TFF_aDCNN: A Pre-Trained Base Model for Intelligent Wideband Spectrum Sensing

Abstract

In the beyond 5 G (B5G)/6 G era, to achieve ultra-dense and ultra-large-capacity intelligent connection of all things, an intelligent wideband spectrum sensing technology is particularly important. However, in an extremely wide frequency range, it is still a challenge to achieve high-precision and high-reconstruction-capability wideband spectrum sensing (WSS) under a very low SNR. We propose a Time-Frequency-Fused adjustable Deep Convolutional Neural Network (TFF_aDCNN). Meanwhile, a novel TFF_aDCNN-based sensing framework is also proposed. In this framework, we can obtain a pre-trained base model with a single distribution by training TFF_aDCNN. Then, for the sensing task in the actual environment, we use the base model for transfer learning, so that a newly trained sensing model can be obtained very quickly (i.e. fine-tuned model). In the TFF_aDCNN, we design a main network and an adjustable auxiliary network, where the former learns complex and abstract signal features, while the latter assists the main network in learning different data distribution patterns during the training process and regulates the focus direction of the main network during the perception process. Simulation results show that TFF_aDCNN can significantly reduce hardware cost and improve reconstruction accuracy and reconstruction capability, when compared with SOMP and SwSOMP-based WSS algorithms, single-dimensional deep learning spectrum sensing method, and deep learning-based WSS (DLWSS), especially at very low SNRs.