Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems

Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems

In this paper, we consider the combination of machine learning (ML) and wireless communication. We design a machine learning generated clusters model in a distributed antenna system (DAS), which is constructed by two different ML clustering algorithms, i.e., k-means algorithm and Gaussian mixture mo...

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Main Authors: Chunlong He, Yuehua Zhou, Gongbin Qian, Xingquan Li, Daquan Feng
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Machine learning
spectral efficiency
energy efficiency
k-means
mixture of Gaussian model
distributed antenna system
Online Access:https://ieeexplore.ieee.org/document/8703037/
id doaj-f868b5e3bad34f1d9a8b34e0abc7e794
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spelling doaj-f868b5e3bad34f1d9a8b34e0abc7e7942021-03-29T22:46:53ZengIEEEIEEE Access2169-35362019-01-017595755958410.1109/ACCESS.2019.29141598703037Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna SystemsChunlong He0https://orcid.org/0000-0003-4316-0672Yuehua Zhou1Gongbin Qian2https://orcid.org/0000-0003-2935-0808Xingquan Li3Daquan Feng4College of Information Engineering, Shenzhen University, Shenzhen, ChinaCollege of Information Engineering, Shenzhen University, Shenzhen, ChinaCollege of Information Engineering, Shenzhen University, Shenzhen, ChinaCollege of Information Engineering, Shenzhen University, Shenzhen, ChinaCollege of Information Engineering, Shenzhen University, Shenzhen, ChinaIn this paper, we consider the combination of machine learning (ML) and wireless communication. We design a machine learning generated clusters model in a distributed antenna system (DAS), which is constructed by two different ML clustering algorithms, i.e., k-means algorithm and Gaussian mixture model-based (GMM) algorithm. Under the communication scenario of DAS with ML generated clusters model, we investigate two different power allocation optimization problems with the interference of maximizing spectral efficiency (SE) and energy efficiency (EE) in DAS, respectively. We compare the SE and EE of DAS with ML generated clusters model and the conventional model. The simulation results verify the effectiveness of DAS with ML generated clusters model, which can obtain the much better performance of SE and EE compared with the conventional communication model in DAS.https://ieeexplore.ieee.org/document/8703037/Machine learningspectral efficiencyenergy efficiencyk-meansmixture of Gaussian modeldistributed antenna system
collection DOAJ
language English
format Article
sources DOAJ
author Chunlong He
Yuehua Zhou
Gongbin Qian
Xingquan Li
Daquan Feng
spellingShingle Chunlong He
Yuehua Zhou
Gongbin Qian
Xingquan Li
Daquan Feng
Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems
IEEE Access
Machine learning
spectral efficiency
energy efficiency
k-means
mixture of Gaussian model
distributed antenna system
author_facet Chunlong He
Yuehua Zhou
Gongbin Qian
Xingquan Li
Daquan Feng
author_sort Chunlong He
title Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems
title_short Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems
title_full Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems
title_fullStr Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems
title_full_unstemmed Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems
title_sort energy efficient power allocation based on machine learning generated clusters for distributed antenna systems
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description In this paper, we consider the combination of machine learning (ML) and wireless communication. We design a machine learning generated clusters model in a distributed antenna system (DAS), which is constructed by two different ML clustering algorithms, i.e., k-means algorithm and Gaussian mixture model-based (GMM) algorithm. Under the communication scenario of DAS with ML generated clusters model, we investigate two different power allocation optimization problems with the interference of maximizing spectral efficiency (SE) and energy efficiency (EE) in DAS, respectively. We compare the SE and EE of DAS with ML generated clusters model and the conventional model. The simulation results verify the effectiveness of DAS with ML generated clusters model, which can obtain the much better performance of SE and EE compared with the conventional communication model in DAS.
topic Machine learning
spectral efficiency
energy efficiency
k-means
mixture of Gaussian model
distributed antenna system
url https://ieeexplore.ieee.org/document/8703037/
work_keys_str_mv AT chunlonghe energyefficientpowerallocationbasedonmachinelearninggeneratedclustersfordistributedantennasystems
AT yuehuazhou energyefficientpowerallocationbasedonmachinelearninggeneratedclustersfordistributedantennasystems
AT gongbinqian energyefficientpowerallocationbasedonmachinelearninggeneratedclustersfordistributedantennasystems
AT xingquanli energyefficientpowerallocationbasedonmachinelearninggeneratedclustersfordistributedantennasystems
AT daquanfeng energyefficientpowerallocationbasedonmachinelearninggeneratedclustersfordistributedantennasystems
_version_ 1724190979514171392

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