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Parallel Digital Twin-driven Deep Reinforcement Learning for User Association and Load Balancing in Dynamic Wireless Networks by Tao, Zhenyu; Xu, Wei; You, Xiaohu is a scholarly article available to read on EtoBox.

What is Parallel Digital Twin-driven Deep Reinforcement Learning for User Association and Load Balancing in Dynamic Wireless Networks about?

Optimization of user association in a densely deployed heterogeneous cellular network is usually challenging and even more complicated due to the dynamic nature of user mobility and fluctuation in user counts. While deep reinforcement learning (DRL) emerges as a promising solution, its application in practice is hindered by high trial-and-error costs in real world and unsatisfactory physical network performance during training. In addition, existing DRL-based user association methods are usually only applicable to scenarios with a fixed number of users due to convergence and compatibility challenges. In this paper, we propose a parallel digital twin (DT)-driven DRL method for user association and load balancing in networks with both dynamic user counts, distribution, and mobility patterns. Our method employs a distributed DRL strategy to handle varying user numbers and exploits a refined neural network structure for faster convergence. To address these DRL training-related challenges, we devise a high-fidelity DT construction technique, featuring a zero-shot generative user mobility model, named Map2Traj, based on a diffusion model. Map2Traj estimates user trajectory patterns and s

Author

Tao, Zhenyu; Xu, Wei; You, Xiaohu

Published

2024

Language

EN