A Machine Learning Method for Prediction of Multipath Channels
This work addresses channel prediction for cellular networks, but it appears incremental as it applies a specific CNN to a simulated scenario without claiming broad breakthroughs.
The paper tackled the problem of predicting mobile communication channel evolution in multipath scenarios using a convolutional neural network, achieving a predictor that meets deployment requirements for base station radio resource schedulers.
In this paper, a machine learning method for predicting the evolution of a mobile communication channel based on a specific type of convolutional neural network is developed and evaluated in a simulated multipath transmission scenario. The simulation and channel estimation are designed to replicate real-world scenarios and common measurements supported by reference signals in modern cellular networks. The capability of the predictor meets the requirements that a deployment of the developed method in a radio resource scheduler of a base station poses. Possible applications of the method are discussed.