Chen, Tianrui and Zhang, Xinruo and You, Minglei and Zheng, Gan and Lambotharan, Sangarapillai (2023) Federated Learning Enabled Link Scheduling in D2D Wireless Networks. IEEE Wireless Communications Letters, 13 (1). pp. 89-92. DOI https://doi.org/10.1109/LWC.2023.3321500
Chen, Tianrui and Zhang, Xinruo and You, Minglei and Zheng, Gan and Lambotharan, Sangarapillai (2023) Federated Learning Enabled Link Scheduling in D2D Wireless Networks. IEEE Wireless Communications Letters, 13 (1). pp. 89-92. DOI https://doi.org/10.1109/LWC.2023.3321500
Chen, Tianrui and Zhang, Xinruo and You, Minglei and Zheng, Gan and Lambotharan, Sangarapillai (2023) Federated Learning Enabled Link Scheduling in D2D Wireless Networks. IEEE Wireless Communications Letters, 13 (1). pp. 89-92. DOI https://doi.org/10.1109/LWC.2023.3321500
Abstract
Centralized machine learning methods for device-to-device (D2D) link scheduling may lead to a computing burden for a central server, transmission latency for decisions, and privacy issues for D2D communications. To mitigate these challenges, a federated learning (FL) based method is proposed to solve the link scheduling problem, where a global model is distributedly trained at local devices, and a server is used for aggregating model parameters instead of training samples. Specially, a more realistic scenario with limited channel state information (CSI) is considered instead of full CSI. Despite a decentralized implementation, simulation results demonstrate that the proposed FL based approach with limited CSI performs close to the conventional optimization algorithm. In addition, the FL based solution achieves almost the same performance as that of the centralized training.
Item Type: | Article |
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Uncontrolled Keywords: | Federated learning; Device-to-device (D2D); Link scheduling |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 11 Oct 2023 13:28 |
Last Modified: | 09 Jan 2024 17:14 |
URI: | http://repository.essex.ac.uk/id/eprint/36559 |
Available files
Filename: FINAL VERSION.pdf