QoS-Aware Scheduling for Cellular Networks Using Deep Reinforcement Learning

Jonathan Robert Malin, Gun Ko, Won Woo Ro

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This research presents a reinforcement learning (RL) approach that provides coarse-grained decisions to maximize QoS requirement satisfaction in mobile networks. Deep reinforcement learning has demonstrated agents that can capture the dynamics of impossibly complex systems. At each scheduling interval, our RL agent provides a scheduling policy that is suitable for optimal resource allocation in a mobile network. By using a deep neural network to approximate the action-value function (Q-function), scheduling decisions can be made using an optimal policy. Utilising a 4G-LTE network simulator and Pytorch, this research explores three scenarios of diverse traffic and UE density. The implementation shows stable and effective performance when compared to baseline static schedulers. Additionally, the RL agent selects the optimal scheduler for both single and mixed traffic simulations. Being both scalable and cheap to compute, the implementation offers a simple and effective method of radio resource management.

Original languageEnglish
Title of host publicationNetwork and Parallel Computing - 18th IFIP WG 10.3 International Conference, NPC 2021, Proceedings
EditorsChristophe Cérin, Depei Qian, Jean-Luc Gaudiot, Guangming Tan, Stéphane Zuckerman
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030935702
Publication statusPublished - 2022
Event18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021 - Paris, France
Duration: 2021 Nov 32021 Nov 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13152 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021

Bibliographical note

Funding Information:
Acknowledgements. This research was in part supported by the MOTIE (Ministry of Trade, Industry & Energy) (No. 10080674, Development of Reconfigurable Artificial Neural Network Accelerator and Instruction Set Architecture) and KSRC (Korea Semiconductor Research Consortium) support program for the development of the future semiconductor device and in part supported by Samsung Electronics Co., Ltd.

Publisher Copyright:
© 2022, IFIP International Federation for Information Processing.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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