An End-to-End Deep Learning Framework for Wideband Signal Recognition

Successful management of the radio spectrum requires, as a first step, detailed information about spectrum occupancy. In this work, we present an end-to-end deep learning (DL) based framework to obtain information from wide spectrum bands through signal detection, localization, and modulation classification. By visually representing the radio signals in spectrograms, we formulate the wideband detection problem as an object detection task from the computer vision field. To this end, the proposed framework consists of two cascaded modules: an object detection network repurposed to detect and classify distinctive signals in wideband spectrograms, and a convolutional neural network (CNN) designed to extend the classification capabilities to support a wide range of analog and digital modulation schemes. To evaluate our framework, we use a public wideband recognition dataset, which we carefully analyze and curate through a series of preprocessing techniques. To tackle the challenges of insufficient training data and class imbalance observed in the dataset, we suggest a training strategy that includes data mixing and transfer learning. Our experimental results on a general test set demonstrate that the proposed approach can detect and classify a variety of narrowband signals with simultaneously high precision (77.1%), recall (81.8%), and localization accuracy, as indicated by an average Intersection over Union (IoU) of 86%.