Machine Learning Applied to Optical Communication Systems

A special issue of Photonics (ISSN 2304-6732). This special issue belongs to the section "Optical Communication and Network".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 5335

Special Issue Editors


E-Mail Website
Guest Editor
Peng Cheng Laboratory, Shenzhen 518055, China
Interests: optics communications; signal processing; modulation/coding; machine learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Peng Cheng Laboratory, Shenzhen 518055, China
Interests: optical communications; short-reach optical interconnects; digital signal processing; machine learning

Special Issue Information

Dear Colleagues,

Optical communication utilizing light for high-speed data transmission has played a vital role in bringing forth the digital age. However, as the demand for data continues to grow, there is a pressing need to further increase the capacity, scalability, and reliability of optical communication systems. New technologies are of vital importance in supporting next-generation optical transport networks. Accompanied by the fast development of computing resources, in recent years, we have witnessed the growing trend of using machine learning (ML) in various applications. ML has found its place in a number of industries, and its application in optical transmission systems is one of the current hot topics to revolutionize traditional approaches in the field of optical communications.

ML algorithms such as the support vector machine, Gaussian mixture model, different types of neural networks, reinforcement learning, etc., have strong ability to analyze vast amounts of data, extract patterns, and make intelligent predictions. These properties make ML extremely suitable for applications in the optical communication domain, which is facing similar problems. By harnessing the power of ML, optical communication issues such as optical performance monitoring, modulation format identification, device imperfection estimation, channel modelling, and linear/nonlinear equalization can potentially be addressed in an efficient manner. On the other hand, optical communication is also well-suited for ML applications since it can easily generate and collect huge amounts of transmission data for ML to build complex mathematical models efficiently.

This Special Issue aims to dive into the exciting intersection of ML and optical communication systems to foster a deeper understanding of how ML can revolutionize optical communications and how optical communications can facilitate ML processing. We encourage researchers to contribute to this hot topic and present their state-of-the-art research or review articles. Potential directions include but are not limited to ML theory and design, performance evaluation, complexity analysis, hardware implementation, etc., for different types of optical communication systems (to solve the aforementioned problems) shown below:

  • ML in short-reach transmission systems (IM/DD or self-coherent);
  • ML in long-haul transmission systems (coherent);
  • ML in optical access networks (e.g., passive optical networks);
  • ML in radio-over-fiber systems;
  • ML in optical wireless communications;
  • ML in visible-light communication systems;
  • ML in underwater optical communications;
  • ML in optical vehicle-to-vehicle communication systems;
  • ML in laser communications in space;
  • ML in chaotic optical communications.

Dr. Jinlong Wei
Dr. Zhaopeng Xu
Guest Editors

Manuscript Submission Information

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Keywords

  • machine learning
  • neural network
  • deep learning
  • optical communications
  • digital signal processing
  • optical performance monitoring
  • modulation format identification
  • device imperfection estimation
  • channel modelling
  • linear and nonlinear equalization

Published Papers (4 papers)

