Diversity Reception Technology in Coherent Optical Communication over Gamma-Gamma Atmospheric Turbulence Channel

One of the key advantages of terahertz (THz) communication is its potential for energy efficiency, making it an attractive option for green communication systems. Coherent THz transmission technology has recently been explored in the literature. However, there exist few error performance results for such a wireless link employing coherent THz technology. In this paper, we explore a comprehensive terrestrial channel model designed for wireless line-of-sight communication using THz frequencies. The performance of coherent THz links is analyzed, and it is found to be notably affected by two significant factors, atmospheric turbulence and pointing errors. These could occur between the terahertz transmitter and receiver in terrestrial links. The exact and asymptotic solutions are derived for bit error rate and interrupt probability for binary phase-shift keying coherent THz systems, respectively, over log-normal and Gamma–Gamma turbulent channels. The asymptotic outage probability analysis is also performed. It is shown that the presented results offer a precise estimation of coherent THz transmission performance and its link budget.

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Atmospheric Turbulence Intensity Image Acquisition Method Based on Convolutional Neural Network

Mu,

Zhou,

Shao

et al. 2024

Remote Sensing

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An algorithmic model of a neural network with channel attention and spatial attention (CASANet) is proposed to estimate the value of atmospheric coherence length, which in turn provides a quantitative description of atmospheric turbulence intensity. By processing the acquired spot image data, the channel attention and spatial attention mechanisms are utilized, and the convolutional neural network learns the interdependence between the channel and space of the feature image and adaptively recalibrates the feature response in terms of the channel to increase the contribution of the foreground spot and suppress the background features. Based on the experimental data, an analysis of the CASANet model subject to turbulence intensity perturbations, fluctuations in outgoing power, and fluctuations in beam quality at the outlet is carried out. Comparison of the results of the convolutional neural network with those of the inverse method and the differential image motion method shows that the convolutional neural network is optimal in three evaluation indexes, namely, the mean deviation, the root-mean-square error, and the correlation coefficient, which are 2.74, 3.35, and 0.94, respectively. The convolutional neural network exhibits high accuracy under moderate and weak turbulence, and the estimation values under strong turbulence conditions are still mostly within the 95% confidence interval. The above results fully demonstrate that the proposed convolutional neural network method can effectively estimate the atmospheric coherence length, which provides technical support for the inversion of atmospheric turbulence intensity based on images.

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Diversity Reception Technology in Coherent Optical Communication over Gamma-Gamma Atmospheric Turbulence Channel

Cited by 4 publications

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Performance of Wavelength Division Multiplexing System Based on V-Cavity Semiconductor Laser

Precise Error Performance of BPSK Modulated Coherent Terahertz Wireless LOS Links with Pointing Errors

Atmospheric Turbulence Intensity Image Acquisition Method Based on Convolutional Neural Network

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