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Abstract
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.
Highlights
In a standard communication system [1], the transmitter sends messages to its legitimate receiver in accordance with a pre-agreed protocol
In the backpropagation through time (BPTT) algorithm, an recurrent neural networks (RNNs) is fed with an input vector of the training data set at each time step and computes the output given the values of the state and the parameter set
NUMERICAL RESULTS the simulation results of the proposed blind synchronization word (SW) estimation method are provided, and its performance is analyzed
Summary
In a standard communication system [1], the transmitter sends messages to its legitimate receiver in accordance with a pre-agreed protocol. Under non-cooperative communication settings, blind frame recognition, which is an estimation of frame information such as frame length and SW, should precede synchronization. We specially address the blind frame synchronization for variable-length frame condition that has not been studied at all except for a very limited contribution [32]. A SW estimation method for the variable-length frame condition was presented in [32], where a neural network is trained to learn all possible SW patterns. Since the previous blind frame synchronization methods presented in [10], [32] are no longer applicable to the variable-length frame scenario, it is worthwhile to propose a SW estimation algorithm working under good channel quality.
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