LAUSR

This article proposes a novel orthogonal frequency-division multiplexing (OFDM) autoencoder featuring convolutional neural networks (CNNs)-based channel estimation for marine communications with complex and fast-changing environments. We demonstrate that the proposed OFDM autoencoder system can be generalized to work under various channel environments, different throughputs, while outperforming the traditional OFDM counterparts, especially when working at high throughputs. In addition, since OFDM systems require accurate channel estimations to function properly, this treatise also proposes a new channel estimation algorithm for OFDM systems that combine the power of deep learning (DL) with the philosophy of super-resolution reconstruction, which uses dense convolutional neural networks (Dense-Nets) to reconstruct low-resolution pilot information images into high-resolution full-channel impulse responses (CIRs). The Dense-Net structure has the characteristics of dense connections and feature multiplexing. The simulation results show that under slow fading, the proposed channel estimator (CE) can estimate the CIRs perfectly. Under fast fading, the proposed CE outperforms the existing learning-based algorithms with fewer neural network parameters. Therefore, the proposed novel autoencoder scheme and the powerful CE are potentially attractive approaches for the Internet of Vessels (IoV).