场景中的智能体：面向资源高效站点特定基站部署的智能体框架

中文摘要 AI 辅助

提出了一种用于无线网络规划中自主站点特定基站（BS）部署的智能体框架。与依赖人工站点勘测或大量射线追踪（RT）模拟且需要大量人工干预的传统方法不同，所提出的框架在有限的RT评估预算下自主探索并优化基站部署，从而实现资源高效的网络规划。为此，首先从三维（3D）无线数字孪生中构建一个连续的、基于几何的部署动作空间。在该动作空间内，一个智能体团队通过有状态的感知-推理-反思循环运作。具体而言，一个放置智能体首先以两种互补模式生成候选基站部署：一种经验引导模式，用于优化有前景的解决方案；一种探索模式，用于避免陷入局部最优。在通过RT评估候选部署后，一个反思智能体结合场景几何解释RT结果，识别诸如阻塞、重叠覆盖和未覆盖区域等限制性能的因素，并将这些诊断转化为后续部署的指导。通过这一迭代过程，无需人工干预即可积累站点特定经验并优化部署方案。在两个真实城市场景中的数值结果表明：1）所提出的方法显著优于启发式、基于学习和大型语言模型（LLM）辅助的方法；2）与最优解决方案相比，它实现了极具竞争力的覆盖率，同时所需的发射器级RT评估显著更少；3）站点特定反思有效地将原始RT反馈转化为改进指导，而双模式机制保持了多样性并有助于摆脱局部最优。

英文摘要

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.

发表机构

• The University of Hong Kong（香港大学）

机构由 AI 辅助整理，请以论文原文为准。