A Deep Learning Based Channel Estimation Scheme for IEEE 802.11p Systems

Han, Seung-Ho; Oh, Yeonji; Song, Changick

doi:10.1109/icc.2019.8761354

Cited by 37 publications

(60 citation statements)

References 9 publications

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“…Inspired by the fact that the auto-encoder (AE) neural networks are useful for solving diverse problems in the physical layer wireless communication systems. AE-DNN channel estimation scheme [11] has been proposed to recover possible estimation and demapping errors resulting from (6), and thus to help in mitigating the error propagation issue of the initial channel estimation. As shown in Fig.…”

Section: G Ae-dnn Estimation Schemementioning

confidence: 99%

“…(ii) RTV-UC that features lower Doppler shifts (400-500 Hz). The comparison of the IEEE 802.11p classical channel estimation schemes, the recently proposed AE-DNN scheme [11], and our proposed STA-DNN schemes are performed over the chosen vehicular channel models in This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.…”

Section: B Ber and Nmse Performancementioning

confidence: 99%

“…Simulation results according to the studied criteria show that integrating DNN with classical estimators attains significant performance improvement. Correcting the estimation error of the initial channel estimatesĥ Initiali [k] (6), which is the main idea of the recently proposed AE-DNN scheme [11] is not sufficient, since it just corrects the demapping error of (5) and neglects the frequency and time correlation of the received symbols. Moreover, employing DNN as a post processing module after the classical STA scheme reflects the performance superiority among other channel estimation schemes, due to the fact that classical STA scheme takes into consideration both frequency and time correlation between the received OFDM symbols.…”

Section: ) Dnn Architecturesmentioning

confidence: 99%

“…Similarly for the other successive layers, the DNN requires 2J 2 J 3 , 2J 3 J 4 , and 2J 4 J 5 operations respectively where J 5 = 2K on denotes the DNN outputs. As presented in [11], the AE-DNN architecture is composed of three hidden layers with J 1 = J 5 = 2K on , J 2 = J 4 = 40, and J 3 = 20 neurons respectively. Thus, AE-DNN requires 4K on J 2 + 2J 2 J 3 multiplications, and 2K on + 2J 2 + J 3 summations.…”

Section: Estimationmentioning

confidence: 99%

“…Motivated by these advantages, DL has been introduced to physical layer in wireless communications and achieved superior performance in various applications [8]- [10]. A DL-based channel estimation scheme for IEEE 802.11p based on auto-encoder deep neural network (AE-DNN) is proposed in [11]. The key ingredient of AE-DNN scheme is to combine DL and data-pilot aided channel estimation schemes such that both are mutually beneficial.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Channel Estimation Schemes for IEEE 802.11p Standard

et al. 2020

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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.

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Section: G Ae-dnn Estimation Schemementioning

confidence: 99%

Section: B Ber and Nmse Performancementioning

confidence: 99%

Section: ) Dnn Architecturesmentioning

confidence: 99%

Section: Estimationmentioning

confidence: 99%