Problem
Research questions and friction points this paper is trying to address.
5G Channel Estimation
Real-Time Inference
Hardware-in-the-Loop
RF Impairments
AI-Native Physical Layer
Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation
🤖 AI Summary
This study addresses performance degradation caused by hardware impairments and the challenges of deploying real-time AI inference in 5G uplink channel estimation. We establish an O-RAN hardware-in-the-loop platform and propose a DMRS-based phase-aware CNN alongside an AI-native physical layer inference workflow. By leveraging a lightweight network trained on empirical hardware data, this approach enables real-time channel estimation that effectively mitigates RF non-idealities. Experimental results demonstrate that the proposed method achieves high-precision, real-time channel reconstruction under realistic hardware impairments, significantly outperforming conventional LS and LMMSE baselines. These findings validate the feasibility and robustness of AI-native architectures for practical deployment in future 6G networks, bridging the gap between theoretical AI models and real-world physical layer implementation.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hardware-in-the-Loop
Phase-Aware CNN
Real-Time Channel Estimation
5G DMRS
AI-Native Physical Layer
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