Kai-Ten Feng Professor

The lab's technologies are broadly applicable to smart healthcare,

enabling non-intrusive patient monitoring, fall detection alerts, and sleep apnea

detection without cameras, thereby preserving patient privacy. Additional application

domains include smart buildings and indoor localization systems, UAV-assisted

communication networks, and 5G/6G heterogeneous network deployments requiring

high-throughput, low-latency transmissions.

Mature technologies ready for transfer include the device-free

human presence detection system, which supports multi-person scenarios with cross-environment

transfer learning and has been validated in real-time on commercial Wi-Fi routers.

The Spatial Skeleton-enhanced Location Tracking indoor localization system is

also available, combining automated map feature extraction, fingerprinting databases,

and particle filter tracking for high-accuracy positioning, with an demonstration

prototype.

The lab is actively developing a 6G-oriented federated edge learning

framework, integrating massive antenna structures and AI-driven radio resource

management to achieve Tbps-level throughput with reduced beam training latency.

Concurrently, an RIS-empowered full-duplex self-interference cancellation scheme

is under development, along with a cooperative mobile robot-assisted wireless

sensing architecture, all of which are open for academic and industry collaboration

opportunities.

Faculty Webpage：Feng's webpage

Laboratory：MINT Laboratory