Machine Learning in Communication Systems and Networks
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 as a promising tool to tackle the challenge in increasingly complex, heterogeneous and dynamic communication environments. Machine learning would be able to contribute to intelligent management and optimization of communication systems and networks by enabling us 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 Original studies (unpublished and not currently under review by another journal) are welcome in relevant areas, including (but not limited to) the following: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
- wireless communications
- mobile communications
- vehicular communications
- 5G/6G systems and networks
- artificial intelligence
- machine learning
- deep learning
|Journal Name||Impact Factor||CiteScore||Launched Year||First Decision (median)||APC|
|2.838||3.7||2011||14.9 Days||2300 CHF||Submit|
|3.847||6.4||2001||15 Days||2400 CHF||Submit|
|2.690||3.7||2012||14.4 Days||2000 CHF||Submit|
|2.536||2.3||2014||13 Days||1800 CHF||Submit|
Journal of Sensor and Actuator Networksjsan
|-||6.9||2012||18.4 Days||1600 CHF||Submit|
Preprints is a platform dedicated to making early versions of research outputs permanently available and citable. MDPI journals allow posting on preprint servers such as Preprints.org prior to publication. For more details about reprints, please visit https://www.preprints.org.