In the domain of highly directive wireless networks, hybrid beamforming is a solution to overcome the complexities of baseband processors and make a good trade-off between transmission performance and cost. Nonetheless, the conventional hybrid beamformer, which employs phase shifters (PSs), has several significant concerns. These include limited amplitude constraints, expensive power consumption, and inefficient hardware architecture that results in exponential complexity in the number of antennas. As a result, this paper proposes a branchwise double phase shifter (B-DPS) architecture as a solution to achieve computationally efficient hybrid beamforming, where each RF chain is connected to two distinct PSs and antenna branches using a power splitting strategy. The proposed B-DPS hybrid beamformer implementation can comfortably rescale the beamforming coverage, thereby maximizing the spectral efficiency in scenarios involving mmWave massive multiple-input and multiple-output systems. Furthermore, we consider zero-forcing with an iterative algorithm as the digital beamformer to solve the joint optimization problem of hybrid beamforming. The outcomes of the simulation results validate the beam coverage of the proposed B-DPS architecture, which outperforms the conventional non-blockwise PSs implementation.

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.

This letter presents a reconfigurable intelligent surface (RIS)-equipped vehicle model to help improve the visual paths of millimeter-wave (mmWave) multiple-input and multiple-output (MIMO) communications. As obstacles disrupt the connections between a base station and its users, in this letter we propose a RIS-equipped vehicle to mitigate the interruptions of obstacles and enrich the service area. We set a RIS on the roof of a vehicle and use it in the MIMO mmWave systems to improve the visual paths and serve several single-antenna users efficiently. To verify the superiority of the channel estimation performance, we also propose a multiple RIS-assisted channel (m-RISC) model and employ an orthogonal matching pursuit (OMP) algorithm in this letter. The simulation result shows the achievable sum rate and the normalized mean squared error performance of the proposed m-RISC model, which outperforms the single RISC (s-RISC) model.

The revised version has been submitted to the IEEE Transactions on Vehicular Technology. A reconfigurable intelligent surface (RIS) is a kind of metasurface that provides an adaptable way of controlling the propagation of radio signals in wireless systems, allowing for improved coverage and system efficiency. Instead of the traditional RIS-assisted single-to-multiple reflection channel, we present a time-variant location-based hybrid reflection channel(TL-HRC) model in this paper. The designed TL-HRC model can easily tune a cooperative situation between the mobile blockage and RIS in millimeter wave massive multiple-input and multiple-output systems and satisfactorily scale a high received power gain. Based on the designed TL-HRC model, we establish a joint active and passive beamforming problem for the weighted sum rate maximization and propose an alternative optimization with a RIS element selection (AO-RES) algorithm to tackle the challenging high dimensional coupled parameters. Through simulation experiments the effectiveness of the proposed TL-HRC-based AO-RES algorithm is validated in terms of the weighted sum rate with the case of perfect channel state information.