Abstract
The fast growth of time-sensitive applications calls for the optimization of radio access network (RAN) scheduling. We consider the problem of RAN scheduling of a mix of periodic and burst traffic and design a reinforcement learning method for the age of information and throughput optimization. The periodic traffic is generated with a fixed frequency and the burst traffic is generated by the Poisson Pareto Burst Process. We firstly formulate the scheduling problem as a non-linear integer programming problem. Then, we focus on the reinforcement learning method modeling and solve it via the Proximal Policy Optimization algorithm. Our evaluations show that the suggested reinforcement algorithm outperforms the classical algorithms without any prior knowledge of the arriving traffic.
Original language | English |
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Title of host publication | 2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings |
Publisher | IEEE |
Publication date | 2022 |
Article number | 9861011 |
ISBN (Electronic) | 9781665482431 |
DOIs | |
Publication status | Published - 2022 |
Event | 95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring - Helsinki, Finland Duration: 19 Jun 2022 → 22 Jun 2022 |
Conference
Conference | 95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring |
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Country/Territory | Finland |
City | Helsinki |
Period | 19/06/2022 → 22/06/2022 |
Sponsor | Huawei Technologies Co., Ltd., Nokia Siemens Networks, pix moving, Samsung, Technology Innovation Institute (TII) |
Series | IEEE Vehicular Technology Conference |
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Volume | 2022-June |
ISSN | 1550-2252 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Age of Information
- Burst Traffic
- Periodic Traffic
- Proximal Policy Optimization
- Reinforcement Learning
- Time-sensitive communications