基于神经网络的亚散粒噪声限接收机的散粒噪声跟踪
Channel-noise tracking for sub-shot-noise-limited receivers with neural networks
摘要：
用于具有相干态的光通信的非高斯接收器可以实现超出由量子噪声极限（QNL）给出的常规检测极限的测量灵敏度。然而，如果在通信信道中存在噪声，则可以可靠地发送的信息量显著降低，除非接收机能够有效地补偿这种噪声。在这里，我们研究了使用深度神经网络作为相位和幅度信道噪声的计算效率估计器，以实现非高斯接收器的噪声跟踪的可靠方法。神经网络使用非高斯接收机收集的数据来实时估计和校正动态信道噪声。使用数值模拟，我们发现这种噪声跟踪方法允许非高斯接收器在相位和强度噪声的广泛强度和带宽范围内保持其对QNL的益处。基于神经网络的噪声跟踪方法还可以包括其他类型的噪声，以确保在具有许多噪声源的信道中的子QNL性能。
英文摘要：
Non-Gaussian receivers for optical communication with coherent states can achieve measurement sensitivities beyond the limits of conventional detection, given by the quantum-noise limit (QNL). However, the amount of information that can be reliably transmitted substantially degrades if there is noise in the communication channel, unless the receiver is able to efficiently compensate for such noise. Here, we investigate the use of a deep neural network as a computationally efficient estimator of phase and amplitude channel noise to enable a reliable method for noise tracking for non-Gaussian receivers. The neural network uses the data collected by the non-Gaussian receiver to estimate and correct for dynamic channel noise in real-time. Using numerical simulations, we find that this noise tracking method allows the non-Gaussian receiver to maintain its benefit over the QNL across a broad range of strengths and bandwidths of phase and intensity noise. The noise tracking method based on neural networks can further include other types of noise to ensure sub-QNL performance in channels with many sources of noise.
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