Deep Reinforcement Learning-Based Dynamic Spectrum Allocation for Next-Generation Wireless Networks

This study has been conducted with the view of increasing demand for efficient spectrum allocation in next-generation wireless networks, as there has been rapid growth in wireless communication devices and increased data traffic. Traditional methods of spectrum allocation are often suboptimal in dynamic environments. The study here proposes a Deep Reinforcement Learning (DRL)-based framework for dynamic spectrum allocation, leveraging pre-trained deep convolutional neural networks (AlexNet and GoogLeNet) for feature extraction. The DRL agent is learned interactively with the network environment based on real-time traffic demands and interference levels in order to make optimal decisions regarding spectrum allocation. The ultimate objectives are to improve spectrum use, latency, and fairness. Simulation results show that the proposed system outperforms conventional methods in terms of spectrum usage, throughput, and fairness, providing an adaptable and scalable solution for intelligent spectrum management in 5G and beyond wireless networks.