AI 中文总结
该研究提出MultiPathFormer，以多径传播为预训练对象，结合Environmental RAG机制，在27种环境预训练后，在多项无线通信下游任务中超越SOTA模型，验证了路径级预训练的有效性。
AI 中文摘要
机器学习领域的最新进展已支持无线基础模型的训练，这类模型旨在基于无线信号支持信道估计、波束预测、定位等任务。现有无线基础模型通常在信道张量上进行预训练，采用子载波、天线或时间维度的掩码重构方式，但忽略了无线传播的物理特性。本研究中，我们提出将多径传播作为核心预训练对象，构建了MultiPathFormer，这是一种自回归基础模型，将每个发射机-接收机链路表示为连续值路径标记的有序序列，并通过下一路径预测任务进行预训练。我们在Transformer主干网络之上引入了Environmental RAG（检索增强生成）机制和首路径码本，利用环境知识提升延迟、功率等路径统计量的估计性能，幅度可达59%。MultiPathFormer在27种环境上完成预训练后，可迁移至未见过的用户场景，经特定场景微调后，在新环境中训练对应模型的性能优于从头训练的模型。在下游任务中，它的性能超越了基于信道的现有最优（SOTA）基础模型，实现了5.57米的平均定位误差、0.914的Top-3波束准确率、0.994的视距分类准确率以及0.561的信道估计归一化均方误差（NMSE）。这些结果表明，路径级预训练能够学习到可复用的无线传播表征。
英文摘要
Recent advances in machine learning have enabled training of wireless foundation models, which aim to support tasks such as channel estimation, beam prediction, and localization based on wireless signals. Existing wireless foundation models typically pretrain on channel tensors using masked reconstruction over subcarriers, antennas, or time but ignore the physical characteristics of wireless propagation. In this work, we propose to instead use multipath propagation as the fundamental pretraining object. We present MultiPathFormer, an autoregressive foundation model that represents each transmitter-receiver link as an ordered sequence of continuous-valued path tokens and pretrains with next-path prediction. We introduce an Environmental RAG (retrieval-augmented generation) mechanism and a first-path codebook on top of the transformer backbone, leveraging environment knowledge to improve path statistics estimation like delay and power by up to 59%. MultiPathFormer pretrained on 27 environments transfers to unseen users and, after scenario-specific fine-tuning, outperforms training the corresponding models from scratch in new environments. Across downstream tasks, it outperforms SOTA channel-based foundation models, achieving 5.57 m mean localization error, 0.914 top-3 beam accuracy, 0.994 line-of-sight classification accuracy, and 0.561 channel estimation NMSE. These results show that path-level pretraining can learn reusable representations of wireless propagation.
