Cooperative spectrum sensing method based on channel attention and parallel CNN-LSTM

Abstract

With the development of 6G networks, enhancing spectrum sensing performance under low signal-to-noise ratio (SNR) conditions has become a crucial research focus. Addressing the challenge of low detection probability under low SNR, we propose a cooperative spectrum sensing method based on a channel attention mechanism and a parallel Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks. This method utilizes the parallel structure of CNN and LSTM to extract spatial and temporal features from the spectrum sensing data, respectively. First, a channel attention mechanism is introduced into the CNN to enhance the focus on important features within the spectrum sensing data during spatial feature extraction, while LSTM is applied individually to the spectrum sensing data of each secondary user to extract temporal features. Then, the features extracted by the CNN and LSTM are flattened and concatenated, followed by feature-level fusion through a fully connected layer to produce the final spectrum sensing result. Simulation results demonstrate that this method achieves a high detection probability, particularly under low SNR conditions. When the SNR is below -10 dB, the average detection probability of the proposed method improves by 5.83% compared to the Parallel CNN and LSTM method at a false alarm probability of 0.1, and by 7.09% at 0.01.