One of the main barriers in front of Free Space Optical (FSO) communication systems is the atmospheric turbulence induced fading. Theoretically, the Maximum Likelihood (ML) detector is the optimum detector. The ML detector requires Channel State Information (CSI), which can be provided in perfect or blind forms. The perfect CSI ML detector requires pilot transmission for channel estimation, which increases the complexity and reduces the data rate The blind CSI ML detector uses blind channel estimation, which leads to performance degradation. In this paper, for the first time, an efficient and low complexity deep learning based detector is presented for FSO system. The proposed deep learning based detector does not require CSI at all, it feeds the received signal directly into a deep neural network. The proposed deep learning based detector is compared with perfect CSI ML detector and blind CSI ML detector. In this paper, log-normal, gamma-gamma, and negative exponential distributions are considered for modeling weak, weak to strong, and saturate atmospheric turbulence regimes, respectively. Results indicate that the performance of proposed deep learning based detector gets close enough to the perfect CSI ML detector, with a significantly lower complexity than the blind CSI ML detector. The proposed detector is almost 80 times faster than blind CSI ML detector. In addition, it does not have an error floor, while one of the main problems of blind CSI ML detector is the error floor. Besides much less complexity, the proposed detector has almost the same performance as blind CSI ML detector at weak atmospheric turbulence regime. The available blind CSI ML detectors are practical only in weak turbulence, because they assume that channel coefficients are constant for the duration of some symbols. However, the proposed deep learning based detector does not consider this assumption, and can be used in all atmospheric turbulence regimes. The performance of the proposed detector degrades when atmospheric turbulence gets stronger. For instance, the performance of the proposed deep learning based detector degrades 7 dB compared with blind CSI ML detector at target bit error rate of $10^{-3}$. However, the proposed deep learning based detector outperforms blind CSI ML detector at high signal to noise ratios, because in this range blind CSI ML detector suffers from the error floor.

自由空间光学（FSO）通信系统前面的主要障碍之一是大气湍流引起的衰落。从理论上讲，最大似然（ML）检测器是最佳检测器。ML检测器需要信道状态信息（CSI），可以以完美或盲目的形式提供。完美的CSI ML检测器需要导频传输进行信道估计，这增加了复杂性并降低了数据速率。盲CSI ML检测器使用了盲信道估计，这会导致性能下降。本文首次提出了一种高效，低复杂度的基于深度学习的FSO系统检测器。所提出的基于深度学习的检测器完全不需要CSI，它将接收到的信号直接馈入深度神经网络。将提出的基于深度学习的检测器与完美CSI ML检测器和盲CSI ML检测器进行了比较。在本文中，考虑对数正态分布，γ-γ分布和负指数分布分别模拟弱，弱到强以及饱和大气湍流状态。结果表明，所提出的基于深度学习的检测器的性能已足够接近理想的CSI ML检测器，并且其复杂度明显低于盲目CSI ML检测器。所提出的检测器几乎是盲CSI ML检测器的80倍。另外，它没有错误基底，而盲CSI ML检测器的主要问题之一是错误基底。除了复杂性降低外，在弱大气湍流条件下，拟议的探测器具有与盲CSI ML探测器几乎相同的性能。可用的盲CSI ML检测器仅在湍流较弱时才实用，因为它们假定在某些符号持续时间内通道系数是恒定的。然而，提出的基于深度学习的探测器不考虑该假设，并且可以在所有大气湍流状态下使用。当大气湍流变强时，提出的探测器的性能会降低。例如，与盲CSI ML检测器相比，建议的基于深度学习的检测器的性能在目标误码率为的情况下降低了7 dB。当大气湍流变强时，提出的探测器的性能会降低。例如，与盲CSI ML检测器相比，建议的基于深度学习的检测器的性能在目标误码率为的情况下降低了7 dB。当大气湍流变强时，提出的探测器的性能会降低。例如，与盲CSI ML检测器相比，建议的基于深度学习的检测器的性能在目标误码率为的情况下降低了7 dB。$\mathrm{1个}{0}^{-3}$。但是，在高信噪比下，提出的基于深度学习的检测器的性能优于盲CSI ML检测器，因为在此范围内，盲CSI ML检测器存在错误底限。