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Journal Article
Deep learning‐based scalable and robust channel estimator for wireless cellular networks
- Authors
- Anseok Lee, Yongjin Kwon, Hanjun Park, Heesoo Lee
- Issue Date
- 2022-12
- Citation
- ETRI Journal, v.44, no.6, pp.915-924
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- Channel Estimator, Cutting-edge, Feature extractioN, Propagation Channel, Reference signal, Two-Stage, Window averaging, Wireless Propagation, Wireless cellular Networks, accurate estimation, deep learning(DL)
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
