OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine

Due to the complex and variable environments of mobile communication, the mobile multiuser networks become a hot topic. To process active complex event in mobile multiuser networks, it is important to predict the system performance. In this work, the authors consider the multiuser networks which uti...

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Main Authors: Han Wang, Lingwei Xu, Ye Tao, Xianpeng Wang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8959122/
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spelling doaj-cd2b0dee67f54ce39beaf05d56db42de2021-03-30T03:08:44ZengIEEEIEEE Access2169-35362020-01-018145571456410.1109/ACCESS.2020.29666908959122OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning MachineHan Wang0https://orcid.org/0000-0001-7347-3763Lingwei Xu1https://orcid.org/0000-0002-2169-6356Ye Tao2https://orcid.org/0000-0001-5470-9451Xianpeng Wang3https://orcid.org/0000-0002-6681-6489Department of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, ChinaDepartment of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, ChinaDepartment of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, ChinaDue to the complex and variable environments of mobile communication, the mobile multiuser networks become a hot topic. To process active complex event in mobile multiuser networks, it is important to predict the system performance. In this work, the authors consider the multiuser networks which utilizes transmit antenna selection (TAS). We derive novel closed-form expressions for the outage probability (OP) in terms of the Meijer's G-function. Then, a extreme learning machine (ELM)-based OP performance prediction algorithm is proposed. We use the theoretical results to generate training data. We test back-propagation (BP) neural network, locally weighted linear regression (LWLR), wavelet neural network, ELM, and support vector machine (SVM) methods. Compared with wavelet neural network, SVM, BP neural network, and LWLR methods, the Monte-Carlo results shows that the proposed prediction algorithm can consistently achieve higher OP performance prediction results.https://ieeexplore.ieee.org/document/8959122/Extreme learning machinemultiuser diversityoutage probabilityperformance prediction
collection DOAJ
language English
format Article
sources DOAJ
author Han Wang
Lingwei Xu
Ye Tao
Xianpeng Wang
spellingShingle Han Wang
Lingwei Xu
Ye Tao
Xianpeng Wang
OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine
IEEE Access
Extreme learning machine
multiuser diversity
outage probability
performance prediction
author_facet Han Wang
Lingwei Xu
Ye Tao
Xianpeng Wang
author_sort Han Wang
title OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine
title_short OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine
title_full OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine
title_fullStr OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine
title_full_unstemmed OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine
title_sort op performance prediction for complex mobile multiuser networks based on extreme learning machine
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description Due to the complex and variable environments of mobile communication, the mobile multiuser networks become a hot topic. To process active complex event in mobile multiuser networks, it is important to predict the system performance. In this work, the authors consider the multiuser networks which utilizes transmit antenna selection (TAS). We derive novel closed-form expressions for the outage probability (OP) in terms of the Meijer's G-function. Then, a extreme learning machine (ELM)-based OP performance prediction algorithm is proposed. We use the theoretical results to generate training data. We test back-propagation (BP) neural network, locally weighted linear regression (LWLR), wavelet neural network, ELM, and support vector machine (SVM) methods. Compared with wavelet neural network, SVM, BP neural network, and LWLR methods, the Monte-Carlo results shows that the proposed prediction algorithm can consistently achieve higher OP performance prediction results.
topic Extreme learning machine
multiuser diversity
outage probability
performance prediction
url https://ieeexplore.ieee.org/document/8959122/
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AT lingweixu opperformancepredictionforcomplexmobilemultiusernetworksbasedonextremelearningmachine
AT yetao opperformancepredictionforcomplexmobilemultiusernetworksbasedonextremelearningmachine
AT xianpengwang opperformancepredictionforcomplexmobilemultiusernetworksbasedonextremelearningmachine
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