A Reinforcement Learning Framework for PAPR Minimization in MU‐MIMO With GNN‐Based CSI Encoding

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A 4.8 dB reduction in peak-to-average power ratio was achieved, enhancing overall system performance.
Observational analysis tackled changes in channel conditions using a Proximal Policy Optimization framework.
Graph Neural Network features captured spatial correlations, effectively supporting dynamic transmission.
The approach offers low computational complexity, ensuring compatibility with existing transceiver designs.

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The framework reduces peak-to-average power ratio in MU-MIMO systems, indicating improved signal quality and reliability.

Ramesh et al. (2025) studied this question.

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