Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs
We consider an uplink multiuser multiple-input multiple-output (MU-MIMO) system with one-bit analog-to-digital converters (ADCs). In this system, the construction of a low-complexity detector is quite challenging due to the non-linearity of an end-to-end channel transfer function. Recently, a superv...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9064574/ |
| _version_ | 1818330202480050176 |
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| author | Seonho Kim Jeongmin Chae Song-Nam Hong |
| author_facet | Seonho Kim Jeongmin Chae Song-Nam Hong |
| author_sort | Seonho Kim |
| collection | DOAJ |
| description | We consider an uplink multiuser multiple-input multiple-output (MU-MIMO) system with one-bit analog-to-digital converters (ADCs). In this system, the construction of a low-complexity detector is quite challenging due to the non-linearity of an end-to-end channel transfer function. Recently, a supervised-learning (SL) detector was proposed by modeling the complex non-linear function as a tractable Bernoulli-mixture model. It achieves an optimal maximum-likelihood (ML) performance, provided the channel state information (CSI) is perfectly known at a receiver. However, when a system-size is large, SL detector is not practical because of requiring a large amount of labeled data (i.e., pilot signals) to estimate the model parameters. We address this problem by proposing a semi-supervised learning (SSL) detector in which both pilot signals (i.e., labeled data) and some part of data signals (i.e., unlabeled data) are used to estimate them via expectation-maximization (EM) algorithm. We further extend the proposed detector for time-varying channels, by leveraging the idea of online learning, which is called online-learning (OL) detector. Simulation results demonstrate that the proposed SSL detector can achieve the almost same performance of the corresponding SL detector with significantly lower pilot overhead. In addition, it is shown that the proposed OL detector is more robust to channel variations compared with the existing detectors. |
| first_indexed | 2024-12-13T13:00:12Z |
| format | Article |
| id | doaj.art-108a1502bc5043c18a1916d8dd096482 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:00:12Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-108a1502bc5043c18a1916d8dd0964822022-12-21T23:45:04ZengIEEEIEEE Access2169-35362020-01-018866088661610.1109/ACCESS.2020.29872129064574Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCsSeonho Kim0https://orcid.org/0000-0001-8833-8927Jeongmin Chae1Song-Nam Hong2https://orcid.org/0000-0002-9535-2521Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USADepartment of Electrical and Computer Engineering, Ajou University, Suwon, South KoreaDepartment of Electrical and Computer Engineering, Ajou University, Suwon, South KoreaWe consider an uplink multiuser multiple-input multiple-output (MU-MIMO) system with one-bit analog-to-digital converters (ADCs). In this system, the construction of a low-complexity detector is quite challenging due to the non-linearity of an end-to-end channel transfer function. Recently, a supervised-learning (SL) detector was proposed by modeling the complex non-linear function as a tractable Bernoulli-mixture model. It achieves an optimal maximum-likelihood (ML) performance, provided the channel state information (CSI) is perfectly known at a receiver. However, when a system-size is large, SL detector is not practical because of requiring a large amount of labeled data (i.e., pilot signals) to estimate the model parameters. We address this problem by proposing a semi-supervised learning (SSL) detector in which both pilot signals (i.e., labeled data) and some part of data signals (i.e., unlabeled data) are used to estimate them via expectation-maximization (EM) algorithm. We further extend the proposed detector for time-varying channels, by leveraging the idea of online learning, which is called online-learning (OL) detector. Simulation results demonstrate that the proposed SSL detector can achieve the almost same performance of the corresponding SL detector with significantly lower pilot overhead. In addition, it is shown that the proposed OL detector is more robust to channel variations compared with the existing detectors.https://ieeexplore.ieee.org/document/9064574/Massive MIMOone-bit ADCMIMO detectionmachine learningsemi-supervised learningEM algorithm |
| spellingShingle | Seonho Kim Jeongmin Chae Song-Nam Hong Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs IEEE Access Massive MIMO one-bit ADC MIMO detection machine learning semi-supervised learning EM algorithm |
| title | Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs |
| title_full | Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs |
| title_fullStr | Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs |
| title_full_unstemmed | Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs |
| title_short | Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs |
| title_sort | machine learning detectors for mu mimo systems with one bit adcs |
| topic | Massive MIMO one-bit ADC MIMO detection machine learning semi-supervised learning EM algorithm |
| url | https://ieeexplore.ieee.org/document/9064574/ |
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