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학술지 Deep learning-based scalable and robust channel estimator for wireless cellular networks
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저자
이안석, 권용진, 박한준, 이희수
발행일
202212
출처
ETRI Journal, v.44 no.6, pp.915-924
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.2022-0209
협약과제
22HH2300, (세부2) 지능형 무선 액세스 기술 개발, 이희수
초록
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.
KSP 제안 키워드
Channel Estimator, Cutting-edge, Feature extractioN, Learning-based, Propagation Channel, Reference signal, Two-Stage, Window averaging, Wireless Propagation, Wireless cellular Networks, accurate estimation
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