Research Topic

Applications of Machine Learning in Free Space Optical Communication

About this Research Topic

Over the last few years, the global need for data transmission has experienced explosive growth. Data traffic is predicted to continuously challenge the capacity of future communication networks in the period of the next generation of communication systems. To this end, free-space optical communications (FSO) are gaining popularity as an effective alternative technology to the limited availability of radio frequency (RF) spectrum. FSO is gaining popularity due to high achievable optical bandwidth, flexibility, greater security, low power consumption in various applications of communications such as surveillance, last-mile connectivity, backhaul, disaster recovery, drone and satellite communications. As the application domain expands, the demand for more intelligent processing, operation, and optimization of tomorrow's communication networks will inevitably arise.

Machine learning must be integrated into the design, planning, and optimization of future optical wireless communication networks in order to actualize this vision of intelligent processing and operation. Particularly, deep learning (DL) has received much interest in recent years as a popular approach for intelligent signal processing in a broad range of applications for optical wireless communication networks. DL methods provide a number of possibilities for creating intelligent communication designs while addressing a range of concerns, such as channel estimation, channel modelling, channel prediction, optimization of modulation, coding schemes, physical layer security and analysis of an application.

Machine learning-driven FSO systems will attract increased research attention from both academia and industry due to their potential applicability in future wireless communication systems. However, before machine learning-based FSO can be properly implemented in future intelligent FSO systems, a number of unresolved concerns must be resolved. The aim of this research topic is to bring together leading researchers in both academia and industry from diversified backgrounds to unlock the potential of intelligent FSO systems for future wireless communication systems.

The themes of interest include, but are not limited to:

● Deep learning/machine learning approaches to free space optical systems
● Deep learning/machine learning based based channel modeling
● Deep learning/machine learning based modulation and coding
● Deep learning/machine learning based in MIMO system for free space optical systems
● Supervised Machine Learning Methods for free space optical applications
● Unsupervised Machine Learning Methods for free space optical applications
● Reinforcement Learning Methods for for free space optical applications
● Deep learning/machine learning based physical-layer secrecy analysis for free space optical systems
● Physical Layer Optimization with deep learning/machine learning for free space optical system
● Applications of deep learning/machine learning for 5G/6G wireless transmission technologies


Keywords: Machine learning, Free space optical communication (FSO), Optimization, Physical layer security, Modulation, Coding, Channel modelling


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Over the last few years, the global need for data transmission has experienced explosive growth. Data traffic is predicted to continuously challenge the capacity of future communication networks in the period of the next generation of communication systems. To this end, free-space optical communications (FSO) are gaining popularity as an effective alternative technology to the limited availability of radio frequency (RF) spectrum. FSO is gaining popularity due to high achievable optical bandwidth, flexibility, greater security, low power consumption in various applications of communications such as surveillance, last-mile connectivity, backhaul, disaster recovery, drone and satellite communications. As the application domain expands, the demand for more intelligent processing, operation, and optimization of tomorrow's communication networks will inevitably arise.

Machine learning must be integrated into the design, planning, and optimization of future optical wireless communication networks in order to actualize this vision of intelligent processing and operation. Particularly, deep learning (DL) has received much interest in recent years as a popular approach for intelligent signal processing in a broad range of applications for optical wireless communication networks. DL methods provide a number of possibilities for creating intelligent communication designs while addressing a range of concerns, such as channel estimation, channel modelling, channel prediction, optimization of modulation, coding schemes, physical layer security and analysis of an application.

Machine learning-driven FSO systems will attract increased research attention from both academia and industry due to their potential applicability in future wireless communication systems. However, before machine learning-based FSO can be properly implemented in future intelligent FSO systems, a number of unresolved concerns must be resolved. The aim of this research topic is to bring together leading researchers in both academia and industry from diversified backgrounds to unlock the potential of intelligent FSO systems for future wireless communication systems.

The themes of interest include, but are not limited to:

● Deep learning/machine learning approaches to free space optical systems
● Deep learning/machine learning based based channel modeling
● Deep learning/machine learning based modulation and coding
● Deep learning/machine learning based in MIMO system for free space optical systems
● Supervised Machine Learning Methods for free space optical applications
● Unsupervised Machine Learning Methods for free space optical applications
● Reinforcement Learning Methods for for free space optical applications
● Deep learning/machine learning based physical-layer secrecy analysis for free space optical systems
● Physical Layer Optimization with deep learning/machine learning for free space optical system
● Applications of deep learning/machine learning for 5G/6G wireless transmission technologies


Keywords: Machine learning, Free space optical communication (FSO), Optimization, Physical layer security, Modulation, Coding, Channel modelling


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

19 October 2021 Abstract
17 December 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

19 October 2021 Abstract
17 December 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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