论文标题
6G中的基于深钢筋学习的基于深度增强学习的横梁管理的关节感应和通信
Joint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G
论文作者
论文摘要
用户位置是用于网络管理和控制的关键信息。但是，在某些设置导致本地化错误的某些设置中，位置不确定性是不可避免的。在本文中，我们考虑了MMWave网络中的用户位置不确定性，并使用基于深入的基于增强学习的光束管理对未来的6G网络进行研究，调查联合视觉辅助的传感和通信。特别是，我们首先从卫星图像中提取基于像素特征的特征，以提高定位精度。然后，我们提出了一种基于UK-Medoids的方法，用于使用位置不确定性的用户聚类，因此将群集结果用于光束管理。最后，我们将DRL算法应用于梁内无线电资源分配。模拟首先表明我们提出的视觉辅助方法可以大大减少本地化误差。将拟议的基于英国的基于DRL的计划（UKM-DRL）与其他两个方案进行了比较：基于K-Means的基于K-Means的基于基于DRL的资源分配（K-DRL）以及基于UK-MEANS的基于CRUSTERS的集群和基于DRL的资源分配（UK-DRL）。该提出的方法比UK-DRL高17.2％，延迟7.7％，吞吐量的两倍以上，延迟比K-DRL低55.8％。
User location is a piece of critical information for network management and control. However, location uncertainty is unavoidable in certain settings leading to localization errors. In this paper, we consider the user location uncertainty in the mmWave networks, and investigate joint vision-aided sensing and communications using deep reinforcement learning-based beam management for future 6G networks. In particular, we first extract pixel characteristic-based features from satellite images to improve localization accuracy. Then we propose a UK-medoids based method for user clustering with location uncertainty, and the clustering results are consequently used for the beam management. Finally, we apply the DRL algorithm for intra-beam radio resource allocation. The simulations first show that our proposed vision-aided method can substantially reduce the localization error. The proposed UK-medoids and DRL based scheme (UKM-DRL) is compared with two other schemes: K-means based clustering and DRL based resource allocation (K-DRL) and UK-means based clustering and DRL based resource allocation (UK-DRL). The proposed method has 17.2% higher throughput and 7.7% lower delay than UK-DRL, and more than doubled throughput and 55.8% lower delay than K-DRL.
