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.
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Tunable Reflectors Enabled Environment Augmentation for Better mmWave WLANs
With significant commercial potentials, millimeter- wave (mmWave) based wireless local area networks (WLANs) have attracted intensive attention lately. Unfortunately, the susceptible transmission characteristics over mmWave bands, especially the vulnerability to blockages, poses significant design challenges. Although existing solutions, such as beamforming, can overcome some of the problems, they usually focus on enhancing end transceivers to adapt to the transmission environments, and sometimes are still less effective. In this paper, by deploying highly-reflective cheap metallic plates as tunable reflectors without damaging the aesthetic nature of the environments, we propose to augment WLAN transmission environments in a way to create more effective alternative indirect line-of-sight (LOS) links by adjusting the orientations of the reflectors. Based on this idea, we design a novel adaptive mechanism, called mmRef, to effectively tune the angels of the deployed reflectors and develop corresponding operational procedures. Our performance study demonstrates our proposed scheme could achieve significant gain by tuning the angles of deployed reflectors in the augmented transmission environment.
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- Award ID(s):
- 1722791
- NSF-PAR ID:
- 10172948
- Date Published:
- Journal Name:
- IEEE/CIC International Conference on Communications in China (ICCC)
- Page Range / eLocation ID:
- 7 to 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCChina.2019.8855899