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Research

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13 pages, 4178 KiB  
Article
Regeneration of 200 Gbit/s PAM4 Signal Produced by Silicon Microring Modulator (SiMRM) Using Mach–Zehnder Interferometer (MZI)-Based Optical Neural Network (ONN)
by Tun-Yao Hung, David W. U Chan, Ching-Wei Peng, Chi-Wai Chow and Hon Ki Tsang
Photonics 2024, 11(4), 349; https://doi.org/10.3390/photonics11040349 - 10 Apr 2024
Viewed by 890
Abstract
We propose and demonstrate a Mach–Zehnder Interferometer (MZI)-based optical neural network (ONN) to classify and regenerate a four-level pulse-amplitude modulation (PAM4) signal with high inter-symbol interference (ISI) generated experimentally by a silicon microing modulator (SiMRM). The proposed ONN has a multiple MZI configuration [...] Read more.
We propose and demonstrate a Mach–Zehnder Interferometer (MZI)-based optical neural network (ONN) to classify and regenerate a four-level pulse-amplitude modulation (PAM4) signal with high inter-symbol interference (ISI) generated experimentally by a silicon microing modulator (SiMRM). The proposed ONN has a multiple MZI configuration achieving a transmission matrix that resembles a fully connected (FC) layer in a neural network. The PAM4 signals at data rates from 160 Gbit/s to 240 Gbit/s (i.e., 80 GBaud to 120 GBaud) were experimentally generated by a SiMRM. As the SiMRM has a limited 3-dB modulation bandwidth of ~67 GHz, the generated PAM4 optical signal suffers from severe ISI. The results show that soft-decision (SD) forward-error-correction (FEC) requirement (i.e., bit error rate, BER < 2.4 × 10−2) can be achieved at 200 Gbit/s transmission, and the proposed ONN has nearly the same performance as an artificial neural network (ANN) implemented using traditional computer simulation. Full article
(This article belongs to the Special Issue Machine Learning Applied to Optical Communication Systems)
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20 pages, 12421 KiB  
Article
Exploration of Four-Channel Coherent Optical Chaotic Secure Communication with the Rate of 400 Gb/s Using Photonic Reservoir Computing Based on Quantum Dot Spin-VCSELs
by Dongzhou Zhong, Tiankai Wang, Yujun Chen, Qingfan Wu, Chenghao Qiu, Hongen Zeng, Youmeng Wang and Jiangtao Xi
Photonics 2024, 11(4), 309; https://doi.org/10.3390/photonics11040309 - 27 Mar 2024
Viewed by 667
Abstract
In this work, we present a novel four-channel coherent optical chaotic secure communication (COCSC) system, incorporating four simultaneous photonic reservoir computers in tandem with four coherent demodulation units. We employ a quartet of photonic reservoirs that capture the chaotic dynamics of four polarization [...] Read more.
In this work, we present a novel four-channel coherent optical chaotic secure communication (COCSC) system, incorporating four simultaneous photonic reservoir computers in tandem with four coherent demodulation units. We employ a quartet of photonic reservoirs that capture the chaotic dynamics of four polarization components (PCs) emitted by a driving QD spin-VCSEL. These reservoirs are realized utilizing four PCs of a corresponding reservoir QD spin-VCSEL. Through these four concurrent photonic reservoir structures, we facilitate high-quality wideband-chaos synchronization across four pairs of PCs. Leveraging wideband chaos synchronization, our COCSC system boasts a substantial 4 × 100 GHz capacity. High-quality synchronization is pivotal for the precise demasking or decoding of four distinct signal types, QPSK, 4QAM, 8QAM and 16QAM, which are concealed within disparate chaotic PCs. After initial demodulation via correlation techniques and subsequent refinement through a variety of digital signal processing methods, we successfully reconstruct four unique baseband signals that conform to the QPSK, 4QAM, 8QAM and 16QAM specifications. Careful examination of the eye diagrams, bit error rates, and temporal trajectories of the coherently demodulated baseband signals indicates that each set of baseband signals is flawlessly retrieved. This is underscored by the pronounced eye openings in the eye diagrams and a negligible bit error rate for each channel of baseband signals. Our results suggest that delay-based optical reservoir computing employing a QD spin-VCSEL is a potent approach for achieving multi-channel coherent optical secure communication with optimal performance and enhanced security. Full article
(This article belongs to the Special Issue Machine Learning Applied to Optical Communication Systems)
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11 pages, 635 KiB  
Article
Enhanced PON and AMCC Joint Transmission with GMM-Based Probability Shaping Techniques
by Haipeng Guo, Chuanchuan Yang, Zhangyuan Chen and Hongbin Li
Photonics 2024, 11(3), 227; https://doi.org/10.3390/photonics11030227 - 29 Feb 2024
Viewed by 718
Abstract
In ITU-T standards, auxiliary management and control channels (AMCCs), as defined, facilitate the rapid deployment and efficient management of wavelength division multiplexing passive optical network (WDM-PON) systems. The super-imposition of an AMCC introduces additional interference to a PON signal, resulting in the degradation [...] Read more.
In ITU-T standards, auxiliary management and control channels (AMCCs), as defined, facilitate the rapid deployment and efficient management of wavelength division multiplexing passive optical network (WDM-PON) systems. The super-imposition of an AMCC introduces additional interference to a PON signal, resulting in the degradation of the performance of the overall transmission. In prior research, we proposed employing a Gaussian mixture model (GMM) to fit a baseband-modulated AMCC signal. Following the analysis of the interference model and the distribution characteristics of received signal errors, we propose a combined optimization method for a transmitter and receiver in this paper. This method, grounded in probabilistic shaping (PS) techniques, optimizes the probability distribution of the transmitted signal based on the AMCC interference model, with the objective of reducing the error rate in PON signal transmission. We have validated this approach within a 50G-PON experimental system by utilizing PAM4 modulation. The experimental results demonstrate the effectiveness of this method for mitigating the impact of baseband-modulated AMCC, thereby reducing the error rate in PON signal transmission. The approach presented in this paper can further minimize the performance degradation introduced by baseband-modulated AMCC in WDM-PON systems, enhancing the efficiency of WDM-PON deployment. Full article
(This article belongs to the Special Issue Machine Learning Applied to Optical Communication Systems)
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Review

