Deep Reinforcement Learning-Based Vehicle-to-Vehicle Resource Allocation 


Vol. 47,  No. 10, pp. 1565-1567, Oct.  2022
10.7840/kics.2022.47.10.1565


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  Abstract

In this paper, we propose a deep reinforcement learning (DRL)-based method to satisfy diverse requirements in vehicular communications. Using the channel as the input, the actor-critic algorithm outputs the resource allocation maximizing the network sum rate while ensuring the requirements.

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[IEEE Style]

J. Moon and B. Shim, "Deep Reinforcement Learning-Based Vehicle-to-Vehicle Resource Allocation," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 10, pp. 1565-1567, 2022. DOI: 10.7840/kics.2022.47.10.1565.

[ACM Style]

Jihoon Moon and Byonghyo Shim. 2022. Deep Reinforcement Learning-Based Vehicle-to-Vehicle Resource Allocation. The Journal of Korean Institute of Communications and Information Sciences, 47, 10, (2022), 1565-1567. DOI: 10.7840/kics.2022.47.10.1565.

[KICS Style]

Jihoon Moon and Byonghyo Shim, "Deep Reinforcement Learning-Based Vehicle-to-Vehicle Resource Allocation," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 10, pp. 1565-1567, 10. 2022. (https://doi.org/10.7840/kics.2022.47.10.1565)