Deep Learning-Based Resource Allocation Scheme for Heterogeneous NOMA Networks
In this paper, we consider downlink power-domain non-orthogonal multiple access (NOMA) in heterogeneous networks (HetNets) and propose resource allocation algorithms for subchannels and transmit powers to improve the sum rate performance while satisfying a minimum data-rate requirement. The proposed...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10226210/ |
| _version_ | 1827857981653385216 |
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| author | Donghyeon Kim Sean Kwon Haejoon Jung In-Ho Lee |
| author_facet | Donghyeon Kim Sean Kwon Haejoon Jung In-Ho Lee |
| author_sort | Donghyeon Kim |
| collection | DOAJ |
| description | In this paper, we consider downlink power-domain non-orthogonal multiple access (NOMA) in heterogeneous networks (HetNets) and propose resource allocation algorithms for subchannels and transmit powers to improve the sum rate performance while satisfying a minimum data-rate requirement. The proposed subchannel allocation scheme is an iterative algorithm to achieve NOMA gain by selecting the best subchannel from the viewpoint of each user, without the constraint of the number of NOMA users on each subchannel. The proposed power allocation scheme for NOMA is a deep neural network (DNN)-based unsupervised learning algorithm, where the output of the subchannel allocation scheme is used, and unsupervised learning is adopted to reduce the training complexity, as compared to supervised learning. Through simulation, we show that the proposed subchannel allocation scheme provides better sum rates compared to the conventional two-sided matching scheme, and the proposed power allocation scheme achieves a comparable sum rate to the interior point method (IPM). |
| first_indexed | 2024-03-12T12:48:11Z |
| format | Article |
| id | doaj.art-2fbb1fcd7dc445968af35e0d3d3a4da3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T12:48:11Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2fbb1fcd7dc445968af35e0d3d3a4da32023-08-28T23:00:31ZengIEEEIEEE Access2169-35362023-01-0111894238943210.1109/ACCESS.2023.330740710226210Deep Learning-Based Resource Allocation Scheme for Heterogeneous NOMA NetworksDonghyeon Kim0https://orcid.org/0000-0002-0697-4354Sean Kwon1https://orcid.org/0000-0003-4743-153XHaejoon Jung2https://orcid.org/0000-0003-1901-2784In-Ho Lee3https://orcid.org/0000-0002-2104-9781Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin-si, South KoreaDepartment of Electrical Engineering, California State University Long Beach, Long Beach, CA, USADepartment of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin-si, South KoreaSchool of Electronic and Electrical Engineering, Hankyong National University, Anseong-si, South KoreaIn this paper, we consider downlink power-domain non-orthogonal multiple access (NOMA) in heterogeneous networks (HetNets) and propose resource allocation algorithms for subchannels and transmit powers to improve the sum rate performance while satisfying a minimum data-rate requirement. The proposed subchannel allocation scheme is an iterative algorithm to achieve NOMA gain by selecting the best subchannel from the viewpoint of each user, without the constraint of the number of NOMA users on each subchannel. The proposed power allocation scheme for NOMA is a deep neural network (DNN)-based unsupervised learning algorithm, where the output of the subchannel allocation scheme is used, and unsupervised learning is adopted to reduce the training complexity, as compared to supervised learning. Through simulation, we show that the proposed subchannel allocation scheme provides better sum rates compared to the conventional two-sided matching scheme, and the proposed power allocation scheme achieves a comparable sum rate to the interior point method (IPM).https://ieeexplore.ieee.org/document/10226210/Non-orthogonal multiple accessheterogeneous networkdeep neural networksubchannel allocationpower allocationsum rate |
| spellingShingle | Donghyeon Kim Sean Kwon Haejoon Jung In-Ho Lee Deep Learning-Based Resource Allocation Scheme for Heterogeneous NOMA Networks IEEE Access Non-orthogonal multiple access heterogeneous network deep neural network subchannel allocation power allocation sum rate |
| title | Deep Learning-Based Resource Allocation Scheme for Heterogeneous NOMA Networks |
| title_full | Deep Learning-Based Resource Allocation Scheme for Heterogeneous NOMA Networks |
| title_fullStr | Deep Learning-Based Resource Allocation Scheme for Heterogeneous NOMA Networks |
| title_full_unstemmed | Deep Learning-Based Resource Allocation Scheme for Heterogeneous NOMA Networks |
| title_short | Deep Learning-Based Resource Allocation Scheme for Heterogeneous NOMA Networks |
| title_sort | deep learning based resource allocation scheme for heterogeneous noma networks |
| topic | Non-orthogonal multiple access heterogeneous network deep neural network subchannel allocation power allocation sum rate |
| url | https://ieeexplore.ieee.org/document/10226210/ |
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