A Comprehensive Review of Spectrum Sensing Techniques in Cognitive Radio Networks: Recent Advances, Datasets, Challenges and Future Research Directions

The demand for radio spectrum has risen due to the rapid expansion of wireless communication technology, which has resulted in poor utilization and significant spectrum scarcity issues. By facilitating dynamic spectrum access, Cognitive Radio Networks (CRNs) have become a viable solution to this problem. Without generating detrimental interference, CRNs enable unlicensed users as Secondary Users (SUs) to profitably exploit the underutilized spectrum assigned to licensed or Primary Users (PUs). Spectrum sensing, one of the basic operations of CRNs, is crucial for identifying empty spectrum bands and ensuring smooth, interference-free transmission. Hence, this review explores a comprehensive analysis of recently developed spectrum sensing techniques used in CRNs, including matched filtering, energy detection, cooperative sensing, machine learning and deep learning. All the techniques are analyzed through their operational principles, advantages, disadvantages, and applicability under various channel conditions. Moreover, it highlights the techniques and datasets used for spectrum sensing. Moreover, it discuss the key performance measures such as probability of detection, sensing time and false alarm rate, which is used for evaluating the spectrum sensing performance. The review work concludes by identifying key challenges and proposing future research directions aimed at improving the efficiency, reliability, and adaptability of spectrum sensing in CRN.