scholarly journals Machine Learning Applications for Short Reach Optical Communication

Photonics ◽  
2022 ◽  
Vol 9 (1) ◽  
pp. 30
Author(s):  
Yapeng Xie ◽  
Yitong Wang ◽  
Sithamparanathan Kandeepan ◽  
Ke Wang
Keyword(s):  
Machine Learning ◽  
Optical Communication ◽  
Optical Communications ◽  
Communication Systems ◽  
Performance Monitoring ◽  
Rapid Development ◽  
Optical Performance Monitoring ◽  
Optical Wireless Communications ◽  
Modulation Format ◽  
Machine Learning Applications

With the rapid development of optical communication systems, more advanced techniques conventionally used in long-haul transmissions have gradually entered systems covering shorter distances below 100 km, where higher-speed connections are required in various applications, such as the optical access networks, inter- and intra-data center interconnects, mobile fronthaul, and in-building and indoor communications. One of the techniques that has attracted intensive interests in short-reach optical communications is machine learning (ML). Due to its robust problem-solving, decision-making, and pattern recognition capabilities, ML techniques have become an essential solution for many challenging aspects. In particular, taking advantage of their high accuracy, adaptability, and implementation efficiency, ML has been widely studied in short-reach optical communications for optical performance monitoring (OPM), modulation format identification (MFI), signal processing and in-building/indoor optical wireless communications. Compared with long-reach communications, the ML techniques used in short-reach communications have more stringent complexity and cost requirements, and also need to be more sensitive. In this paper, a comprehensive review of various ML methods and their applications in short-reach optical communications are presented and discussed, focusing on existing and potential advantages, limitations and prospective trends.

scholarly journals Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods

2019 ◽  
Author(s):  
Muhammad Usman Hadi
Keyword(s):  
Machine Learning ◽  
Optical Communication ◽  
Communication Systems ◽  
Performance Monitoring ◽  
Optical Communication System ◽  
Optical Performance Monitoring ◽  
Fiber Nonlinearities ◽  
Machine Learning Methods ◽  
Optical Communication Systems ◽  
Linearity Improvement

Use of Machine Learning (ML) methodologies in optical communications has paved a new pathway. In this paper, firstly, we discuss the use of ML methodologies for reducing optical fiber nonlinearities, nonlinearity compensation, fault detection and optical performance monitoring. Then we present our recent work where we compare RL-SARSA and SVM based method with conventional method. The results show that RL-SARSA and SVM methods are successful candidates in mitigating the nonlinearities in proposed system as compared to conventional optical communication system.

scholarly journals Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

2022 ◽  
Vol 2 ◽  
Author(s):  
Dativa K. Tizikara ◽  
Jonathan Serugunda ◽  
Andrew Katumba
Keyword(s):  
Machine Learning ◽  
Communication Systems ◽  
Performance Monitoring ◽  
High Capacity ◽  
Machine Learning Algorithms ◽  
Heterogeneous Traffic ◽  
Optical Fiber Communications ◽  
Optical Performance ◽  
Optical Performance Monitoring ◽  
Monitoring Techniques

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 many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.

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