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Digital twin for pre-screening users in MU-MIMO downlink
US & West ·
The DiTUS framework uses a digital twin to pre-screen promising users before instantaneous CSI acquisition in MU-MIMO systems. Spatial covariances are formed from estimated departure angles and path powers, with optional calibration to mitigate bias. Candidates are selected via a projection-energy rule (DiTUS-P) or a greedy log-determinant rule (DiTUS-L).
Why it matters
The method improves efficiency of user scheduling in dense networks, which is important for digital twins of telecom systems.
A GPU-based solver for trajectory planning problems using branch model predictive control is presented. The solver is based on iterative LQR, multiple-shooting formulation, and augmented Lagrangian method for constraint handling. Numerical experiments show superiority over a CPU-based solver on large-scale problems.
The integration of shared autonomous vehicles (SAVs) into microtransit systems, which address the last-mile problem, is investigated. The Atlanta case study shows that SAVs can improve sustainability, convenience, and reliability compared to conventional fixed-route transit.
Function+Data Flow (FDF), a visual domain-specific language for specifying and validating AI/ML pipelines used in building real-time digital twins, is presented. Implemented in DesCartes Builder, it enables composition and reuse of models. An empirical study found FDF makes AI-based twin development more accessible and reliable.