NetAgent: Agentic Wireless Base-Station Planning with Differentiable Propagation Optimization

Reliable wireless service and quiet-zone protection depend on base-station placement. This is a constrained inverse-design problem in which sparse transmitter decisions are judged by the dense propagation fields they induce. Locations, power allocation, and station count interact: strengthening one region can illuminate another that should remain quiet. Electromagnetic simulation captures these effects but makes configuration search costly, while fast channel models miss local building geometry. A radio-map generator offers another route when transmitter locations are not prescribed: gradients of regional objectives shape a candidate field whose dominant peak identifies a deployable site. NetAgent trains a RadioDiff-based architecture without its original condition encoder and freezes the resulting radio-map prior. Short deterministic denoising allows joint optimization of spatial latents and source power weights; station coordinates are read from the optimized single-source fields. One shared multi-source optimization produces weight-ranked nested deployments, from which a constraint-first rule selects station count without separate searches. An agentic interface converts drawn or natural-language requests into validated regional constraints. On thirty OpenStreetMap-derived scenarios scored by an independent electromagnetic simulator, NetAgent Auto-$K$ reaches $0.5885$ mean target attainment in $8.21$s with $1.27$ stations on average, compared with $0.4661$ for AutoBS and $0.6431$ for a $101.82$s exhaustive ray-tracing reference.