Deep Reinforcement Learning-Driven Hybrid Precoding for Efficient Mm-Wave Multi-User MIMO Systems
High route loss and line-of-sight requirements are two of the fundamental challenges of millimeter-wave (mm-wave) communications that are mitigated by incorporating sensor technology. Sensing gives the deep reinforcement learning (DRL) agent comprehensive environmental feedback, which helps it bette...
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2025
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| Online Access: | http://eprints.uthm.edu.my/12720/1/J19648_a89117491fecf426e024e0bcce680759.pdf http://eprints.uthm.edu.my/12720/ https://doi.org/10.3390/jsan14010020 |
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| author | Salh, Adeb Alhartomi, Mohammed A Ali Hussain, Ghasan Jing Jing, Chang M. Shah, Nor Shahida Alzahrani, Saeed Alsulami, Ruwaybih Alharbi, Saad Hakimi, Ahmad S. Almehmadi, Fares |
| author_facet | Salh, Adeb Alhartomi, Mohammed A Ali Hussain, Ghasan Jing Jing, Chang M. Shah, Nor Shahida Alzahrani, Saeed Alsulami, Ruwaybih Alharbi, Saad Hakimi, Ahmad S. Almehmadi, Fares |
| author_sort | Salh, Adeb |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | High route loss and line-of-sight requirements are two of the fundamental challenges of millimeter-wave (mm-wave) communications that are mitigated by incorporating sensor technology. Sensing gives the deep reinforcement learning (DRL) agent comprehensive environmental feedback, which helps it better predict channel fluctuations and modify beam patterns accordingly. For multi-user massive multiple-input multiple-output (mMIMO) systems, hybrid precoding requires sophisticated real-time low-complexity power allocation (PA) approaches to achieve near-optimal capacity. This study presents a unique angular-based hybrid precoding (AB-HP) framework that minimizes radio frequency (RF) chain and channel estimation while optimizing energy efficiency (EE) and spectral efficiency (SE). DRL is essential for mm-wave technology to make adaptive and intelligent decision-making possible, which effectively transforms wireless communication systems. DRL optimizes RF chain usage to maintain excellent SE while drastically lowering hardware complexity and energy consumption in an AB-HP architecture by dynamically learning optimal precoding methods using environmental angular information. This article
proposes enabling dual optimization of EE and SE while drastically lowering beam training overhead by incorporating maximum reward beam training driven (RBT) in the DRL. The proposed RBT-DRL improves system performance and flexibility by dynamically modifying the number of active RF chains in dynamic network situations. The simulation results show that RBT-DRL-driven beam training guarantees good EE performance for mobile users while increasing SE in mm-wave structures. Even though total power consumption rises by 45%, the SE improves by 39%, increasing from 14 dB to 20 dB, suggesting that this
strategy could successfully achieve a balance between performance and EE in upcoming B5G networks. |
| format | Article |
| id | my.uthm.eprints-12720 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2025 |
| publisher | Mdpi |
| record_format | eprints |
| spelling | my.uthm.eprints-127202025-06-26T00:01:09Z http://eprints.uthm.edu.my/12720/ Deep Reinforcement Learning-Driven Hybrid Precoding for Efficient Mm-Wave Multi-User MIMO Systems Salh, Adeb Alhartomi, Mohammed A Ali Hussain, Ghasan Jing Jing, Chang M. Shah, Nor Shahida Alzahrani, Saeed Alsulami, Ruwaybih Alharbi, Saad Hakimi, Ahmad S. Almehmadi, Fares TK Electrical engineering. Electronics Nuclear engineering High route loss and line-of-sight requirements are two of the fundamental challenges of millimeter-wave (mm-wave) communications that are mitigated by incorporating sensor technology. Sensing gives the deep reinforcement learning (DRL) agent comprehensive environmental feedback, which helps it better predict channel fluctuations and modify beam patterns accordingly. For multi-user massive multiple-input multiple-output (mMIMO) systems, hybrid precoding requires sophisticated real-time low-complexity power allocation (PA) approaches to achieve near-optimal capacity. This study presents a unique angular-based hybrid precoding (AB-HP) framework that minimizes radio frequency (RF) chain and channel estimation while optimizing energy efficiency (EE) and spectral efficiency (SE). DRL is essential for mm-wave technology to make adaptive and intelligent decision-making possible, which effectively transforms wireless communication systems. DRL optimizes RF chain usage to maintain excellent SE while drastically lowering hardware complexity and energy consumption in an AB-HP architecture by dynamically learning optimal precoding methods using environmental angular information. This article proposes enabling dual optimization of EE and SE while drastically lowering beam training overhead by incorporating maximum reward beam training driven (RBT) in the DRL. The proposed RBT-DRL improves system performance and flexibility by dynamically modifying the number of active RF chains in dynamic network situations. The simulation results show that RBT-DRL-driven beam training guarantees good EE performance for mobile users while increasing SE in mm-wave structures. Even though total power consumption rises by 45%, the SE improves by 39%, increasing from 14 dB to 20 dB, suggesting that this strategy could successfully achieve a balance between performance and EE in upcoming B5G networks. Mdpi 2025 Article PeerReviewed text en http://eprints.uthm.edu.my/12720/1/J19648_a89117491fecf426e024e0bcce680759.pdf Salh, Adeb and Alhartomi, Mohammed A and Ali Hussain, Ghasan and Jing Jing, Chang and M. Shah, Nor Shahida and Alzahrani, Saeed and Alsulami, Ruwaybih and Alharbi, Saad and Hakimi, Ahmad and S. Almehmadi, Fares (2025) Deep Reinforcement Learning-Driven Hybrid Precoding for Efficient Mm-Wave Multi-User MIMO Systems. Journal Sensor Actuator Networks, 14 (20). pp. 1-31. https://doi.org/10.3390/jsan14010020 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Salh, Adeb Alhartomi, Mohammed A Ali Hussain, Ghasan Jing Jing, Chang M. Shah, Nor Shahida Alzahrani, Saeed Alsulami, Ruwaybih Alharbi, Saad Hakimi, Ahmad S. Almehmadi, Fares Deep Reinforcement Learning-Driven Hybrid Precoding for Efficient Mm-Wave Multi-User MIMO Systems |
| title | Deep Reinforcement Learning-Driven Hybrid Precoding for Efficient Mm-Wave Multi-User MIMO Systems |
| title_full | Deep Reinforcement Learning-Driven Hybrid Precoding for Efficient Mm-Wave Multi-User MIMO Systems |
| title_fullStr | Deep Reinforcement Learning-Driven Hybrid Precoding for Efficient Mm-Wave Multi-User MIMO Systems |
| title_full_unstemmed | Deep Reinforcement Learning-Driven Hybrid Precoding for Efficient Mm-Wave Multi-User MIMO Systems |
| title_short | Deep Reinforcement Learning-Driven Hybrid Precoding for Efficient Mm-Wave Multi-User MIMO Systems |
| title_sort | deep reinforcement learning-driven hybrid precoding for efficient mm-wave multi-user mimo systems |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.uthm.edu.my/12720/1/J19648_a89117491fecf426e024e0bcce680759.pdf http://eprints.uthm.edu.my/12720/ https://doi.org/10.3390/jsan14010020 |
| url_provider | http://eprints.uthm.edu.my/ |