A Comprehensive Review on Cognitive Radio Networks: Applications, Challenges and Research Trends

Abstract

The exponential growth in wireless communication demand, driven by mobile devices and emerging technologies, has revealed the limitations of traditional spectrum management strategies, which often result in inefficient spectrum usage and increased interference. Cognitive Radio (CR) technology has emerged as a promising solution, enabling dynamic and intelligent spectrum utilization through the ability to sense, analyze, and adapt to the radio frequency environment. A key challenge in CR networks is Wide Band Spectrum Sensing (WBSS), crucial for identifying and evaluating available spectrum across a broad frequency range. While traditional spectrum sensing techniques are effective in narrowband scenarios, they struggle with the complexities of wideband analysis. Recent advancements in Machine Learning (ML) offer new opportunities to enhance WBSS capabilities by improving spectrum sensing accuracy and efficiency. This research explores the application of various ML algorithms, including Artificial Neural Networks (ANN), Naive Bayes, Random Forest, Decision Trees, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNNs), to address these challenges. Additionally, the study highlights the potential of a non-parametric dual transfer framework for Cooperative Spectrum Sensing (CSS), which significantly improves performance without extensive parameter tuning. This paper provides a comprehensive analysis of ML approaches applied to WBSS, comparing their effectiveness against conventional techniques and discussing the implications for CR systems. It also includes a literature review on Wireless Body Sensor Systems (WBSS) using ML in Cognitive Radio Ad-hoc Networks (CRAHNs), emphasizing ML applications in optimizing spectrum sensing and management. The reviewed studies demonstrate significant advancements in detection accuracy, particularly in low Signal-to-Noise Ratio (SNR) environments, through innovative approaches like deep learning, transfer learning, and cooperative sensing. The paper concludes by discussing the integration of ML into CR systems, highlighting its potential to enhance spectrum management, reduce interference, and improve wireless communication system performance in dynamic and complex environments, with specific focus on healthcare and other critical applications.