Modulation Recognition with Untrained Deep Neural Network for IoT and Mobile Applications

This paper presents a novel denoising algorithm with the untrained deep neural network (DNN) for Radio Frequency (RF) signal modulation recognition considering the mobile and Internet of Things (IoT) applications. In denoising with the untrained DNN, which is called deep image prior (DIP), a DNN is used as a deep generative network to generate the less noisy signal from the pure noise. Considering denoiser for RF signal, which can't apply the denoising algorithm selectively, we propose a new DIP algorithm called hybrid DIP to improve the data reproducing capability when the noise level is low. The experimental results show that the proposed method can increase the classification accuracy of the received RF signals, especially with low signal-to-noise ratio (SNR), with minimal impact on the signal with high SNR.