AoI and Throughput Optimization for Hybrid Traffic in Cellular Uplink Using Reinforcement Learning

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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 languageEnglish
Title of host publication2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings
PublisherIEEE Signal Processing Society
Publication date2022
ISBN (Electronic)9781665482431
DOIs
Publication statusPublished - 2022
Event95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring - Helsinki, Finland
Duration: 19 Jun 202222 Jun 2022

Conference

Conference95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring
Country/TerritoryFinland
CityHelsinki
Period19/06/202222/06/2022
SponsorHuawei Technologies Co., Ltd., Nokia Siemens Networks, pix moving, Samsung, Technology Innovation Institute (TII)
SeriesIEEE Vehicular Technology Conference
Volume2022-June
ISSN1550-2252

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Age of Information
  • Burst Traffic
  • Periodic Traffic
  • Proximal Policy Optimization
  • Reinforcement Learning
  • Time-sensitive communications

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