Bayesian Optimization for Multimodal Heterogeneous Network Orchestration via Hybrid Probability Process
In the era of 5G and beyond, heterogeneous network orchestration has become a tremendous issue. The dilemma facing future systems is how to allocate integrated resources to satisfy multifarious services, which is an imperative but arduous task in forming a systematic mathematical model and quantifyi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8808905/ |
| _version_ | 1818936355130966016 |
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| author | Hai Wang Geng Zhang Hao Jiang Jing Wu Xing Yang Mo Zhou |
| author_facet | Hai Wang Geng Zhang Hao Jiang Jing Wu Xing Yang Mo Zhou |
| author_sort | Hai Wang |
| collection | DOAJ |
| description | In the era of 5G and beyond, heterogeneous network orchestration has become a tremendous issue. The dilemma facing future systems is how to allocate integrated resources to satisfy multifarious services, which is an imperative but arduous task in forming a systematic mathematical model and quantifying the model with its multi-layer uncertainty characteristics. Aiming at the statistical representation and optimization in multimodal heterogenous networks for 5G and beyond, we propose a novel hybrid probability process (HPP) as a generalized surrogate model and a weighted degenerated upper confidence bound (WDUCB) criterion for Bayesian optimization (BO). We apply the proposed HPP-WDUCB combination to our developed simulation platform and configure several applications of the integration of space information network in next generation communication systems. And we compared the proposed method with other surrogate models and acquisition strategies from a range of perspectives. The experiment results yield significant applicability and excellent performance in multimodal system representation and optimization which provides an effective statistical modeling and orchestration references for network tuning. |
| first_indexed | 2024-12-20T05:34:45Z |
| format | Article |
| id | doaj.art-820b4a3ab3be44659b03eba6029792aa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T05:34:45Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-820b4a3ab3be44659b03eba6029792aa2022-12-21T19:51:39ZengIEEEIEEE Access2169-35362019-01-01711795411796710.1109/ACCESS.2019.29366068808905Bayesian Optimization for Multimodal Heterogeneous Network Orchestration via Hybrid Probability ProcessHai Wang0https://orcid.org/0000-0002-0171-8087Geng Zhang1Hao Jiang2https://orcid.org/0000-0002-8533-1612Jing Wu3Xing Yang4Mo Zhou5Electronic Information School, Wuhan University, Hubei, ChinaChina Electric Power Research Institute, Beijing, ChinaElectronic Information School, Wuhan University, Hubei, ChinaElectronic Information School, Wuhan University, Hubei, ChinaElectronic Information School, Wuhan University, Hubei, ChinaElectronic Information School, Wuhan University, Hubei, ChinaIn the era of 5G and beyond, heterogeneous network orchestration has become a tremendous issue. The dilemma facing future systems is how to allocate integrated resources to satisfy multifarious services, which is an imperative but arduous task in forming a systematic mathematical model and quantifying the model with its multi-layer uncertainty characteristics. Aiming at the statistical representation and optimization in multimodal heterogenous networks for 5G and beyond, we propose a novel hybrid probability process (HPP) as a generalized surrogate model and a weighted degenerated upper confidence bound (WDUCB) criterion for Bayesian optimization (BO). We apply the proposed HPP-WDUCB combination to our developed simulation platform and configure several applications of the integration of space information network in next generation communication systems. And we compared the proposed method with other surrogate models and acquisition strategies from a range of perspectives. The experiment results yield significant applicability and excellent performance in multimodal system representation and optimization which provides an effective statistical modeling and orchestration references for network tuning.https://ieeexplore.ieee.org/document/8808905/Bayesian optimizationheterogeneous network orchestration5G and beyondhybrid probability processweight degenerate upper confidence bound |
| spellingShingle | Hai Wang Geng Zhang Hao Jiang Jing Wu Xing Yang Mo Zhou Bayesian Optimization for Multimodal Heterogeneous Network Orchestration via Hybrid Probability Process IEEE Access Bayesian optimization heterogeneous network orchestration 5G and beyond hybrid probability process weight degenerate upper confidence bound |
| title | Bayesian Optimization for Multimodal Heterogeneous Network Orchestration via Hybrid Probability Process |
| title_full | Bayesian Optimization for Multimodal Heterogeneous Network Orchestration via Hybrid Probability Process |
| title_fullStr | Bayesian Optimization for Multimodal Heterogeneous Network Orchestration via Hybrid Probability Process |
| title_full_unstemmed | Bayesian Optimization for Multimodal Heterogeneous Network Orchestration via Hybrid Probability Process |
| title_short | Bayesian Optimization for Multimodal Heterogeneous Network Orchestration via Hybrid Probability Process |
| title_sort | bayesian optimization for multimodal heterogeneous network orchestration via hybrid probability process |
| topic | Bayesian optimization heterogeneous network orchestration 5G and beyond hybrid probability process weight degenerate upper confidence bound |
| url | https://ieeexplore.ieee.org/document/8808905/ |
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