Research Areas Research Projects

Communication-Computing Co-Design for Real-Time Computer Vision Services

Communication-Computing Co-Design
Goal
  • Maximizing the performance of semantic communications and implementing a real-time computer vision transmission system
Approach
  • Joint optimization of communication and computing resources
  • Breaking through the tradeoff 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

6G Vertical Communications
Goal
  • Maximizing energy efficiency in 6G vertically integrated networks with mobile devices, UAVs, and satellites
Approach
  • Wireless energy harvesting UAV communication systems
  • Context-aware low-power satellite communication networks
  • Energy-efficient integrated terrestrial–aerial–satellite networks
Issues
  • Minimizing energy consumption in UAV systems with wireless energy transfer and harvesting
  • Maximizing the lifetime of LEO satellite networks while ensuring end-to-end QoS
  • Maximizing energy efficiency in 6G integrated terrestrial–aerial–satellite networks

Machine Learning-based Communications and Networks

ML-based Communications
Goal
  • Achieve fully autonomic operation, maximum throughput, and minimum cost through machine learning
Approach
  • Solve CN & SON issues by applying ML algorithms
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

Bio-Inspired Networks
Goal
  • Achieve self-organized networking with low overhead while guaranteeing user quality of service
Approach
  • Apply bio-inspired algorithms (e.g., ACO, flocking, firefly synchronization/desynchronization)
Issues

Next-Generation Wireless Communications and Networks

Next-Generation Wireless Networks
Goal
  • Maximize data rate and energy efficiency while guaranteeing seamless connectivity
Issues