Channel estimation of massive MIMO FSO communication system using deep attention residual U-Net
Al-Imran, Md Shahriar Nazim, Huy Nguyen, Yeong Min Jang
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.