发表机构
Hanoi University of Science and Technology (HUST)（河内理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无人机空对地路径损耗预测中环境漂移问题，提出LiLib轻量级持续学习方案，通过窗口残差测试触发专家库重用或新建，显著降低预测误差并恢复吞吐量，证明记忆优于重适应。
AI 中文摘要
作为中继或基站的无人机需要准确的空对地路径损耗预测以进行速率自适应和位置选择，但传播条件会随着无人机在郊区、城市和高层建筑区域之间移动而变化，且相同区域经常被重访。通过遗忘来适应的在线回归器必须从头重新学习每个环境，而在所有数据上训练的单一模型则平均了不兼容的机制。我们提出LiLib，一种轻量级持续学习方案，其中无人机维护一个小型的递归最小二乘专家库。一个窗口化残差测试检测漂移；随后一个短探测阶段要么重用最佳存储的专家，要么创建新的专家。在基于四个标准城市化轮廓的模拟中，LiLib将预测RMSE从5.89 dB（最佳滑动窗口基线）降低到4.03 dB（p < 0.001），在返回已知环境后不久将误差从12.3 dB降低到5.7 dB，并在速率自适应中恢复了机制感知预言机吞吐量的99%。该库在不到0.5 KB中存储四个专家，并在无标签情况下以92%纯度识别机制。当第二架无人机用同伴的库初始化时，其环境变化后的误差减半。LiLib未达到预言机性能，且在阴影效应强时相似机制可能被合并。结果表明，对于重复出现的漂移，记忆比重适应更有效。
英文摘要
UAVs that act as relays or base stations need accurate air-to-ground path-loss predictions for rate adaptation and placement, but propagation conditions change as a UAV moves between suburban, urban and high-rise areas, and the same areas are often revisited. Online regressors that adapt by forgetting must relearn each environment from scratch, whereas a single model trained on all data averages incompatible regimes. We propose LiLib, a lightweight continual-learning scheme in which a UAV maintains a small library of recursive-least-squares experts. A windowed residual test detects drift; a short probe phase then either reuses the best stored expert or creates a new one. In simulations based on four standard urbanization profiles, LiLib reduces prediction RMSE from 5.89 dB (best sliding-window baseline) to 4.03 dB (p < 0.001), lowers the error shortly after a return to a known environment from 12.3 dB to 5.7 dB, and recovers 99% of the throughput of a regime-aware oracle in rate adaptation. The library stores four experts in under 0.5 KB, and identifies regimes with 92% purity without labels. When a second UAV is initialized with the library of a peer, its error after environment changes halves. LiLib does not reach the oracle, and similar regimes may be merged when shadowing is strong. The results indicate that, for recurring drift, remembering is more effective than re-adapting.
