用本地大模型让设备间通信自动优化，省电又快。
PEARL: Peer-Enhanced Adaptive Radio via On-Device LLM
- 用设备状态和邻居信息指导无线参数调整
- 比传统方法节能16%，推理延迟低于20毫秒
- 适合需要低功耗实时通信的移动设备
我们提出PEARL（基于本地大模型的同行增强自适应无线电），一种用于设备间（D2D）通信的协作跨层优化框架。在先前单设备本地大模型工作的基础上，PEARL通过融合发布者与订阅者状态，指导Wi-Fi Aware（WA）参数选择。设计了一种上下文感知奖励机制，以应用容忍度归一化延迟，并根据设备电池状态调节能耗，为基于KL的微调提供更丰富的监督信号。研究了两种轻量级变体：PEARL（Head + 低秩适配，LoRA）实现最佳综合性能，而PEARL-Lite（仅头部）在近似相同目标得分下实现亚20毫秒推理。在基于真实测量的合成场景中，PEARL在目标得分上优于启发式与紧凑模型基线，并在协同低电量情况下最高降低16%能耗。结果表明，同伴感知上下文、奖励对齐训练及头部高效设计使大模型适用于持续运行的本地跨层控制。代码、真实演示与数据集见https://github.com/abman23/pearl。
原文摘要 · Abstract (English)
We present PEARL (Peer-Enhanced Adaptive Radio via On-Device LLM), a framework for cooperative cross-layer optimization in device-to-device (D2D) communication. Building on our previous work on single-device on-device LLMs, PEARL extends the paradigm by leveraging both publisher and subscriber states to guide Wi-Fi Aware (WA) parameter selection. A context-aware reward, which normalizes latency by application tolerances and modulates energy by device battery states, provides richer supervision for KL-based finetuning. We study two lightweight variants: PEARL (Head + Low-Rank Adaptation (LoRA)) achieves the best overall performance, while PEARL-Lite (Head-only) delivers sub-20 ms inference at near-identical objective scores. Across synthetic scenarios grounded in real measurements, PEARL improves objective scores over heuristic and compact model baselines and reduces energy by up to 16% in cooperative low-battery cases. These results demonstrate that peer-aware context, reward-aligned training, and head-based efficiency make LLMs practical for always-on, on-device cross-layer control. Code, real-world demo, and dataset are available at https://github.com/abman23/pearl
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