Search papers, labs, and topics across Lattice.
This study systematically explores 6G beamforming optimization (6GBO) through both supervised and unsupervised machine learning methods, revealing that network features significantly outperform device, environmental, and vision features in predictive power. Imbalance-aware experiments demonstrated superior performance metrics, including recall, F1-score, and ROC-AUC values for network features. Additionally, unsupervised clustering analysis indicated that deployment environment and device type are more influential than mobility attributes, highlighting key factors for future optimization strategies in 6G networks.
Network features dominate in predicting 6G beamforming performance, outpacing other feature groups in critical metrics.
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
