Machine Learning in Communication Systems and Networks, 3rd Edition

Topic Information

Dear Colleagues,

Recent advances in machine learning, including the availability of powerful computing platforms, have received huge amounts of attention from related academic, research and industry communities. Machine learning is considered as a promising tool to tackle the challenges in increasingly complex, heterogeneous and dynamic communication environments. Machine learning can contribute to intelligent management and optimization of communication systems and networks by enabling 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 communication application ranges. The objective of this Topic is to share and discuss recent advances and future trends of machine learning for intelligent communication. Original studies (that are unpublished and not currently under review by another journal) are welcome in the relevant areas, including (but not limited to) the following: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	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Journal of Sensor and Actuator Networks jsan	4.2	9.4	2012	21.6 Days	CHF 2000	Submit
Photonics photonics	1.9	3.5	2014	14.8 Days	CHF 2400	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Telecom telecom	2.4	5.4	2020	26.3 Days	CHF 1200	Submit

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