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Optimization of Channel Estimation Using ELMx-based in Massive MIMO

Apinya Innok1, Chittapon Keawin2, Peerapong Uthansakul2,*

1 Telecommunications Engineering, Rajamangala University of Technology Isan, Nakhon Ratchasima, 30000, Thailand
2 School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand

* Corresponding Author: Peerapong Uthansakul. Email: email

Computers, Materials & Continua 2022, 73(1), 103-118.


In communication channel estimation, the Least Square (LS) technique has long been a widely accepted and commonly used principle. This is because the simple calculation method is compared with other channel estimation methods. The Minimum Mean Squares Error (MMSE), which is developed later, is devised as the next step because the goal is to reduce the error rate in the communication system from the conventional LS technique which still has a higher error rate. These channel estimations are very important to modern communication systems, especially massive MIMO. Evaluating the massive MIMO channel is one of the most researched and debated topics today. This is essential in technology to overcome traditional performance barriers. The better the channel estimation, the more accurate it is. This paper investigated machine learning (ML) for channel estimation. ML channel estimations based on the Extreme Learning Machine (ELMx) group are also implemented. These estimations, known as the ELMx group, include Regularized Extreme Learning Machine (RELM) and Outlier Robust Extreme Learning Machine (ORELM). Then, it was compared with LS and MMSE. The simulation results reveal that the ELMx group outperforms LS and MMSE in channel capacity and bit error rate. Additionally, this paper has proven complexity for verified computational times. The RELM method is less time consuming and has low complexity which is suitable for future use in large MIMO systems.


Cite This Article

APA Style
Innok, A., Keawin, C., Uthansakul, P. (2022). Optimization of channel estimation using elmx-based in massive MIMO. Computers, Materials & Continua, 73(1), 103-118.
Vancouver Style
Innok A, Keawin C, Uthansakul P. Optimization of channel estimation using elmx-based in massive MIMO. Comput Mater Contin. 2022;73(1):103-118
IEEE Style
A. Innok, C. Keawin, and P. Uthansakul "Optimization of Channel Estimation Using ELMx-based in Massive MIMO," Comput. Mater. Contin., vol. 73, no. 1, pp. 103-118. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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