用手机端大模型智能判断何时换Wi-Fi，减少卡顿和频繁切换。
On-Device LLM for Context-Aware Wi-Fi Roaming
- 在设备端运行大模型，结合位置时间等信息选最优信号源。
- 自适应调整切换阈值，实测比传统方法减少37%无效切换。
- 适合边缘计算、智能终端场景，尤其对移动用户有帮助。
无线局域网（Wi-Fi）中的漫游是动态移动环境中维持无缝连接的关键挑战。传统基于阈值或启发式的方法常导致“粘滞”或“过度切换”。本文首次提出在设备端部署大语言模型（LLM），实现应用层的高层推理，并将实时动作下发至物理层/媒体访问控制层。该模型解决两大任务：(i) 上下文感知的接入点（AP）选择，通过结构化提示融合环境线索（如位置、时间）确定最佳BSSID；(ii) 动态阈值调节，由模型自适应判断是否进行漫游。为满足边缘硬件的严苛延迟与资源限制，采用链式思维提示、参数高效微调和量化等优化技术。在室内与室外数据集上的实验表明，该方法优于传统启发式及深度强化学习基线，在漫游稳定性与信号质量间取得良好平衡。结果验证了应用层大模型推理在下一代边缘系统中用于底层无线控制的巨大潜力。
原文摘要 · Abstract (English)
Roaming in Wireless LAN (Wi-Fi) 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.
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