Citydeploy bench tests better ways to place city wireless transmitters
CityDeploy-Bench: Benchmarking Physics-Grounded Spatial Set Planning for Multi-Transmitter Network Deployment
Machine Learning
Summary
Setting up many wireless transmitters across a city is tricky because signals can block or interfere with each other. The authors created CityDeploy-Bench, a tool to test how well different planning methods place transmitters considering real-world physics of signal paths. They found that planning works best when methods understand how transmitters interact together, not just individually. This shows that placing transmitters is a teamwork problem needing to think about their combined effects. The authors also provide data and tools to help others improve city wireless network setups.
What this means in practice
- •For telecom network engineers: Improve citywide wireless transmitter layouts by testing planners that model complex interference and signal paths more accurately.
- •For smart city infrastructure teams: Design and evaluate urban network deployments with tools that simulate realistic radio signal interactions for better coverage.
- •For wireless network optimization vendors: Develop software products that help customers plan multi-transmitter deployments with physics-grounded performance predictions.$Commercial implications: Enables commercial tools that incorporate physical signal modeling into multi-transmitter placement decisions for city networks.
Authors
Chenyang Yuan, Xiaoyuan Cheng
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.