# Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning

arXiv: https://arxiv.org/abs/2207.06537 · HTML: https://mlsanity.com/paper/2207.06537/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 69 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation](https://mlsanity.com/paper/1806.07377/) (2018)
- [Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks](https://mlsanity.com/paper/1907.03289/) (2019)
- [Open, Programmable, and Virtualized 5G Networks: State-of-the-Art and the Road Ahead](https://mlsanity.com/paper/2005.10027/) (2020)
- [VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications](https://mlsanity.com/paper/1907.09319/) (2019)
- [Exploring the Practical Limits of Cooperative Awareness in Vehicular Communications](https://mlsanity.com/paper/1503.06590/) (2015)
- [Enhanced C-V2X Mode-4 Subchannel Selection](https://mlsanity.com/paper/1807.04819/) (2018)
- [Study of the Impact of PHY and MAC Parameters in 3GPP C-V2V Mode 4](https://mlsanity.com/paper/1807.10699/) (2018)
- [Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage](https://mlsanity.com/paper/1904.12653/) (2019)
- [Geo-Based Scheduling for C-V2X Networks](https://mlsanity.com/paper/1906.10370/) (2019)

## Abstract (arXiv)

Performance of vehicle-to-vehicle (V2V) communications depends highly on the employed scheduling approach. While centralized network schedulers offer high V2V communication reliability, their operation is conventionally restricted to areas with full cellular network coverage. In contrast, in out-of-cellular-coverage areas, comparatively inefficient distributed radio resource management is used. To exploit the benefits of the centralized approach for enhancing the reliability of V2V communications on roads lacking cellular coverage, we propose VRLS (Vehicular Reinforcement Learning Scheduler), a centralized scheduler that proactively assigns resources for out-of-coverage V2V communications textitbefore vehicles leave the cellular network coverage. By training in simulated vehicular environments, VRLS can learn a scheduling policy that is robust and adaptable to environmental changes, thus eliminating the need for targeted (re-)training in complex real-life environments. We evaluate the performance of VRLS under varying mobility, network load, wireless channel, and resource configurations. VRLS outperforms the state-of-the-art distributed scheduling algorithm in zones without cellular network coverage by reducing the packet error rate by half in highly loaded conditions and achieving near-maximum reliability in low-load scenarios.