Spectrum Sensing Methodology Using Artificial Intelligence for Cognitive Radio Network

Spectrum sensing is an essential part of cognitive radio networks (CRNs) that enables efficient utilisation of the radio frequency spectrum and dynamic spectrum access. Conventional spectrum sensing technologies, such as energy detection, matched filtering, and cyclostationary features recognition, are often computationally complex, noise-sensitive, and unable to detect signals at low signal-to-noise ratio (SNR) levels. Artificial intelligence (AI)-based approaches have become a viable substitute to deal with these issues. CRNs can provide more precise, flexible, and effective spectrum sensing owing to AI approaches. In intricate and dynamic situations, these algorithms can identify primary users, predict spectrum usage, and categorise spectrum accessibility. Major AI-driven techniques for spectrum sensing are reviewed in this work, with an emphasis on supervised, unsupervised, and reinforcement learning techniques. The suggested approach supports the creation of intelligent, independent CRNs for next-generation wireless networks by improving spectrum efficacy, cutting down on sensing time, and facilitating real-time decision-making.