Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems

In this paper, a support vector machine (SVM) technique has been applied to an antenna allocation system with multiple antennas in multiuser downlink communications. Here, only the channel magnitude information is available at the transmitter. Thus, a subset of transmit antennas that can reduce mult...

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Autores principales: Lin, Huifa, Shin, Won-Yong, Joung, Jingon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514960/
https://www.ncbi.nlm.nih.gov/pubmed/33267185
http://dx.doi.org/10.3390/e21050471
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author Lin, Huifa
Shin, Won-Yong
Joung, Jingon
author_facet Lin, Huifa
Shin, Won-Yong
Joung, Jingon
author_sort Lin, Huifa
collection PubMed
description In this paper, a support vector machine (SVM) technique has been applied to an antenna allocation system with multiple antennas in multiuser downlink communications. Here, only the channel magnitude information is available at the transmitter. Thus, a subset of transmit antennas that can reduce multiuser interference is selected based on such partial channel state information to support multiple users. For training, we generate the feature vectors by fully utilizing the characteristics of the interference-limited setup in the multiuser downlink system and determine the corresponding class label by evaluating a key performance indicator, i.e., sum rate in multiuser communications. Using test channels, we evaluate the performance of our antenna allocation system invoking the SVM-based allocation and optimization-based allocation, in terms of sum-rate performance and computational complexity. Rigorous testing allowed for a comparison of a SVM algorithm design between one-vs-one (OVO) and one-vs-all (OVA) strategies and a kernel function: (i) OVA is preferable to OVO since OVA can achieve almost the same sum rate as OVO with significantly reduced computational complexity, (ii) a Gaussian function is a good choice as the kernel function for the SVM, and (iii) the variance (kernel scale) and penalty parameter (box constraint) of an SVM kernel function are determined by 21.56 and 7.67, respectively. Further simulation results revealed that the designed SVM-based approach can remarkably reduce the time complexity compared to a traditional optimization-based approach, at the cost of marginal sum rate degradation. Our proposed framework offers some important insights for intelligently combining machine learning techniques and multiuser wireless communications.
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spelling pubmed-75149602020-11-09 Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems Lin, Huifa Shin, Won-Yong Joung, Jingon Entropy (Basel) Article In this paper, a support vector machine (SVM) technique has been applied to an antenna allocation system with multiple antennas in multiuser downlink communications. Here, only the channel magnitude information is available at the transmitter. Thus, a subset of transmit antennas that can reduce multiuser interference is selected based on such partial channel state information to support multiple users. For training, we generate the feature vectors by fully utilizing the characteristics of the interference-limited setup in the multiuser downlink system and determine the corresponding class label by evaluating a key performance indicator, i.e., sum rate in multiuser communications. Using test channels, we evaluate the performance of our antenna allocation system invoking the SVM-based allocation and optimization-based allocation, in terms of sum-rate performance and computational complexity. Rigorous testing allowed for a comparison of a SVM algorithm design between one-vs-one (OVO) and one-vs-all (OVA) strategies and a kernel function: (i) OVA is preferable to OVO since OVA can achieve almost the same sum rate as OVO with significantly reduced computational complexity, (ii) a Gaussian function is a good choice as the kernel function for the SVM, and (iii) the variance (kernel scale) and penalty parameter (box constraint) of an SVM kernel function are determined by 21.56 and 7.67, respectively. Further simulation results revealed that the designed SVM-based approach can remarkably reduce the time complexity compared to a traditional optimization-based approach, at the cost of marginal sum rate degradation. Our proposed framework offers some important insights for intelligently combining machine learning techniques and multiuser wireless communications. MDPI 2019-05-06 /pmc/articles/PMC7514960/ /pubmed/33267185 http://dx.doi.org/10.3390/e21050471 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lin, Huifa
Shin, Won-Yong
Joung, Jingon
Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
title Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
title_full Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
title_fullStr Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
title_full_unstemmed Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
title_short Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
title_sort support vector machine-based transmit antenna allocation for multiuser communication systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514960/
https://www.ncbi.nlm.nih.gov/pubmed/33267185
http://dx.doi.org/10.3390/e21050471
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