- Research Areas
- Communicaton-Computing Co-Design for Real-Time Computer Vision Services
- Goal
- Maximizing the performance of semantic communications and implementing a real-time computer vision transmission system
- Approach
- Joint optimization of communication and computing resources
- Breakthrough the tradeoff performances among fidelity, energy, and delay
- Issues
- Maximizing accuracy while ensuring end-to-end latency for real-time intelligent surveillance services
- Minimizing energy consumption while guaranteeing precision for real-time immersive 3D/VR monitoring services
- Minimizing energy-latency costs while ensuring fidelity for real-time interactive XR conferencing services
- Implementation and performance evaluation of a real-time computer vision transmission system in virtual and real environments
- Energy-Efficient Protocol for 6G Vertical Communications
- Goal
- Maximizing energy efficiency in 6G vertically integrated networks with mobile devices, UAVs, and satellites
- Approach
- Research on wireless energy harvesting UAV communication systems
- Research on context-aware low-power satellite communication networks
- Research on energy-efficient integrated terrestrial–aerial–satellite networks
- Issues
- Minimizing energy consumption in UAV systems with wireless energy transfer and harvesting
- Maximizing the lifetime of satellite networks while ensuring end-to-end QoS in LEO satellite communication networks
- Maximizing energy efficiency in 6G integrated terrestrial–aerial–satellite networks
- Machine Learning-based Communications and Networks
- Goal
- Achieve fully autonomic operation, maximum throughput, minimum cost through machine learning
- Approach
- Solve CN & SON issues by applying ML algorithm
- Issues
- ML-based Optimization of Communication Networks
- Deep Learning-based Resource Allocation and Optimization
- Multi-Agent Reinforcement Learning Algorithms for Optimization
- Computing and Networking for Machine Learning Services
- Bio-Inspired Self-Organizing Communication and Networks
- Goal
- Achieve a self-organized networking with low overhead while gauranteeing user quality of service
- Approach
- Apply bio-inspired algorithms (e.g., ACO, Flocking algorithm, Firefly synch/desync., etc)
- Issues
- Bio-Inspired Routing
- Bio-Inspired Resource Management
- Bio-Inspired Energy Saving
- Bio-Inspired Fast Consensus
- Bio-Inspired System Modeling and Engineering
- Next-Generation Wireless Communications and Networks
- Goal
- Maximize data rate and energy efficiency while guaranteeing the seamless connectivity
- Issues
- Wireless Energy Harvesting
- Cooperative Interference Management
- Distributed Resource Management & Access Control
- Ultra-Low Power Management
- Seamless Connectivity Management