用AI提升5G基站选址效率，解决规划与建设脱节问题
TelePlanNet: An AI-Driven Framework for Efficient Telecom Network Planning
- 结合大语言模型与强化学习，实现用户意图到基站规划的自动对齐
- 在真实场景中将规划一致性提升至78%，优于人工方法
- 适合运营商用于大规模、动态化网络规划，支持多目标优化
5G基站选址是网络规划中的关键挑战，需兼顾覆盖范围、成本、用户满意度及实际约束。传统人工方法效率低，难以保证规划与施工的一致性；现有AI工具虽提升部分效率，仍难以应对动态环境和多目标需求。为此，我们提出TelePlanNet——一个面向基站选址的AI驱动框架，采用三层架构实现高效规划与大规模自动化。通过大语言模型（LLMs）实时处理用户输入并对齐规划意图，结合改进的群体相对策略优化（GRPO）强化学习训练规划模型，有效解决多目标优化问题，评估候选站点并输出可落地方案。实验结果表明，该框架将规划一致性提升至78%，显著优于人工方法，为电信运营商提供高效、可扩展的网络规划工具。
原文摘要 · Abstract (English)
The selection of base station sites is a critical challenge in 5G network planning, which requires efficient optimization of coverage, cost, user satisfaction, and practical constraints. Traditional manual methods, reliant on human expertise, suffer from inefficiencies and are limited to an unsatisfied planning-construction consistency. Existing AI tools, despite improving efficiency in certain aspects, still struggle to meet the dynamic network conditions and multi-objective needs of telecom operators' networks. To address these challenges, we propose TelePlanNet, an AI-driven framework tailored for the selection of base station sites, integrating a three-layer architecture for efficient planning and large-scale automation. By leveraging large language models (LLMs) for real-time user input processing and intent alignment with base station planning, combined with training the planning model using the improved group relative policy optimization (GRPO) reinforcement learning, the proposed TelePlanNet can effectively address multi-objective optimization, evaluates candidate sites, and delivers practical solutions. Experiments results show that the proposed TelePlanNet can improve the consistency to 78%, which is superior to the manual methods, providing telecom operators with an efficient and scalable tool that significantly advances cellular network planning.
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