Year: 2025
Type: article
Abstract: This paper introduces a novel autoencoder-based channel estimation framework specifically designed for OFDM systems in the complex and rapidly time-varying maritime channel. We design a novel autoencoder architecture that integrates attention mechanisms with long short-term memory networks to adapt to the challenges posed by maritime communication. Additionally, to enhance the OFDM system’s ability to acquire precise channel response and improve operational efficiency, we introduce an improved fast super-resolution convolutional neural network. This enhancement is achieved through the incorporation of a residual denoising module specifically designed to mitigate the adverse effects of additive noise. By jointly training the autoencoder and the channel estimation network, we significantly enhance the reliability of maritime OFDM communication systems. Simulation results demonstrate that the proposed channel estimation network accurately estimates channel response across different pilot numbers, and the joint channel estimation method based on the autoencoder can be extended to accommodate different transmission rates and sea states.
Source: Electronics
Authors: Yongjie Yang, Wenming Chao, Li Ma, Fandi Meng, Zhixuan Hu
Institution: Nantong University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G1195673862, G3832685802, G6956463424
OpenAlex ID: W4413836277