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arXiv · 2508.07009

Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systems

Xintong Chen·Zhenyu Jiang·Jiangbin Lyu·Liqun Fu
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Abstract

Intelligent Reflecting Surfaces (IRSs) have potential for significant performance gains in next-generation wireless networks but face key challenges, notably severe double-pathloss and complex multi-user scheduling due to hardware constraints. Active IRSs partially address pathloss but still require efficient scheduling in cell-level multi-IRS multi-user systems, whereby the overhead/delay of channel state acquisition and the scheduling complexity both rise dramatically as the user density and channel dimensions increase. Motivated by these challenges, this paper proposes a novel scheduling framework based on neural Channel Knowledge Map (CKM), designing Transformer-based deep neural networks (DNNs) to predict ergodic spectral efficiency (SE) from historical channel/throughput measurements tagged with user positions. Specifically, two cascaded networks, LPS-Net and SE-Net, are designed to predict link power statistics (LPS) and ergodic SE accurately. We further propose a low-complexity Stable Matching-Iterative Balancing (SM-IB) scheduling algorithm. Numerical evaluations verify that the proposed neural CKM significantly enhances prediction accuracy and computational efficiency, while the SM-IB algorithm effectively achieves near-optimal max-min throughput with greatly reduced complexity.

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Xintong Chen, Zhenyu Jiang, Jiangbin Lyu, Liqun Fu. 2025-08-09. Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systems. https://arxiv.org/abs/2508.07009

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