Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation

[2608.14709] Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2608.14709 (eess) [Submitted on 11 Aug 2026] Title: Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation Authors: Javad Zolfaghari-Bengar , Rakibul Rony , Elisa Gomez-de-Lope , Alejandro Villena-Rodriguez , Abhinav Mahadevan , Nicolas Kourtellis View a PDF of the paper titled Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation, by Javad Zolfaghari-Bengar and 5 other authors View PDF HTML (experimental) 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. Comments: This demo paper has been accepted at IEEE CSCN 2026 Subjects: Signal Processing (eess.SP) ; Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT); Machine Learning (cs.LG) Cite as: arXiv:2608.14709 [eess.SP] (or arXiv:2608.14709v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2608.14709 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Javad Zolfaghari-Bengar [ view email ] [v1] Tue, 11 Aug 2026 10:45:37 UTC (1,048 KB) Full-text links: Access Paper: View a PDF of the paper titled Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation, by Javad Zolfaghari-Bengar and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-08 Change to browse by: cs cs.CV cs.IT cs.LG eess math math.IT References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation#609112
• CNN-based compensation of faulty planar phased-array radiation patterns#7728
• Knowledge Distillation for mmWave Beam Prediction Using Sub-6 GHz Channels#798758
• Knowledge Distillation for mmWave Beam Prediction Using Sub-6 GHz Channels#797344
• GreenDyGNN: Runtime-Adaptive Energy-Efficient Communication for Distributed GNN Training#138903