Topics of CDSSC 2026 征稿主题
We invite submissions of original research papers, reviews, and case studies addressing topics related to the three core pillars of CDSSC 2026: Communications, Data Science, and Smart Computing. Contributions that bridge multiple areas or present novel interdisciplinary insights are especially encouraged.
Topics of interest include, but are not limited to:
| Communications | Smart Computing | |
|---|---|---|
| Next-generation wireless communication systems (6G and beyond) | Edge, fog, and cloud computing | |
| Optical communications and networks | Intelligent computing paradigms | |
| Satellite communications and integrated terrestrial-satellite networks | Embedded AI and AIoT (Artificial Intelligence of Things) | |
| Vehicular communications and connected autonomous vehicles | Smart sensors and pervasive computing | |
| Green communications and energy-efficient networking | Autonomous systems and robotics | |
| Network softwarization, SDN, and NFV | Human-centric computing and human-computer interaction | |
| Communication protocols and architectures | Digital twins and cyber-physical systems | |
| Millimeter-wave and terahertz communications | Smart grids, smart cities, and intelligent infrastructure | |
| Massive MIMO, reconfigurable intelligent surfaces, and advanced antenna technologies | High-performance computing for AI and data-intensive applications | |
| Data Science | Trustworthy computing and system security | |
| Big data analytics and architectures | Cross-cutting and Emerging Topics | |
| Machine learning and deep learning methodologies | Integration of communications, sensing, and computing | |
| Data mining and knowledge discovery | AI-native network architectures | |
| Data visualization and interactive analytics | Sustainability in computing and communication systems | |
| Streaming data processing and real-time analytics | Open-source tools, platforms, and benchmarks | |
| Federated learning and distributed data analytics | ||
| Data quality, governance, and provenance | ||
| Ethical AI, fairness, and interpretability in data science | ||
| Multimodal data fusion and analysis | ||
| Data-driven decision support systems | ||