Weighted Sum-Rate Maximization by Joint Antenna Grouping and Movable RIS Deployment

Reconfigurable intelligent surface (RIS) is emerging as a key technology for next-generation wireless communications, offering the ability to reconfigure the wireless propagation environment. This paper investigates an RIS-assisted wireless communication system, where the base station (BS) side utilizes the antenna grouping technology. In addition, the RIS is mounted on a slide rail, enabling dynamic adjustment of its location. Given that the channel conditions are dependent on the RIS location, we jointly optimize the transmit beamforming of the BS, the passive beamforming of the RIS, and the RIS location to maximize the system’s weighted sum-rate (WSR). To efficiently solve this joint optimization problem, we first propose an iterative optimization algorithm based on product-manifolds. Additionally, in order to adapt to large-scale scenarios, we further introduce an enhanced gradient-based manifold meta-learning (E-GMML) method. Extensive numerical experiments demonstrate the critical importance of RIS location optimization and show that the proposed algorithm significantly improves the WSR while achieving lower complexity compared to traditional methods.