Advances in Adaptive Threshold Techniques in Cognitive Radio
A Survey
Keywords:
Threshold, Advances, Adaptive, Technique, Cognitive RadioAbstract
Cognitive radio technology has emerged as a transformative solution to the growing spectrum scarcity and demand for wireless communication services. At the core of cognitive radio's effectiveness lies spectrum sensing, a fundamental process for dynamically allocating unused spectrum bands. In this context, adaptive thresholding techniques assume a pivotal role, ensuring the precise detection of primary users and, consequently, the efficient use of available spectrum resources. This survey paper delves into recent advancements in adaptive threshold estimation techniques within the domain of cognitive radio. Our aim is to provide a comprehensive, structured, and insightful overview of these techniques, with a primary focus on global thresholding methods. We classify these adaptive threshold estimation techniques into two principal categories: local and global Adaptive Thresholding Techniques (ATTs). Within the realm of local ATTs, we explore notable techniques such as Cell Averaging CFAR (CA-CFAR) and its various iterations, encompassing 'greatest of' (GO-CFAR), 'smallest of' (SO-CFAR), heterogeneous clutter estimating (HCE-CFAR), 'trimmed mean' (TM-CFAR), and ordered statistics (OS-CFAR). Additionally, we captured variants of local ATTs like the maximum likelihood and algebraic product (MLAP-CFAR) and the goodness-of-fit and generalized likelihood test with dual censoring (GGDC-CFAR). On the global front, we further categorize ATTs into parametric and non-parametric methods. In the parametric domain, we scrutinize techniques such as Recursive One-sided Hypothesis Testing (ROHT) and First Order Statistical Testing (FOST) Algorithm, while in the non-parametric arena, we explore enhancements in approaches like Otsu, Modified Otsu's Algorithm (MOA), Non-parametric Amplitude Quantization Method (NPAQM), and the Autonomous Global Threshold Adjustment Algorithm (AGTAA). Our pilgrimage guides us through each of these techniques, unveiling their underlying principles, tangible applications, and the specific scenarios in which they excel. The paper identifies key challenges, and presenting a taxonomy of global thresholding techniques based on their working principles. This taxonomy provides a holistic view of the landscape, guiding us toward more accurate spectrum sensing and the realization of an ultra-efficient cognitive radio network. Furthermore, we delve into real-world applications and case studies that demonstrate the practical implications of these advancements. As cognitive radio technology continues to evolve, this paper serves as an invaluable resource for researchers, engineers, and practitioners, offering a holistic view of cutting-edge adaptive thresholding techniques. Moreover, it identifies critical challenges and future directions in this dynamic field. Ultimately, the advances in adaptive threshold estimation presented in this paper contribute to improving spectrum sensing accuracy and realizing more efficient cognitive radio networks.