A mobility-aware 6G-enabled V2X communications using multi-user MC-CDMA-OTFS and car-following Markov mobility model with multi-agent deep Q-learning

Nguyen Huu Trung; Nguyen Thuy Anh

doi:10.15625/2525-2518/23063

Author affiliations

Authors

Nguyen Huu Trung School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, 1 Dai Co Viet, Bach Mai Ward, Ha Noi, Viet Nam https://orcid.org/0000-0002-0928-1684
Nguyen Thuy Anh School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, 1 Dai Co Viet, Bach Mai Ward, Ha Noi, Viet Nam

DOI:

https://doi.org/10.15625/2525-2518/23063

Keywords:

6G, IoV, V2X, MC-CDMA, OTFS, multi-user, Markov chain

Abstract

This paper presents a novel 6G-Enabled vehicle-to-everything (V2X) communication framework designed for the Intelligent Internet of Vehicles (IoV). The proposed system integrates the multi-user access and interference suppression capabilities of multi-carrier code division multiple access (MC-CDMA) with the delay–Doppler domain robustness of orthogonal time frequency space (OTFS) modulation. This hybrid design ensures reliable transmission in high-mobility and dense vehicular environments. Vehicle mobility is modeled using a combination of car-following dynamics and Markov chain-based lane-changing behavior, capturing both longitudinal and lateral motion patterns. The communication system further employs minimum mean square error (MMSE) and zero-forcing (ZF) techniques for multi-user detection at the receiver. To cope with rapid channel variations caused by mobility, we introduce a Multi-Agent Deep Q-learning (DQL) framework for mobility-aware channel tracking. Each agent learns adaptive channel update strategies based on historical channel estimates and mobility context. Simulation results show that the proposed MC-CDMA–OTFS system with DQL tracking achieves up to a 2.5 dB SNR gain at BER = 10⁻³ and improves throughput compared to a conventional OFTS baseline. These findings highlight the potential of the proposed hybrid design as a robust physical-layer foundation for next-generation 6G V2X networks.

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References

1. Roger S., Botella-Mascarell C., Martín-Sacristán D., García-Roger D., Monserrat J. F., Svensson T. – Sustainable mobility in B5G/6G: V2X technology trends and use cases. IEEE Open J. Veh. Technol., 5 (2024) 459–472. https://doi.org/10.1109/ojvt.2024.3375451.

2. Xu X., Yao L., Bilal M., Wan S., Dai F., Choo K.-K. R. – Service migration across edge devices in 6G-enabled internet of vehicles networks. IEEE Internet Things J., 9 (2022) 1930–1937. https://doi.org/10.1109/jiot.2021.3089204.

3. Gimenez-Guzman J. M., Leyva-Mayorga I., Popovski P. – Semantic V2X communications for image transmission in 6G systems. IEEE Network, 38 (2024) 48–54. https://doi.org/10.1109/mnet.2024.3420214.

4. Kulaç S., Şahin M. – Bit error rate performance improvement for orthogonal time frequency space modulation with a selective decode-and-forward cooperative communication scenario in an internet of vehicles system. Sensors, 24 (2024) 5324. https://doi.org/10.3390/s24165324.

5. Khan N., Coleri S. – Event-triggered reinforcement learning based joint resource allocation for ultra-reliable low-latency V2X communications. IEEE Trans. Veh. Technol., 73 (2024) 16991–17006. https://doi.org/10.1109/tvt.2024.3424398.

6. Khan I. A., Mohammed S. K. – A low-complexity OTFS channel estimation method for fractional delay-doppler scenarios. IEEE Wireless Commun. Lett., 12 (2023) 1484–1488. https://doi.org/10.1109/lwc.2023.3274936.

7. Hidalgo C., Arizala A., Iturbe-Olleta N., Brazalez A., Zubizarreta A., Asua E., Rastelli J. P. – A cooperative driving framework for platooning using V2X messages in urban environments. IEEE Trans. Veh. Technol., 74 (2025) 16813–16826. https://doi.org/10.1109/tvt.2025.3578725.

8. Das S. S., Rangamgari V., Tiwari S., Mondal S. C. – Time domain channel estimation and equalization of CP-OTFS under multiple fractional dopplers and residual synchronization errors. IEEE Access, 9 (2021) 10561–10576. https://doi.org/10.1109/access.2020.3046487.

