AI/ML for Wireless Systems

I investigate localization methods that estimate user position from wireless channel information. By learning the relationship between channel-derived features and spatial location, the system can infer user positions with high accuracy in complex environments.

The visualization below shows example localization results, where the predicted trajectory closely follows the ground-truth path. This demonstrates the potential of wireless channel information as a sensing and localization resource.

Beyond estimating the current user position, I also study mobility prediction based on localization history. By modeling how user trajectories evolve over time, the system can predict near-future movement and provide a basis for anticipatory network control.

The visualization below shows example mobility prediction results, where the predicted future trajectory is generated from past movement observations. This type of prediction is an important step toward proactive wireless resource management.

Building on localization and mobility prediction, my current research investigates how future user position estimates can be translated into predicted large-scale fading coefficients (LSFCs). Since LSFCs play a key role in wireless resource allocation, predicting them in advance can provide useful prior information before actual channel realization or pilot-based estimation.

This direction aims to connect mobility-aware user state prediction with communication-oriented network control, enabling wireless systems to prepare for upcoming channel and interference conditions.

A long-term goal of this research is to integrate localization, mobility prediction, and future LSFC prediction into a unified framework for proactive wireless resource allocation. Instead of making decisions only from the current network state, such a framework would allow the network to anticipate future user locations and channel conditions, and allocate resources more effectively in advance.

This proactive perspective can support more efficient scheduling, subchannel assignment, power control, and network coordination in dynamic wireless environments.