Radio Frequency in the Automatic Modulation Recognition (AMR): MIMO System Integrating Artificial Intelligence

Abstract

Automatic modulation recognition (AMR) serves a critical role in the absence of previous knowledge by identifying the scheme for modulation of the incoming signals for subsequent processing. High-performance DL-AMR techniques for messaging systems can now be developed thanks to recent advances in deep learning (DL). Because deep neural networks offer powerful capabilities for obtaining and categorizing features, DL-AMR systems have demonstrated outstanding results compared to standard control detection methods, exhibiting high identification accuracy and minimal error messages. Although DL-AMR techniques have great potential, they also raise complexity issues that hinder their practical implementation in Bluetooth systems. The goal of this research is to present an overview of recent DL-AMR research with an emphasis on suitable model designs and standard datasets. Additionally, we present thorough experiments that evaluate the state-of-the-art models for SISO systems from the standpoint of accuracy and complexity. We also suggest using DL-AMR in the novel multiple-input multiple-output (MIMO) situation with precoding. Lastly, the topic of current issues and potential future research avenues is covered.