AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent
  Surfaces: Deployment, Prototyping, Experiments, and Opportunities
Li-Hsiang Shen Kai-Ten Feng Ta-Sung Lee Yuan-Chun Lin Shih-Cheng Lin Chia-Chan Chang Sheng-Fuh Chang
The requirement of wireless data demands is increasingly high as the
sixth-generation (6G) technology evolves. Reconfigurable intelligent surface
(RIS) is promisingly deemed to be one of 6G techniques for extending service
coverage, reducing power consumption, and enhancing spectral efficiency. In
this article, we have provided some fundamentals of RIS deployment in theory
and hardware perspectives as well as utilization of artificial intelligence
(AI) and machine learning. We conducted an intelligent deployment of RIS
(i-Dris) prototype, including dual-band auto-guided vehicle (AGV) assisted RISs
associated with an mmWave base station (BS) and a receiver. The RISs are
deployed on the AGV with configured incident/reflection angles. While, both the
mmWave BS and receiver are associated with an edge server monitoring downlink
packets for obtaining system throughput. We have designed a federated
multi-agent reinforcement learning scheme associated with several AGV-RIS
agents and sub-agents per AGV-RIS consisting of the deployment of position,
height, orientation and elevation angles. The experimental results presented
the stationary measurement in different aspects and scenarios. The i-Dris can
reach up to 980 Mbps transmission throughput under a bandwidth of 100 MHz with
comparably low complexity as well as rapid deployment, which outperforms the
other existing works. At last, we highlight some opportunities and future
issues in leveraging RIS-empowered wireless communication networks.