Chaotic Frequency Hopping Prediction Based on Temporal Convolutional Network.

Chaotic sequence is often used in FH communications because of its good properties. Chaotic theory proves that chaotic sequence is predictable in phase space and provides a theoretical basis for the prediction of chaotic FH sequence. Temporal convolutional network (TCN) has excellent sequence modeling ability for its causal convolution structure and stacked dilated convolutional layers. In this paper, we firstly perform phase space reconstruction for FH sequence generated by three commonly used chaotic maps, i.e., Logistic map, Chebyshev map and Tent map. Then, TCN based sequence prediction model is established to implement FH sequence prediction in phase space. The simulation results show that the model can effectively predict chaotic FH sequences while embedding dimension and delay time for phase space reconstruction are appropriately chosen.