Proposes CALO framework that maps grouped fractional variables through constraint-preserving transforms, uses straight-through estimator for discrete assignments, and a regret-driven hinge loss against BCD to achieve feasible real-time joint allocation in double-active RIS networks.
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CALO: Constraint-Aware Learning Optimization for Joint Resource Allocation in Double-Active RIS-Assisted Wireless Networks
Proposes CALO framework that maps grouped fractional variables through constraint-preserving transforms, uses straight-through estimator for discrete assignments, and a regret-driven hinge loss against BCD to achieve feasible real-time joint allocation in double-active RIS networks.