– ICON LAB
ICON LAB: We apply machine learning techniques and neural networks to various communication systems. We have developed a high-performance Wi-Fi system with Spiking Neural Networks (SNNs), a trajectory prediction system for self-driving cars, and a new radio signal receiving system that can avoid interferences in radars.
– Spiking Neural Networks (SNNs)
SNNs are next generation neural networks using spikes for delivering information. They show high accuracy with consuming less amount of energy than other neural networks. Because of this characteristic, SNNs can increase communication performances of portable devices dramatically when they are implemented in the portable devices. Currently, we apply SNNs to various communication protocols and improve learning rules of SNNs.
– Autonomous driving
We develop key primitives for realizing autonomous driving system using V2X and machine learning. In particular, we focus on predicting future trajectory of neighbors, which is imperative to safe driving of self-driving vehicles. To this end, vehicles share their kinematic data (e.g., position, velocity, acceleration, yaw rate) via periodic beaconing and use machine learning techniques for predicting trajectories of neighbors.
– Radar signal processing
This research topic is concerned with the design of orthogonal waveforms and AI-based mutual interference suppression techniques in spectrum-sharing radar networks for coexistence and independent operation of radars.
– AI-inspired wireless communications and networks
Our goal in this topic lies in designing intelligent algorithms for improving wireless communications and network systems. For this purpose, we leverage model-based approach (mathematical optimization) as well as data-driving approach (machine learning). To demonstrate the validity of our algorithms, we evaluate performances using several techniques: mathematical models, network simulations and software-defined radio platform (e.g., USRP).