Linear algebra finds extensive applications in multi-beam beamforming, optimum beamforming, spatial filtering, channel estimation, MIMO processing, machine learning and inference, microwave imaging, and other applications. This workshop bridges the connection between the mathematical foundations of linear algebra and signal processing and their applications in spectrum awareness for AI/ML systems. The workshop is unique because it will focus on explainable AI drawn from domain-specific concepts from linear algebra and signal processing.
We aim to provide valuable and actionable insights that are useful for early-/mid-career professionals and established professionals within the spectrum domain. We call for papers that span theories in Linear Algebra to practical systems operating on real-work electronic platforms such as RF-SoCs. We encourage the submission of papers that highlight applications, providing authors with a platform to demonstrate how their research in applied mathematics and scientific computing tackles real-world challenges in the field of wireless communications with spectral coexistence.
The interdisciplinary nature of the workshop calls papers including linear algebra, signal processing, and AI/ML applications for wireless. We invite submissions from research, industrial, and educational communities across disciplines of mathematics, communication theory, data science, and electronic engineering.
