Topic Editors

School of Engineering and Technology, University of Hertfordshire, Hatfield AL10 9AB, UK
Department of Electrical and Electronic Engineering, University of Hertfordshire, Hatfield AL10 9EU, UK
Dr. Oluyomi Simpson
School of Engineering and Technology, University of Hertfordshire, Hatfield AL10 9AB, UK

Machine Learning in Communication Systems and Networks, 2nd Edition

Abstract submission deadline
20 April 2025
Manuscript submission deadline
20 July 2025
Viewed by
1036

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 Original research (unpublished and not currently under review by another journal) is welcome in relevant areas, including (but not limited to) the following: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

  • wireless communications
  • mobile communications
  • vehicular communications
  • 5G/6G systems and networks
  • artificial intelligence
  • machine learning
  • deep learning

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 (1 paper)

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35 pages, 1273 KiB  
Review
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
Viewed by 690
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
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