6G物联网波束成形优化中，网络特征预测力最强，环境与设备类型决定聚类结果。
Multi-perspective Imbalance-Conscious 6G Beamforming Optimization and Performance
- 融合多视角特征，用机器学习优化6G波束成形
- 网络特征在召回率、F1值等指标上全面领先
- 适合研究6G通信与智能优化的工程师
本研究系统性地采用监督与无监督机器学习方法，探索6G物联网波束成形优化（6GBO）。比较了网络、环境、设备和视觉特征组对6GBO的预测能力。结果显示，网络特征在召回率、F1分数和ROC-AUC方面均优于其他特征组。无监督分析表明，部署环境与设备类型是聚类的主要影响因素，而非移动性属性。解释性分析显示，带宽、物联网传感器和移动性在所有特征组中具有更高全局重要性。未来将引入深度学习与强化学习，以预测吞吐量/时延或优化由信噪比提升等指标决定的奖励。
原文摘要 · Abstract (English)
The study presents a systematic machine learning (ML) study of 6G-IoT beamforming optimization (6GBO) using supervised and unsupervised approaches. We compared the predictive power of network, environmental, device, and vision feature groups for 6GBO. Additionally, it addressed other unsupervised perspectives that can enhance 6GBO, including clustering network scenarios using methods such as K-means, DBSCAN, and hierarchical clustering. Several imbalance-aware experiments revealed that network features possess better prediction power than device, environmental, and vision feature groups, as evidenced by their recall, F1-score and ROC-AUC values. For unsupervised ML exploration (assessed using Elbow, Silhouette score, and Davies-Bouldin Index methods), the results indicate that the deployment environment and type of device primarily influence clustering, rather than mobility-based attributes. Furthermore, the explainability analysis showed that bandwidth, IoT sensors, and mobility possess higher global feature importance across the feature groups. In the future, we would apply deep and reinforcement learning techniques to predict throughput/latency or to optimize rewards determined by performance indicators like SNR enhancement
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。