Alshorbaji, M.A., Lawey, A. and Zaidi, S.A.R. (2024) Energy Efficient Bandwidth Allocation and Routing in Electromagnetic Nano-Networks via Reinforcement Learning. In: 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM). 11th International Conference on Wireless Networks and Mobile Communications, 23-25 Jul 2024, Leeds, UK. IEEE ISBN 979-8-3503-7787-3
Abstract
Electromagnetic nano-networks operating in the THz band offer a promising solution for enabling communication among many nanoscale devices. However, the inherent limitations of nano-nodes, such as restricted energy and processing resources and short communication range, pose significant challenges for efficient data transmission. While prior research has explored Reinforcement Learning (RL) for optimising traffic routing in electromagnetic nano-networks, this paper proposes a novel approach that jointly optimises routing and sub-channel bandwidth allocation to minimise network energy consumption using RL. We leverage the Q-learning algorithm to develop a dynamic single-hop or multi-hop routing scheme that considers each node's location, energy storage capability, and the available sub-channel bandwidth. Our model formulates a reward function that balances these multiple objectives and enables the selection of optimal transmission policies for each nano-node. Our findings suggest carefully choosing the number of hops and increasing bandwidth in sub-channels can lead to substantial energy savings in nano-networks.
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: | Electromagnetic Nano-Networks, Reinforcement Learning, Q-Learning, Multi-hop Routing, Bandwidth Allocation, Energy Efficiency |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Jul 2024 08:40 |
Last Modified: | 20 Sep 2024 14:08 |
Published Version: | https://ieeexplore.ieee.org/document/10658288 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/WINCOM62286.2024.10658288 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214862 |