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Deep Learning Aided Blind Synchronization Word Estimation
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DOI：10.1109/ACCESS.2021.3058351.png)
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Abstract
En 中文
In this paper, we address a blind frame synchronization problem where the receiver acts as an eavesdropper in the wiretap channel. A challenging condition is considered, where the receiver has completely no prior information except that an unknown synchronization word (SW) is repeated in a nonperiodic fashion. Although an autocorrelation method was proposed for the fixed frame length scenario, the scheme is not applicable to the variable-length scenario. To solve the problem, we propose a deep learning-aided blind SW estimation method where recurrent neural networks (RNNs) are used as a symbol predictor that predicts a symbol from an observation of preceding symbols. The prediction confidence of the RNN-based predictor is used for the localization of the SW symbols in the received signal. Two RNNs fed with the received signal forward and backward are used for accurate SW localization. It has been verified by simulation that the proposed schemes estimate the SW well when the amount of the received signal is sufficiently large. It is straightforward to get synchronization to the received signal with the correctly estimated SW.
Keywords:
Synchronization
Estimation
Receivers
Signal to noise ratio
Standards
Recurrent neural networks
Logic gates
Blind frame synchronization
deep learning
frame recognition
non-cooperative communication
synchronization word
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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Cited Papers
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