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One of the main problems encountered with Free Space Optical (FSO)Communication system is the atmospheric turbulence. Although many solutionsexist for combating this effect, they have either high complexity or lowperformance. In this paper, a comprehensive investigation is developed, andthree new effective Deep Learning (DL) based solutions are proposed. Thispaper, for the first time, deploys Deep Learning, transceiver learning, as wellas transmitter learning in FSO communication. In addition, this is the firsttime that DL approach is implemented in FSO-Multi-Input Multi-Output (MIMO)communication. Results of the proposed structure are compared with the state ofthe art MQAM based FSO system with Maximum Likelihood Detection. Wide range ofatmospheric turbulence, from weak to the strong regime, are considered; resultsindicate that the proposed structures despite less complexity, have the sameperformance as the outstanding conventional structure. In addition, indifferent MIMO structures (different combiners and the different number oftransceiver apertures), the proposed structure still achieve the performance ofthe state of the art conventional system.

Three novel efficient Deep Learning-based approaches for compensating atmospheric turbulence in FSO communication system