Work

Channel estimation of massive MIMO FSO communication system using deep attention residual U-Net

Year: 2024

Type: article

Abstract: Channel estimation in massive-MIMO FSO systems is critical for ensuring reliable data transmission . However, conventional estimators offer limited benefits due to the computational difficulty of accurately estimating the channel. This paper presents a novel approach to estimate channels using an attention residual U-Net (ARU-Net) architecture which utilizes the advantages of both attention and residual connection. In the simulation, the channel matrix has been represented as a 2D image. The proposed model significantly outperforms traditional channel estimation methods and other deep learning models in terms of MSE ( 1 0 − 5 at 25 dB SNR), especially in atmospheric turbulence and other noises.

Source: ICT Express

DOI: 10.1016/j.icte.2024.09.012

Authors: Al-Imran, Md. Shahriar Nazim, Huy Nguyen, Yeong Min Jang

Institution: Kookmin University

Language: English

Topic: Optical Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,500

Funders: Korea Research Institute for Defense Technology Planning and Advancement, National Research Foundation Singapore, National Research Foundation of Korea, Defense Acquisition Program Administration