I-Hong Hou
Papers
2
Total Citations
32
H-Index
2
About
I-Hong Hou is a leading researcher in the co-design of learning and communication systems for next-generation wireless networks. His work focuses on the critical intersection of ultra-reliable low-latency communications (URLLC) and intelligent inference, addressing fundamental challenges in enabling real-time, mission-critical applications. Hou’s most impactful contribution is the development of frameworks that jointly optimize feature length selection and transmission scheduling for remote inference systems, where neural networks must infer time-varying targets—such as robot movement—from progressively received sensory data. This work, published in 2023 and garnering 20 citations, pioneers a novel approach to balancing communication efficiency with inference accuracy. He also co-authored a seminal guest editorial on URLLC in wireless networks (2019, 12 citations), which helped shape the research agenda for moving beyond human-centric, delay-tolerant mobile networks toward systems capable of supporting ultra-high reliability and low latency. Hou’s research is instrumental in enabling the next generation of autonomous systems, industrial IoT, and tactile internet applications, making him a key figure in the evolution of intelligent, responsive wireless infrastructure.
Research Focus
Key Achievements
Top Papers
- 1
- 2