Objectives

This project aims to lay the foundations of utilizing Riemannian-geometric tools, particularly, geometric machine learning (G-ML) and conic geometric optimization (CGO), to advance the knowledge of designing dynamic networks with low latency, low complexity, and high data rate. Such goal is accomplished through synergistic integration of three components, which span multiple network layers (i.e., PHY, MAC, NET), as follows. First, environment-aware beamforming codebooks are developed using low-latency unsupervised G-ML models, thanks to representing the covariance matrices of spatially-correlated wireless channels over Riemannian manifolds. Second, wireless link scheduling and resource allocation mechanisms are developed using low-latency supervised G-ML models, which are based on jointly modeling the Riemannian-geometric and temporal structures of interference-aware network topologies over Riemannian manifolds. Finally, locations of relays are optimized for maximizing the network flow rate using low-complexity CGO, given the representation of relay-enhanced network topologies over Riemannian manifolds.