Cognitive Radio Networks: Enhancing Spectrum Utilization
DOI:
https://doi.org/10.15662/IJARCST.2019.0206002Keywords:
Cognitive Radio Networks, Spectrum Utilization, Dynamic Spectrum Access, Spectrum Sensing, Spectrum Management, Primary Users, Secondary Users, Cooperative SensingAbstract
With the exponential growth of wireless communication services, spectrum scarcity has become a critical challenge. Traditional fixed spectrum allocation policies lead to inefficient utilization of the available frequency bands, resulting in underused spectrum resources. Cognitive Radio Networks (CRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic spectrum access and improving spectrum utilization. CRNs allow secondary users to opportunistically access licensed frequency bands without causing harmful interference to primary users. This paper explores the fundamental concepts, techniques, and challenges associated with cognitive radio technology, emphasizing spectrum sensing, spectrum management, spectrum mobility, and spectrum sharing. Through an extensive review of existing literature, we analyze different spectrum sensing methods such as energy detection, matched filter detection, and cyclostationary feature detection, along with strategies for dynamic spectrum access. The research methodology involves comparative analysis of spectrum utilization efficiency and interference mitigation across diverse CRN frameworks. Key findings suggest that collaborative and cooperative spectrum sensing techniques outperform individual sensing by enhancing detection accuracy and reducing false alarms. Furthermore, intelligent spectrum management strategies improve overall network throughput and fairness among users. The workflow of CRN involves spectrum sensing, decision-making, spectrum allocation, and seamless handover to ensure uninterrupted communication. While CRNs present significant advantages in maximizing spectrum efficiency and enabling flexible communication, challenges remain in terms of hardware complexity, security threats, and reliable spectrum sensing under noisy conditions. The paper concludes by highlighting future research directions, including advanced machine learning techniques for spectrum prediction, enhanced security frameworks, and integration with emerging 5G technologies to further optimize spectrum utilization and network performance.
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