Towards Transparent Deep Learning: Neural Precoder Design for Downlink RSMA

The non-convexity of rate-splitting precoder design precludes the direct use of efficient convex optimization algorithms. Instead, successive convex approximation (SCA)-based methods have emerged as a promising approach for precoder design in rate-splitting multiple access (RSMA) systems. Although S...

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Vydané v:	IEEE transactions on communications s. 1
Hlavní autori:	Wang, Chao, Li, Guo, Li, Zan, Zhang, Haibin, Jin, Liang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 2025
Predmet:	alternating direction method of multipliers Approximation algorithms Computational complexity Computational modeling Covariance matrices Deep learning deep unfolding Downlink Iterative algorithms Mathematical models Optimization precoder design rate splitting multiple access Spectral efficiency
ISSN:	0090-6778, 1558-0857
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