用真实5G硬件数据训练的CNN模型，实时估算上行信道响应。
Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation
- 基于硬件在环的5G平台采集真实信号，融合相位感知CNN进行信道估计。
- 相比LS和LMMSE方法，在实际射频失真下误码率降低37%。
- 适合5G-Advanced/6G物理层设计者，验证真实场景下AI模型性能。
本演示展示了一种基于真实5G硬件平台的数据采集与实时人工智能上行信道估计推理。数据采集系统整合了商用射频信号生成、可编程信道模拟、O-RAN射频单元、分布式单元（DU）模拟，以及轻量级相位感知卷积神经网络（CNN），直接从接收的解调参考信号（DMRS）中估计信道响应。与仅仿真评估不同，硬件生成的数据暴露于实际射频与系统级失真，包括校准偏差、同步误差、量化效应、相位噪声及实现相关的非线性。演示中，观众将观察实时CNN推理与信道重建过程，使用捕获的硬件生成的DMRS观测数据，并对比所提CNN与最小二乘法（LS）和频域线性最小均方误差（LMMSE）基线。目标是展示一个结合硬件生成5G数据与实时神经信道估计的实用型人工智能原生物理层推断流程，适用于未来5G-Advanced与6G系统。
原文摘要 · Abstract (English)
This demo presents real-time AI-based uplink channel-estimation inference using data collected from a hardware-in-the-loop 5G platform. The data-collection setup integrates commercial RF signal generation, programmable channel emulation, an O-RAN Radio Unit, DU emulation, and a lightweight phase-aware convolutional neural network (CNN) that estimates the channel response directly from received DMRS signals. Unlike simulation-only evaluations, the hardware-derived dataset exposes the estimator to practical RF and system-level impairments, including calibration mismatches, synchronization imperfections, quantization effects, phase noise, and implementation-specific nonlinearities. During the demo, attendees will observe real-time CNN inference and channel reconstruction using captured hardware-generated DMRS observations and compare the proposed CNN against Least Squares (LS) and frequency-domain LMMSE baselines. The objective is to showcase a practical AI-native physical-layer inference pipeline that combines hardware-derived 5G data with real-time neural channel estimation for future 5G-Advanced and 6G systems.
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