Home »
Academic Articles »
Applied »
Machine Learning for Self-Coherent Detection Short-Reach Optical Communications
Machine Learning for Self-Coherent Detection Short-Reach Optical Communications
Qi Wu
(State Key Laboratory of Advanced Optical Communication System and Networks, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
Zhaopeng Xu
(Peng Cheng Laboratory, Shenzhen 518055, China)
Yixiao Zhu
(State Key Laboratory of Advanced Optical Communication System and Networks, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
Yikun Zhang
(State Key Laboratory of Advanced Optical Communication System and Networks, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
Honglin Ji
(Peng Cheng Laboratory, Shenzhen 518055, China)
Yu Yang
(Peng Cheng Laboratory, Shenzhen 518055, China)
Gang Qiao
(Peng Cheng Laboratory, Shenzhen 518055, China)
Lulu Liu
(Peng Cheng Laboratory, Shenzhen 518055, China)
Shangcheng Wang
(Peng Cheng Laboratory, Shenzhen 518055, China)
Junpeng Liang
(Peng Cheng Laboratory, Shenzhen 518055, China)
Jinlong Wei
(Peng Cheng Laboratory, Shenzhen 518055, China)
Jiali Li
(Peng Cheng Laboratory, Shenzhen 518055, China)
Zhixue He
(Peng Cheng Laboratory, Shenzhen 518055, China)
Qunbi Zhuge
(State Key Laboratory of Advanced Optical Communication System and Networks, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
Weisheng Hu
(State Key Laboratory of Advanced Optical Communication System and Networks, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
Journal: Photonics
•
• MDPI AG
• ISSN: 2304-6732
Abstract
Driven by emerging technologies such as the Internet of Things, 4K/8K video applications, virtual reality, and the metaverse, global internet protocol traffic has experienced an explosive growth in recent years. The surge in traffic imposes higher requirements for the data rate, spectral efficiency, cost, and power consumption of optical transceivers in short-reach optical networks, including data-center interconnects, passive optical networks, and 5G front-haul networks. Recently, a number of self-coherent detection (SCD) systems have been proposed and gained considerable attention due to their spectral efficiency and low cost. Compared with coherent detection, the narrow-linewidth and high-stable local oscillator can be saved at the receiver, significantly reducing the hardware complexity and cost of optical modules. At the same time, machine learning (ML) algorithms have demonstrated a remarkable performance in various types of optical communication applications, including channel equalization, constellation optimization, and optical performance monitoring. ML can also find its place in SCD systems in these scenarios. In this paper, we provide a comprehensive review of the recent progress in SCD systems designed for high-speed optical short- to medium-reach transmission links. We discuss the diverse applications and the future perspectives of ML for these SCD systems.