Aug 26-28, 2026 · Osaka, Japan.
http://www.ieee-fine.org/2026/Track Chairs:
Engin Zeydan, CTTC, Spain
ezeydan@gmail.com
Leonardo Maccari, Ca' Foscari University of Venice, Italy
leonardo.maccari@unive.it
Muge Sayit, University of Essex, U.K.
muge.sayit@essex.ac.uk
As we transition toward 6G, non-terrestrial networks, quantum networks and highly integrated AI-native ecosystems, the complexity of network infrastructures goes beyond the capacity of traditional human-centric management. The shift from static configurations to dynamic, programmable, and ultra-dense environments demands a fundamental evolution in how we observe, model, and control network behavior. Network Performance Analysis, Automation, Analytics, and Management represent a fundamental part of this evolution. Without rigorous mathematical foundations for performance analysis and robust data pipelines, managing future networks will be impossible.
This track focuses on the critical intersection of network theory, high-fidelity telemetry, and the lifecycle management of the data that fuels modern operational intelligence. We invite papers that move beyond "black-box" AI applications to explore the white-box mathematical models and algorithmic frameworks that ensure network stability, reliability, and efficiency. We seek classical and modern network theory to solve optimization challenges in real-world, large-scale infrastructures, and the production and publication of data-sets that can be used to study the performance of networks at all layers.
Submissions should emphasize observability, provable performance, reproducibility, open source implementations and open protocols, and automation architectures that bridge the gap between theoretical modeling and operational reality. We also emphasize the need for sustainable and carbon-aware solutions that can reduce the impact of networks, which calls for new metrics, new protocols and new definitions of performance itself.
Details of paper submission and publication can be found here.
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Call for Papers published.
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