用深度强化学习优化无线网络动态资源分配，效果优于传统方法。
Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks
- 用DQN和PPO等DRL算法实现基站资源动态分配
- 不同学习率与调度策略显著影响系统性能表现
- 适合研究智能无线网络优化的工程师与学者
本报告研究深度强化学习（DRL）算法在无线通信系统中动态资源分配的应用。构建包含基站、多天线及用户设备的仿真环境，使用RLlib库对比Deep Q-Network（DQN）与Proximal Policy Optimization（PPO）等DRL算法的性能。重点分析不同学习率与调度策略对资源分配效率的影响。结果表明，算法选择与学习率设置显著影响系统表现，相比传统方法，DRL能实现更高效的资源分配。
原文摘要 · Abstract (English)
This report investigates the application of deep reinforcement learning (DRL) algorithms for dynamic resource allocation in wireless communication systems. An environment that includes a base station, multiple antennas, and user equipment is created. Using the RLlib library, various DRL algorithms such as Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) are then applied. These algorithms are compared based on their ability to optimize resource allocation, focusing on the impact of different learning rates and scheduling policies. The findings demonstrate that the choice of algorithm and learning rate significantly influences system performance, with DRL providing more efficient resource allocation compared to traditional methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。