Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

The rapid development of information and wireless communication technologies together with the large increase in number end-users have made radio spectrum more crowded than ever. Besides, providing a stable reliable service is challenging, as electromagnetic environments are evolving becoming sophisticated. Accordingly, there an urgent need for intelligent systems that can improve efficiency quality to provide agile management network resources, so better meet needs future users. Specifically, Automatic Modulation Recognition (AMR) plays essential role most especially emergence Software Defined Radio (SDR). AMR indispensable task while performing sensing Cognitive (CR). Thanks significant advancements Deep Learning (DL) applications, new powerful tools been provided which tackle problems this space. Thus, today, integrating DL models into has gained attention many researchers. This work aims comprehensive state-of-the-art review recent Machine (ML) based methods Single-Input Single-Output (SISO) Multiple-Input Multiple-Output (MIMO) systems. Furthermore, architecture each model will be identified along detailed comparison terms specifications performance. Finally, outline open problems, challenges, potential research directions discussion conclusion.