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 Original studies (that are unpublished and not currently under review by another journal) are welcome in the relevant areas, including (but not limited to) the following: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	2.9	6.1	2011	15 Days	CHF 2400	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Journal of Sensor and Actuator Networks jsan	4.8	11.3	2012	24.4 Days	CHF 2000	Submit
Photonics photonics	2.1	3.9	2014	13.9 Days	CHF 2400	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit
Telecom telecom	2.8	5.2	2020	22.8 Days	CHF 1400	Submit

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Published Papers (1 paper)

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All Applied Sciences Electronics JSAN Photonics Sensors Telecom

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19 pages, 2205 KB  

Open AccessFeature PaperArticle

ScionPathML: Enabling an Empirical Measurement Dataset and Benchmarks for Path-Aware Networking

by Damien Rossi, Sina Keshvadi and Yogesh Sharma

Telecom 2026, 7(4), 81; https://doi.org/10.3390/telecom7040081 - 2 Jul 2026

Viewed by 162

Abstract

Path-aware networking architectures, such as SCION, give endpoints explicit visibility into multiple inter-domain paths, opening new opportunities for data-driven path selection, reliability prediction, and automated diagnosis. However, the lack of standardized, machine learning-ready datasets collected from live path-aware deployments has slowed progress in [...] Read more.

Path-aware networking architectures, such as SCION, give endpoints explicit visibility into multiple inter-domain paths, opening new opportunities for data-driven path selection, reliability prediction, and automated diagnosis. However, the lack of standardized, machine learning-ready datasets collected from live path-aware deployments has slowed progress in this domain. We present ScionPathML, an open-source measurement and data-standardization pipeline that abstracts the complexity of SCION’s tooling to continuously collect longitudinal performance measurements (RTT, packet loss, jitter, bandwidth, and per-hop latency) in formats directly usable by ML pipelines. Using a four-week, multi-region campaign across four vantage points on the SCIONLab testbed, we release a public dataset capturing path availability, churn, lifetimes, and end-to-end performance across concurrently available paths. To demonstrate its application, we define four reproducible benchmark tasks, including short-horizon performance forecasting, path failure prediction, anomaly detection, and multi-objective path recommendation, each accompanied by baseline models and evaluation protocols. Our results show that live SCION path performance exhibits an exploitable temporal structure, enabling accurate short-term predictions and early detection of availability drops. Together, the dataset, benchmarks, and open tooling substantially lower the barrier for ML researchers and provide a reproducible foundation for accelerating innovation in path-aware networking. Full article

(This article belongs to the Topic Machine Learning in Communication Systems and Networks, 3rd Edition)

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