A deep neural network aided channel estimation for maritime wireless communications

Guocai Yuan; Shiyu Jiang; Min Jing; Hongming Zhang

doi:10.1117/12.3035636

10 July 2024 A deep neural network aided channel estimation for maritime wireless communications

Guocai Yuan, Shiyu Jiang, Min Jing, Hongming Zhang

Proceedings Volume 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024); 132232P (2024) https://doi.org/10.1117/12.3035636
Event: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2024), 2024, Wuhan, China

Abstract

In 6G, maritime wireless communications will play an important role for providing high data-rate services ubiquitously. However, due to the complex and rapidly changing marine environment, an accurate channel estimation is usually hard to achieve for maritime wireless communication systems. In this paper, a deep neural network (DNN) aided receiver design is proposed for the orthogonal frequency division multiplexing (OFDM) based maritime wireless communications. Due to the complicated and rapidly changing marine environment, the channel conditions are hard to be accurately estimated. Hence, the attainable performance of the OFDM based maritime wireless systems may be severely degraded. In order to tackle this issue, in this paper, a DNN aided marine channel estimation scheme is conceived for the OFDM based maritime wireless system, where SGD, Adam, SignSGD optimizers are considered. The attainable system performance is validated by simulations, showing that the proposed DNN-aided channel estimation scheme is capable of attaining a competitive system performance compared to the existing schemes. Furthermore, the proposed DNN-aided channel estimation scheme is able to achieve a better system performance at the low signal to noise ratio (SNR) regime in comparison to the existing channel estimation schemes.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guocai Yuan, Shiyu Jiang, Min Jing, and Hongming Zhang "A deep neural network aided channel estimation for maritime wireless communications", Proc. SPIE 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024), 132232P (10 July 2024); https://doi.org/10.1117/12.3035636

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KEYWORDS

Orthogonal frequency division multiplexing

Wireless communications

Error analysis

Neural networks

Signal to noise ratio

Telecommunications

Simulations

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