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Mobile Networks and Applications
Erschienen in: Mobile Networks and Applications 3/2022

05.12.2018

Deep Learning Based Cooperative Resource Allocation in 5G Wireless Networks

verfasst von: Dan Huang, Yuan Gao, Yi Li, Mengshu Hou, Wanbin Tang, Shaochi Cheng, Xiangyang Li, Yunchuan Sun

Erschienen in: Mobile Networks and Applications | Ausgabe 3/2022

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Abstract

Wireless personal communication has become popular with the rapid development of 5G communication systems. Critical demands on transmission speed and QoS make it difficult to upgrade current wireless personal communication systems. In this paper, we develop a novel resource allocation method using deep learning to squeeze the benefits of resource utilization. By generating the convolutional neural network using channel information, resource allocation is to be optimized. The deep learning method could help make full use of the small scale channel information instead of traditional resource optimization, especially when the channel environment is changing fast. Simulation results indicate the fact that the performance of our proposed method is close to MMSE method and better than ZF method, and the time consumption of computation is smaller than traditional method.
Vorheriger Artikel Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks
Nächster Artikel Measuring Creative Experience from Visitors’ Paths and Abstract Multi-sensory Artefacts

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Metadaten
Titel
Deep Learning Based Cooperative Resource Allocation in 5G Wireless Networks
verfasst von
Dan Huang
Yuan Gao
Yi Li
Mengshu Hou
Wanbin Tang
Shaochi Cheng
Xiangyang Li
Yunchuan Sun
Publikationsdatum
05.12.2018
Verlag
Springer US
Erschienen in
Mobile Networks and Applications / Ausgabe 3/2022
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-018-1178-9

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