A Real-Time C-V2X Beamforming Selector Based on Effective Sequence to Sequence Prediction Model Using Transitional Matrix Hard Attention

Abstract

For C-V2X systems, the selection of the best beam in a real-time mode becomes an increasingly critical and yet open topic. Most of the existing approaches adopt either conventional ARIMA or ANN. Recently, there has been research on adopting sequence-to-sequence (Seq2Seq) predictors with attentions to extract time series features and emphasis on critical information to achieve data prediction. In this paper, a Seq2Seq predictor integrating with a Transitional Matrix based Hard attention is presented and validated through an artificial test dataset with predefined transitional states. At first, the transition probability matrix is generated from previous time series data and fed into the 'hard' attention module of Seq2Seq predictor to determine the weights during the training phase. Secondly, the presented Seq2Seq predictor was implemented and adopted to predict the best beams of a C-V2X beamforming selector built up by the authors. Experiments were conducted and captured data were used to validate the performance of the predictor. When compared with baseline models, the presented predictor can achieve an enhanced prediction accuracy in a gain of 10-12%.