Open AccessLow-latency resource elements scheduling based on deep reinforcement learning model for UAV video in 5G network
Author(s) -
Jilian Jiang,
Yuhe Qiu,
Yanqing Su,
Jian Zhou
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1827/1/012071
Subject(s) - computer science , scheduling (production processes) , latency (audio) , reinforcement learning , distributed computing , real time computing , computer network , mathematical optimization , artificial intelligence , telecommunications , mathematics
We consider the problem of resource elements allocation in a network environment with multiple users. Previous studies have done a lot of works using traditional methods in terms of bandwidth allocation, which is sufficient to serve for 4G network. However, it cannot be neglected to provide more efficient and intelligent scheduling policies in haste, due to growing demands on high resolution video and image transmission in 5G network. To fit the condition taking resource elements as scheduling unit in 5G network, we proposed deep Q network (DQN) algorithm based on the requirement of low time latency and high resource utilization rate to solve resource elements (RE) scheduling problem. Ultimately, we give out the optimal allocation scheme of resource elements (RE) for four users in fixed condition of time latency and resource utilization rate.