Interference Reduction In Sensing Mechanism Of Cognitive Radio Using Fuzzy Based Smartcity Technique
Keywords:
Interference, reduction, mechanism, cognitive, radio, fuzzy, based, smartcity, techniqueAbstract
Cognitive Radio (CR) technology has emerged as a key solution to spectrum scarcity by enabling dynamic spectrum access through intelligent sensing and adaptive transmission. However, one of the major limitations of CR systems is the presence of interference in the sensing mechanism, which degrades detection accuracy, increases false alarms, and reduces overall spectrum utilization efficiency. This challenge becomes more critical in the context of smart city environments, where dense wireless communication networks coexist and demand reliable spectrum management. This study proposes an interference reduction framework for cognitive radio systems using a Fuzzy-Based Smart City Technique. The fuzzy logic controller is employed to intelligently evaluate uncertain sensing parameters such as signal-to-noise ratio (SNR), channel occupancy probability, and interference levels, thereby improving decision-making in dynamic spectrum allocation. By leveraging the adaptive capabilities of fuzzy logic, the proposed technique minimizes interference, enhances spectrum sensing accuracy, and ensures reliable connectivity across heterogeneous smart city networks. Simulation results indicate that the fuzzy-based smart city approach outperforms conventional sensing methods by reducing false detection rates, improving spectrum efficiency, and supporting seamless communication in complex urban environments. This research provides a robust pathway toward interference-resilient cognitive radio deployment in future smart city applications. The conventional Signal to noise ratio(SNR) that cause interference in the cognitive radio sensing mechanism was 8dB, On the other hand, when Fuzzy based smart city technique was incorporated into the system, it automatically increased to10.32dB and the conventional interference to noise ratio INR that cause interference in the cognitive radio sensing mechanism was6dB. Meanwhile, when Fuzzy based smart city technique was integrated into the system, it simultaneously reduced to5.3dB. Finally, with these results obtained, it definitely meant that the percentage interference reduction in sensing mechanism of cognitive radio when Fuzzy based smart city technique was integrated into the system was13.2%.
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