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Structure-utilized, Adaptive, and Efficient ML-based Proportional-Fair Scheduling in MIMO Networks for Non-stationary Channels

Cheng, YirongRice University2025

… ML-based methods typically assume stationary channel distributions, making them vulnerable to performance degradation under dynamic network conditions such as user mobility and location changes. In this work, I develop a new ML-based scheduling framework that adapts to non-stationary wireless …

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