A Meta-Learning-Based Precoder Optimization Framework for Rate-Splitting Multiple Access

In this letter, we propose the use of a meta-learning based precoder optimization framework to directly optimize the Rate-Splitting Multiple Access (RSMA) precoders with partial Channel State Information at the Transmitter (CSIT). By exploiting the overfitting of the compact neural network to maximi...

Full description

Bibliographic Details

Published in:	IEEE wireless communications letters Vol. 13; no. 2; pp. 347 - 351
Main Authors:	Cerna Loli, Rafael, Clerckx, Bruno
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.02.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Artificial neural networks Channel state information Complexity theory Decoding Interference Learning Metalearning Multiple access Neural networks non-convex optimization Optimization partial channel state information at the transmitter (CSIT) Rate-splitting multiple access (RSMA) Splitting
ISSN:	2162-2337, 2162-2345
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!