Linear Gaussian Wireless Networks
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Mutual Information Optimization via K-Recursion and Automatic Differentiation for Linear Gaussian Wireless Networks
new Abstract: We present a differentiable framework for end-to-end mutual information (MI) optimization over linear Gaussian directed acyclic graphs (DAGs). The framework targets network-wide design under global constraints, such as a total transmit power budget, and covers MIMO precoding, amplify-and-forward relays, RIS-aided channels, and branching/merging topologies within a common linear Gaussian model. Its core ingredient is a \emph{K-recursion} that analytically propagates all...