Generalized automatic modulation recognition method based on distributed learning in the presence of data mismatch problem

Abstract

Deep learning-based automatic modulation recognition (DL-AMR) methods are mainly based on centralized learning and decentralized learning. These methods have been developed for many applications in heterogeneous wireless networks (HWNs). However, the centralized DL-AMR methods involve high communications cost and computation cost and the decentralized DL-AMR methods are not robust since these methods are based on the assumption of uniform data distribution. In the practical HWNs, most sub-networks are independent and their heterogeneous datasets often do not match. In order to solve these problems, we propose a generalized AMR (GAMR) method based on distributed learning by considering the data mismatch scenario. First, each sub-network trains its own model by means of initialization model download from fusion center and formative modulated datasets. Second, sub-networks upload model weight of all sub-networks to the fusion center for re-training a generalized model, which will be immediately distributed to sub-networks for updating local model. Repeat these two steps until the global model converges successfully. Finally, simulation results are given to confirm advantages of the proposed GAMR method in different scenarios of HWNs.