Federated Learning for RIS-Assisted UAV-Enabled Wireless Networks: Learning-Based Optimization for UAV Trajectory, RIS Phase Shifts and Weighted Aggregation

Chong Huang; Gaojie Chen; Pei Xiao; De Mi; Yunsheng Zhang; Hui Tang; Chen Lu; Rahim Tafazolli

doi:10.1109/IECON51785.2023.10312474

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Federated Learning for RIS-Assisted UAV-Enabled Wireless Networks: Learning-Based Optimization for UAV Trajectory, RIS Phase Shifts and Weighted Aggregation

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Federated Learning for RIS-Assisted UAV-Enabled Wireless Networks: Learning-Based Optimization for UAV Trajectory, RIS Phase Shifts and Weighted Aggregation

Chong Huang, Gaojie Chen, Pei Xiao, De Mi, Yunsheng Zhang, Hui Tang, Chen Lu and Rahim Tafazolli

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, pp.1-6

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society (Singapore, 16/10/2023–19/10/2023)

16/10/2023

DOI: https://doi.org/10.1109/IECON51785.2023.10312474

Abstract

Autonomous aerial vehicles

Correlation

deep learning

Federated learning

Industrial electronics

reconfigurable intelligent surface

Reflection

reinforcement learning

Simulation

unmanned aerial vehicle

unreliable communication

Wireless networks

This paper investigates a learning-based approach autonomously and jointly optimizing the trajectory of unmanned aerial vehicle (UAV), phase shifts of reconfigurable intelligent surfaces (RIS), and aggregation weights for federated learning (FL) in wireless communications, forming an autonomous RIS-assisted UAV-enabled network. The proposed network considers practical RIS reflection models and FL transmission errors in wireless communications. To optimize the RIS phase shifts, a double cascade correlation network (DCCN) is introduced. Additionally, the deep deterministic policy gradient (DDPG) algorithm is employed to address the optimization problem of UAV trajectory and FL aggregation weights based on the results obtained from DCCN. Simulation results demonstrate the substantial improvement in FL performance within the autonomous RIS-assisted UAV-enabled network setting achieved by the proposed algorithms compared to the benchmarks.

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Title: Federated Learning for RIS-Assisted UAV-Enabled Wireless Networks: Learning-Based Optimization for UAV Trajectory, RIS Phase Shifts and Weighted Aggregation
Creators: Chong Huang (Author) - University of Surrey, School of Computer Science and Electronic Engineering
Gaojie Chen (Author) - University of Surrey, School of Computer Science and Electronic Engineering
Pei Xiao (Author) - University of Surrey, School of Computer Science and Electronic Engineering
De Mi - Birmingham City University
Yunsheng Zhang - Shenzhen Institute of Information Technology
Hui Tang - Shenzhen Institute of Information Technology
Chen Lu - Shenzhen Institute of Information Technology
Rahim Tafazolli (Author) - University of Surrey, School of Computer Science and Electronic Engineering
Publication Details: IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, pp.1-6
Conference: IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society (Singapore, 16/10/2023–19/10/2023)
Publisher: IEEE
Number of pages: 6
Publication Date: 16/10/2023
Date accepted for publication: 15/07/2023
Identifiers: 99843666302346
Copyright: © 2023 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.”
Academic Unit: School of Computer Science and Electronic Engineering
Language: English
Resource Type: Conference proceeding

Federated Learning for RIS-Assisted UAV-Enabled Wireless Networks: Learning-Based Optimization for UAV Trajectory, RIS Phase Shifts and Weighted Aggregation

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