Deep Convolutional Linear Precoder Neural Network for Rate Splitting Strategy of Aerial Computing Networks

Aerial computing networks arefacing the challenge of massive node access, where user devices generally have stringent latency and robustness requirements. Rate Splitting Multiple Access (RSMA) is a general and robust multiple access framework for the aerial computing communication architecture, whic...

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Vydané v:	IEEE transactions on network science and engineering Ročník 11; číslo 6; s. 5228 - 5243
Hlavní autori:	Wang, Zhijie, Ma, Ruhui, Shi, Hongjian, Cai, Zinuo, Lin, Liwei, Guan, Haibing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.11.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Aerial computing networks Artificial neural networks Communication Computational modeling Computing time Convolutional neural networks CSI feedback deep convolution neural network Design optimization Edge computing Feature extraction Feedback Information management linear precoder Messages Multiple access Network latency Neural networks Optimization Radio access networks rate splitting Receivers Splitting User requirements
ISSN:	2327-4697, 2334-329X
On-line prístup:	Získať plný text
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