CNN-aided timing synchronization in OFDM systems by exploiting lightweight cascaded mode

Qing, Chaojin; Yang, Na; Tang, Shuhai; Chen, Jinliang; Wang, Jiafan

doi:10.1016/j.icte.2023.03.003

Cited by 2 publications

(2 citation statements)

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“…In addition, the lightweight convolutional neural network (CNN) is utilized for the STO estimation in OFDM systems. 34,35 Mahesh et al 36 propose an STO estimation method by using the DNN model for the OFDM system. However, these methods do not consider the variation of Doppler shifts in different paths, which is not suitable for high mobility environments.…”

Section: Introductionmentioning

confidence: 99%

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Timing‐deviation and frequency‐offset estimations for multicarrier transmission in high mobility environments using deep neural network

Wu,

Geng,

Chen

et al. 2023

Int J Communication

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SummaryThe multicarrier technology used in fourth‐generation (4G) and fifth‐generation (5G) communications is quite susceptible to multipath fading and Doppler frequency shift, which is more prominent in high mobility environments. Therefore, the accuracy of timing and frequency synchronization impacts the overall system performance significantly. Existing synchronization methods rely on the correlation of the preamble sequences and are, hence, vulnerable to severe multipath effect and Doppler effect. In this article, we propose a novel scheme leveraging deep neural network (DNN) to achieve high‐precision synchronization in high mobility environments. Concretely, a convolutional neural network (CNN) architecture is proposed to extract the hidden features of received signals for timing deviation estimation. In the following, a fully connected (FC) model is demonstrated to classify the optimal carrier frequency offset (CFO) estimate from several CFO candidates. The proposed synchronization scheme is assessed under extended vehicular A (EVA) channel with various Doppler frequency shifts. Simulation results corroborate that the proposed DNN‐based scheme achieves a significant performance gain over the conventional correlation methods, and the proposed timing deviation estimation scheme exhibits an excellent complexity reduction; wherefore, it is extremely promising for multicarrier transmission systems in high mobility environments.

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Section: Introductionmentioning

confidence: 99%

“…Also, Mintao et al 33 construct an ELM‐based timing synchronization network for the OFDM systems. In addition, the lightweight convolutional neural network (CNN) is utilized for the STO estimation in OFDM systems 34,35 . Mahesh et al 36 propose an STO estimation method by using the DNN model for the OFDM system.…”

Section: Introductionmentioning

confidence: 99%