Topcu, A., Lawey, A.Q. orcid.org/0000-0003-3571-4110 and Zaidi, S.A.R. (2024) Joint Power and Flexible Numerology Allocation in 5G Networks Using Deep Reinforcement Learning. In: 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM). The 11th International Conference on Wireless Networks and Mobile Communications, 23-25 Jul 2024, Leeds, UK. IEEE ISBN 979-8-3503-7787-3
Abstract
This study presents a novel power allocation optimisation strategy that does not rely on traditional power constraints. Unlike previous works in the literature, we introduce a ratio between the allocated and requested data rates and incorporate this ratio into the reward function of our deep reinforcement learning algorithm. The highest reward of 1 is achieved when the allocated and requested data rates are equal. Additionally, we jointly optimise power and numerology allocation, considering the users' delay and data rate requirements. Any numerology can be allocated to users as long as their requirements are satisfied. This approach enables users to be allocated optimum numerology and transmit power. By addressing the challenge posed by greedy users, our approach enhances the flexibility and performance of the power and numerology allocation process.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Numerology allocation, power allocation, reinforcement learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Jul 2024 09:58 |
Last Modified: | 24 Sep 2024 08:27 |
Published Version: | https://ieeexplore.ieee.org/document/10655993 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/WINCOM62286.2024.10655993 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214781 |