Machine Learning in Communication Systems and Networks, 2nd Edition

Topic Information

Dear Colleagues,

Recent advances in machine learning, including the availability of powerful computing platforms, have received huge attention from related academic, research, and industry communities. Machine learning is considered a promising tool to tackle the challenge in increasingly complex, heterogeneous, and dynamic communication environments. Machine learning would be able to contribute to the intelligent management and optimization of communication systems and networks by enabling them to predict changes, find patterns of uncertainties in the communication environment, and make data-driven decisions.

This Topic will focus on machine learning-based solutions to manage complex issues in communication systems and networks across various layers and within various ranges of communication applications. The objective of the Topic is to share and discuss recent advances and future trends of machine learning for intelligent communication. Original research (unpublished and not currently under review by another journal) is welcome in relevant areas, including (but not limited to) the following:

• Fundamental limits of machine learning in communication;
• Design and implementation of advanced machine learning algorithms (including distributed learning) in communication;
• Machine learning for physical layer and cross-layer processing (e.g., channel modeling and estimation, interference avoidance, beamforming and antenna configuration, etc.);
• Machine learning for adaptive radio resource allocation and optimization;
• Machine learning for network slicing, virtualization, and software-defined networking;
• Service performance optimization and evaluation of machine learning-based solutions in various vertical applications (e.g., healthcare, transport, aquaculture, farming, etc.);
• Machine learning for anomaly detection in communication systems and networks;
• Security, privacy, and trust of machine learning over communication systems and networks.

Prof. Dr. Yichuang Sun
Dr. Haeyoung Lee
Dr. Oluyomi Simpson
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400	Submit
Electronics electronics	2.9	4.7	2012	15.6 Days	CHF 2400	Submit
Journal of Sensor and Actuator Networks jsan	3.5	7.6	2012	20.4 Days	CHF 2000	Submit
Photonics photonics	2.4	2.3	2014	15.5 Days	CHF 2400	Submit
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600	Submit
Telecom telecom	-	3.1	2020	26.1 Days	CHF 1200	Submit

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Published Papers (2 papers)

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All Applied Sciences Electronics JSAN Photonics Sensors Telecom

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18 pages, 4835 KiB  

Open AccessArticle

VLCMnet-Based Modulation Format Recognition for Indoor Visible Light Communication Systems

by Xin Zheng, Ying He, Chong Zhang and Pu Miao

Photonics 2024, 11(5), 403; https://doi.org/10.3390/photonics11050403 - 26 Apr 2024

Viewed by 266

Abstract

In indoor visible light communication (VLC), the received signals are subject to severe interference due to factors such as high-brightness backgrounds, long-distance transmissions, and indoor obstructions. This results in an increase in misclassification for modulation format recognition. We propose a novel model called [...] Read more.

In indoor visible light communication (VLC), the received signals are subject to severe interference due to factors such as high-brightness backgrounds, long-distance transmissions, and indoor obstructions. This results in an increase in misclassification for modulation format recognition. We propose a novel model called VLCMnet. Within this model, a temporal convolutional network and a long short-term memory (TCN-LSTM) module are utilized for direct channel equalization, effectively enhancing the quality of the constellation diagrams for modulated signals. A multi-mixed attention network (MMAnet) module integrates single- and mixed-attention mechanisms within a convolutional neural network (CNN) framework specifically for constellation image classification. This allows the model to capture fine-grained spatial structure features and channel features within constellation diagrams, particularly those associated with high-order modulation signals. Experimental results obtained demonstrate that, compared to a CNN model without attention mechanisms, the proposed model increases the recognition accuracy by 19.2%. Under severe channel distortion conditions, our proposed model exhibits robustness and maintains a high level of accuracy. Full article

(This article belongs to the Topic Machine Learning in Communication Systems and Networks, 2nd Edition)

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35 pages, 1273 KiB  

Open AccessReview

A Survey of PAPR Techniques Based on Machine Learning

by Bianca S. de C. da Silva, Victoria D. P. Souto, Richard D. Souza and Luciano L. Mendes

Sensors 2024, 24(6), 1918; https://doi.org/10.3390/s24061918 - 16 Mar 2024

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Abstract

Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technology used in Fourth Generation (4G) and Fifth Generation (5G) wireless communication systems, and it will likely be essential to Sixth Generation (6G) wireless communication systems. However, OFDM introduces a high Peak to Average Power [...] Read more.

Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technology used in Fourth Generation (4G) and Fifth Generation (5G) wireless communication systems, and it will likely be essential to Sixth Generation (6G) wireless communication systems. However, OFDM introduces a high Peak to Average Power Ratio (PAPR) in the time domain due to constructive interference among multiple subcarriers, increasing the complexity and cost of the amplifiers and, consequently, the cost and complexity of 6G networks. Therefore, the development of new solutions to reduce the PAPR in OFDM systems is crucial to 6G networks. The application of Machine Learning (ML) has emerged as a promising avenue for tackling PAPR issues. Along this line, this paper presents a comprehensive review of PAPR optimization techniques with a focus on ML approaches. From this survey, it becomes clear that ML solutions offer customized optimization, effective search space navigation, and real-time adaptability. In light of the demands of evolving 6G networks, integration of ML is a necessity to propel advancements and meet increasing prerequisites. This integration not only presents possibilities for PAPR reduction but also calls for continued exploration to harness its potential and ensure efficient and reliable communication within 6G networks. Full article

(This article belongs to the Topic Machine Learning in Communication Systems and Networks, 2nd Edition)

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