Learning-Based mmWave V2I Environment Augmentation through Tunable Reflectors

To support the demand of multi-Gbps sensory data exchanges for enhancing (semi)-autonomous driving, millimeter-wave bands (mmWave) vehicular-to- infrastructure (V2I) communications have attracted intensive attention. Unfortunately, the vulnerability to blockages over mmWave bands poses significant design challenges, which can be hardly addressed by manipulating end transceivers, such as beamforming techniques. In this paper, we propose to enhance mmWave V2I communications by augmenting the transmission environments through reflection, where highly-reflective cheap metallic plates are deployed as tunable reflectors without damaging the aesthetic nature of the environments. In this way, alternative indirect line-of-sight (LOS) links are established by adjusting the angle of reflectors. Our fundamental challenge is to adapt the time-consuming reflector angle tuning to the highly dynamic vehicular environment. By using deep reinforcement learning, we propose the learning-based Fast Reflection (LFR) algorithm, which autonomously learns from the observable traffic pattern to select desirable reflector angles in advance for probably blocked vehicles in near future. Simulation results demonstrate our proposal could effectively augment mmWave V2I transmission environments with significant performance gain.