Yelalwar, 2018;Koçkaya and Develi, 2020;Brito et al, 2021;Agarwal et al, 2022;Liang et al, 2019).…”