As 6G networks move from vision to reality, intelligence, adaptability, and autonomy are becoming foundational design principles. Future 6G systems will operate over highly heterogeneous and dynamic infrastructures, supporting ultra-reliable low-latency communications, massive connectivity, integrated sensing and communication, and stringent energy-efficiency requirements. Meeting these challenges calls for a paradigm shift in how networks are modeled, controlled, and optimized.

In this context, 6G-TWIN is pleased to promote Special Session 6 at CoDIT 2026, entitled:

“Data-Driven and AI-Based Methods for Control and Optimization in 6G Systems”

This special session provides a dedicated forum for researchers and practitioners working at the intersection of artificial intelligence, control theory, optimization, and next-generation communication systems.

Why this session matters for 6G and Digital Twins

AI-driven and data-centric approaches are rapidly transforming how communication networks are designed and operated. Learning-based models enable predictive resource allocation, proactive network management, and real-time orchestration of distributed network functions, paving the way toward truly autonomous 6G infrastructures.

At the same time, ensuring reliability, robustness, safety, and interpretability remains a critical challenge—particularly for mission-critical and cyber-physical 6G applications. This special session explicitly aims to bridge data-driven learning with control-theoretic foundations, fostering solutions that are not only powerful, but also trustworthy and scalable.

These topics strongly resonate with the 6G-TWIN vision, where AI-powered digital twins are leveraged to model, predict, and optimize network behavior across the RAN, core, edge, and cloud domains.

Scope and topics of interest

The session welcomes original contributions on (but not limited to):

• AI-enabled modeling and prediction for 6G networks
• Learning-based control and optimization for radio access and core networks
• Resource allocation, scheduling, and network slicing using ML/AI
• Reinforcement learning for autonomous network management and orchestration
• Integration of foundation models and large-scale neural architectures in 6G control
• Robustness, reliability, and safety of AI-driven control in communication systems
• AI-based methods for joint communication, sensing, and computation
• Security, privacy, and adversarial resilience in learning-enabled network control
• Applications in edge/cloud computing, IoT, vehicular and aerial networks, and cyber-physical 6G platforms

Both theoretical advances and applied, system-level contributions are encouraged.

Submission guidelines and key dates

• Paper submission deadline: February 07, 2026
• Notification of acceptance: April 30, 2026
• Final paper & registration deadline: May 20, 2026

Papers must:

• Be written in English
• Describe original, unpublished work
• Be limited to 6 pages, IEEE double-column conference format

Submissions are handled electronically via PaperCept:
👉 http://controls.papercept.net/conferences/scripts/start.pl
(Select “Submit a Contribution to CoDIT 2026”)

Join the discussion

This special session aims to bring together the control, AI, and networking communities to shape the foundations of intelligent, autonomous, and resilient 6G systems. If your work explores how data-driven intelligence can enhance control and optimization in future networks, we warmly invite you to contribute.

We look forward to exciting discussions and high-quality contributions at CoDIT 2026.