A novel spectrogram based lightweight deep learning for IoT spectrum monitoring

Abstract

The integration of cognitive radio network (CRNet) with internet of things (IoT) holds tremendous potential for creating more intelligent and advanced technological ecosystems. By allowing IoT devices to dynamically access and utilize the available radio spectrum, CRNet-based IoT systems can enable transparent connectivity, faster data transmission, and efficient resource utilization. The successful deployment of CRNet-based IoT systems highly depends on spectrum sensing implementation, which makes selecting the appropriate spectrum sensing technique extremely critical. However, several challenges are associated with the current spectrum sensing techniques, including limited representation of signal features, susceptibility to noise, inadequate hardware compatibility, and significant performance degradation using deep learning models. To deal with these issues, a novel approach is proposed integrating spectrogram representation and lightweight convolutional neural network models. Unlike raw digital representation, spectrograms allow efficient and consistent feature extraction using time–frequency representation of the received signals. Lightweight convolutional neural network models leverage advanced deep learning techniques to achieve fast and accurate predictions from spectrograms. They require few parameters to process while reduce the computational burden. The proposed approach is a promising solution for performing spectrum sensing in resource-constrained environments paving the way for efficient use of radio spectrum in IoT networks. The performance of the proposed approach is evaluated and compared to the existing techniques. Spectrogram based lightweight deep learning achieved high efficiency while required less computational resources.