Machine Learning and Intelligent Communications for Scalable Information Systems: Advances, Applications, and Challenges
SCOPE AND DETAILS:
Emerging artificial intelligence (AI) and machine learning (ML) technologies, coupled with advanced intelligent communications, are driving a paradigm shift across academic research and industrial applications. From multi-modality learning to federated intelligence, these technologies are not only revolutionizing wireless communication systems (e.g., physical layer optimization, intelligent decision-making, and secure data transmission) but also enabling deep integration with diverse fields such as healthcare, industry, manufacturing, education, culture, governance, and digital humanities. The integration of ML and intelligent communications breaks through traditional domain boundaries—for instance, realizing secure transmission of medical data via federated learning, optimizing industrial IoT communication for smart manufacturing, and facilitating cross-cultural humanistic exchange through intelligent language processing.
MLICOM 2026 aims to build a comprehensive academic platform that bridges ML/AI algorithms, intelligent communication technologies, and cross-domain applications. We welcome high-quality original research papers (theoretical, experimental, or application-oriented) focusing on recent advancements, challenges, and innovative solutions at the intersection of ML and intelligent communications. By covering a broad spectrum of fields including digital humanities, the conference seeks to foster interdisciplinary dialogue among academia, industry, and public sectors, and serve as a catalyst for the development of next-generation intelligent communication systems and their practical deployments.
TOPICS:
The conference solicits submissions on topics including (but not limited to) the following categories:
1. ML & Intelligent Communications Foundations
l Intelligent cloud/edge/cloud-edge collaborative communications
l ML-driven resource allocation (spectrum, power, computation, bandwidth)
l Green/energy-aware intelligent communications (sustainable network design)
l Intelligent software-defined radios (SDR) and flexible communication architectures
l Intelligent massive MIMO, beamforming, and antenna dynamic configuration
l Cooperative/distributed coding & signal processing for intelligent networks
l Wireless sensor networks (WSN) & IoT: ML-optimized data transmission
l Satellite/radar communications: Intelligent signal processing & anti-interference
l Cognitive radio networks: ML-based spectrum sensing & sharing
l Federated learning (FL)/distributed learning for secure communication systems
l Multi-modality learning for cross-media communication (text, image, audio, video)
l Pre-trained models & transfer learning in intelligent communications
l Generative AI for communication content generation & error correction
l ML-driven network security, privacy preservation, and trust management
2. Cross-Domain Applications of ML
2.1 Intelligent Healthcare
l ML-optimized telemedicine communication systems
l Secure transmission of medical big data (FL-based privacy-preserving solutions)
l Intelligent communication for wearable medical devices & real-time health monitoring
2.2 Industry & Manufacturing
l Industrial IoT (IIoT) communication optimization for smart factories
l ML-driven predictive maintenance via real-time industrial data transmission
l Intelligent communication in collaborative robotics & automated production lines
2.3 Education & E-Learning
l Intelligent remote teaching communication platforms (adaptive bandwidth allocation)
l ML-based personalized learning: Communication optimization for educational data
l Secure transmission of educational big data & cross-institutional collaboration
l AI-driven interactive communication tools
2.4 Culture & Digital Content
l Intelligent communication for digital cultural heritage preservation
l ML-based cross-cultural communication
l Secure transmission & analysis of cultural big data
l AI-driven cultural content dissemination
2.5 Government & Public Services
l Smart governance communication platforms
l Secure communication for e-government
l AI-driven traffic/transportation communication
l Environmental monitoring: ML-based communication for sensor networks
2.6 Other Emerging Applications
l Intelligent unmanned systems (vehicles, drones): Communication & adaptive sensing
l Smart agriculture: IoT communication optimization for crop monitoring & precision farming
l Financial technology (FinTech): Secure communication for ML-driven risk control & transaction processing
l Accessible communication: ML-optimized solutions for people with disabilities
l ML-based semantic communication & cultural adaptation
l Digital archaeology: Communication optimization for remote sensing data
l AI-assisted preservation of endangered languages: Intelligent speech/text communication
l ML-based analysis of humanistic communication patterns
3. Other Relevant Topics
l Datasets & evaluation metrics for ML-empowered intelligent communications
l Low-resource language processing in cross-lingual communication
l Edge AI for resource-constrained communication devices
l Quantum machine learning for next-generation secure communications
l Ethical & regulatory issues of AI-driven intelligent communications
IMPORTANT DATES
l Manuscript submission deadline: December 1, 2026
l Notification of acceptance: December 20, 2026
l Submission of final revised paper: January 30, 2027
l Publication of special issue (tentative): April 30, 2027
Main Guest Editor
Yu Weng, Minzu University of China, China, wengyu@muc.edu.cn
Guest Editors
Honghao Gao, Shanghai University, China, gaohonghao@shu.edu.cn
Dr. Zheng Liu, Minzu University of China, China, liuzheng@muc.edu.cn
