想做无线信道估计或波束预测的可以看看，它用路径预测代替传统掩码重建，环境RAG让延迟估计直接涨59%，效果比现有模型好。
MultiPathFormer将每个收发链路表示为连续路径令牌的有序序列，采用自回归下一路径预测进行预训练。它引入环境RAG机制和首路径码本，使时延和功率等路径统计估计提升最高59%。模型在27个环境中预训练，可迁移到未见用户，经场景微调后在新环境中优于从零训练。在下游任务中，MultiPathFormer实现5.57米平均定位误差、0.914前3波束准确率、0.994视距分类准确率和0.561信道估计NMSE，全面超越现有基于信道的模型。
MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation
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.