{"ID":2884410,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.07009","arxiv_id":"2508.07009","title":"Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systems","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.","short_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 schedulin...","url_abs":"https://arxiv.org/abs/2508.07009","url_pdf":"https://arxiv.org/pdf/2508.07009v1","authors":"[\"Xintong Chen\",\"Zhenyu Jiang\",\"Jiangbin Lyu\",\"Liqun Fu\"]","published":"2025-08-09T15:14:03Z","proceeding":"cs.IT","tasks":"[\"cs.IT\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}