Speaker
Description
Millimeter waves within the sub-terahertz band offer a plethora of applications in next-generation wireless communication. However, they also introduce severe real-time and hardware limitations, making conventional wideband multi-beam beamforming exceedingly complex. For example, an $N$-element array using true-time delay beamformers needs $\mathcal{O}(N^2)$ time delays or phase shifts, while FFT-based beamformers experience the beam-squint problem. Thus, this lecture addresses how structured matrix analysis can lead to algorithmic and architectural solutions to address these obstacles.
The central object of the talk is the delay Vandermonde matrix (DVM), a new structured matrix whose elements are defined via true-time delays in the spatio-temporal domain. By exploring its structure, we derive fast DVM algorithms that reduce computational and hardware complexity from $\mathcal{O}(N^2)$ to $\mathcal{O}(N \log N)$ and also to $\mathcal{O}(N)$ for $N$ beams. These results position DVM-based beamformers as a superclass of FFT-based multibeam beamformers. Unlike FFT beams, DVM beamformers are inherently wideband and eliminate beam squint, while simultaneously reducing chip area, power consumption, and system cost.
Beyond this, DVM factorization enhances AI-chip design by using structured weight matrices in neural network layers, enabling real-time wireless communication. This model-driven approach constrains learning through a structured neural network, enabling real-time inference on massive data streams at gigahertz rates. Taken together, the lecture emphasizes that structured matrices connect algorithmic complexity, hardware feasibility, and future wireless communication powered by AI.