Implementation of Monte Carlo and ANFIS techniques for detection threshold estimation in cognitive radio

This research explores the implementation of Monte Carlo and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques for detection threshold estimation in cognitive radio networks. Accurate detection threshold estimation is essential for effective spectrum sensing, minimizing false alarms, and optimizing spectrum utilization. The study first outlines conventional spectrum sensing methods and their limitations, particularly in dealing with noise uncertainty and dynamic spectral environments. Monte Carlo simulations are employed to statistically model detection scenarios and derive optimal threshold values, while ANFIS leverages machine learning and fuzzy logic to adaptively adjust thresholds in real time. A comparative analysis of both techniques is conducted, evaluating their efficiency, computational complexity, and adaptability in cognitive radio applications. The findings demonstrate that Monte Carlo offers a robust probabilistic approach suitable for static environments, while ANFIS enhances real-time adaptability, making it more effective for dynamic spectrum sensing. This research significantly contributes to improving cognitive radio performance, ensuring reliable spectrum access, and reducing interference in wireless communication networks.