Multisegment Mapping Network for Massive MIMO Detection
The massive multiple-input multiple-output (MIMO) technology is one of the core technologies of 5G, which can significantly improve spectral efficiency. Because of the large number of massive MIMO antennas, the computational complexity of detection has increased significantly, which poses a signific...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2021/9989634 |
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| author | Yongzhi Yu Jianming Wang Limin Guo |
| author_facet | Yongzhi Yu Jianming Wang Limin Guo |
| author_sort | Yongzhi Yu |
| collection | DOAJ |
| description | The massive multiple-input multiple-output (MIMO) technology is one of the core technologies of 5G, which can significantly improve spectral efficiency. Because of the large number of massive MIMO antennas, the computational complexity of detection has increased significantly, which poses a significant challenge to traditional detection algorithms. However, the use of deep learning for massive MIMO detection can achieve a high degree of computational parallelism, and deep learning constitutes an important technical approach for solving the signal detection problem. This paper proposes a deep neural network for massive MIMO detection, named Multisegment Mapping Network (MsNet). MsNet is obtained by optimizing the prior detection networks that are termed as DetNet and ScNet. MsNet further simplifies the sparse connection structure and reduces network complexity, which also changes the coefficients of the residual structure in the network into trainable variables. In addition, this paper designs an activation function to improve the performance of massive MIMO detection in high-order modulation scenarios. The simulation results show that MsNet has better symbol error rate (SER) performance and both computational complexity and the number of training parameters are significantly reduced. |
| format | Article |
| id | doaj-art-1c1e003d86a04d7397a32787f2b814d8 |
| institution | Kabale University |
| issn | 1687-5869 1687-5877 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-1c1e003d86a04d7397a32787f2b814d82025-02-03T01:27:20ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772021-01-01202110.1155/2021/99896349989634Multisegment Mapping Network for Massive MIMO DetectionYongzhi Yu0Jianming Wang1Limin Guo2College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaThe massive multiple-input multiple-output (MIMO) technology is one of the core technologies of 5G, which can significantly improve spectral efficiency. Because of the large number of massive MIMO antennas, the computational complexity of detection has increased significantly, which poses a significant challenge to traditional detection algorithms. However, the use of deep learning for massive MIMO detection can achieve a high degree of computational parallelism, and deep learning constitutes an important technical approach for solving the signal detection problem. This paper proposes a deep neural network for massive MIMO detection, named Multisegment Mapping Network (MsNet). MsNet is obtained by optimizing the prior detection networks that are termed as DetNet and ScNet. MsNet further simplifies the sparse connection structure and reduces network complexity, which also changes the coefficients of the residual structure in the network into trainable variables. In addition, this paper designs an activation function to improve the performance of massive MIMO detection in high-order modulation scenarios. The simulation results show that MsNet has better symbol error rate (SER) performance and both computational complexity and the number of training parameters are significantly reduced.http://dx.doi.org/10.1155/2021/9989634 |
| spellingShingle | Yongzhi Yu Jianming Wang Limin Guo Multisegment Mapping Network for Massive MIMO Detection International Journal of Antennas and Propagation |
| title | Multisegment Mapping Network for Massive MIMO Detection |
| title_full | Multisegment Mapping Network for Massive MIMO Detection |
| title_fullStr | Multisegment Mapping Network for Massive MIMO Detection |
| title_full_unstemmed | Multisegment Mapping Network for Massive MIMO Detection |
| title_short | Multisegment Mapping Network for Massive MIMO Detection |
| title_sort | multisegment mapping network for massive mimo detection |
| url | http://dx.doi.org/10.1155/2021/9989634 |
| work_keys_str_mv | AT yongzhiyu multisegmentmappingnetworkformassivemimodetection AT jianmingwang multisegmentmappingnetworkformassivemimodetection AT liminguo multisegmentmappingnetworkformassivemimodetection |