AI-Based Resource Allocation in Heterogeneous Wireless Networks: Implementing and Testing Deep Q-Learning Algorithms

Abstract

This research investigates the application of Deep Q-Learning (DQL) algorithms for resource allocation in heterogeneous wireless networks (HetNets). The primary objective is to develop, implement, and test DQL algorithms to optimize resource distribution, enhancing network performance and user experience. The study utilized the Network Simulator 3 (NS-3) tool to create a simulated HetNet environment, encompassing macro cells, microcells, and femtocells. Key performance metrics such as throughput, latency, packet loss, and energy consumption were analyzed. The findings indicate significant improvements in network performance with the DQL algorithm, including a 15.85% increase in total throughput, a 29.04% reduction in total latency, a 42.67% decrease in packet loss, and an 18.16% reduction in energy consumption compared to traditional heuristic methods. These results suggest that AI-driven resource allocation can effectively manage dynamic network conditions, ensuring efficient utilization of resources. The study's implications highlight the potential for DQL to enhance scalability, reduce operational costs, and support sustainable network management practices. Future research should focus on real-world deployment and further exploration of advanced AI techniques to optimize resource allocation strategies in diverse network environments.