用于实时5G信道估计的硬件在环相位感知CNN
Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation
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中文总结 AI 辅助
本研究提出一种硬件在环相位感知CNN，结合硬件生成的5G数据实现实时5G信道估计，性能优于LS和LMMSE基准，可用于未来5G-Advanced及6G系统。
中文摘要 AI 辅助
本演示展示了使用硬件在环5G平台采集的数据进行的实时基于AI的上行链路信道估计推理。数据采集装置集成了商用RF信号生成器、可编程信道仿真器、O-RAN射频单元、DU仿真器，以及一个轻量型相位感知卷积神经网络（CNN），该网络可直接从接收的DMRS信号中估计信道响应。与仅基于仿真的评估不同，硬件生成的数据集使估计器面临实际RF和系统级损伤，包括校准失配、同步缺陷、量化效应、相位噪声及特定实现的非线性。演示期间，参会者将观察使用捕获的硬件生成DMRS观测值进行的实时CNN推理与信道重建，并将所提CNN与最小二乘（LS）及频域LMMSE基准进行对比。目标是展示一种实用的AI原生物理层推理管线，其结合硬件生成的5G数据与实时神经信道估计，以应用于未来5G-Advanced及6G系统。
英文摘要
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.
发表机构
- Keysight AI Labs（是德科技AI实验室）
机构由 AI 辅助整理，请以论文原文为准。
