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.
|Title of host publication||Network and Parallel Computing - 18th IFIP WG 10.3 International Conference, NPC 2021, Proceedings|
|Editors||Christophe Cérin, Depei Qian, Jean-Luc Gaudiot, Guangming Tan, Stéphane Zuckerman|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||13|
|Publication status||Published - 2022|
|Event||18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021 - Paris, France|
Duration: 2021 Nov 3 → 2021 Nov 5
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021|
|Period||21/11/3 → 21/11/5|
Bibliographical noteFunding 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.
© 2022, IFIP International Federation for Information Processing.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)