Jump to: Research

17 pages, 4198 KiB  
Review
Machine Learning for Self-Coherent Detection Short-Reach Optical Communications
by Qi Wu, Zhaopeng Xu, Yixiao Zhu, Yikun Zhang, Honglin Ji, Yu Yang, Gang Qiao, Lulu Liu, Shangcheng Wang, Junpeng Liang, Jinlong Wei, Jiali Li, Zhixue He, Qunbi Zhuge and Weisheng Hu
Photonics 2023, 10(9), 1001; https://doi.org/10.3390/photonics10091001 - 31 Aug 2023
Cited by 3 | Viewed by 1722
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Machine Learning Applied to Optical Communication Systems)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Planned Paper 1

Title: A Survey of Machine Learning Techniques for Dynamic Bandwidth Allocation Algorithms in Passive Optical Networks

Authorship: Mohammad Zehri (1)(2), José Ramon Piney(2), David Rincón-Rivera (2), Ali Bazzi (1)

Affiliation:

(1) Department of Computer and Communication Engineering, Lebanese International University (LIU), Beirut 14404, Lebanon
(2) Dept. of Network Engineering, Universitat Politècnica de Catalunya (UPC) - BarcelonaTech, Castelldefels, Barcelona, 08860 Spain

 

Planned Paper 2

Title: Stability optimization of visible light indoor positioning algorithm based on single LED and Camera: using attention mechanism convolutional neural network

Authorship: Xun Zhang, et al.

Affiliation: Institute Supérieur d'Electronique de Paris (ISEP), France

Abstract: With the advancement of image sensor technology and the widespread adoption of LED lighting devices, camera-based visible light indoor positioning (VLP) technology has emerged as a research hotspot in recent years. It offers low-cost, high-precision positioning capabilities that are easily integrated into existing multimedia devices, robots, and other applications, promising broad prospects and market opportunities. In single LED-based positioning algorithms, height calculation often relies on the geometric relationships of LEDs. However, the inevitable presence of camera roll and pitch angles affects height and position calculations. Current solutions typically involve using gyroscopes or IMU sensors for image correction to enhance positioning accuracy. However, this approach unavoidably increases algorithm complexity and imposes high demands on sensor precision. In this paper, we propose a positioning algorithm based on an attention mechanism convolutional neural network model, aiming to mitigate the error impact caused by roll and pitch angles. We conducted experiments with rotation angles ranging from ±15 degrees for testing and comparison.

 

Planned Paper 3

Title: Artificial neural networks for short-haul fiber optic communications

Authorship:  Prof. Shiva Kumar, et al.

Affiliation: McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4L8, Canada.

 

Planned Paper 4

Title: TBD

Authorship: Prof. Sujan Rajbhandari, et al.

Affiliation: University of Strathclyde, Glasgow, UK.

 

Planned Paper 5

Title: TBD

Authorship: Zhaopeng Xu, et al. 
Affiliation: Peng Cheng Laboratory, Shenzhen 518055, China

 

Planned Paper 6

Title: TBD

Authorship: Junwen Zhang, et al. 
Affiliation: Fudan University

 

Planned Paper 7

Title:  ML based optical wireless communication

Authorship: Hyunchae Chun, et al. 

Affiliation:  Incheon National University

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