A Foundation Model for Cross-Band CSI Reconstruction

Hongpu Zhang, Shu Sun, Ruifeng Gao, Tongjia Zhang, Feng Yang

Acquiring dense high-frequency channel state information (CSI) is costly in multi-band low-altitude wireless systems because pilot resources are limited and channel dimensions change with the carrier frequency, bandwidth, and antenna array size. We address cross-band CSI reconstruction, which recovers dense target-band CSI from dense source-band CSI and sparse, noisy target-band pilots. We propose a foundation model that represents every band in a common power-angle-delay spectrum and uses radio-frequency metadata as auxiliary conditioning for an encoder-decoder. The model uses pilot-guided cross-attention to fuse source-band structure with target-band pilot, allowing one model to handle heterogeneous band pairs. It is trained by pilot-densification pretraining followed by supervised cross-band fine-tuning. On ray-tracing, 3GPP, and DeepMIMO datasets, the model lowers average normalized mean-square error by 6.1 dB over the state-of-the-art pair-specific baseline. It also transfers to two unseen pairs without paired fine-tuning, achieving 7.5 and 7.1 dB gains over fully supervised baselines, and remains effective when target pilots are noisy.