A Generalized CNN Model with Automatic Hyperparameter Tuning for Millimeter Wave Channel Prediction

Chengfang Yue, Hui Tang, Jun Yang, and Li Chai


Abstract :  This paper focuses on millimeter wave (mmWave) channel prediction by machine learning (ML) methods. Previous ML-based mmWave channel predictors have limitations on requirements of the amount of training data, model generalization ability, robustness to noise, etc. In this paper, we propose a CNN model with a novel feature selection strategy for mmWave channel prediction. Automatic hyperparameter tuning (AHT) algorithms are embedded in the training process to iteratively optimize the predictive performance of the proposed CNN. The diversification strategy is leveraged to enhance the robustness of the AHT procedure against different communication scenarios. To improve the generalization ability of the prediction model, the input features are designed to capture the correlation between the physical environment and the channel characteristics. In parallel, the Cartesian coordinates of the transmitter (Tx) and receiver (Rx) are transformed into polar ones to reduce the model’s sensitivity to coordinate noise. Numerical results demonstrate the effectiveness of the proposed CNN model in predicting mmWave channel characteristics in various communication scenarios.

Index terms : Automatic hyperparameter tuning, channel prediction, CNN, diversification strategy, feature selection, mmWave communication.