6G Technologies for Vehicular Ad Hoc Networks: A Survey of Enablers, Architectures, and Open Challenges
Keywords:
6G, VANET, V2X, Terahertz, Reconfigurable intelligent surface, Integrated sensing and communication, Edge intelligence, Nonterrestrial networks
Abstract
Vehicular ad hoc networks (VANETs) are evolving from best-effort safety-message broadcasting toward a data-intensive substrate for cooperative automated driving, remote driving, and immersive in-vehicle services. These applications demand sub-millisecond latency, reliability beyond 1−10−7, centimetre-level positioning, and multi-Gbps throughput under extreme mobility, which exceeds what DSRC, LTEV2X, and 5G NR-V2X can jointly guarantee. This survey reviews the sixth-generation (6G) technologies most relevant to VANETs: terahertz and sub-THz communications, reconfigurable intelligent surfaces, integrated sensing and communication, native artificial intelligence, edge and fog computing with digital twins, integrated space–air–ground networks, and next-generation security and privacy mechanisms. For each enabler we summarise the operating principle, its specific benefit for vehicular links, representative research results, and the vehicular-specific obstacles that remain. We then propose a layered reference architecture for 6G-enabled VANETs, compare the enablers against key performance indicators, and identify open research directions, including mobilityaware beam management at THz, real-time RIS control, trustworthy and explainable AI, and standardisation of IMT-2030 V2X.
References
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18. Liu, F., et al.: Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond. IEEE Journal on Selected Areas in Communications 40(6), 1728–1767 (2022). https://doi.org/10.1109/JSAC.2022.3156632 19. Liu, F., Yuan, W., Masouros, C., Yuan, J.: Radar-assisted predictive beamforming for vehicular links: Communication served by sensing. IEEE Transactions on Wireless Communications 19(11), 7704–7719 (2020). https://doi.org/10.1109/ TWC.2020.3015735 20. Lu, Y., Huang, X., Zhang, K., Maharjan, S., Zhang, Y.: Blockchain empowered asynchronous federated learning for secure data sharing in Internet of Vehicles. IEEE Transactions on Vehicular Technology 69(4), 4298–4311 (2020). https:// doi.org/10.1109/TVT.2020.2973651 21. Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials 19(4), 2322–2358 (2017). https://doi.org/10.1109/COMST.2017. 2745201 22. Noor-A-Rahim, M., et al.: 6G for vehicle-to-everything (V2X) communications:
Enabling technologies, challenges, and opportunities. Proceedings of the IEEE 110(6), 712–734 (2022). https://doi.org/10.1109/JPROC.2022.3173031 23. Quraishi, S.J., Raj, D., Ather, D., Gupta, A.K., Setia, S., Pathak, S.: A comparative analysis of post-quantum cryptographic schemes over discrete algebraic structures for enhanced information security in large-scale networks. Journal of Discrete Mathematical Sciences and Cryptography 29(9-A), 3435–3451 (2026). https://doi.org/10.47974/JDMSC-2860 24. Rai, S., Upadhyay, A.K., Sharma, D., Raj, D., Gupta, A.K., Ather, D.: Quantum cryptography—A modern approach. Journal of Discrete Mathematical Sciences and Cryptography 26(7), 1991–2006 (2023). https://doi.org/10.47974/ JDMSC-1839 25. Raj, D., Ather, D., Sagar, A.K.: Advancing vehicular ad-hoc network solutions in emerging economies: A comparative analysis of V2V protocols through simulation studies. SN Computer Science 5, 1077 (2024). https://doi.org/10.1007/ s42979-024-03411-1 26. Raj, D., Ather, D., Sagar, A.K.: Integrative strategies for enhancing energy efficiency in AI-driven heterogeneous networks using adaptive load balancing. In: Data