@ CNSM 2026 - 26th October 2026, Alcalá de Henares (Madrid), Spain
Beyond 5G and 6G wireless systems are expected to handle significantly increased data rates, provide ultra-low latency and enhanced connectivity to massive numbers of devices, and bring improvements in network energy efficiency. This new generation of networking systems aims to be fully autonomous networks (AN) with management capabilities, such as self-configuration, self-healing, self-optimizing, and self-evolving aspects, that today’s networks do not support as their management is largely manual with some automated assistance.
This workshop focuses on novel research in algorithms, architectures, approaches, and applications in the autonomous management of 5G and 6G systems. We encourage original paper submissions from academia and industry presenting work in progress or novel research on the most recent advances, frameworks, models, and approaches for management of autonomous networks using enabling techniques, such as AI/ML, network digital twins, network programmability, network softwarization, network function virtualization, software-defined networking, and blockchain. We are also interested in articles revising the state-of-the-art of this topic, showing recent major advances and discoveries, significant gaps in the research, current standardization status, and new future issues, especially related to sustainability.
We invite submissions of original research papers, as well as vision papers and experience reports.
All times in Anywhere on Earth (AoE) timezone.
The aim of the workshop is to share new findings, exchange ideas and discuss research challenges on the following topics:
Authors are invited to submit original and unpublished work, not submitted concurrently for publication elsewhere.
Submissions should use the IEEE 2-column conference style. Paper limit: 6 pages (regular), 4 pages (position papers), including references.
Submission is via TBC.
To encourage reproducibility, we encourage the authors, whenever it is possible, to include in their paper a link to an anonymised GitHub repository with all source code, scripts and data needed for the reproduction of their results.
Note that the authors should adhere to ethic and professional standards of IEEE. Please refer to IEEE Code of Ethics and IEEE Policy of AI-Generated Text.
