Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systems
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| Format: | Preprint |
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2025
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| _version_ | 1866913982753800192 |
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| author | Chen, Xintong Jiang, Zhenyu Lyu, Jiangbin Fu, Liqun |
| author_facet | Chen, Xintong Jiang, Zhenyu Lyu, Jiangbin Fu, Liqun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07009 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systems Chen, Xintong Jiang, Zhenyu Lyu, Jiangbin Fu, Liqun Information Theory Artificial Intelligence Machine Learning 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. |
| title | Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systems |
| topic | Information Theory Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.07009 |