Deep Federated Representations for Distributed and Secure Spectrum Sensing in Large-Scale CRNs
Nada Abdel Khalek, Walaa Hamouda · 2025
Spectrum sensing in large-scale cognitive radio networks (CRNs) presents significant challenges, as it typically necessitates numerous static secondary users (SUs) to determine the spectrum state. Current cooperative spectrum sensing (CSS) methods require SUs to transmit their private sensing data to a central unit. This centralized approach not only raises security concerns but also leads to considerable communication overhead. To address these issues, this paper introduces FeRAP, a novel CSS framework based on unsupervised federated representation learning. We leverage the mobility of multiple SUs to collect spectrum sensing data, allowing them to collaboratively yet distributively train a learning model to determine the spectrum state. The FeRAP framework employs a novel deep federated$\beta$variational autoencoder ($\beta$-VAE) for distributed representation learning, which identifies independent latent variables and learns disentangled representations of the sensing data in a lowerdimensional space. Furthermore, Affinity Propagation (AP) is then trained locally on the learned representations at each cooperating SU to securely and autonomously infer the spectrum state. FeRAP is a fully data-driven solution, requiring no modelbased assumptions or prior knowledge of channel or signal characteristics for training. Numerical results demonstrate that FeRAP's CSS performance is on par with supervised deep learning-based CSS techniques. Extensive simulations conducted under various network settings and propagation environments confirm the effectiveness and scalability of FeRAP.