Abstract
In cognitive radio (CR) systems, the accurate detection of spectrum holes is a cornerstone for efficient spectrum utilization. However, the increasing complexity of CR environments, particularly those with multiple primary users (PUs), has made precise spectrum sensing a paramount challenge. To address this challenge, this study introduces the ATC model, a novel deep learning architecture that integrates a parallel combination of attention mechanism-based networks and a Convolutional Neural Network (CNN). This hybrid design enables the model to capture both spatial and temporal features from the distinct statistics of sensing signals, thereby enhancing the accuracy of spectrum state detection. The model employs a Graph Attention Network (GAT) to extract complex topological features from graph-structured data derived from received signal strength, dynamically highlighting the most relevant information. To complement this, a CNN processes the sample covariance matrix of sensing signals, unlocking localized statistical correlations and hierarchical feature representations by treating the matrix as an image. Temporal dynamics, such as PU activity patterns, are modeled using a Transformer encoder, which leverages a self-attention mechanism to learn sequential features effectively. The proposed model is evaluated using both simulated and real-world datasets. For the simulated datasets, the model is assessed and compared with baseline methods under multi-PU scenarios across different channel models. For the real-world dataset, the experimental setup is configured for a single-PU scenario due to practical data collection limitations. In both cases, the ATC model demonstrates improved performance over the benchmarked spectrum sensing methods, exhibiting higher accuracy and robustness within the respective evaluation settings.