Neural Network Based Cooperative Spectrum Sensing in Cognitive Radios

This book presents a comprehensive study on cooperative spectrum sensing in Cognitive Radio Networks using advanced neural network and optimization techniques. It addresses the critical issue of spectrum underutilization caused by static spectrum allocation policies and highlights how intelligent, adaptive radio systems can enhance spectral efficiency while protecting licensed primary users from interference.The work systematically analyzes the limitations of conventional spectrum sensing methods under practical wireless impairments such as shadowing, multipath fading, and noise uncertainty. To overcome these challenges, the book proposes and evaluates several intelligent algorithms, including Enhanced Fuzzy C-Means (EFCM), Improved Whale Optimization Algorithm combined with Convolutional Neural Networks (IWOA-CNN), and Deep Belief Networks integrated with Fruit Fly Optimization Algorithm (DBN-FOA). Through extensive simulations, the proposed approaches demonstrate superior performance compared to existing methods in terms of detection accuracy, delay, throughput, fairness index, energy consumption, and network lifetime. The book also provides a detailed theoretical background.