发表机构
University of Hertfordshire; Joint Admissions & Matn. Board; Lightenet Technologies Ltd.（赫特福德大学; 联合招生与 matn 委员会; 莱特尼特科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究通过监督与无监督ML方法，对比多类特征对6G物联网波束成形优化的预测能力，明确关键影响因素，为后续应用深度学习与强化学习优化奠定基础。
AI 中文摘要
本研究针对6G物联网波束成形优化（6GBO）开展了系统的机器学习（ML）研究，采用监督与无监督方法。我们对比了网络、环境、设备及视觉特征组对6GBO的预测能力；还探讨了可提升6GBO的其他无监督视角，包括用K-means、DBSCAN、层次聚类等方法对网络场景进行聚类。多项感知不平衡实验显示，网络特征组的召回率、F1值及ROC-AUC值均优于设备、环境及视觉特征组，具备更强预测能力。针对无监督ML探索（通过肘法、轮廓系数、戴维斯-布尔丁指数评估），结果表明部署环境与设备类型对聚类的影响大于基于移动性的属性。此外，可解释性分析显示，带宽、物联网传感器及移动性在各特征组中具有更高的全局特征重要性。未来，我们将应用深度学习与强化学习技术预测吞吐量/延迟，或优化由信噪比增强等性能指标确定的奖励。
英文摘要
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
Comments9 pages
