UQML Based Precoder Optimization for RSMA-LEO Satellite Networks

In this letter, unsupervised quantum machine learning (UQML) is proposed to optimize linear precoding for rate-splitting multiple access (RSMA) in low earth orbit (LEO) satellite-terrestrial systems. This approach enhances common and private stream transmission from the LEO satellite to a ground sta...

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Bibliographic Details
Published in:IEEE wireless communications letters p. 1
Main Authors: Wafula, Celine Nerima, Shin, Soo Young
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
LEO satellite
Linear precoding
Logic gates
Low earth orbit satellites
Optimization
Precoding
Quantum circuit
quantum machine learning
Qubit
Resource management
RSMA
Satellites
Training
unsupervised
Wireless communication
ISSN:2162-2337, 2162-2345
Online Access:Get full text
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Summary:In this letter, unsupervised quantum machine learning (UQML) is proposed to optimize linear precoding for rate-splitting multiple access (RSMA) in low earth orbit (LEO) satellite-terrestrial systems. This approach enhances common and private stream transmission from the LEO satellite to a ground station (GS), to address 6G challenges in resource allocation, interference management, and capacity. Simulation results demonstrate significant spectral efficiency gains, indicating UQML's potential for future satellite communication systems.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2025.3603334