Comparison of different models for ground-level atmospheric turbulence strength (Cn^2) prediction with a new model according to local weather data for FSO applications

Abstract

Atmospheric parameters strongly affect the performance of free-space optical communication (FSOC) systems when the optical wave is propagating through the inhomogeneous turbulence transmission medium. Developing a model to get an accurate prediction of the atmospheric turbulence strength ($C_n^2$) according to meteorological parameters (weather data) becomes significant to understand the behavior of the FSOC channel during different seasons. The construction of a dedicated free-space optical link for the range of 0.5 km at an altitude of 15.25 m built at Thanjavur (Tamil Nadu) is described in this paper. The power level and beam centroid information of the received signal are measured continuously with weather data at the same time using an optoelectronic assembly and the developed weather station, respectively, and are recorded in a data-logging computer. Existing models that exhibit relatively fewer prediction errors are briefed and are selected for comparative analysis. Measured weather data (as input factors) and $C_n^2$ (as a response factor) of size $[177,147\times 4]$ are used for linear regression analysis and to design mathematical models more suitable in the test field. Along with the model formulation methodologies, we have presented the contributions of the input factors’ individual and combined effects on the response surface and the coefficient of determination ($R^2$) estimated using analysis of variance tools. An $R^2$ value of 98.93% is obtained using the new model, model equation V, from a confirmatory test conducted with a testing data set of size $[2000\times 4]$. In addition, the prediction accuracies of the selected and the new models are investigated during different seasons in a one-year period using the statistics of day, week-averaged, month-averaged, and seasonal-averaged diurnal $C_n^2$ profiles, and are verified in terms of the sum of absolute error (SAE). A $C_n^2$ prediction maximum average SAE of $2.3\times {10}^{-13}{\mathrm{m}}^{-2/3}$ is achieved using the new model in a longer range of dynamic meteorological parameters during the different local seasons.