A DRL agent learns a direct mapping from channel state information to near-optimal beamforming and hybrid RIS configurations, reaching 95% of the spectral efficiency of alternating optimization at far lower runtime complexity.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
eess.SP 2years
2026 2verdicts
CONDITIONAL 2representative citing papers
DRL learns antenna activation ratio and power coefficients to optimize energy efficiency in cell-free massive MIMO, achieving 50% EE gain and 3350x speedup over sequential convex approximation.
citing papers explorer
-
Deep Reinforcement Learning for Hybrid RIS Assisted MIMO Communications
A DRL agent learns a direct mapping from channel state information to near-optimal beamforming and hybrid RIS configurations, reaching 95% of the spectral efficiency of alternating optimization at far lower runtime complexity.
-
Deep Reinforcement Learning-Based Dynamic Resource Allocation in Cell-Free Massive MIMO
DRL learns antenna activation ratio and power coefficients to optimize energy efficiency in cell-free massive MIMO, achieving 50% EE gain and 3350x speedup over sequential convex approximation.