In recent years, extensive research has been conducted to explore the utilization of machine learning algorithms in various direct-detected and self-coherent short-reach communication applications. These applications encompass a wide range of tasks, including bandwidth request prediction, signal quality monitoring, fault detection, traffic prediction, and digital signal processing (DSP)-based equalization. As a versatile approach, machine learning demonstrates the ability to address stochastic phenomena in optical systems networks where deterministic methods may fall short. However, when it comes to DSP equalization algorithms, their performance improvements are often marginal, and their complexity is prohibitively high, especially in cost-sensitive short-reach communications scenarios such as passive optical networks (PONs). They excel in capturing temporal dependencies, handling irregular or nonlinear patterns effectively, and accommodating variable time intervals. Within this extensive survey, we outline the application of machine learning techniques in short-reach communications, specifically emphasizing their utilization in high-bandwidth demanding PONs. Notably, we introduce a novel taxonomy for time-series methods employed in machine learning signal processing, providing a structured classification framework. Our taxonomy categorizes current time series methods into four distinct groups: traditional methods, Fourier convolution-based methods, transformer-based models, and time-series convolutional networks. Finally, we highlight prospective research directions within this rapidly evolving field and outline specific solutions to mitigate the complexity associated with hardware implementations. We aim to pave the way for more practical and efficient deployment of machine learning approaches in short-reach optical communication systems by addressing complexity concerns.

翻译：近年来，已有大量研究致力于探索机器学习算法在各种直接检测与自相干短距通信应用中的运用。这些应用涵盖广泛的任务，包括带宽请求预测、信号质量监测、故障检测、流量预测以及基于数字信号处理（DSP）的均衡。作为一种多功能方法，机器学习展现出处理光系统网络中确定性方法可能难以应对的随机现象的能力。然而，在DSP均衡算法方面，其性能提升往往有限，且复杂度极高，尤其是在成本敏感的短距通信场景中，如无源光网络（PONs）。它们在捕捉时间依赖性、有效处理不规则或非线性模式以及适应可变时间间隔方面表现出色。在本篇全面综述中，我们概述了机器学习技术在短距通信中的应用，特别强调了它们在高带宽需求的PONs中的使用。值得注意的是，我们为机器学习信号处理中采用的时间序列方法引入了一种新颖的分类法，提供了一个结构化的分类框架。我们的分类法将当前的时间序列方法划分为四个不同的类别：传统方法、基于傅里叶卷积的方法、基于Transformer的模型以及时间序列卷积网络。最后，我们指出了这一快速发展领域内的前瞻性研究方向，并概述了具体解决方案以降低硬件实现相关的复杂度。我们旨在通过解决复杂度问题，为在短距光通信系统中更实际、更高效地部署机器学习方法铺平道路。