OPTIMIZING SPECTRUM HANDOFF AND RESOURCE UTILIZATION IN COGNITIVE RADIO NETWORKS USING ARTIFICIAL NEURAL NETWORKS

Abstract

Cognitive Radio Networks (CRNs) are designed to improve spectrum efficiency by enabling dynamic spectrum access. In such networks, spectrum handoff is a crucial process that ensures seamless communication by transferring communication from a congested channel to a less congested one. However, the optimization of spectrum handoff and resource utilization remains a significant challenge due to the dynamic and unpredictable nature of wireless environments. Traditional spectrum handoff techniques struggle to efficiently manage the allocation of resources in CRNs, often leading to delays, high energy consumption, and inefficient bandwidth usage. The problem becomes even more complex as the network grows, demanding advanced techniques that can intelligently predict and manage resource utilization during handoff. This paper proposes a novel approach using Artificial Neural Networks (ANNs) to optimize spectrum handoff and resource utilization in CRNs. The ANN model is trained to predict the best spectrum handoff decision based on factors such as signal strength, traffic load, and interference. The network's performance is assessed by comparing ANN-based decisions with traditional handoff mechanisms, focusing on throughput, energy consumption, and handoff delay. The results show that the proposed ANN-based approach significantly outperforms traditional methods in terms of reduced handoff delays, improved spectrum utilization, and lower energy consumption.