Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 14; p. 4861
• Main Authors: Le, Ha An, Van Chien, Trinh, Nguyen, Tien Hoa, Choo, Hyunseung, Nguyen, Van Duc
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 16.07.2021; MDPI
• Subjects: channel estimation; Communications systems; Computer architecture; Deep learning; Estimates; frequency selective channels; Machine learning; Mean square errors; MIMO communication; MIMO-OFDM; Network reliability; Neural networks; Normal distribution; Orthogonal Frequency Division Multiplexing; Performance evaluation; Propagation; Random variables; Receivers & amplifiers; Time series; Transmitters; Wireless communications; Wireless networks
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21144861

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.