Authors - N. Mangaiyarkarasi, J. Arputha Vijaya Selvi, T. Pasupathi Abstract - Free Space Optical (FSO) communication systems offersultra high bandwidth, large data rate and very secure data transmission, making them a feasible solution for next generation communication networks. However, performance of the FSO communication system is greatly impacted by adverse atmospheric conditions such as heavy turbulence, rain, and fog, which set up errors and degrade the quality of the signal. In this paper an adaptive neural network-based approach is presented to mitigate errors under different seasons of atmospheric conditions. This method exploits a convolutional neural network (CNN) based architecture to predict and compensate the atmospheric induced distortions, thereby improving the performance of the FSO communication system. It significantly improves the Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR). Real-time atmospheric data such as temperature (T in C), relative humidity (%), atmospheric pressure (Pa), and wind speed (ms-1) are collected and the CNN dynamically changes the parameters to optimize performance. Achieved result shows that the neural network model significantly improves the robustness and reliability of the communication system. This method triggers the way for more resilient FSO networks, which is more crucial for the implementation of 5G/6G and beyond communication infrastructures.
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