Hybrid Spectrum Sensing Framework for Cognitive Radio Networks in Dynamic Environments

Abstract

ABSTRACT Performance issues with spectrum sensing are caused by multipath fading and shadowing, and obstacle blocking. This paper presents a novel Hybrid spectrum sensing framework that cooperatively combines energy detection (ED), matched filter detection (MFD), and cyclostationary feature detection (CFD) to detection reliability in cognitive radio networks. To mitigate fading and shadowing effects, cooperative spectrum sensing (CSS) is incorporated, leveraging multiple secondary users (SUs) for spatial diversity, achieving P d = 0.80 at SNR = −20 dB using the OR fusion rule The key novelty lies in a dynamic switching mechanism that adaptively selects among ED, MFD, and CFD based on real‐time signal and PU information availability, enabling operation under full, limited, or absent PU knowledge. Monte Carlo simulations in the low SNR region (−20 to −5 dB) demonstrate that the proposed hybrid detector achieves P d values of 1, 0.98, and 0.95 for CFD, ED, and MFD, respectively, at P f = 0.1. Moreover, it achieves energy efficiencies of 1.86 bits/J (non‐CSS) and 2.05 bits/J (CSS), emphasizing the importance of energy‐efficient spectrum access in power‐constrained CR‐IoT applications. The framework also minimizes average detection delay to 1.16 ms, ensuring faster PU identification. Owing to its adaptability and energy‐aware operation, the proposed method is suitable for TV white spaces, CR‐IoT, 5G‐and‐beyond networks, VANETs, and disaster‐recovery communications.

Affiliated Institutions

• Indian Institute of Management Lucknow IN

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