面向实时5G/6G信道估计的相位感知CNN：硬件在环验证
Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation
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中文总结 AI 辅助
该研究针对5G/6G信道估计的相位预测难题，提出相位感知输入编码结合轻量级CNN的方案，经O-RAN硬件在环测试平台验证，实现高准确率、强泛化性与实时推理能力。
中文摘要 AI 辅助
在5G/6G无线系统中，准确且及时的信道估计对于在复杂、快速变化的无线电条件下确保可靠通信至关重要。本研究聚焦于基于导频的信道估计，利用深度学习重构整个子载波网格的幅度和相位，特别强调使用从端到端O-RAN测试平台收集的仿真数据进行评估。该测试平台包含硬件在环（Hardware-in-the-loop）和受控信道仿真，以更好地反映超出纯软件仿真的部署条件。研究解决了经典估计器（如LS、MMSE）以及基于深度学习的方法的主要局限，这些方法因±π处的不连续性难以进行相位预测、对不同UE和天线配置的泛化能力差，且在实时部署时计算效率低下。所提出的系统结合了利用正弦和余弦表示的相位感知输入编码与轻量级卷积神经网络（CNN）架构。该设计实现了高准确性、稳定的相位重构、对测试平台衍生数据集的强泛化能力，以及适用于边缘设备的实时推理。
英文摘要
In 5G/6G wireless systems, accurate and timely channel estimation is critical to ensure reliable communication under complex, fast-changing radio conditions. This work focuses on pilot-based channel estimation using deep learning to reconstruct both magnitude and phase across the full subcarrier grid, with particular emphasis on evaluation using emulated data collected from an end-to-end O-RAN testbed. The testbed includes hardware in the loop and controlled channel emulation to better reflect deployment conditions beyond pure software simulation. It addresses major limitations in classical estimators such as LS and MMSE, as well as deep learning-based approaches that struggle with phase prediction due to discontinuities at $\pm π$, poor generalization to different UE and antenna configurations, and computational inefficiency for real-time deployment. The proposed system combines a phase-aware input encoding using sine and cosine representations with a lightweight Convolutional Neural Network (CNN) architecture. This design achieves high accuracy, stable phase reconstruction, strong generalization across testbed-derived datasets, and real-time inference suitable for edge devices.
发表机构
- Keysight AI Labs（是德科技人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。
