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Journal Article Hybrid Beamforming and Deep-Learning-Enabled Precoding for O-RAN mmWave Massive MIMO
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Authors
Ngo Hoang Tu, Minhyun Kim, Kyungchun Lee
Issue Date
2025-12
Citation
IEEE Transactions on Wireless Communications, v.25, pp.4053-4069
ISSN
1536-1276
Publisher
IEEE
Language
Korean
Type
Journal Article
DOI
https://dx.doi.org/10.1109/TWC.2025.3607838
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.
Keyword
cellular massive MIMO, deep learning, hybrid beamforming, millimeter wave, non-grid-of-beams, Open radio access network (O-RAN)