# ASIANComNet — Asian Conference on Communication and Networks

- **Submission deadline**: 2026-08-31
- **Notification date**: 2026-09-10
- **Conference date**: 2026-10-11
- **Location**: Hanoi, Vietnam
- **Conference Partner Index**: 45.1/100 (ranked #2822, confidence 0.25, algorithm 1.1) — how this is computed: https://www.myhuiban.com/ranking
- **Trackers**: 1
- **Attendees**: 0
- **Canonical page**: https://www.myhuiban.com/conference/4846

## Call for papers

Track 1: Mobile computing, communications, 5G and beyond • 5G and 6G Technologies • Cell-free Networks • Cloud-RAN, Programmable RAN • Ultra Large Cell Technologies for 5G and beyond 5G networks • 5G and Beyond Small Cell Technologies • Network Slicing and Multi-service Architectures • Cloud-based 5G and Beyond Mobile Architectures • Spectrum sensing, fusion, decision-making, and allocation • Signaling process, PHY/link layer protocols, and optimization • Resource optimization, network, and distributed network computing • Dynamic spectrum access, spectrum sharing, spectrum management • AI/ML for cognitive radio network • VNF/SDN (NFV, VIM, VNFs, service function chaining, network slicing, and Open Flow) • Quantum communications, and network computing resources • Quantum Computing in Disrupting the Moore's Law • Application of Quantum Computing in Cyber Security • Quantum Computing in Large Scale AI. • Sensor networks, mesh networks, MIMO, massive MIMO, mmWave, V2X, 5G and 6G • Edge computing, IoT connectivity, and energy harvesting • LEO (Low Earth Orbit) satellite/HAPS (High Altitude Platform System) Communication & its scale and applications • LEOS/HAPS communication integration with terrestrial mobile networks • LEOS/HAPS communication network reliability • Industrial IoT, e.g., manufacturing, logistics, and supply chain • Industry control network, networking theory, and algorithms • Wireless embedded sensor systems, body sensors, smart cities & security • Cognitive radio and Soft defined radio • Future generation communications and pervasive computing • Peer-to-peer network computing and overlaying networks • Directional antenna and networking • FDMA/OFDMA modulations, synchronization, and power optimization • Security & privacy, attacking models, confidentiality & security in communications • Services, middleware, and multimedia on wireless networks • QoS, reliability, performance, and communication theory • Wireless network simulations, implementation, and applications • Optical Networks and free space optical communications • Ultra-reliability and Low-latency communications • Terahertz for Future Networks • Digital Twins of Complex Systems with 5G & Future Networks • Tactile Internet Track 2: IoT and applications • IoT technologies for energy monitoring, efficiency, harvesting, etc. • IoT Architecture with embedded AI • AI for IoT edge computing • Low-power AI for IoT and Distributed AI for IoT • IoT with SDGs (Sustainable Development Goals) • Intelligent Transportation Systems • Big Data and Information Integrity in IoT • Non-Terrestrial Networks for IoT/AI • Beyond 5G, 6G technologies for IoT/AI • Digital Twins in IoT applications • Cryptography, Key Management, Authentication, and Authorization for IoT • Biometrics Applications in Enhancing IoT Security and Privacy • Blockchain for Securing 6G-enabled IoT-based Applications • Security Awareness and Effective Training Approaches in IoT • Applying Machine Learning Techniques in IoT Security • Blockchain and Distributed Ledger Technology for IoT Security and Privacy • Blockchain-based Security and Privacy in Resilient IoT-enabled 5G and Beyond • Strategies for Proactive Cybersecurity Incident Prevention and Response in IoT • Edge Computing and Intelligence in AI and IoT • Machine Learning for IoT Applications • Mobile deployment of Large Language Models (LLMs) • LLMs for AIoT applications • AI and IoT Solutions for Smart Cities • Security and Privacy in AI-driven IoT Systems • 5G and Its Impact on AI and IoT • Human-Machine Interaction in IoT Environments • IoT Sensors and Actuators: Innovations and Advances • AI-driven Predictive Maintenance in IoT • Energy-Efficient AI Algorithms for IoT Devices • IoT in Healthcare: Applications and Challenges • Industrial IoT (IIoT) and AI for Manufacturing • AI and IoT in Precision Farming • Ethical Considerations in AI-powered IoT Systems • IoT Standards and Interoperability • Robotic Process Automation (RPA) in IoT • AI-driven Automation in Supply Chain Management • IoT Analytics and Big Data Processing • AI in Edge Devices: Challenges and Solutions • Wireless Sensor Networks in AI and IoT • IoT for Environmental Monitoring and Sustainability • AI and IoT in Transportation and Logistics • Cross-domain Integration of AI and IoT Technologies Track 3: Privacy, Security for Networks • Privacy enhancement, policy, access control, and regulation • Privacy with surveillance, big data, machine learning, and IoT • Privacy for healthcare, human-computer interaction, and other applications • Network security, cybersecurity risk assessment, malware analysis • Cryptography, cryptographic algorithm, post-quantum cryptography • Attacks, DDoS, ransomware, and cybersecurity attacks and detection • Cyber network, configuration, cloud, IoT, and