Deep Learning Based Channel Estimation Schemes for IEEE 802.11p Standard
| Autor: | Ahmad Nimr, Gerhard Fettweis, Marwa Chafii, Abdul Karim Gizzini |
|---|---|
| Přispěvatelé: | Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Institute of Computer Engineering (TUD), Technische Universität Dresden = Dresden University of Technology (TU Dresden) |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science Channel estimation IEEE 80211p standard 02 engineering and technology [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] General Materials Science IEEE 802.11p Network packet business.industry Deep learning General Engineering deep learning Estimator 020206 networking & telecommunications [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF] Computer engineering Bit error rate 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence vehicular channels business lcsh:TK1-9971 DNN Communication channel |
| Zdroj: | IEEE Access IEEE Access, IEEE, 2020, pp.113751-113765. ⟨10.1109/ACCESS.2020.3003286⟩ IEEE Access, Vol 8, Pp 113751-113765 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.3003286 |
| Popis: | International audience; IEEE 802.11p standard is specially developed to define vehicular communications requirements and support cooperative intelligent transport systems. In such environment, reliable channel estimation is considered as a major critical challenge for ensuring the system performance due to the extremely time-varying characteristic of vehicular channels. The channel estimation of IEEE 802.11p is preamble based, which becomes inaccurate in high mobility scenarios. The major challenge is to track the channel variations over the course of packet length while adhering to the standard specifications. The motivation behind this paper is to overcome this issue by proposing a novel deep learning based channel estimation scheme for IEEE 802.11p that optimizes the use of deep neural networks (DNN) to accurately learn the statistics of the spectral temporal averaging (STA) channel estimates and to track their changes over time. Simulation results demonstrate that the proposed channel estimation scheme STA-DNN significantly outperforms classical channel estimators in terms of bit error rate. The proposed STA-DNN architectures also achieve better estimation performance than the recently proposed auto-encoder DNN based channel estimation with at least 55.74% of computational complexity decrease. INDEX TERMS Channel estimation, deep learning, DNN, IEEE 802.11p standard, vehicular channels. |
| Databáze: | OpenAIRE |
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