Deep Learning-Driven Beam and Blockage Forecasting for Reliable mmWave Vehicular Networks

In the rapidly advancing field of millimeter-wave (mmWave) communications, ensuring a stable and efficient connection poses a substantial challenge due to the dynamic environmental conditions. Predicting beam direction and potential blockage is crucial for maintaining uninterrupted service, particularly in mobile scenarios. This study introduces an innovative deep-learning approach for predicting beam direction and blockage events in vehicular networks. We propose a unique technique centered on the Gated Recurrent Unit-based Beam and Blockage Prediction (GBBP) model to improve prediction accuracy. Our multivariate GBBP model anticipates the optimal serving beam and forecasts future blockage events, outperforming baseline models such as Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and Attention-BiLSTM. The proposed GBBP model demonstrates superior performance in modeling the complex dynamics of mmWave communication channels, achieving high accuracy across various evaluation metrics, including Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and blockage accuracy (~99%).