A Novel CNN-based Autoencoder with Channel Feedback for Intelligent Maritime Communications
Bin Lin, Xiaoling Han, Nan Wu, Jiaye Li, Haocheng Wang, Shuai Shao · 2022 IEEE/CIC International Conference on Communications in China (ICCC) · 2022
Driven by the rapid growth of maritime business, the research of reliable maritime communications has attracted great attention from both academic and industry. This paper proposes a novel convolutional neural networks (CNN) -based autoencoder with channel feedback (CNN-AE-CF) for intelligent maritime communications with complex and changeable environment. A CNN-AE-CF is comprised of CNN layers, which inherits the breakthrough characteristics of CNN, such as generalization, feature learning, classification, and fast training convergence. In the CNN-AE-CF, we introduce a feedback channel and a feedback decoder at the transmitter to decode the feedback signals. Then, the transmitter combines feedback signals with input signals for secondary encoding. We leverage Rician fading channel to simulate the marine environment, in which CNN-AE-CF is trained. Finally, simulation results illustrate the superiority in terms of reliability and robustness. The proposed autoencoder is promising for intelligent maritime communications.