- Wireless communications protocols for AIoT
- Sensors and actuator networks for AIoT
- Power consumption optimization in Wireless
Sensor Networks
- Drones/UAVs for aerial surveillance
- D2D communication for emergency/disaster
management
- Body area network wearable indoor localization
with deep learning model
- Middleware for Fog/Edge infrastructures
- Fog/Edge resources allocation
- Low-power wide-area (LPWA) networks
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- Smart campus and smart cities applications
- Big data analysis in AIoT
- Big data processing with fog computing
- Big data and AIoT data analysis
- Big data and information integrity in AIoT
- Scheduling for Fog/Edge infrastructures with
machine learning algorithms
- Fog/Edge storage allocation with clustering
machine learning methods
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UAV Networks and
Applications(無人機網路及其應用)
- UAV as a flying base-station, to enhance the
capability of wireless network
infrastructure
- UAV as a relay node, to extend the communication
range of the wireless network
- UAV as mobile sensor, to collect environmental
data from the area without deployed
sensor
- UAV as a gateway, to collect offline sensor data
from sensors without backhaul
connection
- Optimization of the deployment and trajectory
- Precise positioning for UAV flocking
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Application of
Deep Learning in Wireless Communications and
Networking(深度學習在無線通訊和網路的應用)
- Selection of optimal connection between UE and
BS
- Selection of optimal channel to avoid data
collision
- Optimal Resource allocation for UE of a BS
- Optimal approach to offload mobile network to
Wi-Fi
- Optimal approach to allocate computing resource
- Optimization for effectively packet forwarding
- Automatic trajectory design and collision
avoidance
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Beyond 5G (後5G行動通訊網路)
- Heterogeneous Networks (HetNet)
- Coordinated Multi-Point (CoMP)
- Massive MIMO
- Multi-RAT
- Small Cell Enhancement
- Cloud-RAN
- SDN-based Control Plane in 5G
- Device to Device Communication(D2D)
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[大學部專題]智慧家庭