wireless communications • Multistage attacks, data security, AI, and intrusion detection • Risk assessment, management, and network monitoring • Blockchain, cryptocurrency, smart contracts, identity management, and voting • Blockchain applications, e.g., smart grid, healthcare, industrial control systems • Cyber authentication and access control • Deep learning for attack behavior, prediction, and game theory • AI/ML and deep learning for security and privacy Track 4: Dedicated Technologies for Wireless Networks • AI/ML-based physical layer technologies for B5G and 6G • Beamforming in a massive MIMO system based on AI/ML • AI/ML-based non-orthogonal multiple access (NOMA) techniques • AI/ML-aided Channel modeling • AI/ML in network design and planning • AI/ML for coverage and capacity optimization • AI/ML-based network load balancing and traffic steering • Intelligent network slicing • AI/ML for network deployment automation • AI/ML for service quality assurance and improvement • AI/ML self-driving networks • AI/ML for network energy saving and efficiency improvement • Reinforce Learning for Autonomous Networks and Federated Learning in Networking • Artificial intelligence-generated content (AIGC) for wireless security • Large language model (LLM) for wireless security • Machine learning/deep learning-driven device identification using radio frequency fingerprint, Physical layer channel features, and network traffic features • Deep learning enhanced physical layer security • Deep learning-enhanced RF security • Adversarial machine learning in wireless communications, including adversarial erosion attacks, poisoning attacks, and Trojan/backdoor attacks • Defensive and anticipatory aspects of adversarial machine learning in wireless communications • AI/ML for Security and privacy of deep learning-based wireless sensing • AI/ML for Intrusion and anomaly detection for wireless networks Track 5: Emerging Trends of AI/ML • Data sets for 5G/6G testbeds and trials • Distributed AI/ML for communication networks • Distributed multi-agent reinforcement learning aided wireless networks • Edge learning for wireless networks • Federated learning for wireless communications • Distributed intelligence in wireless communications • Standardization of AI/ML in network architectures. • AI/ML in network planning and 5G and beyond use case • AI/ML in Network Diagnostics • AI/ML in Network characteristics forecasts • AI/ML techniques for security incident identification and forecast • AI/ML techniques for precise synthesizing and efficient mobile traffic forecast • AI/ML–aided forecasting techniques for QoS improvement, and QoE inference • AI/ML techniques for multi-tenant environments service level agreement forecast • AI/ML techniques for Complex event recognition and forecasting • AI/ML techniques for Network Optimization and Control • AI/ML techniques for Transport and FH/BH networks • AI/ML techniques for E2E slicing • AI/ML techniques for E2E service assurance • AI/ML techniques for Resource reservation • AI/ML techniques for Resource allocation (jointly through slice-based demand prediction) • AI/ML techniques for autonomous slice management -slice isolation, and slice Optimization • AI/ML solutions for control and orchestration • AI/ML techniques for cross-layer optimization framework • AI/ML solutions for anomaly detection, and management analytics • AI/ML- aaS in network management and orchestration • AI/ML solutions for Management of traffic, Dynamic load balancing, Efficient per-flow scheduling, MEC, and NFV orchestrators, Resource allocation for service function chaining, and Dynamic resource sharing in NFV infrastructure. Track 6: Signal Processing for Wireless Communications • Channel estimation, acquisition, and equalization • Compressive sensing and sparse signal processing algorithms • Decentralized. cooperative signal processing and Distributed signal processing for edge learning and computing • Interference management techniques in communications systems • Localization, positioning, and tracking techniques • Architectures for signal demodulation and decoding • Signal processing for integrated communications and sensing, artificial intelligence, data analytics, and machine learning • Signal processing for green communications, energy harvesting, and wireless power transfer • Signal processing for millimeter,THz communication systems, multi-antenna, MIMO, and/or multi-user systems • Signal processing for optical communications and semantic communications • Signal processing for security enhancement, particularly physical layer security and privacy • Signal processing for sensor networks, smart cities, and IoT applications • Signal processing for single-carrier, OFDM / OFDMA, multicarrier systems including new waveforms • Signal processing for smart grid and powerline communications • Signal processing for software defined and cognitive radio • Signal processing for emerging wireless hardware architectures (e.g., reconfigurable intelligent surfaces, metasurface-based antennas, holographic MIMO) • Signal processing techniques for commercial/standardized and emerging systems • Signal processing tech

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