Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation

[2608.14676] Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation 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:2608.14676 (eess) [Submitted on 4 Aug 2026] Title: Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation Authors: Javad Zolfaghari-Bengar , Rakibul Rony , Elisa Gomez-de-Lope , Alejandro Villena-Rodriguez , Abhinav Mahadevan , Nicolas Kourtellis View a PDF of the paper titled Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation, by Javad Zolfaghari-Bengar and 5 other authors View PDF HTML (experimental) Abstract: In 5G/6G wireless systems, accurate and timely channel estimation is critical to ensure reliable communication under complex, fast-changing radio conditions. This work focuses on pilot-based channel estimation using deep learning to reconstruct both magnitude and phase across the full subcarrier grid, with particular emphasis on evaluation using emulated data collected from an end-to-end O-RAN testbed. The testbed includes hardware in the loop and controlled channel emulation to better reflect deployment conditions beyond pure software simulation. It addresses major limitations in classical estimators such as LS and MMSE, as well as deep learning-based approaches that struggle with phase prediction due to discontinuities at $\pm \pi$, poor generalization to different UE and antenna configurations, and computational inefficiency for real-time deployment. The proposed system combines a phase-aware input encoding using sine and cosine representations with a lightweight Convolutional Neural Network (CNN) architecture. This design achieves high accuracy, stable phase reconstruction, strong generalization across testbed-derived datasets, and real-time inference suitable for edge devices. Comments: Accepted at the IEEE Conference on Standards for Communications and Networking (CSCN) 2026 Subjects: Signal Processing (eess.SP) ; Machine Learning (cs.LG) Cite as: arXiv:2608.14676 [eess.SP] (or arXiv:2608.14676v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2608.14676 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Javad Zolfaghari-Bengar [ view email ] [v1] Tue, 4 Aug 2026 11:05:13 UTC (1,535 KB) Full-text links: Access Paper: View a PDF of the paper titled Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation, 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.LG eess 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

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