Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
Open accessAbstract
This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. Fast channel variations in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In response, we model the resource sharing as a multi-agent reinforcement learning problem, which is then solved using a fingerprint-based deep Q-network method that is amenable to a distributed implementation. The V2V links, each acting as an agent, collectively interact with the communication environment, receive distinctive observations yet a common reward, and learn to improve spectrum and power allocation through updating Q-networks using the gained experiences. We demonstrate that with a proper reward design and training mechanism, the multiple V2V agents successfully learn to cooperate in a distributed way to simultaneously improve the sum capacity of V2I links and payload delivery rate of V2V links.
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Due to the dynamic nature of the C-V2X networks and the associated short coherence time interval, centralized solutions are inadequate due to difficulties in acquiring the instantaneous CSI of all links.
“Due to the dynamic nature of the C-V2X networks and the associated short coherence time interval, centralized solutions are inadequate due to difficulties in acquiring the instantaneous CSI of all links [36].”
From Explainable AI-Aided Feature Selection and Model Reduction for DRL-Based V2X Resource Allocation (2025) · cites this paper as [36]
Cites
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Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
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Mao, H., Alizadeh, M., Menache, I., & Kandula, S. (2016) · Proceedings of the 15th ACM Workshop on Hot Topics in Networks · doi:10.1145/3005745.3005750 · Cited by 1,353 · cited 1× · see citing works
“In particular, recent success of deep RL in humanlevel video game play [7] and AlphaGo [8] has sparked a flurry of interest in applying RL techniques to solve problems from a wide variety of areas and remarkable progress has been made ever since [9], [10], [11].”
BackgroundMulti-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
Nasir, Y. S., & Guo, D. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933973 · Cited by 660 · see citing works
Machine learning for vehicular networks: Recent advances and application examples
Ye, H., Liang, L., Ye Li, G., Kim, J., Lu, L., & Wu, M. (2018) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2018.2811185 · Cited by 303 · see citing works
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Wu, Q., Zhao, Y., Fan, Q., Fan, P., Wang, J., & Zhang, C. (2023) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3221271 · Cited by 191 · see citing works
“g i is the small-scale fading effect, which is assumed to be exponentially distributed with unit mean [33].”
Survey on artificial intelligence (AI) techniques for vehicular ad-hoc networks (VANETs)
Mchergui, A., Moulahi, T., & Zeadally, S. (2022) · Vehicular Communications · doi:10.1016/j.vehcom.2021.100403 · Cited by 165 · see citing works
Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective
Nguyen, D. C., Cheng, P., Ding, M., Lopez-Perez, D., Pathirana, P. N., Li, J., Seneviratne, A., Li, Y., & Poor, H. V. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3024783 · Cited by 160 · see citing works
Resource Allocation in V2X Networks: From Classical Optimization to Machine Learning-Based Solutions
Parvini, M., Schulz, P., & Fettweis, G. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3380509 · Cited by 27 · see citing works
Urmonov, O., Aliev, H., & Kim, H. (2023) · IEEE Systems Journal · doi:10.1109/jsyst.2022.3197880 · Cited by 24 · see citing works
Multi-agent deep reinforcement learning based resource management in heterogeneous V2X networks
Zhao, J., Hu, F., Li, J., & Nie, Y. (2025) · Digital Communications and Networks · doi:10.1016/j.dcan.2023.06.003 · Cited by 21 · see citing works
Explainable AI-Aided Feature Selection and Model Reduction for DRL-Based V2X Resource Allocation
Khan, N., Abdallah, A., Celik, A., Eltawil, A. M., & Coleri, S. (2025) · IEEE Transactions on Communications · doi:10.1109/tcomm.2025.3554655 · Cited by 20 · see citing works
Joint Resource Allocation for V2X Sensing and Communication Based on MADDPG
Zhong, Z., & Peng, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3527049 · Cited by 7 · see citing works
- Authors
- Liang, L., Ye, H., & Li, G. Y.
- Affiliations
- Intel (United States) (US)
- Georgia Institute of Technology (US)
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2019-10
- Volume
- 37
- Issue
- 10
- Pages
- 2282-2292
- ISSN
- 0733-8716 (ISSN-L)
- 1558-0008
- OpenAlex
- W2968764495
- Cited by
- 559
- References
- 4
- Citations by year
- 2026: 56 · 2025: 94 · 2024: 105 · 2023: 83 · 2022: 88 · 2021: 89 · 2020: 36 · 2019: 7 · 2012: 1
- FWCI
- 19.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- IEEE Journal on Selected Areas in Communications (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Intel Corporation
- National Science Foundation (1731017, 1815637)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Keywords
- spectrum sharing, vehicular networks, multi-agent reinforcement learning, vehicle-to-vehicle communication, vehicle-to-infrastructure links, deep Q-network, distributed resource allocation, power allocation, reward function design, sum capacity, channel state information
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