Research

Reconfigurable Intelligent Surface (RIS)

Reconfigurable Intelligent Surfaces (RIS) enable 6G wireless networks by intelligently shaping the propagation environment through programmable electromagnetic responses. RIS can be classified into passive RIS, which control phase (and amplitude), and active RIS, which integrate low-power amplification to mitigate path loss. Their performance is governed by challenging optimization and signal processing problems, including high-dimensional nonconvex joint beamforming and surface configuration, hardware impairments, imperfect CSI, and power constraints. We focus on scalable optimization and signal processing methods for active and passive RIS, aiming to achieve efficient, robust, and practical reconfigurable wireless systems.

Fluid/Movable Antenna Systems (FAS)

Fluid/Movable Antenna Systems (FAS) introduce a new spatial dimension by allowing antenna elements to dynamically move within a constrained region, thereby exploiting location diversity and enhancing spatial degrees of freedom. When integrated with RIS, including FAS-RIS, Fluid-RIS (FRIS), and Fluid-Active-RIS architectures (FARIS), these systems create new opportunities and challenges in performance analysis and system design, such as joint antenna position selection, RIS configuration, amplification control, and robustness under mobility and CSI uncertainty. We focus on analytical performance characterization and optimization-driven signal processing for FAS-RIS systems, aiming to develop scalable and practical frameworks that fully exploit fluid antenna mobility and RIS reconfigurability for 6G

Rydberg Atomic Receiver (RAR)

Rydberg Atomic Receiver (RAR) exploits quantum-mechanical interactions between electromagnetic fields and highly excited Rydberg atoms to realize ultra-sensitive RF reception beyond the limitations of conventional antenna-based front-ends. Unlike classical receivers, RAR inherently operates with magnitude-domain measurements and quantum-limited noise characteristics, which fundamentally reshape MIMO signal modeling and processing. We focus on quantum-mechanics-based MIMO receiver design for RAR systems, including signal modeling, multi-user detection, beamforming and precoding/decoding strategies for ISAC, and scalable algorithms that bridge quantum sensing physics with modern MIMO signal processing for 6G