Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS)...
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/14/4861 |
| _version_ | 1827686890461986816 |
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| author | Ha An Le Trinh Van Chien Tien Hoa Nguyen Hyunseung Choo Van Duc Nguyen |
| author_facet | Ha An Le Trinh Van Chien Tien Hoa Nguyen Hyunseung Choo Van Duc Nguyen |
| author_sort | Ha An Le |
| collection | DOAJ |
| description | Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures. |
| first_indexed | 2024-03-10T09:24:02Z |
| format | Article |
| id | doaj.art-c2c6f6401fe94900889a9a13c1ea122a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:24:02Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c2c6f6401fe94900889a9a13c1ea122a2023-11-22T04:57:07ZengMDPI AGSensors1424-82202021-07-012114486110.3390/s21144861Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication SystemsHa An Le0Trinh Van Chien1Tien Hoa Nguyen2Hyunseung Choo3Van Duc Nguyen4School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, VietnamSchool of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 100000, VietnamSchool of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, VietnamCollege of Computing, Sungkyunkwan University (SKKU), Seoul 08826, KoreaSchool of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, VietnamChannel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures.https://www.mdpi.com/1424-8220/21/14/4861machine learningchannel estimationMIMO-OFDMfrequency selective channels |
| spellingShingle | Ha An Le Trinh Van Chien Tien Hoa Nguyen Hyunseung Choo Van Duc Nguyen Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems Sensors machine learning channel estimation MIMO-OFDM frequency selective channels |
| title | Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems |
| title_full | Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems |
| title_fullStr | Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems |
| title_full_unstemmed | Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems |
| title_short | Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems |
| title_sort | machine learning based 5g and beyond channel estimation for mimo ofdm communication systems |
| topic | machine learning channel estimation MIMO-OFDM frequency selective channels |
| url | https://www.mdpi.com/1424-8220/21/14/4861 |
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