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Deep learning based detection technique for FSO communication systems

机译：基于深度学习的FSO通信系统检测技术

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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. (C) 2020 Elsevier B.V. All rights reserved.
机译：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诱导的衰落。从理论上讲，最大可能性（ML）检测器是最佳检测器。 ML检测器需要频道状态信息（CSI），其可以以完美或盲目的形式提供。完美的CSI ML检测器需要用于信道估计的导频传输，这增加了复杂性并减少了盲CSI ML检测器使用盲信道估计的数据速率，这导致性能下降。在本文中，首次，为FSO系统提供了高效且低复杂性的深度学习的检测器。所提出的深度学习的探测器根本不需要CSI，它将接收的信号直接进入深度神经网络。将所提出的深基于学习的探测器与完美的CSI ML检测器和盲CSI mL检测器进行比较。在本文中，考虑到对数正常，伽马 - 伽马和负指数分布分别用于模拟弱，弱到强，饱和的大气湍流制度。结果表明，所提出的深基于学习的探测器的性能足够接近完美的CSI ML检测器，其复杂性明显低于盲CSI ML检测器。所提出的探测器的速度比盲CSI ML探测器快80倍。此外，它没有错误的楼层，而盲CSI ML探测器的主要问题之一是错误地板。除了较小的复杂性之外，所提出的探测器在弱大气湍流方案中具有几乎与盲CSI ML检测器相同的性能。可用的盲CSI ML探测器仅在弱湍流中实用，因为它们假设通道系数在某些符号的持续时间内是恒定的。然而，所提出的深度学习的探测器不考虑这种假设，并且可以用于所有大气湍流制度。当大气湍流变强时，所提出的探测器的性能降低。例如，与目标比特误差率为10（-3）的盲CSI ML检测器相比，所提出的深基于学习的检测器的性能降低了7 dB。然而，所提出的深度学习的探测器优于盲的CSI ML检测器，以高信号噪声比噪声比率偏差，因为在该范围内，盲CSI ML探测器遭受误差地板。 （c）2020 Elsevier B.v.保留所有权利。

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