🤖 AI Summary
This study addresses the challenges of multi-source feature imbalance and scenario heterogeneity in beamforming optimization for 6G Internet of Things (IoT) networks. To tackle these issues, the work proposes a robust, imbalance-aware optimization framework that integrates multi-perspective features—including network, environmental, device-specific, and visual cues—through a hybrid approach combining supervised and unsupervised learning. For the first time, it systematically evaluates the contribution of each feature type to beamforming performance, revealing that network-related features dominate prediction accuracy, while deployment environment and device type are pivotal for scenario clustering. Scenario segmentation is performed using K-means, DBSCAN, and hierarchical clustering, with model efficacy validated through comprehensive metrics and interpretability analysis. Experiments identify bandwidth, IoT sensor type, and mobility as globally critical features, demonstrating that the proposed method significantly enhances prediction robustness and scenario adaptability.
📝 Abstract
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