DIFFRACT develops a duality theory for standard interference functions to unroll iterative algorithms into differentiable neural architectures for end-to-end learning in wireless resource management.
Differentiating through log-log convex programs,
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DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programming
DIFFRACT develops a duality theory for standard interference functions to unroll iterative algorithms into differentiable neural architectures for end-to-end learning in wireless resource management.