Wireless communication channel modeling based on ML

Nowadays, wireless communication plays an important role in various fields of our lives, and in order to ensure good communication performance, reliable channel models are important. In this paper, we present a comprehensive survey of Machine Learning (ML) based channel modeling methods to help with the estimation and prediction accuracy on wireless channel modeling. To begin with, we start with a review of the traditional methods of wireless communication channel modeling, basically empirical method and deterministic method. Then, we introduce several ML methods to address limitations of traditional ways will be used for channel modeling, such as Support Vector Machine (SVM), Random Forests, autoencoder, Deep Learning, etc. Lastly, we demonstrate the application of ML methods including deep learning, SVM, and random forests on wireless channel modeling. Through learning the underlying channel distribution, Deep Learning methods have already demonstrated remarkable performance in channel modeling for different scenarios in some previous learning. Also, by training with suitable kernels and conducting sufficient training iterations, optimal hyperplanes can be identified, Support Vector Machines (SVM) can then be utilized to predict channel characteristics based on input features. Random Forests can identify the most relevant features influencing the channel and optimize system design by handling complex and high-dimensional data through iterative feature selection and splitting criteria. These methods achieve competitive accuracy with respect to traditional methods, however, there are still challenges and issues to be addressed in the application of machine learning methods for communication channel modeling.