Reconfigurable Intelligent Surfaces Assisted Multicast Transmissions With Finite-Alphabet Inputs

Given its ability to intelligently modify the wireless propagation environment, reconfigurable intelligent surface (RIS) emerges as a promising technology to enhance the capacity of multicast systems. This paper analyzes the ergodic rate (ER) of a RIS-assisted multicast system with finite-alphabet inputs by exploiting only statistical channel state information. To reveal important insights for system design, asymptotic analyses are performed on the ER in the region of high signal-to-noise ratio (SNR). It is observed that the ER converges to some constant in the high-SNR regime and the convergence rate is influenced by the diversity order and the array gain. On this basis, a novel array gain maximization problem is formulated, and two efficient algorithms are proposed to improve the array gain and the ER. Numerical results are presented to verify the accuracy of the derived results and the effectiveness of the proposed methods.