Machine Learning in Wireless Communication

In recent years, the technology of wireless communication has developed rapidly. For instance, 5G and 6G are a next-generation communication technology. Because of the potential of 5G and 6 G, large quantities of data can be generated, which can be utilized to enhance the performance of neural networks. Recently, convolutional neural networks have achieved remarkable performance in various applications, including voice recognition, channel prediction, and so on. Recently, there have been a lot of studies in this field, including physical, radio, and wireless. Recently, great progress has been made in the area of deep learning in the physical layer. This research covers a broad spectrum of topics. A novel method for automatic modulation recognition is proposed in this paper. This paper gives a comparative analysis of traditional AMR and DL-based AMR, and also describes CNN arithmetic. In addition to CNN-based 2D AMRs, they are also included in Star Map, Time Rate Entry, Eye Graph, etc. In the following section, we will show you an AMR algorithm for RNNs. Finally, this paper sums up and forecasts its future.