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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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/ |
work_keys_str_mv |
AT hanwang opperformancepredictionforcomplexmobilemultiusernetworksbasedonextremelearningmachine AT lingweixu opperformancepredictionforcomplexmobilemultiusernetworksbasedonextremelearningmachine AT yetao opperformancepredictionforcomplexmobilemultiusernetworksbasedonextremelearningmachine AT xianpengwang opperformancepredictionforcomplexmobilemultiusernetworksbasedonextremelearningmachine |
_version_ |
1724184024945000448 |