论文标题
MMWave系统中关节梁宽度和功率优化的深度加固学习
Deep Reinforcement Learning for Joint Beamwidth and Power Optimization in mmWave Systems
论文作者
论文摘要
本文研究了毫米波通信系统中的关节梁宽和传输功率优化问题。提出了一种基于强化的学习方法。具体而言，定制的深Q网络是离线训练的，该网络可以在线部署时做出实时决策。仿真结果表明，在性能和复杂性方面，所提出的方法显着优于常规方法。此外，还展示了强大的泛化能力，这进一步增强了所提出方法的实用性。
This paper studies the joint beamwidth and transmit power optimization problem in millimeter wave communication systems. A deep reinforcement learning based approach is proposed. Specifically, a customized deep Q network is trained offline, which is able to make real-time decisions when deployed online. Simulation results show that the proposed approach significantly outperforms conventional approaches in terms of both performance and complexity. Besides, strong generalization ability to different system parameters is also demonstrated, which further enhances the practicality of the proposed approach.
