用强化学习优化基站部署，兼顾定位精度与用户速率
Access Point Deployment for Localizing Accuracy and User Rate in Cell-Free Systems
- 融合D-最优与欧氏准则，构建联合优化目标
- 相比传统方法，整体性能提升20%，最低性能提升120%
- 适合研究大规模无蜂窝网络部署的工程师和学者
下一代移动网络旨在实现无缝覆盖与网络化感知。借助多视角感知与多节点协同传输，无蜂窝系统成为实现该愿景的有前途技术。本文针对无蜂窝系统中接入点（AP）部署问题，旨在平衡感知精度与用户速率。通过融合D-最优性与欧氏准则，提出一种新型集成度量作为多目标优化目标函数，分别用于解决最大总和与最大最小性能问题，以保障多用户通信与目标跟踪场景下的整体与最低性能。为求解高维非凸多目标优化问题，采用软演员-评论家（SAC）算法，避免陷入局部最优。数值结果表明，所提基于SAC的AP部署方法在整体性能上提升20%，最低性能提升120%。
原文摘要 · Abstract (English)
Evolving next-generation mobile networks is designed to provide ubiquitous coverage and networked sensing. With utility of multi-view sensing and multi-node joint transmission, cell-free is a promising technique to realize this prospect. This paper aims to tackle the problem of access point (AP) deployment in cell-free systems to balance the sensing accuracy and user rate. By merging the D-optimality with Euclidean criterion, a novel integrated metric is proposed to be the objective function for both max-sum and max-min problems, which respectively guarantee the overall and lowest performance in multi-user communication and target tracking scenario. To solve the corresponding high dimensional non-convex multi-objective problem, the Soft actor-critic (SAC) is utilized to avoid risk of local optimal result. Numerical results demonstrate that proposed SAC-based APs deployment method achieves $20\%$ of overall performance and $120\%$ of lowest performance.
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