Agents in the Scene: An Agentic Framework for Resource-Efficient Site-Specific Base Station Deployment

Abstract

An agentic framework is proposed for autonomous site-specific base station (BS) deployment in wireless network planning. In contrast to conventional approaches that rely on manual site surveys or extensive ray-tracing (RT) simulations with significant human intervention, the proposed framework autonomously explores and optimizes BS deployment under a limited RT evaluation budget, enabling resource-efficient network planning. To this end, a continuous, geometry-grounded deployment action space is first constructed from three-dimensional (3D) wireless digital twins. Within this action space, an agent team operates through a stateful perception--reasoning--reflection loop. Specifically, a Placement Agent first generates candidate BS deployments in two complementary modes: an experience-guided mode that refines promising solutions, and an exploration mode that avoids getting stuck in local optima. After the candidate deployments are evaluated through RT, a Reflection Agent interprets the RT results together with the scene geometry, identifies performance-limiting factors such as blockage, overlapping coverage, and uncovered areas, and converts these diagnoses into guidance for subsequent deployment. Through this iterative process, site-specific experience is accumulated and deployment plans are optimized without human intervention. Numerical results in two realistic urban scenarios show that: 1) the proposed approach substantially outperforms heuristic, learning-based, and large language model (LLM)-assisted methods; 2) it achieves highly competitive coverage against the optimal solution while requiring substantially fewer transmitter-level RT evaluations; 3) site-specific reflection effectively turns raw RT feedback into refinement guidance, whereas the dual-mode mechanism preserves diversity and facilitates escape from local optima.