Wireless roaming is a critical yet challenging task for maintaining seamless connectivity in dynamic mobile environments. Conventional threshold-based or heuristic schemes often fail, leading to either sticky or excessive handovers. We introduce the first cross-layer use of an on-device large language model (LLM): high-level reasoning in the application layer that issues real-time actions executed in the PHY/MAC stack. The LLM addresses two tasks: (i) context-aware AP selection, where structured prompts fuse environmental cues (e.g., location, time) to choose the best BSSID; and (ii) dynamic threshold adjustment, where the model adaptively decides when to roam. To satisfy the tight latency and resource budgets of edge hardware, we apply a suite of optimizations-chain-of-thought prompting, parameter-efficient fine-tuning, and quantization. Experiments on indoor and outdoor datasets show that our approach surpasses legacy heuristics and DRL baselines, achieving a strong balance between roaming stability and signal quality. These findings underscore the promise of application-layer LLM reasoning for lower-layer wireless control in future edge systems.

本文解决了无线漫游中无缝连接面临的挑战，特别是传统方法常常导致的粘滞或过度切换问题。提出了一种新颖的方法，通过在应用层使用大型语言模型进行上下文感知的接入点选择和动态阈值调整，最终实验结果表明该方法在漫游稳定性和信号质量之间实现了良好的平衡，有望影响未来边缘系统的无线控制。

基于设备的上下文感知Wi-Fi漫游大型语言模型