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

TL;DR

This paper introduces a neural Channel Knowledge Map framework using Transformer-based DNNs to improve scheduling efficiency and accuracy in multi-user active IRS systems, addressing challenges of channel estimation and complexity.

Contribution

It proposes a novel neural CKM framework with cascaded networks for accurate channel prediction and a low-complexity scheduling algorithm, advancing IRS multi-user system optimization.

Findings

Neural CKM improves spectral efficiency prediction accuracy.

Proposed scheduling achieves near-optimal throughput.

Method reduces computational complexity significantly.

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.