用因果关系筛选关键信号，大幅减少毫米波波束搜索开销。
Causal Beam Selection for Reliable Initial Access in AI-driven Beam Management
- 通过因果发现构建信号依赖图，只选真正相关的输入特征。
- 相比传统方法，输入选择时间减少94.4%，波束扫描开销降低59.4%。
- 适合6G毫米波系统中追求高效可靠的波束管理场景。
高效可靠的波束对齐是6G及以后毫米波多输入多输出（MIMO）系统的关键需求，要求通信快速、自适应并能应对现实不确定性。现有基于深度学习的波束对齐方法常忽略输入与输出间的潜在因果关系，导致可解释性差、泛化能力弱以及不必要的波束扫描开销。本文提出一种融合因果发现的因果感知深度学习框架，引入新型两阶段因果波束选择算法，识别用于波束预测的最小相关输入集。首先，通过因果发现构建贝叶斯图，刻画接收功率输入与最优波束间的依赖关系；随后，该图指导深度学习分类器进行因果特征选择。仿真结果表明，所提方法性能媲美传统方案，同时输入选择时间降低94.4%，波束扫描开销减少59.4%，仅聚焦于因果相关特征。
原文摘要 · Abstract (English)
Efficient and reliable beam alignment is a critical requirement for mmWave multiple-input multiple-output (MIMO) systems, especially in 6G and beyond, where communication must be fast, adaptive, and resilient to real-world uncertainties. Existing deep learning (DL)-based beam alignment methods often neglect the underlying causal relationships between inputs and outputs, leading to limited interpretability, poor generalization, and unnecessary beam sweeping overhead. In this work, we propose a causally-aware DL framework that integrates causal discovery into beam management pipeline. Particularly, we propose a novel two-stage causal beam selection algorithm to identify a minimal set of relevant inputs for beam prediction. First, causal discovery learns a Bayesian graph capturing dependencies between received power inputs and the optimal beam. Then, this graph guides causal feature selection for the DL-based classifier. Simulation results reveal that the proposed causal beam selection matches the performance of conventional methods while drastically reducing input selection time by 94.4% and beam sweeping overhead by 59.4% by focusing only on causally relevant features.
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