Multi-head deep Q-learning for continuous beamforming with selective MC-CDMA operation in V2X highway communications
Abstract This study investigates a large-scale dynamic Vehicle-to-Everything (V2X) communication network, in which multiple Roadside Units (RSUs) are deployed along highways to enable high-speed vehicular links. To ensure robust and adaptive performance under fast-varying conditions, we propose an i...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-16016-2 |
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| _version_ | 1849344055574528000 |
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| author | Nguyen Huu Trung Nguyen Thuy Anh Fuqiang Liu |
| author_facet | Nguyen Huu Trung Nguyen Thuy Anh Fuqiang Liu |
| author_sort | Nguyen Huu Trung |
| collection | DOAJ |
| description | Abstract This study investigates a large-scale dynamic Vehicle-to-Everything (V2X) communication network, in which multiple Roadside Units (RSUs) are deployed along highways to enable high-speed vehicular links. To ensure robust and adaptive performance under fast-varying conditions, we propose an integrated framework that combines resource block-based MC-CDMA modulation with dynamic beamforming optimized for complex propagation environments. A custom code mapper and resource element (RE) allocator are introduced to support interference-aware transmission and enhance signal robustness in dense deployment scenarios. The MC-CDMA scheme enables extended-range coverage per RSU, outperforming traditional OFDM-based transmission in terms of reliability and scalability. To further optimize performance, a Deep Reinforcement Learning (DRL) model is employed to jointly handle beam tracking and time-varying channel conditions. Specifically, a physics-inspired Deep Q-Learning (DQL) strategy is proposed, using a force-arm-based mechanism to adaptively correct beam misalignment caused by mobility and Doppler effects. Simulation results demonstrate that the proposed system achieves significant improvements in bit error rate (BER), bitrate stability, handover smoothness, and spectral efficiency. When equipped with a large-scale antenna array, the system ensures continuous beam tracking and substantially outperforms conventional RL-based techniques. These results highlight its potential for future 6G-enabled V2X deployments, where scalability, adaptability, and robust link quality are essential. |
| format | Article |
| id | doaj-art-8180c4f532d648878c4c8a7544fb42b0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8180c4f532d648878c4c8a7544fb42b02025-08-20T03:42:45ZengNature PortfolioScientific Reports2045-23222025-08-0115112410.1038/s41598-025-16016-2Multi-head deep Q-learning for continuous beamforming with selective MC-CDMA operation in V2X highway communicationsNguyen Huu Trung0Nguyen Thuy Anh1Fuqiang Liu2School of Electrical and Electronic Engineering, Hanoi University of Science and TechnologySchool of Electrical and Electronic Engineering, Hanoi University of Science and TechnologySchool of Electronics and Information Engineering, Tongji UniversityAbstract This study investigates a large-scale dynamic Vehicle-to-Everything (V2X) communication network, in which multiple Roadside Units (RSUs) are deployed along highways to enable high-speed vehicular links. To ensure robust and adaptive performance under fast-varying conditions, we propose an integrated framework that combines resource block-based MC-CDMA modulation with dynamic beamforming optimized for complex propagation environments. A custom code mapper and resource element (RE) allocator are introduced to support interference-aware transmission and enhance signal robustness in dense deployment scenarios. The MC-CDMA scheme enables extended-range coverage per RSU, outperforming traditional OFDM-based transmission in terms of reliability and scalability. To further optimize performance, a Deep Reinforcement Learning (DRL) model is employed to jointly handle beam tracking and time-varying channel conditions. Specifically, a physics-inspired Deep Q-Learning (DQL) strategy is proposed, using a force-arm-based mechanism to adaptively correct beam misalignment caused by mobility and Doppler effects. Simulation results demonstrate that the proposed system achieves significant improvements in bit error rate (BER), bitrate stability, handover smoothness, and spectral efficiency. When equipped with a large-scale antenna array, the system ensures continuous beam tracking and substantially outperforms conventional RL-based techniques. These results highlight its potential for future 6G-enabled V2X deployments, where scalability, adaptability, and robust link quality are essential.https://doi.org/10.1038/s41598-025-16016-26GV2XMC-CDMAContinuous beamformingMulti-Head deep Q-Learning |
| spellingShingle | Nguyen Huu Trung Nguyen Thuy Anh Fuqiang Liu Multi-head deep Q-learning for continuous beamforming with selective MC-CDMA operation in V2X highway communications Scientific Reports 6G V2X MC-CDMA Continuous beamforming Multi-Head deep Q-Learning |
| title | Multi-head deep Q-learning for continuous beamforming with selective MC-CDMA operation in V2X highway communications |
| title_full | Multi-head deep Q-learning for continuous beamforming with selective MC-CDMA operation in V2X highway communications |
| title_fullStr | Multi-head deep Q-learning for continuous beamforming with selective MC-CDMA operation in V2X highway communications |
| title_full_unstemmed | Multi-head deep Q-learning for continuous beamforming with selective MC-CDMA operation in V2X highway communications |
| title_short | Multi-head deep Q-learning for continuous beamforming with selective MC-CDMA operation in V2X highway communications |
| title_sort | multi head deep q learning for continuous beamforming with selective mc cdma operation in v2x highway communications |
| topic | 6G V2X MC-CDMA Continuous beamforming Multi-Head deep Q-Learning |
| url | https://doi.org/10.1038/s41598-025-16016-2 |
| work_keys_str_mv | AT nguyenhuutrung multiheaddeepqlearningforcontinuousbeamformingwithselectivemccdmaoperationinv2xhighwaycommunications AT nguyenthuyanh multiheaddeepqlearningforcontinuousbeamformingwithselectivemccdmaoperationinv2xhighwaycommunications AT fuqiangliu multiheaddeepqlearningforcontinuousbeamformingwithselectivemccdmaoperationinv2xhighwaycommunications |