Primary User Detection in Cognitive Radios: Challenges, Techniques, and Emerging Solutions

Abstract

Cognitive Radio Networks (CRNs) address spectrum scarcity through intelligent spectrum management, enabling dynamic spectrum access for secondary users. However, traditional spectrum sensing techniques struggle with noise sensitivity and unstable Primary User (PU) dynamics, particularly in low Signal-to-Noise Ratio (SNR) environments. This paper proposes an Attention-based Deep Cognitive Network (ADCN) that integrates convolutional layers for spatial feature extraction, Long Short-Term Memory (LSTM) networks for temporal dependency modeling, and a self-attention mechanism to dynamically prioritize critical time-frequency characteristics. The paper presents a prototype of Attention-based Deep Cognitive Network (ADCN), which aims at improving the detection of PU under noisy and dynamic conditions. The suggested architecture combines the convolutional layers (as a spatial feature extractor) with Long Short-Term Memory (LSTM) networks (as a practical model of time dependencies) as well as the use of self-attention to highlight important time–frequency features. The data utilized to train and test the model is the CSRD2025, and the levels of SNR used are between -20 dB and 10 dB. As shown in the experimental results, ADCN attains a bit error rate of 0.12 at -20 dB, which is considerably better than Energy Detection (0.60) and Matched Filter Detection (0.30). The model also provides lesser false alarm rates and greater rates of detection and is adaptable to various patterns of PU activity. These results indicate that ADCN would be a powerful and efficient solution to next-generation CRNs, which can be used to optimize the spectrum and work in low-SNR settings.