Hybrid Beamforming and Deep-Learning-Enabled Precoding for O-RAN mmWave Massive MIMO

Hybrid Beamforming and Deep-Learning-Enabled Precoding for O-RAN mmWave Massive MIMO | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 8 September 2025 V1 Latest version Share on Hybrid Beamforming and Deep-Learning-Enabled Precoding for O-RAN mmWave Massive MIMO Authors : Ngo Hoang Tu 0000-0003-1944-6056 [email protected] , Minhyun Kim , and Kyungchun Lee Authors Info & Affiliations https://doi.org/10.22541/au.175735393.34865031/v1 Published IEEE Transactions on Wireless Communications Version of record Peer review timeline 161 views 213 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This work investigates cellular millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems within the open radio access network (O-RAN) architecture, integrating the compatible spectrum, air interface, and networking entities of beyond fifth-generation wireless networks. To overcome O-RAN fronthaul (O-FH) load limitations and the short wavelength inherent in mmWave bands, we design a hybrid beamforming architecture with digital and analog beamformers generated at the O-RAN distributed unit and O-RAN radio unit, respectively. Using the information theory, we develop non-grid-of-beams analog beamformers to maximize the sum-spectral efficiency (SE) under constant-modulus constraints. For digital precoding, we apply a successive convex approximation method with second-order cone program procedures to maximize sum-SE, while addressing transmit power and limited O-FH load constraints, and ensuring user quality of service requirements. Sub-optimal digital combiners are also designed based on the inherent characteristics of the user side. However, the current optimization approach suffers from long execution times, posing challenges for near-real-time beamforming configurations. To address this issue, we propose an efficient deep learning (DL)-based digital precoding scheme with short execution time, low computational complexity, and high performance. Numerical results demonstrate that the proposed DL-based precoding scheme provides superior performance compared to benchmark schemes, generalizes well to environments with imperfect CSI and user mobility, and scales effectively to massive MIMO configurations. Supplementary Material File (techrxiv j14__ieee_twc_oran_cellular_dynamic_hbf__camera_ready_submission.pdf) Download 1.55 MB Information & Authors Information Version history V1 Version 1 08 September 2025 Peer review timeline Published IEEE Transactions on Wireless Communications Version of Record 1 Jan 2026 Published Copyright This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License Keywords and deep learning cellular massive mimo hybrid beamforming millimeter wave non-grid-of-beams open radio access network (o-ran) Authors Affiliations Ngo Hoang Tu 0000-0003-1944-6056 [email protected] View all articles by this author Minhyun Kim View all articles by this author Kyungchun Lee View all articles by this author Metrics & Citations Metrics Article Usage 161 views 213 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ngo Hoang Tu, Minhyun Kim, Kyungchun Lee. Hybrid Beamforming and Deep-Learning-Enabled Precoding for O-RAN mmWave Massive MIMO. Authorea . 08 September 2025. DOI: https://doi.org/10.22541/au.175735393.34865031/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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