RIS-Assisted Hybrid Beamforming with Connected Autonomous User Vehicle Localization for Millimeter Wave MIMO Systems

Reconfigurable intelligent surfaces (RIS) have recently been investigated for intelligently reforming the blockage or mobility-aware wireless propagation environment as well as improving spectral efficiency and localization error performance. A blockage generally impacts the visual path of the millimeter wave (mmWave) channel and increases its signaling overhead. Thus, the system performance may suffer due to interruptions caused by static or mobile blockers, such as buildings, trees, vehicles or people. In this paper, we consider an RIS-assisted mmWave multiple-input and multiple-output (MIMO) channel model to overcome the blockage awareness problem. In order to solve the subarray rate maximization problem, we propose a conjugate gradient-based low-cost hybrid beamforming algorithm. Next, we consider a channel covariance splitting method and propose a double-step iterative algorithm to perform the localization error-probability of the user autonomous vehicle.  Finally, the simulation results show and verify the proposed algorithm in terms of the RIS-assisted equivalent channel for the mmWave vehicle-to-vehicle MIMO systems.