Deep-Learning-Based Channel Estimation for Chaotic Wireless Communication
Hui‐Ping Yin, Xiaohui Zhao, Jun-Ling Yao, Hai‐Peng Ren
Abstract
A deep-learning-based channel estimation method for chaotic wireless communication is proposed in this letter, which is based on a deep neural network (DNN) pre-trained by the stacked denoising autoencoder (SDAE) structure. The DNN learns the channel parameters by using the autocorrelation function (ACF) of the received signal in the sense of minimizing the mean squared error (MSE). Numerical results demonstrate that the proposed scheme learns the channel very well and significantly outperforms the conventional schemes in terms of the channel estimation MSE, as well as the BER performance of the communication system. The proposed channel estimation method based on the ACF of chaotic signal is robust to the noise because of the effect of the double noise resistance operation including the autocorrelation operation and the denoising autoencoder. The proposed scheme is a blind identification method, which uses the received signal directly, by this way, saves the valuable bandwidth resource without any probe signal.