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

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Abstrakt

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.

OriginalsprogEngelsk
Titel2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings
ForlagIEEE Signal Processing Society
Publikationsdato2022
ISBN (Elektronisk)9781665482431
DOI
StatusUdgivet - 2022
Begivenhed95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring - Helsinki, Finland
Varighed: 19 jun. 202222 jun. 2022

Konference

Konference95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring
Land/OmrådeFinland
ByHelsinki
Periode19/06/202222/06/2022
SponsorHuawei Technologies Co., Ltd., Nokia Siemens Networks, pix moving, Samsung, Technology Innovation Institute (TII)
NavnIEEE Vehicular Technology Conference
Vol/bind2022-June
ISSN1550-2252

Bibliografisk note

Publisher Copyright:
© 2022 IEEE.

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