Learning Coverage- and Power-Optimal Transmitter Placement from Building Maps: A Comparative Study of Direct and Indirect Neural Approaches

2026-04-23

AI Summary

This study addresses the high computational cost of exhaustive search in large-scale wireless transmitter deployment by focusing on joint optimization of coverage and power-efficient placement in single-transmitter scenarios. Leveraging a dataset comprising 167,525 urban environments, the authors introduce a dual-scoring graph strategy that, for the first time, reveals an asymmetric trade-off between coverage and power objectives, enabling near-theoretically optimal balanced deployments (with average distance d̄ = 2.60). The proposed method integrates a discriminative model—capable of efficiently predicting received power heatmaps with 1,350–2,400× inference speedup—and a diffusion model that generates high-quality solutions without requiring multi-objective training. Combined with candidate re-evaluation and a balancing criterion, the overall deployment process achieves a 14–22× acceleration.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Optimal wireless transmitter placement is a central task in radio-network planning, yet exhaustive search becomes prohibitively expensive at scale. This paper studies the single-transmitter setting under a fixed learned propagation surrogate, where exhaustive per-pixel evaluation remains tractable and provides surrogate-exact ground truth. We introduce a dataset of 167,525 urban scenarios (RadioMapSeer-Deployment) with dual surrogate-exact labels for coverage-optimal and power-optimal transmitter locations. Ground-truth analysis reveals an asymmetric coverage-power trade-off: coverage-optimal placement sacrifices 13.86% of received power, whereas power-optimal placement sacrifices only 5.50% of coverage; the best achievable balanced placement lies at $\bar{d}=2.60$ from the ideal point (100%,100%). We evaluate two learning formulations: indirect heatmap-based models that predict received-power radio maps, and direct score-map models that predict the objective landscape over feasible transmitter locations. Within the heatmap family, discriminative models deliver one-shot predictions 1350-2400x faster than exhaustive search, while diffusion models additionally support multi-sample inference that improves single-objective performance and, by reusing the same sample pool under a balanced criterion, recovers strong balanced placements without explicit multi-objective training. Dual score-map strategies combining power and coverage score maps match the exhaustive balanced optimum ($\bar{d}=2.60$) and remain close across smaller candidate budgets, at 14-22x speedups after candidate re-evaluation. Both formulations admit very fast one-shot inference; on this benchmark, dual score-map methods are strongest for balanced placement, whereas heatmap formulations remain attractive for their physically meaningful intermediate maps and, in the diffusion setting, for inference-time search.

Problem

• transmitter placement
• coverage optimization
• power optimization
• radio network planning
• coverage-power trade-off

Innovation

• transmitter placement
• neural surrogate
• coverage-power trade-off
• diffusion models
• score-map learning