Cross-Band Channel Impulse Response Prediction: Leveraging 3.5 GHz Channels for Upper Mid-Band

TL;DR

This paper introduces CIR-UNext, a deep learning framework that predicts 7 GHz channel impulse responses using 3.5 GHz data, enhancing 6G network applications with high accuracy and efficiency.

Contribution

It presents a novel deep learning approach combining ray tracing data with attention U-Net models for accurate cross-band channel prediction in 6G networks.

Findings

AU-Net-Aux achieves median gain error of 0.58 dB

Phase prediction error is 0.27 rad

Channel2ComMap outperforms existing throughput prediction methods

Abstract

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.