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Taylan Şahin, Mate Boban, Ramin Khalili and Adam Wolisz, "iVRLS: In-coverage Vehicular Reinforcement Learning Scheduler," Proceedings of 93rd IEEE Vehicular Technology Conference (VTC 2021-Spring), Virtual Conference, April 2021.


Cellular networks enable high reliability of vehicle-to-vehicle (V2V) communications thanks to centralized, efficient coordination of radio resources. Collision-free transmissions are possible, where base stations could allocate orthogonal resources to the vehicles. However, in case of limited resources in relation to the data traffic load, the resource allocation task becomes a challenge. Current solutions propose heuristic algorithms that focus on resource reuse, often based on the location of the vehicles. Such schedulers are mainly designed assuming ideal network coverage conditions and are prone to performance degradation in case of coverage loss. Further, they typically rely on frequent scheduling updates, which increases the dependency on coverage. In this paper, we propose a reinforcement learning-based approach to scheduling V2V communications. Our solution, called iVRLS, delivers higher reliability than an enhanced version of a state-of-the-art benchmark algorithm in case of intermittent coverage conditions, while requiring less frequent scheduling. Following this approach, we enable a unified scheduler deployment irrespective of coverage, which offers graceful performance behavior across varying coverage conditions, thus making iVRLS a robust alternative to existing schedulers.

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Taylan Şahin
Mate Boban
Ramin Khalili
Adam Wolisz

BibTeX reference

    author = {{\c{S}}ahin, Taylan and Boban, Mate and Khalili, Ramin and Wolisz, Adam},
    doi = {10.1109/vtc2021-spring51267.2021.9448993},
    title = {{iVRLS: In-coverage Vehicular Reinforcement Learning Scheduler}},
    publisher = {IEEE},
    issn = {2577-2465},
    isbn = {978-1-7281-8964-2},
    address = {Virtual Conference},
    booktitle = {93rd IEEE Vehicular Technology Conference (VTC 2021-Spring)},
    month = {4},
    year = {2021},

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