Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning

2025-09-29

AI Summary

Traditional resource allocation methods in heterogeneous wireless networks (HetNets) suffer from poor adaptability to dynamic user load and time-varying channel conditions. To address this, this paper proposes a deep reinforcement learning (DRL) framework that jointly optimizes transmit power, bandwidth allocation, and user scheduling. A multi-objective reward function is designed to simultaneously maximize throughput, energy efficiency, and fairness. The framework comparatively evaluates Proximal Policy Optimization (PPO) and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms under realistic base station deployments and benchmarks them against three classical heuristic approaches. Experimental results demonstrate that the proposed DRL framework significantly improves resource utilization efficiency and overall network performance across diverse dynamic scenarios. Furthermore, it reveals critical trade-offs among algorithm selection, reward function design, and environmental generalizability. This work provides a scalable, end-to-end solution for intelligent resource management in HetNets.

Technology Category

• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Search and Optimization: Learning to Search
• Machine Learning: Online Learning & Bandits

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Dynamic resource allocation in heterogeneous wireless networks (HetNets) is challenging for traditional methods under varying user loads and channel conditions. We propose a deep reinforcement learning (DRL) framework that jointly optimises transmit power, bandwidth, and scheduling via a multi-objective reward balancing throughput, energy efficiency, and fairness. Using real base station coordinates, we compare Proximal Policy Optimisation (PPO) and Twin Delayed Deep Deterministic Policy Gradient (TD3) against three heuristic algorithms in multiple network scenarios. Our results show that DRL frameworks outperform heuristic algorithms in optimising resource allocation in dynamic networks. These findings highlight key trade-offs in DRL design for future HetNets.

Problem

• Optimizing resource allocation in heterogeneous wireless networks
• Balancing throughput, energy efficiency, and fairness objectives
• Overcoming limitations of traditional methods in dynamic conditions

Innovation

• Deep reinforcement learning optimizes wireless network resources
• Framework jointly controls power, bandwidth, and scheduling
• PPO and TD3 algorithms outperform traditional heuristic methods