Accurate cross-band channel prediction is essential for 6G networks, particularly in the upper mid-band (FR3, 7-24 GHz), where penetration loss and blockage are severe. Although ray tracing (RT) provides high-fidelity modeling, it remains computationally intensive, and high-frequency data acquisition is costly. To address these challenges, we propose CIR-UNext, a deep learning framework designed to predict 7 GHz channel impulse responses (CIRs) by leveraging abundant 3.5 GHz CIRs. The framework integrates an RT-based dataset pipeline with attention U-Net (AU-Net) variants for gain and phase prediction. The proposed AU-Net-Aux model achieves a median gain error of 0.58 dB and a phase prediction error of 0.27 rad on unseen complex environments. Furthermore, we extend CIR-UNext into a foundation model, Channel2ComMap, for throughput prediction in MIMO-OFDM systems, demonstrating superior performance compared with existing approaches. Overall, CIR-UNext provides an efficient and scalable solution for cross-band prediction, enabling applications such as localization, beam management, digital twins, and intelligent resource allocation in 6G networks.

翻译：准确的跨频段信道预测对于6G网络至关重要，尤其在上中频段（FR3，7-24 GHz），该频段的穿透损耗与遮挡效应极为严重。尽管射线追踪技术能提供高保真度的信道建模，但其计算开销巨大，且高频数据采集成本高昂。为应对这些挑战，我们提出了CIR-UNext——一种深度学习框架，旨在通过利用丰富的3.5 GHz信道冲激响应数据来预测7 GHz信道冲激响应。该框架将基于射线追踪的数据集处理流程与用于增益和相位预测的注意力U-Net变体相结合。所提出的AU-Net-Aux模型在未见过的复杂环境中实现了0.58 dB的中值增益误差与0.27弧度的相位预测误差。此外，我们将CIR-UNext扩展为基础模型Channel2ComMap，用于MIMO-OFDM系统的吞吐量预测，其性能显著优于现有方法。总体而言，CIR-UNext为跨频段预测提供了一种高效且可扩展的解决方案，能够支持6G网络中的定位、波束管理、数字孪生及智能资源分配等应用。