Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since alot of OPM depends on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach to OPM as an emerging technique that has only recently been applied to this domain.
翻译:未来通信系统面临着对高容量、动态带宽、可靠性和不同交通的更大需求。为满足这些需求,网络变得更加复杂,因此随着新设计方法和监测技术的逐步发展,需要新的设计方法和监测技术。近年来,机器学习作为一种大有希望的技术,成为有助于这一演变的前沿技术。光纤通信可以提供大多数应用所需的高能力,但是,需要提高可扩缩性和适应用户需求和链接条件的变化。准确的性能监测是这一转变的一个组成部分。在本文件中,我们审查了机器学习算法应用的光学性能监测技术。此外,由于OPM的很多内容取决于对信号类型的了解,我们还审查了调整格式的识别和比特率识别工作。我们还简要地介绍了OPM的神经形态方法,这是最近才应用于该领域的一种新兴技术。