K. Mei, J. Liu, X. Zhang, N. Rajatheva and J. Wei, "Performance Analysis on Machine Learning-Based Channel Estimation," in IEEE Transactions on Communications, vol. 69, no. 8, pp. 5183-5193, Aug. 2021, doi: 10.1109/TCOMM.2021.3083597
Performance analysis on machine learning-based channel estimation
|Author:||Mei, Kai1; Liu, Jun1; Zhang, Xiaochen1;|
1College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
2Center for Wireless Communications, University of Oulu, Oulu 90570, Finland
|Online Access:||PDF Full Text (PDF, 0.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021101450964
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-10-14
Recently, machine learning-based channel estimation has attracted much attention. The performance of machine learning-based estimation has been validated by simulation experiments. However, little attention has been paid to the theoretical performance analysis. In this paper, we investigate the mean square error (MSE) performance of machine learning-based estimation. Hypothesis testing is employed to analyze its MSE upper bound. Furthermore, we build a statistical model for hypothesis testing, which holds when the linear learning module with a low input dimension is used in machine learning-based channel estimation, and derive a clear analytical relation between the size of the training data and performance. Then, we simulate the machine learning-based channel estimation in orthogonal frequency division multiplexing (OFDM) systems to verify our analysis results. Finally, the design considerations for the situation where only limited training data is available are discussed. In this situation, our analysis results can be applied to assess the performance and support the design of machine learning-based channel estimation.
IEEE transactions on communications
|Pages:||5183 - 5193|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the National Science Foundation of China (NSFC) (Grants 61931020).
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