ATIC: Autoencoder Transformer-Based Detector for Interweave Cognitive Radios

The widespread adoption of wireless communication technologies has intensified spectrum demand, exacerbating spectrum scarcity despite underutilization in many frequency bands. Cognitive radio systems offer a solution by supporting dynamic spectrum access, particularly through passive spectrum monitoring for opportunistic use. Existing machine learning (ML) and deep learning (DL) based white space detectors fall short of the accuracy needed for large-scale networks. This paper presents the Autoencoder-Transformer Integrated Cognitive Radio (ATIC) framework, a lightweight model that leverages the Transformer's self-attention mechanism to effectively capture complex spectraltemporal patterns without extensive preprocessing. ATIC's robustness is validated on both field-collected 5G Non-Standalone (NSA) and synthetic datasets, achieving an accuracy of 99.77 %. Furthermore, ATIC's architecture supports parallel processing across multiple primary resource blocks (PRBs), optimizing inference time as PRB count increases and achieving a 32% reduction compared to convolutional neural network (CNN).