Planning with Language and Generative Models: Toward General Reward-Guided Wireless Network Design
This work addresses the problem of efficient AP planning for wireless network designers, offering a domain-agnostic solution that is incremental over existing generative approaches.
The paper tackled the challenge of intelligent access point (AP) deployment in wireless networks by proposing a diffusion-based generative inference model guided by a unified reward function, which outperformed other methods and demonstrated scalability and generalization on a new large-scale dataset.
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.