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

arXiv: https://arxiv.org/abs/2508.07009 · HTML: https://mlsanity.com/paper/2508.07009/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 9 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [On Embeddings for Numerical Features in Tabular Deep Learning](https://mlsanity.com/paper/2203.05556/) (2022)
- [A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G](https://mlsanity.com/paper/2309.07460/) (2023)
- [Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement](https://mlsanity.com/paper/1911.05326/) (2019)
- [Intelligent Surfaces Empowered Wireless Network: Recent Advances and The Road to 6G](https://mlsanity.com/paper/2312.16918/) (2023)
- [Active RIS vs. Passive RIS: Which Will Prevail in 6G?](https://mlsanity.com/paper/2103.15154/) (2021)
- [Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication](https://mlsanity.com/paper/1910.01573/) (2019)
- [Environment-Aware Beam Selection for IRS-Aided Communication with Channel Knowledge Map](https://mlsanity.com/paper/2111.11289/) (2021)
- [Multi-Pair D2D Communications Aided by An Active RIS over Spatially Correlated Channels with Phase Noise](https://mlsanity.com/paper/2208.07840/) (2022)
- [Active-IRS-Aided Wireless Communication: Fundamentals, Designs and Open Issues](https://mlsanity.com/paper/2301.04311/) (2023)

## Abstract (arXiv)

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.