A hybrid ensembling and autoencoder scheme for improving sensing reliability in cognitive radio networks.

ABSTRACT: This paper proposes a hybrid ensemble classifier with denoising autoencoder (ECDAE) framework to address reliability and robustness challenges in cooperative spectrum sensing (CSS) for cognitive radio networks (CRNs). The proposed framework first employs an ensemble classifier (EC) to dynamically reconfigure the sensing time, optimizing performance while minimizing cost. The EC accurately estimates the sensing samples based on target detection probabilities, false alarm rates, and channel conditions. Subsequently, a denoising autoencoder (DAE) eliminates soft-combined energies from false-sensing users (FSUs) before soft fusion. The results show that EC surpasses other methods, including random forest (RFC), neural networks (NN), decision trees (DT), k-nearest neighbors (KNN), Gaussian naiv

SUBMITTER: Gul N 

PROVIDER: S-EPMC12753086 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature