Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/4150 2019 2019 Electronic and communication Department, Institute of Laser for Postgraduate Studies, University of Baghdad, Baghdad, Iraq; 3Department of Computer Networks, Information College of Engineering, Al-Nahrain University, Iraq Hasan Hasan Department of Information and Communication, Al-khwarizmi College of Engineering, University of Baghdad, Iraq Lwaa F. Abdulameer The current exponential growth in the demand for bandwidth is the most urgent challenge for next-generation wireless systems. One of the most appropriate techniques to overcome this situation is Free-space optical (FSO) communication due to the provision of an ample bandwidth. The main disadvantage of FSO communication systems is that the optical beam, propagating through atmospheric turbulence, can be distorted to an unacceptable level. In this work, a 64x64 MIMO-FSO system with Non-Orthogonal Multiple Access (NOMA) and QPSK modulation scheme is assessed. We compare the Bit Error Rate (BER) performance of the system under 4 theoretical turbulence channel models: Log-Normal, Gamma-Gamma, Fisher-Snedecor, Negative Exponential, as well as 4 real seasonal LogCn² datasets. Classical Maximum Likelihood (ML) detection was compared against the deep learning-based ML detection using a Deep Neural Network (DNN) as well as an Autoencoder model. We found that the autoencoder model has outperformed the classical ML detection in terms of BER performance, especially for the weaker user, when NOMA is considered. It was also found that using real datasets that represent real turbulence conditions the proposed system is highly effective and can serve as intelligent fronthaulbackhaul solutions for dense IoT networks such as smart cities, autonomous vehicles, and industrial automation. 2025 2025 392 403 10.54216/JISIoT.170225 https://www.americaspg.com/articleinfo/18/show/4150