One-Bit Massive MIMO Precoding Using Unsupervised Deep Learning
The recently emerged symbol-level precoding (SLP) technique is a promising solution in multi-user wireless communication systems due to its ability to transform harmful multi-user interference (MUI) into useful signals, thereby improving system performance. Conventional symbol-level precoding design...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10418502/ |
| _version_ | 1827308200150433792 |
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| author | Mohsen Hosseinzadeh Hassan Aghaeinia Mohammad Kazemi |
| author_facet | Mohsen Hosseinzadeh Hassan Aghaeinia Mohammad Kazemi |
| author_sort | Mohsen Hosseinzadeh |
| collection | DOAJ |
| description | The recently emerged symbol-level precoding (SLP) technique is a promising solution in multi-user wireless communication systems due to its ability to transform harmful multi-user interference (MUI) into useful signals, thereby improving system performance. Conventional symbol-level precoding designs have a significant computational complexity that makes their practical implementation difficult and imposes excessive computational complexity on the system. To deal with this problem, we suggest a new deep learning (DL) based approach that utilizes low-complexity designs of symbol-level precoding. This paper focuses on DL-based one-bit precoding approaches for downlink massive multiple-input multiple-output (MIMO) systems, where one-bit digital-to-analog converters (DACs) are used to reduce cost and power. Unlike previous works, the optimized one-bit precoder for multiuser massive MIMO system (HDL-O1PmMIMO) for a wide range of signal-to-noise-ratio (SNR) has a low computational complexity, making it suitable for real precoding scenarios. In this paper, we first design an unsupervised DL-based precoder (UDL-O1PmMIMO) to address the low SNR scenarios, using which we then design a hybrid DL-based precoder (HDL-O1PmMIMO) to address both low and high SNR scenarios. The method suggested in this article utilizes a novel residual DL network structure, which helps overcome the problem of training very deep networks. Additionally, a novel customized cost function, specifically for one-bit precoding in massive MIMO systems, is introduced to optimize the performance of the system in handling interference. The results of an experiment conducted on a general test set using Python and MATLAB show that the proposed approach outperforms existing methods in three aspects: it has a lower bit error rate, it takes less time to generate the precoded vector, and it is more resistant to imperfect channel estimation. |
| first_indexed | 2024-04-24T18:54:19Z |
| format | Article |
| id | doaj.art-bd0c4133d91a40a09b56feacd78a8312 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:54:19Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bd0c4133d91a40a09b56feacd78a83122024-03-26T17:46:51ZengIEEEIEEE Access2169-35362024-01-0112346683468010.1109/ACCESS.2024.336086210418502One-Bit Massive MIMO Precoding Using Unsupervised Deep LearningMohsen Hosseinzadeh0https://orcid.org/0000-0002-0630-2515Hassan Aghaeinia1https://orcid.org/0000-0002-4286-4662Mohammad Kazemi2https://orcid.org/0000-0001-5177-1874Department of Electrical Engineering, Amirkabir University of Technology, Tehran, IranDepartment of Electrical Engineering, Amirkabir University of Technology, Tehran, IranDepartment of Electrical and Electronics Engineering, Bilkent University, Ankara, TurkeyThe recently emerged symbol-level precoding (SLP) technique is a promising solution in multi-user wireless communication systems due to its ability to transform harmful multi-user interference (MUI) into useful signals, thereby improving system performance. Conventional symbol-level precoding designs have a significant computational complexity that makes their practical implementation difficult and imposes excessive computational complexity on the system. To deal with this problem, we suggest a new deep learning (DL) based approach that utilizes low-complexity designs of symbol-level precoding. This paper focuses on DL-based one-bit precoding approaches for downlink massive multiple-input multiple-output (MIMO) systems, where one-bit digital-to-analog converters (DACs) are used to reduce cost and power. Unlike previous works, the optimized one-bit precoder for multiuser massive MIMO system (HDL-O1PmMIMO) for a wide range of signal-to-noise-ratio (SNR) has a low computational complexity, making it suitable for real precoding scenarios. In this paper, we first design an unsupervised DL-based precoder (UDL-O1PmMIMO) to address the low SNR scenarios, using which we then design a hybrid DL-based precoder (HDL-O1PmMIMO) to address both low and high SNR scenarios. The method suggested in this article utilizes a novel residual DL network structure, which helps overcome the problem of training very deep networks. Additionally, a novel customized cost function, specifically for one-bit precoding in massive MIMO systems, is introduced to optimize the performance of the system in handling interference. The results of an experiment conducted on a general test set using Python and MATLAB show that the proposed approach outperforms existing methods in three aspects: it has a lower bit error rate, it takes less time to generate the precoded vector, and it is more resistant to imperfect channel estimation.https://ieeexplore.ieee.org/document/10418502/Massive MIMOone-bit DACprecodingunsupervised deep learning |
| spellingShingle | Mohsen Hosseinzadeh Hassan Aghaeinia Mohammad Kazemi One-Bit Massive MIMO Precoding Using Unsupervised Deep Learning IEEE Access Massive MIMO one-bit DAC precoding unsupervised deep learning |
| title | One-Bit Massive MIMO Precoding Using Unsupervised Deep Learning |
| title_full | One-Bit Massive MIMO Precoding Using Unsupervised Deep Learning |
| title_fullStr | One-Bit Massive MIMO Precoding Using Unsupervised Deep Learning |
| title_full_unstemmed | One-Bit Massive MIMO Precoding Using Unsupervised Deep Learning |
| title_short | One-Bit Massive MIMO Precoding Using Unsupervised Deep Learning |
| title_sort | one bit massive mimo precoding using unsupervised deep learning |
| topic | Massive MIMO one-bit DAC precoding unsupervised deep learning |
| url | https://ieeexplore.ieee.org/document/10418502/ |
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