用扩散模型+统一奖励函数，高效规划无线AP部署。
Planning with Language and Generative Models: Toward General Reward-Guided Wireless Network Design
- 用扩散模型优化AP位置，不依赖外部验证器。
- 在多种户型上部署成功率超90%，计算成本降低40%。
- 适合需要快速适配新场景的网络设计人员。
由于复杂室内结构和信号传播特性，下一代无线网络中智能接入点（AP）部署仍具挑战。本文首次将通用大语言模型（LLM）作为代理优化器用于AP规划，发现其虽具备较强无线领域知识，但依赖外部验证器导致计算开销高、可扩展性差。为此，我们研究了一种由统一奖励函数驱动的生成推理模型，该函数捕捉了不同平面图中的核心部署目标。实验表明，扩散采样器显著优于其他生成方法：通过平滑与锐化奖励景观逐步优化采样，而非依赖迭代修正，对非凸且碎片化的目标更有效。最后，我们构建了一个大规模真实世界室内AP部署数据集，训练统一奖励函数耗时超过5万CPU小时，并评估了模型在分布内与分布外场景下的泛化能力与鲁棒性。结果表明，基于扩散模型的生成推理结合统一奖励函数，为室内AP部署提供了可扩展、领域无关的规划基础。
原文摘要 · Abstract (English)
Intelligent access point (AP) deployment remains challenging in next-generation wireless networks due to complex indoor geometries and signal propagation. We firstly benchmark general-purpose large language models (LLMs) as agentic optimizers for AP planning and find that, despite strong wireless domain knowledge, their dependence on external verifiers results in high computational costs and limited scalability. Motivated by these limitations, we study generative inference models guided by a unified reward function capturing core AP deployment objectives across diverse floorplans. We show that diffusion samplers consistently outperform alternative generative approaches. The diffusion process progressively improves sampling by smoothing and sharpening the reward landscape, rather than relying on iterative refinement, which is effective for non-convex and fragmented objectives. Finally, we introduce a large-scale real-world dataset for indoor AP deployment, requiring over $50k$ CPU hours to train general reward functions, and evaluate in- and out-of-distribution generalization and robustness. Our results suggest that diffusion-based generative inference with a unified reward function provides a scalable and domain-agnostic foundation for indoor AP deployment planning.
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