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Deep learning-based scalable and robust channel estimator for wireless cellular networks

  • Anseok, Lee (Intelligent Wireless Access Research Section, Mobile Communication Research Division, Telecommunications & Media Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Yongjin, Kwon (Intelligent Wireless Access Research Section, Mobile Communication Research Division, Telecommunications & Media Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Hanjun, Park (Intelligent Wireless Access Research Section, Mobile Communication Research Division, Telecommunications & Media Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Heesoo, Lee (Intelligent Wireless Access Research Section, Mobile Communication Research Division, Telecommunications & Media Research Laboratory, Electronics and Telecommunications Research Institute)
  • Received : 2022.05.24
  • Accepted : 2022.10.21
  • Published : 2022.12.10
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Abstract

In this paper, we present a two-stage scalable channel estimator (TSCE), a deep learning (DL)-based scalable, and robust channel estimator for wireless cellular networks, which is made up of two DL networks to efficiently support different resource allocation sizes and reference signal configurations. Both networks use the transformer, one of cutting-edge neural network architecture, as a backbone for accurate estimation. For computation-efficient global feature extractions, we propose using window and window averaging-based self-attentions. Our results show that TSCE learns wireless propagation channels correctly and outperforms both traditional estimators and baseline DL-based estimators. Additionally, scalability and robustness evaluations are performed, revealing that TSCE is more robust in various environments than the baseline DL-based estimators.

Keywords

Acknowledgement

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (2021-0-00972, Development of Intelligent Wireless Access Technologies)

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