9. Abushattal A., Yazgan A. – Artificial noise physical layer security for V2X joint radar communication systems-based OTFS waveform. IEEE Access, 13 (2025) 87884–87897. https://doi.org/10.1109/access.2025.3571491.

10. Umakoglu I., Namdar M., Basgumus A. – Deep learning-assisted signal detection for OTFS-NOMA systems. IEEE Access, 12 (2024) 119105–119115. https://doi.org/10.1109/access.2024.3449812.

11. Bagadi K., C. V. R., Sathish K., Alibakhshikenari M., Virdee B. S., Kouhalvandi L., Olan-Nunez K. N., Pau G., See C. H., Dayoub I., et al. – Detection of signals in MC–CDMA using a novel iterative block decision feedback equalizer. IEEE Access, 10 (2022) 105674–105684. https://doi.org/10.1109/access.2022.3211392.

12. Yan Y., Ma M. – Novel frequency-domain oversampling receiver for CP MC-CDMA systems. IEEE Commun. Lett., 19 (2015) 661–664. https://doi.org/10.1109/lcomm.2015.2391103.

13. Tang Z., Jiang Z., Pan W., Zeng L. – The estimation method of sensing parameters based on OTFS. IEEE Access, 11 (2023) 66035–66049. https://doi.org/10.1109/access.2023.3289976.

14. Xie T., Li S. – Secondary transmission and channel estimation for symbiotic radio with orthogonal time frequency space modulation. IEEE Open J. Veh. Technol., 6 (2025) 1872–1880. https://doi.org/10.1109/ojvt.2025.3572387.

15. Abid M. H., Talin I. A., Matin M. A., Kadir M. I. – Wavelet-aided OTFS: A 2-D multicarrier modulation format for high-mobility communications. IEEE Open J. Commun. Soc., 6 (2025) 2660–2678. https://doi.org/10.1109/ojcoms.2025.3533027.

16. Sheikh-Hosseini M., Rahdari F., Ghasemnezhad H., Ahmadi S., Uysal M. – A comparative performance evaluation of OFDM, GFDM, and OTFS in impulsive noise channels. IEEE Open J. Commun. Soc., 6 (2025) 2693–2705. https://doi.org/10.1109/ojcoms.2024.3506723.

17. Li X., Guo T. N., Mackenzie A. B. – Multi-agent reinforcement learning with measured difference reward for multi-association in ultra-dense mmWave network. IEEE Access, 10 (2022) 118747–118758. https://doi.org/10.1109/access.2022.3221455.

18. Abid M. H., Talin I. A., Kadir M. I. – Reconfigurable intelligent surface-aided orthogonal time frequency space and its deep learning-based signal detection. IEEE Access, 11 (2023) 47321–47338. https://doi.org/10.1109/access.2023.3273297.

19. Annu, Rajalakshmi P. – Towards 6G V2X sidelink: Survey of resource allocation-mathematical formulations, challenges, and proposed solutions. IEEE Open J. Veh. Technol., 5 (2024) 344–383. https://doi.org/10.1109/ojvt.2024.3368240.

20. Jamal M., Ullah Z., Naeem M., Abbas M., Coronato A. – A hybrid multi-agent reinforcement learning approach for spectrum sharing in vehicular networks. Future Internet, 16 (2024) 152. https://doi.org/10.3390/fi16050152.

21. Solaija M. S. J., Zegrar S. E., Arslan H. – Delay-Doppler-based key generation using OTFS. IEEE Wireless Commun. Lett., 12 (2023) 1474–1478. https://doi.org/10.1109/lwc.2023.3279320.

22. Ma Y., Ma G., Ai B., Liu J., Wang N., Zhong Z. – OTFCS-modulated waveform design for joint grant-free random access and positioning in C-V2X. IEEE J. Sel. Areas Commun., 42 (2024) 103–119. https://doi.org/10.1109/jsac.2023.3322796.

23. Ge Y., Deng Q., González D. G., Guan Y. L., Ding Z. – OTFS signaling for SCMA with coordinated multi-point vehicle communications. IEEE Trans. Veh. Technol., 72 (2023) 9044–9057. https://doi.org/10.1109/tvt.2023.3248119.