The 13^th International
Conference on Wireless Networks and Mobile Communications

Call for papers

Topics of Interest:

WINCOM 2026 solicits high-quality and original research contributions in wireless and mobile networking, including (but not limited to) the following tracks:

Track 1: Wireless and Mobile Communications

• Wireless and Mobile Networks
• Future Internet and Next-Generation Networking
• Green and Sustainable Communication Systems
• Risk-Aware and Resilient Wireless Networks
• Modeling and Performance Evaluation of SDN, NFV, and Network Slicing
• Satellite Communications and Non-Terrestrial Networks (NTN)
• Millimeter-Wave and Terahertz Communications
• Massive MIMO and Multi-User MIMO Systems
• Localization, Positioning, and Mapping
• Cross-Layer Design and Optimization (PHY–MAC–Network Layers)
• Multi-Hop Communications: Ad Hoc, WSN, DTN, VANET
• Vehicular and Intelligent Transportation Networks
• Implementation, Testbeds, and Experimental Prototypes
• 6G Network Programmability and Softwarization
• Sub-Networking and Micro-Networks for 6G
• Semantic Communications and Goal-Oriented Networking
• Routing, QoS/QoE, and Risk-Aware Traffic Engineering
• Optical, Quantum, and Hybrid Communication Networks
• Communication Theory and Information Theory
• Cognitive and Self-Adaptive Networking
• Intelligent Reflecting Surfaces (IRS) and Closed-Loop Cognitive Systems
• Radio Resource Management, Allocation, and Scheduling
• Channel Modeling, Capacity Estimation, and Equalization
• Advanced Signal Processing for Wireless Communications

Track 2: Security, Privacy, and Cybersecurity

• Security and Cybersecurity in Wireless and Mobile Networks
• Risk-Aware Security and Threat Modeling for 5G/6G Networks
• Trust, Privacy, and Blockchain-Based Platforms
• Cybersecurity Challenges in 6G Networks
• Secure xApps and rApps for Programmable Networks
• Attacks, Threats, and Vulnerability Analysis in 6G
• Security of Foundational Models and Large-Scale AI Models
• Secure and Privacy-Preserving Machine Learning
• ML-Based Device and Traffic Fingerprinting
• Energy and Cost-Aware Security Mechanisms
• Coding and Information-Theoretic Security
• Privacy Preservation at the Edge and in Distributed Systems
• Differential Privacy and Secure Aggregation
• Secure Over-the-Air Updates and Software Supply Chains
• Security for Big Data and Distributed AI Systems
• Resilient and Trustworthy AI for Communication Networks

Track 3: IoT, Smart Cities, and New Applications

• Internet of Things (IoT) Architectures and Applications
• IoT for Smart Cities and Smart Infrastructures
• Risk-Aware IoT Systems and Critical Infrastructure Protection
• Digital Twins for Communication Networks and Smart Systems
• QoS/QoE in Next-Generation Networks
• Ultra-Low-Power IoT Technologies and Embedded Architectures
• Cloud, Edge, and Fog Computing for IoT
• Intelligent Vehicles and Vehicular Communications
• Autonomous Driving and Cooperative Perception
• Industry 5.0 and Cyber-Physical Systems
• Robotics Communications and Networked Control Systems
• Co-Design of Communication, Control, and Computing
• Mobility Management and Service Continuity
• M2M and Massive Machine-Type Communications (mMTC)
• XR (VR/AR/MR) and Metaverse Networking
• Multimedia Streaming and Immersive Services

Track 4: ML & AI in Communications network

• Machine Learning and Artificial Intelligence for Communication Networks
• AI-Native and AI-Driven Network Architectures
• Semantic Learning and Semantic Communications for Networks
• Semantic Federated Learning and Knowledge-Oriented Aggregation
• Large Language Models (LLMs) for Network Management and Automation
• Foundational Models for Telecom and Networking
• Reinforcement Learning for Resource Allocation and Control
• Open Radio Access Networks (O-RAN) and Open Networking
• RAN Intelligent Controllers (RIC), xApps, and rApps
• Deep Learning for Emerging Network Applications
• Secure, Robust, and Risk-Aware Learning Methods
• Resilient and Trustworthy AI Systems
• Contrastive, Transfer, and Continual Learning
• Edge Intelligence and On-Device Learning
• Federated Learning and Distributed Intelligence
• Model-Mediated and Knowledge-Centric Learning
• AI for Edge, Fog, and Cloud Computing