Mining and Information Security (ICDMIS 2024). LNNS, vol. 1385, pp. 149–162. Springer, Singapore (2026). https://doi.org/10.1007/978-981-96-6046-9_10 27. Raj, D., Sagar, A.K.: Vehicular ad-hoc networks: A review on applications and security. In: Communications in Computer and Information Science, vol. 1921, pp. 241–255. Springer, Cham (2023). https://doi.org/10.1007/ 978-3-031-45124-9_19 28. Raj, D., Sagar, A.K., Ather, D.: Optimized channel allocation algorithm for performance enhancement in heterogeneous networks. MATRIX Academic International Online Journal of Engineering and Technology 8(1), 1–12 (2025). https://www.maiojet.com/index.php/matrix/article/view/74 29. Raj, D., Sagar, A.K., Ather, D.: Optimizing vertical handover using multi-criteria decision making in heterogeneous networks. Journal of Information Technology Management 17(Special Issue), 63–86 (2025). https://doi.org/10.22059/jitm. 2025.102922
30. Rappaport, T.S., et al.: Wireless communications and applications above 100GHz: Opportunities and challenges for 6G and beyond. IEEE Access 7, 78729–78757 (2019). https://doi.org/10.1109/ACCESS.2019.2921522 31. Saad, W., Bennis, M., Chen, M.: A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Network 34(3), 134–142 (2020). https://doi.org/10.1109/MNET.001.1900287 32. Shukla, R.S., Ather, D., Raj, D., Jain, A., Sarkar, T., Rakhra, M.: A secure and intelligent framework for cyber threat detection using machine learning. Journal of Discrete Mathematical Sciences and Cryptography 29(6), 2441–2457 (2026). https://doi.org/10.47974/JDMSC-2752 33. Singh, M.K., Pal, K., Reddy, L.C., Verma, A.S., Raj, D.: Secure and efficient integration of blockchain, digital twins, and metaverse for real-time asset management. In: Proc. 3rd ICDT 2025, Greater Noida, India. pp. 25–29. IEEE (2025). https://doi.org/10.1109/ICDT63985.2025.10986435 34. Tang, F., Kawamoto, Y., Kato, N., Liu, J.: Future intelligent and secure vehicular network toward 6G: Machine-learning approaches. Proceedings of the IEEE 108(2), 292–307 (2020). https://doi.org/10.1109/JPROC.2019.2954595 35. Wang, C.-X., et al.: On the road to 6G: Visions, requirements, key technologies, and testbeds. IEEE Communications Surveys & Tutorials 25(2), 905–974 (2023).
https://doi.org/10.1109/COMST.2023.3249835 36. Wang, M., Zhu, T., Zhang, T., Zhang, J., Yu, S., Zhou, W.: Security and privacy in 6G networks: New areas and new challenges. Digital Communications and Networks 6(3), 281–291 (2020). https://doi.org/10.1016/j.dcan.2020.07.003 37. Wei, Z., et al.: Integrated sensing and communication signals toward 5G-A and 6G: A survey. IEEE Internet of Things Journal 10(13), 11068–11092 (2023). https: //doi.org/10.1109/JIOT.2023.3235618 38. Wu, Q., Zhang, R.: Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network. IEEE Communications Magazine 58(1), 106–112 (2020). https://doi.org/10.1109/MCOM.001.1900107 39. Ye, H., Li, G.Y., Juang, B.-H.F.: Deep reinforcement learning based resource allocation for V2V communications. IEEE Transactions on Vehicular Technology 68(4), 3163–3173 (2019). https://doi.org/10.1109/TVT.2019.2897134 40. Zeng, Y., Zhang, R., Lim, T.J.: Wireless communications with unmanned aerial vehicles: Opportunities and challenges. IEEE Communications Magazine 54(5), 36–42 (2016). https://doi.org/10.1109/MCOM.2016.7470933
Published
2026-10-29
How to Cite
Dharm Raj. (2026). 6G Technologies for Vehicular Ad Hoc Networks: A Survey of Enablers, Architectures, and Open Challenges. MATRIX Academic International Online Journal Of Engineering And Technology, 9(2), 31-42. Retrieved from https://www.maiojet.com/index.php/matrix/article/view/119
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