Multiband Cooperative Spectrum Sensing Meets Vehicular Network: Relying on CNN-LSTM Approach

Abstract

A vehicular network is expected to empower all aspects of the intelligent transportation system (ITS) and aim at improving road safety and traffic efficiency. In view of the fact that spectrum scarcity becomes more severe owing to the increasing number of connected vehicles, implying spectrum sensing technology in vehicular network, i.e., cognitive vehicular network, has emerged as a promising solution to provide opportunistic usage of licensed spectrum. However, some unique features of vehicular networks, such as high movement and dynamic topology, take on high challenges for spectrum sensing. Recently, machine learning-based approaches, especially deep learning, for spectrum sensing have attracted sufficient interest. In this work, we investigate a learning-based cooperative spectrum sensing (CSS) approach for multiband spectrum sensing in the cognitive vehicular network. Specifically, we integrate two powerful deep learning models, i.e., the convolutional neural network (CNN) to exploit the features from sensing data, and the long-short-term memory (LSTM) network is then utilized to extract temporal correlations given input as the generated features by the CNN structure. Instead of the predefined decision threshold typically set in conventional approaches, our proposed approach could eliminate the impact of impertinent threshold value setting. Extensive simulations have been conducted to evaluate the effectiveness of the proposed method in achieving satisfactory spectrum sensing performance, particularly in terms of higher detection accuracy, robustness in low signal-to-noise ratio (SNR) environments, and a significant reduction in spectrum sensing time compared to other methods.