Framework transforms complex chance-constrained problems into convex SOCPs for individual constraints and uses copulas for joint constraints under moment, support, and data-driven ambiguity sets, demonstrated on beamforming.
Maximizing signal to interference noise ratio for massive mimo: A stochastic neurodynamic approach
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Distributionally Robust Complex Chance-Constrained Optimization
Framework transforms complex chance-constrained problems into convex SOCPs for individual constraints and uses copulas for joint constraints under moment, support, and data-driven ambiguity sets, demonstrated on beamforming.