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 4480
Special Issue Editors
Interests: optics communications; signal processing; modulation/coding; machine learning
Special Issues, Collections and Topics in MDPI journals
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
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Photonics is an international peer-reviewed open access monthly journal published by MDPI.
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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
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: TBD
Authorship: Xun Zhang, et al.
Affiliation: Institute Supérieur d'Electronique de Paris (ISEP), France
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