@article{4757,
  author = {Ricardo Rodríguez Jorge},
  title = {Adaptive Weighted Ensemble Clustering for Robust Latent Structure Discovery in RF Signal Datasets},
  journal = {Digital Signal Processing and Artificial Intelligence for Automatic Learning},
  year = {2026},
  volume = {5},
  number = {2},
  doi = {https://doi.org/10.6025/dspaial/2026/5/2/106-129},
  url = {https://www.dline.info/dspai/fulltext/v5n2/dspaiv5n2_3.pdf},
  abstract = {Radio frequency (RF) signal datasets exhibit complex nonlinear distributions, environmental noise, and
heterogeneous transmission behaviors that challenge traditional clustering algorithms. Conventional
ensemble approaches often rely on static weighting strategies and overlook local structural uncertainty,
limiting their robustness in dynamic wireless environments. To address these limitations, this study introduces
an Adaptive Weighted Ensemble Clustering framework designed for robust latent structure discovery in RF
signal datasets. The proposed methodology integrates multiple clustering algorithms, including centroidbased,
density aware, hierarchical, and graph based methods, into a unified consensus architecture. By
constructing a performance-driven weighted co-association matrix and incorporating local uncertainty
estimation, the framework dynamically optimizes algorithmic contributions while suppressing unstable
partitions. High order topological enhancement further refines neighborhood relationships, ensuring coherent
subgroup formation in sparse spectral regions. Experimental evaluation demonstrates that the adaptive
consensus model significantly outperforms standalone clustering baselines, achieving a consensus silhouette
score of 0.722 and exhibiting exceptional reproducibility across subsampling trials. Co-association heatmap
analysis reveals distinct block-diagonal structures, confirming the reliable identification of latent transmission
subgroups. The framework’s dynamic adaptation capability ensures sustained performance under varying
signal to noise ratios and heterogeneous dataset distributions. By bridging the gap between algorithmic
instability and the stringent reliability requirements of modern RF analytics, this work establishes a scalable
foundation for unsupervised spectrum intelligence, anomaly detection, and AI-driven wireless security. Future
research will integrate self supervised representation learning and optimize computational efficiency for
real-time edge deployment.},
}