Deep Learning-Based Resource Allocation in UAV-RIS-Aided Cell-Free Hybrid NOMA/OMA Networks

Chong Huang; Gaojie Chen; Yun Wen; Zihuai Lin; Yue Xiao; Pei Xiao

doi:10.1109/GLOBECOM54140.2023.10437110

Back

Deep Learning-Based Resource Allocation in UAV-RIS-Aided Cell-Free Hybrid NOMA/OMA Networks

Conference proceeding

Open access

Deep Learning-Based Resource Allocation in UAV-RIS-Aided Cell-Free Hybrid NOMA/OMA Networks

Chong Huang, Gaojie Chen, Yun Wen, Zihuai Lin, Yue Xiao and Pei Xiao

IEEE Conference on Global Communications (GLOBECOM 2023)

IEEE Global Communications Conference

2023 IEEE Global Communications Conference: Wireless Communications (Globecom 2023) (Kuala Lumpur, Malaysia, 04/12/2023–08/12/2023)

26/02/2024

DOI: https://doi.org/10.1109/GLOBECOM54140.2023.10437110

Abstract

unmanned aerial vehicle

reinforcement learning

deep learning

Reconfigurable intelligent surface

This paper investigates a deep learning-based algorithm to optimize the unmanned aerial vehicle (UAV) trajectory and reconfigurable intelligent surface (RIS) reflection coefficients in UAV-RIS-aided cell-free (CF) hybrid non-orthogonal multiple-access (NOMA)/orthogonal multiple-access (OMA) networks. The practical RIS reflection model and user grouping optimization are considered in the proposed network. A double cascade correlation network (DCCN) is proposed to optimize the RIS reflection coefficients , and based on the results from DCCN, an inverse-variance deep reinforcement learning (IV-DRL) algorithm is introduced to address the UAV trajectory optimization problem. Simulation results show that the proposed algorithms significantly improve the performance in UAV-RIS-assisted CF networks.

Files and links (2)

pdf

Deep Learning-Based Resource Allocation - AAM832.77 kBDownload View

Author's Accepted Manuscript Open Access

url

https://globecom2023.ieee-globecom.org/View

Event WebsiteConference website

Metrics

255 File views/ downloads

183 Record Views

Details

Title: Deep Learning-Based Resource Allocation in UAV-RIS-Aided Cell-Free Hybrid NOMA/OMA Networks
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
Yun Wen (Author) - University of Surrey, School of Computer Science and Electronic Engineering
Zihuai Lin (Author) - The University of Sydney
Yue Xiao (Author) - University of Electronic Science and Technology of China
Pei Xiao (Author) - University of Surrey, School of Computer Science and Electronic Engineering
Publication Details: IEEE Conference on Global Communications (GLOBECOM 2023)
Conference: 2023 IEEE Global Communications Conference: Wireless Communications (Globecom 2023) (Kuala Lumpur, Malaysia, 04/12/2023–08/12/2023)
Series: IEEE Global Communications Conference
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Number of pages: 6
First online publication date: 26/02/2024
Date accepted for publication: 01/08/2023
Identifiers: 99789566502346; WOS:001178562006034
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

Deep Learning-Based Resource Allocation in UAV-RIS-Aided Cell-Free Hybrid NOMA/OMA Networks

Abstract

Files and links (2)

Metrics

Details

Usage Policy