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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE wireless communications letters s. 1
Hlavní autori: Wafula, Celine Nerima, Shin, Soo Young
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 2025
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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