Ultra-Reliable Deep-Reinforcement-Learning-Based Intelligent Downlink Scheduling for 5G New Radio-Vehicle to Infrastructure Scenarios
Author:
Affiliation:
1. State Grid Chongqing Information and Telecommunication Company, Chongqing 400012, China
2. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
Abstract
Publisher
MDPI AG
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Link
https://www.mdpi.com/1424-8220/23/20/8454/pdf
Reference23 articles.
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3. Robust Modulation Classification Over α-Stable Noise Using Graph-Based Fractional Lower-Order Cyclic Spectrum Analysis;Yan;IEEE Trans. Veh. Technol.,2020
4. Mota, M.P., Araujo, D.C., Costa Neto, F.H., de Almeida, A.L.F., and Cavalcanti, F.R. (2019, January 9–13). Adaptive Modulation and Coding Based on Reinforcement Learning for 5G Networks. Proceedings of the 2019 IEEE Globecom Workshops (GC Wkshps), Waikoloa, HI, USA.
5. Tsurumi, S., and Fujii, T. (2018, January 10–12). Reliable Vehicle-to-Vehicle Communication Using Spectrum Environment Map. Proceedings of the 2018 International Conference on Information Networking (ICOIN), Chiang Mai, Thailand.
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