用神经网络地图预测信道性能，优化多用户有源智能反射面调度。
Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systems
- 构建神经信道知识图谱，基于历史数据预测频谱效率。
- 预测精度提升显著，调度计算复杂度大幅降低。
- 适合高密度多用户无线系统中的实时资源分配场景。
智能反射表面（IRS）在下一代无线网络中具有显著性能提升潜力，但面临严重双重路径损耗及硬件限制下的复杂多用户调度挑战。有源IRS部分缓解路径损耗问题，但在小区级多IRS多用户系统中，随着用户密度和信道维度增加，信道状态获取开销与调度复杂度急剧上升。为此，本文提出一种基于神经信道知识图谱（CKM）的新型调度框架，设计基于Transformer的深度神经网络（DNN），从带用户位置标签的历史信道/吞吐量测量数据中预测遍历频谱效率（SE）。具体构建两级级联网络——LPS-Net与SE-Net，分别精准预测链路功率统计（LPS）与遍历SE。进一步提出低复杂度的稳定匹配-迭代均衡（SM-IB）调度算法。数值评估表明，所提神经CKM显著提升预测精度与计算效率，而SM-IB算法有效实现近似最优的最大最小吞吐量，且复杂度大幅降低。
原文摘要 · Abstract (English)
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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