arXiv：cs.LG· Chenyang Yuan, Xiaoyuan Cheng·· 4 小时前
CityDeploy-Bench：为多发射机网络部署打造物理落地的空间集合规划基准
CityDeploy-Bench: Benchmarking Physics-Grounded Spatial Set Planning for Multi-Transmitter Network Deployment
AI 导读
研究团队发布 CityDeploy-Bench，将多发射机城市无线网络部署重构为统一 ray-tracing 验证器下的物理落地空间集合规划问题，并同步开源 CityDeploy-Data 与基准框架。实验显示，随着物理耦合增强，部署质量越来越取决于学习到的效用能否捕捉发射机间的集体交互，仅靠更强的搜索无法弥补关系结构的缺失。
正文
Abstract:Automating city-scale wireless deployment remains challenging under complex urban propagation and network-wide interference. We introduce \textbf{CityDeploy-Bench}, a benchmark that reframes multi-transmitter deployment as \emph{physics-grounded spatial set planning} under a unified ray-tracing verifier. The benchmark separates utility representation from planning dynamics, enabling controlled comparison between direct scalar rewards, relational models, and higher-order interaction structures across diverse planners. Our experiments reveal a clear transition in planning behavior as physical coupling grows. Deployment quality becomes increasingly dependent on whether the learned utility captures collective transmitter interactions, whereas stronger search alone cannot compensate for missing relational structure. This establishes multi-transmitter deployment as a coordination problem over physically interacting sets rather than a collection of independent spatial decisions. We release CityDeploy-Data and the benchmark framework as a reproducible testbed for research linking decision learning with physically grounded wireless network design.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11065 [cs.LG] |
| (or arXiv:2610.11065v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11065 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chenyang Yuan [view email]
[v1]
Thu, 8 Oct 2026 01:25:48 UTC (190,001 KB)
来源：arXiv：cs.LG · arxiv.org