My long-term vision is to build Intelligent, Integrated, and Resilient wireless systems that can perceive, adapt, and sustain themselves in 6G and beyond. My research bridges statistical signal processing and AI-driven optimization to design interpretable and intelligent communication systems. Specifically, I integrate Variational Bayesian (VB) inference, Deep Learning (DL), and GenAI to address challenges from massive MIMO channel estimation to cross-layer resource allocation.
My research reflects expertise across 3 thrusts:
1) Statistical Signal Processing Foundations: develop robust and low complexity receiver architectures through VB-based joint channel estimation and data detection (JED) for time-varying massive MIMO, including a novel work addressing nonlinear MIMO systems such as low-resolution Sigma-Delta ADCs.
2) AI-Enabled 6G Frontier Technologies: Pushing the boundaries into emerging paradigms such as near-field communications for extremely large antenna arrays (ELAAs) using the VB method.
3) Network Intelligence: AI-Driven Optimization Cross Layers: develop advanced optimization techniques using DRL-based approach (iterative algorithms and DL-aided design) for cross-layer QoS routing, joint optimization of fluid antenna system (FAS) and rate-splitting multiple access (RSMA), and resource allocation in highly energy-constrained networks (IRS-NOMA, WET-Multihop).