Quantum Deep Reinforcement Learning for Secure Multi-User UAV RSMA Networks

Mohamed Amine Ouamri; Abuzar B. M. Adam; Mohammed A. M. Elhassan

doi:10.1109/ntms65597.2025.11076787

ScienceGate Book Chapters

JOURNAL ARTICLE

Quantum Deep Reinforcement Learning for Secure Multi-User UAV RSMA Networks

Mohamed Amine Ouamri Abuzar B. M. Adam Mohammed A. M. Elhassan

Year: 2025 Pages: 86-92

DOI: 10.1109/ntms65597.2025.11076787

Get Full-Text PDF Get Analytical Report

Keywords:

Reinforcement learning Control (management) Artificial neural network Stability (learning theory) Scheme (mathematics) Quantum Key (lock) Control theory (sociology)

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.21

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

UAV Applications and Optimization

Physical Sciences → Engineering → Aerospace Engineering

Advanced Wireless Communication Technologies

Physical Sciences → Engineering → Electrical and Electronic Engineering

Wireless Communication Security Techniques

Physical Sciences → Engineering → Electrical and Electronic Engineering

Quantum Deep Reinforcement Learning for Secure Multi-User UAV RSMA Networks

Metrics

Topics

Related Documents

Deep Reinforcement Learning for RSMA-Based Multi-Functional Wireless Networks

Deep Reinforcement Learning-Based Sum-Rate Maximization for Uplink Multi-user SIMO-RSMA Systems

Comparative Study of Gradient Descent and Deep Reinforcement Learning in Multi-User RSMA Systems

Deep Graph Reinforcement Learning for UAV-Enabled Multi-User Secure Communications

Enhancing Secrecy in UAV RSMA Networks: Deep Unfolding Meets Deep Reinforcement Learning