Closed-form expression for conditional mutual information in linear Gaussian DAGs, constructed from AD primitives, enables gradient-based optimization of multi-terminal wireless rate regions.
Information gradient for directed acyclic graphs: A score-based framework for end-to-end mutual information maximization,
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K-recursion plus automatic differentiation yields a topology-agnostic optimizer for end-to-end mutual information in linear Gaussian wireless DAGs that recovers water-filling optima where known.
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Differentiable Conditional Mutual Information for Multi-Terminal Linear Gaussian Wireless Networks
Closed-form expression for conditional mutual information in linear Gaussian DAGs, constructed from AD primitives, enables gradient-based optimization of multi-terminal wireless rate regions.
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Mutual Information Optimization via K-Recursion and Automatic Differentiation for Linear Gaussian Wireless Networks
K-recursion plus automatic differentiation yields a topology-agnostic optimizer for end-to-end mutual information in linear Gaussian wireless DAGs that recovers water-filling optima where known.