Spectrum Sensing using Hybrid Model with Attention Mechanism for Low SNR Environments

With the rising demand for wireless spectrum, effective spectrum sensing is essential for optimal resource allocation in cognitive radio systems. Detecting signals in low Signal-to-Noise Ratio (SNR) settings, such as -20 dB, is difficult owing to the high noise levels. This research offers a comparative analysis of two hybrid deep learning model that incorporates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Again, Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are incorporated together with attention mechanisms, to increase signal detection accuracy and likelihood of detection at low SNRs. Experimental results show that the CNN-LSTM with attention mechanism outperforms standard models, with a 66.72% accuracy at SNR of -20dB and 61.9% detection probability for signals at -20 dB SNR. Conversely, the CNN-RNN outperforms standard models, with a 61.86% accuracy at SNR of -20dB and 64.68% detection probability for signals at -20 dB SNR. The application of attention processes allows the model to focus on the most important signal information, reducing the influence of noise.