FCNIC 2026 invites the submission of original research papers in Future Communication Networks and Intelligent Computing, covering theoretical, experimental, and applied perspectives, for publication in the conference proceedings. To ensure high standards and quality, each submission undergoes anonymous peer review by an average of three independent reviewers. Accepted papers will be presented as either oral presentations or posters at the conference. Topics of interest include (but are not limited to) the following areas:
Track 1: Intelligent Network Architectures and Automation
Next-Generation Network Architectures, Protocols, and Algorithms
Digital Twin for Networks: Modeling, Simulation, and Autonomous Control
AI-Driven Radio Resource Management and Optimizatio
Data-Driven Network Measurement and Automated Operation
Network Intelligence for Sustainability (Green Networks)
Track 2: 6G, Terahertz, and Integrated Space-Air-Ground-Sea Networks
6G Vision, Key Performance Indicators, and Enabling Technologies
Non-Terrestrial Networks (NTN) and Space-Air-Ground-Sea Convergence
High-Frequency (THz/Optical) Communications and Intelligent Antenna System
Integrated Sensing, Communication, and Computing (ISCC): Architectures and Algorithm
Spectrum- and Energy-Efficient Techniques for 6G
Track 3: Edge Computing and Distributed Resource Intelligence
Edge AI: Distributed Learning, Inference, and Collaborative Systems
Computing Power Networking: Architecture, Scheduling, and Orchestration
High-Performance Computing as a Service for Networked Infrastructures
AI for Network Digital Twin and Automated Management
Track 4: Security, Privacy, and Trust in Intelligent Systems
Security and Privacy for Integrated Sensing and Communication (ISAC)
AI-Powered Network Defense and Adaptive Intrusion Detection
Trustworthy AI for Networks: Explainability, Robustness, and Fairness
Decentralized Security Mechanisms and Blockchain Applications
Privacy-Preserving Distributed Computing and Federated Learning
Track 5: AI and Machine Learning Foundations for Future Networks
Machine Learning Theory and Algorithms for Network Optimization
Graph Neural Networks for Network Modeling and Analysis
Generative AI for Network Traffic Synthesis and Security
Reinforcement Learning for Autonomous Network Control and Decision Making
Scalable and Efficient Distributed/Federated Learning Frameworks