Accuracy in discriminating between different received coherent signals is integral to the operation of many free-space communications protocols, and is often difficult when the receiver measures a weak signal. Here we design an optical communication scheme that uses balanced homodyne detection in combination with an unsupervised generative machine learning and convolutional neural network (CNN) system, and demonstrate its efficacy in a realistic simulated coherent quadrature phase shift keyed (QPSK) communications system. Additionally, we design the neural network system such that it autonomously learns to correct for the noise associated with a weak QPSK signal, which is distributed to the receiver prior to the implementation of the communications. We find that the scheme significantly reduces the overall error probability of the communications system, achieving the classical optimal limit. We anticipate that these results will allow for a significant enhancement of current cla...

中文翻译：

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机器智能增强了相干光通信

区分不同的接收到的相干信号的准确性是许多自由空间通信协议的操作所不可或缺的，并且在接收机测量微弱信号时通常很困难。在这里，我们设计了一种光通信方案，该方案将平衡零差检测与无监督的生成式机器学习和卷积神经网络（CNN）系统结合使用，并证明了其在现实的模拟相干正交相移键控（QPSK）通信系统中的功效。此外，我们设计了神经网络系统，使其能够自主学习以纠正与弱QPSK信号相关的噪声，该噪声在实施通信之前已分配给接收器。我们发现该方案显着降低了通信系统的整体错误概率，从而达到了经典的最佳极限。我们预计，这些结果将大大提高目前的水平。