Machine-learning-based channel predictors must operate under stringent latency, memory, and computational constraints while remaining robust to noisy and time-varying observations. This paper develops a causal channel-prediction framework based on three single-layer gated recurre…
This paper develops a data-driven reachability framework for linear systems whose disturbances are modeled by probabilistic zonotopes (PZs), combining bounded deterministic and Gaussian stochastic components. In contrast to methods that require a precisely known disturbance model…