A Deep Learning-Based Approach for Cell Outage Compensation in NOMA Networks
Cell outage compensation enables a network to react to a catastrophic cell failure quickly and serve users in the outage zone uninterruptedly. Utilizing the promising benefits of non-orthogonal multiple access (NOMA) for improving the throughput of cell edge users, we propose a newly NOMA-based cell...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9749853/ |
| _version_ | 1818150000092250112 |
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| author | Elaheh Vaezpour Layla Majzoobi Mohammad Akbari Saeedeh Parsaeefard Halim Yanikomeroglu |
| author_facet | Elaheh Vaezpour Layla Majzoobi Mohammad Akbari Saeedeh Parsaeefard Halim Yanikomeroglu |
| author_sort | Elaheh Vaezpour |
| collection | DOAJ |
| description | Cell outage compensation enables a network to react to a catastrophic cell failure quickly and serve users in the outage zone uninterruptedly. Utilizing the promising benefits of non-orthogonal multiple access (NOMA) for improving the throughput of cell edge users, we propose a newly NOMA-based cell outage compensation scheme. In this scheme, the compensation is formulated as a mixed integer non-linear program (MINLP) where outage zone users are associated to neighboring cells and their power are allocated with the objective of maximizing spectral efficiency, subject to maintaining the quality of service for the rest of the users. Owing to the importance of immediate management of cell outage and handling the computational complexity, we develop a low-complexity suboptimal solution for this problem in which the user association scheme is determined by a newly heuristic algorithm, and power allocation is set by applying an innovative deep neural network (DNN). The complexityof our proposed method is in the order of polynomial basis, which is much less than the exponential complexity of finding an optimal solution. Simulation results demonstrate thatthe proposed method approaches the optimal solution. Moreover, the developed scheme greatly improves fairness and increases the number of served users. |
| first_indexed | 2024-12-11T13:15:58Z |
| format | Article |
| id | doaj.art-227e45280ee54d29b1ed32c79e7d9a32 |
| institution | Directory Open Access Journal |
| issn | 2644-1330 |
| language | English |
| last_indexed | 2024-12-11T13:15:58Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj.art-227e45280ee54d29b1ed32c79e7d9a322022-12-22T01:06:04ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302022-01-01314916610.1109/OJVT.2022.31646859749853A Deep Learning-Based Approach for Cell Outage Compensation in NOMA NetworksElaheh Vaezpour0Layla Majzoobi1https://orcid.org/0000-0002-2890-6694Mohammad Akbari2https://orcid.org/0000-0002-9339-1201Saeedeh Parsaeefard3Halim Yanikomeroglu4https://orcid.org/0000-0003-4776-9354Communication Technology Department, ICT Research Institute, Tehran, IranCommunication Technology Department, ICT Research Institute, Tehran, IranCommunication Technology Department, ICT Research Institute, Tehran, IranDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, ON, CanadaDepartment of Systems and Computer Engineering, Carleton University, Ottawa, ON, CanadaCell outage compensation enables a network to react to a catastrophic cell failure quickly and serve users in the outage zone uninterruptedly. Utilizing the promising benefits of non-orthogonal multiple access (NOMA) for improving the throughput of cell edge users, we propose a newly NOMA-based cell outage compensation scheme. In this scheme, the compensation is formulated as a mixed integer non-linear program (MINLP) where outage zone users are associated to neighboring cells and their power are allocated with the objective of maximizing spectral efficiency, subject to maintaining the quality of service for the rest of the users. Owing to the importance of immediate management of cell outage and handling the computational complexity, we develop a low-complexity suboptimal solution for this problem in which the user association scheme is determined by a newly heuristic algorithm, and power allocation is set by applying an innovative deep neural network (DNN). The complexityof our proposed method is in the order of polynomial basis, which is much less than the exponential complexity of finding an optimal solution. Simulation results demonstrate thatthe proposed method approaches the optimal solution. Moreover, the developed scheme greatly improves fairness and increases the number of served users.https://ieeexplore.ieee.org/document/9749853/Cell outage compensationdeep neural network (DNN)nonorthogonal multiple access (NOMA)self-healing |
| spellingShingle | Elaheh Vaezpour Layla Majzoobi Mohammad Akbari Saeedeh Parsaeefard Halim Yanikomeroglu A Deep Learning-Based Approach for Cell Outage Compensation in NOMA Networks IEEE Open Journal of Vehicular Technology Cell outage compensation deep neural network (DNN) nonorthogonal multiple access (NOMA) self-healing |
| title | A Deep Learning-Based Approach for Cell Outage Compensation in NOMA Networks |
| title_full | A Deep Learning-Based Approach for Cell Outage Compensation in NOMA Networks |
| title_fullStr | A Deep Learning-Based Approach for Cell Outage Compensation in NOMA Networks |
| title_full_unstemmed | A Deep Learning-Based Approach for Cell Outage Compensation in NOMA Networks |
| title_short | A Deep Learning-Based Approach for Cell Outage Compensation in NOMA Networks |
| title_sort | deep learning based approach for cell outage compensation in noma networks |
| topic | Cell outage compensation deep neural network (DNN) nonorthogonal multiple access (NOMA) self-healing |
| url | https://ieeexplore.ieee.org/document/9749853/ |
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