Energy-Efficient Resource Management for Multi-UAV NOMA Networks Based on Deep Reinforcement Learning

Cellular-connected unmanned aerial vehicles (UAVs) play an essential role in cellular networks. Combined with non-orthogonal multiple access (NOMA) technique, UAVs can provide better performance in various communication scenarios. In this paper, we investigate a NOMA-enhanced UAV-assisted cellular n...

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Bibliographic Details
Published in:IEEE Vehicular Technology Conference pp. 01 - 05
Main Authors: Lin, Xiangda, Yang, Helin, Lin, Kailong, Xiao, Liang, Shi, Zhaoyuan, Yang, Wanting, Xiong, Zehui
Format: Conference Proceeding
Language:English
Published: IEEE 24.06.2024
Subjects:
Cellular networks
Clustering algorithms
deep reinforcement learning
Energy efficiency
NOMA
non-orthogonal multiple access
Partitioning algorithms
Quality of service
resource management
unmanned aerial vehicles
Vehicular and wireless technologies
ISSN:2577-2465
Online Access:Get full text
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Summary:Cellular-connected unmanned aerial vehicles (UAVs) play an essential role in cellular networks. Combined with non-orthogonal multiple access (NOMA) technique, UAVs can provide better performance in various communication scenarios. In this paper, we investigate a NOMA-enhanced UAV-assisted cellular network where multiple UAVs are deployed as aerial base stations to provide communication services for mobile ground users in the presence of a malicious jammer. We propose a two-step learning-based resource scheduling approach. First, an algorithm based on K-means clustering is proposed to partition ground users (GUs) to reduce mutual interference. Moreover, a cooperative multi-agent twin delayed deep deterministic algorithm is proposed to jointly optimize UAVs' trajectories, power allocation and GU association to maximize the system energy efficiency (EE) while guaranteeing minimum quality-of-service (QoS) requirements. Extensive results demonstrate that the proposed solution can efficiently improve EE and QoS performances under jamming attacks compared with existing popular approaches.
ISSN:2577-2465
DOI:10.1109/VTC2024-Spring62846.2024.10683182