A Real-Time Seq2Seq Beamforming Prediction Model for C-V2X Links

The chapter presents the research on a real-time deep-learning beamforming prediction model for C-V2X systems. Machine Learning (ML) for modeling and predicting wireless channels of vehicular communications has attracted increasing interest recently. In C-V2X systems, long-haul communication is critically needed, sometimes, without sacrificing congestion factor. Beamforming emerges as such an enabling approach to enlarge coverage by choosing right beams, instantaneously. In both the latest Wi-Fi and C-V2X standards, beamforming selections are to scan all the beams and pick up optimum beams during antenna training phase within a beacon interval (BI). Simulations and experiments conducted by the authors identified such scheme will lead to medium to significant degradation in performances sometimes during the whole BI under certain situations. To respond to this vital situation, this paper presented and studied a deep-learning beamforming prediction model to forecast optimum beams within each BI. In this chapter, a real-time sequence-to-sequence (Seq2Seq) beamforming prediction model is presented and implemented. Experiment data validated the effectiveness of the proposed prediction model under the Dearborn Campus of the University of Michigan, resulting in an enhancement of prediction accuracy of 50–75%.