A Personalized Solution for Deep Learning-Based mmWave Beam Selection

Abstract

With the increasing sensing and computing power of vehicles, beam selection solutions based on deep learning (DL) and sensor data have attracted attention in vehicle-to-infrastructure (V2I) scenarios. The existing DL-based beam selection solutions generally provide a single global model for all vehicles. However, since the data distributions across vehicles are generally different in practice, the single model may not be suitable for all vehicles. In this letter, we propose a novel personalized beam selection solution, in which each user has a tailored model. Furthermore, we propose a mask-based pre-training and fine-tuning algorithm to accomplish the personalization for beam selection. Simulation results demonstrate that the proposed personalized solution has better performance than the conventional baselines.