CityDeploy-Bench：面向多发射机网络部署的物理基础空间集规划基准

中文摘要 AI 辅助

在复杂的城市传播环境与全网干扰下，自动化城市级无线部署仍具挑战性。我们推出CityDeploy-Bench，这一基准将多发射机部署重新定义为在统一射线追踪验证器下的物理基础空间集规划。该基准将效用表示与规划动态分离，支持在各类规划器间对直接标量奖励、关系模型及高阶交互结构进行受控比较。实验表明，随着物理耦合增强，规划行为会出现明显转变：部署质量愈发依赖学习到的效用是否能捕捉发射机间的集体交互，仅靠更强的搜索无法弥补缺失的关系结构。这确立了多发射机部署是物理交互集合上的协调问题，而非一系列独立空间决策。我们发布CityDeploy-Data及该基准框架，作为连接决策学习与物理基础无线网络设计研究的可复现测试平台。

英文摘要

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.

发表机构

• University of Sheffield（谢菲尔德大学）
• University College London（伦敦大学学院）

机构由 AI 辅助整理，请以论文原文为准。