Mathematical Modeling and Learning-Based Control of Information Dynamics in Complex Networks and Communication Systems
A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 377
Editors
Interests: cellular RAN; MIMO transmission; AI for networking; SAGIN; industrial internet
Special Issues, Collections and Topics in MDPI journals
Interests: mobility support for the next-generation Internet (IPv6); internet audio-visual media; communications for intelligent vehicles
Special Issues, Collections and Topics in MDPI journals
Interests: space-air-ground integrated network; mega constellation operation management; distributed optimization; robust hypothesis testing
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence security; system security
Special Issue Information
Dear Colleagues,
Complex networks and communication systems underpin today’s information infrastructure, from wireless cellular network, vehicular networks, and industrial IoT networks to autonomous systems and cyber–physical platforms. Understanding how information diffuses, congests, and stabilizes over such networks requires rigorous mathematical models and principled control methodologies. Typical phenomena include diffusion and consensus, rumor/epidemic spreading, resource contention, and cascading failures across multilayer network topologies. Meanwhile, learning-based approaches (e.g., reinforcement learning and data-driven methods) are increasingly used to design and operate networked systems, raising new mathematical questions regarding their stability, robustness, and performance guarantees. In addition, the security and reliability of learning-enabled networked systems, such as agentic AI and large-scale ML systems, pose new mathematical challenges in relation to systems’ security, adversarial robustness, and trustworthy decision making.
This Special Issue aims to gather high-quality contributions on mathematical modeling, analysis, and the learning-enabled control of informational dynamics in complex networks and communication systems. We welcome original research articles and reviews developing graph- and network-theoretic models, stochastic/probabilistic formulations (Markov and point processes, and queuing models), optimization and control methods (distributed optimization, optimal/robust control, and model predictive control), and learning-based control with theoretical analysis (convergence, generalization, regret, and stability). Application-driven papers are encouraged when they offer clear mathematical novelty and insights, including in relation to communication networks, multi-agent systems, and networked sensing and resilience.
We look forward to your contributions.
Dr. Bo Qian
Dr. Manabu Tsukada
Dr. Ting Ma
Dr. Shaofeng Li
Prof. Dr. Enbin Song
Guest Editors
Manuscript Submission Information
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Keywords
- complex networks
- information dynamic modeling
- stochastic processes and network analysis
- vehicular networks and autonomous systems
- partial differential equations
- topological data analysis
- automated theorem proving
- learning-based control
- human–AI agents–device collaboration
- agentic AI security and ML system security
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