G-VAP: A Generative Variational Autoencoder Approach for Enhanced Cooperative Sensing
Nada Abdel Khalek, Walaa Hamouda · 2024
In interweave cognitive radio (CR), users can transmit data only when licensed bands are vacant. Deep learning (DL) allows CRs to intelligently sense and identify channel availability. However, most supervised DL detectors require a substantial amount of labeled training data, which can be challenging to obtain in interweave CR. In this paper, we introduce G-VAP, an unsupervised deep generative approach for cooperative spectrum sensing (CSS). G-VAP utilizes an advanced variant of variational autoencoders (VAEs) called β-VAE to identify independent latent variables and encourage accurate and disentangled sensing data representation in a lower dimensional latent space. Furthermore, G-VAP utilizes the affinity propagation (AP) algorithm for unsupervised clustering to detect primary user activity. Unlike other unsupervised clustering methods, AP’s performance is not reliant on the initialization of cluster centroids. Furthermore, G-VAP leverages the cooperation among CRs to sustain a high detection performance. Our approach is fully data-driven, operates without any model-driven assumptions, and does not require prior knowledge of channel or signal characteristics for training. Numerical results indicate that G-VAP for CSS performs comparably to benchmark supervised DL-based CSS. Extensive simulations have been conducted in diverse network settings, propagation environments, and fading conditions, which have proved the effectiveness of G-VAP.