Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation
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 th...
| Authors | Javad Zolfaghari-Bengar, Rakibul Rony, Elisa Gomez-de-Lope et al. |
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| Primary category | eess.SP |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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