Signal, Information, and Communications Lab

SIC-X Lab develops theory-grounded signal processing and learning for resource-constrained wireless and AI systems. Our goal is to achieve near-optimal performance under limited bits, computation, and information, for communications and for AI. Challenge: Sensing and communications impose conflicting objectives on waveform and beam pattern design. Challenge: Preserving semantic meaning under channel impairments while ensuring robustness and generalization. At SIC-X Lab, we design signal processing and learning algorithms that remain near-optimal when resources are fundamentally limited, from quantization bits, computation, and capacity to pilots and observable information. Grounded in optimization and estimation theory, our work spans MIMO, integrated sensing and communications, terrestrial–non-terrestrial networks, and cell-free systems. We bring the same principles to AI, building learning frameworks that are communication-efficient, low-bit, and robust for next-generation wireless systems.