Cooperative Spectrum Sensing Based on LSTM-CNN Combination Network in Cognitive Radio System

Abstract

Cognitive radio (CR), as an emerging technology to improve the utilization of radio spectrum, the fundamental of CR technology is spectrum sensing, due to the detection performance being affected by various factors, spectrum sensing is challenging to achieve accurately. In recent years, many spectrum sensing algorithms have been proposed, such as energy detection algorithm, matched filter detection algorithm, cyclic stationary detection algorithm, etc. However, these algorithms are model-driven and require certain prior information. If the model assumptions are inaccurate or the prior information is challenging to obtain, the algorithms' detection performance will be degraded. The development of artificial intelligence technology and deep learning provides a new way to realize spectrum sensing. In this paper, we design a cooperative spectrum sensing model based on the parallel connection of convolutional neural network (CNN) and long-short-term memory (LSTM), which makes full use of the complementary feature extraction capabilities of CNN and LSTM networks. Among them, CNN is used to extract hidden spatial features, and LSTM network is used to extract time features. Both CNN and LSTM can process the original dataset directly avoiding information feature loss when the network is connected serially. Experimental result shows that the detection performance of the proposed algorithm outperforms the conventional cooperative detection algorithm under low SNR condition. For example, when the number of cooperative users is 9 and the transmit power is 10, the detection probability of the proposed algorithm in this paper can reach more than 90%, which is much higher than the detection performance of other spectrum detection algorithms.