移动通信2024，Vol.48Issue(4)：129-134,6.DOI:10.3969/j.issn.1006-1010.20220902-0001
基于深度强化学习的移动通信网载波调整算法
Carrier Adjustment Algorithm for Mobile Communication Networks Based on Deep Reinforcement Learning
吕晓阳 1沈一飞 1吴兵1
作者信息
- 1. 中国移动通信集团广东有限公司深圳分公司,广东 深圳 518033
- 折叠
摘要
Abstract
To address the issue of accurately and timely adjusting carriers in mobile communication networks,a sector expansion(reduction)algorithm based on deep reinforcement learning(DRL)is proposed.The model-based reinforcement learning method is utilized to establish a multi-model combination of the probability dynamic model of capacity indicators.This model is trained using historical data from the real environment,and a virtual environment is constructed based on it.Then,a neural network is used to build an agent,which interacts with the virtual environment that generates virtual samples using the short rollout technique.Finally,the Deep Q-Network(DQN)algorithm is used to optimize the agent's strategy using virtual samples,providing suggestions for sector expansion(reduction)operations.Experimental results indicate that the trained agent's carrier adjustment recommendations achieve a high level of accuracy.关键词
移动通信网络/载波调整/深度强化学习/多模型组合Key words
mobile communication network/carrier adjustment/deep reinforcement learning/multi-model combination分类
信息技术与安全科学引用本文复制引用
吕晓阳,沈一飞,吴兵..基于深度强化学习的移动通信网载波调整算法[J].移动通信,2024,48(4):129-134,6.
