Strategies for Enhanced Signal Modulation Classifications Under Unknown Symbol Rates and Noise Conditions

Abstract

Radio frequency signal modulation classifications find broad applications in cognitive sensing and RF spectrum coexistence. Recently, deep neural networks have been shown to be a powerful tool for automatic modulation classification (AMC). Accounting for different signal variations is paramount towards reliable classifications. In this paper, we examine the performance of AMC under varying sampling rates and signal-to-noise ratio (SNR). We consider a dynamic environment where the signal modulation and channel conditions can be assumed constant over a number of consecutive observations. We also show that a single ResNet can be used for both modulation classification and estimating SNR which allows network training and testing at the same noise levels. It is shown that significant signal modulation classification accuracy improvement can be achieved using multiple observations and known SNR.