QoS Control Under Perfect and Imperfect CSI in Intelligent Reflecting Surface-Assisted Multi-Cast Multi-Group Communication Systems

To address the explosion demand for data-intensive applications, enhancing wireless transmission capacity has become crucial for today’s networks. This paper focuses on improving the quality of service (QoS) and user satisfaction in intelligent reflecting surface (IRS)-assisted multicast multi-group systems by managing the actual transmitted data instead of sending all source data. A key challenge of this problem is determining the ergodic capacity when the signal-to-noise-plus-interference (SINR) distribution in IRS-assisted wireless systems is significantly complicated. To address this issue, we propose a deep neural network (DNN)-based framework to predict the long-term network capacity accurately. We adapt well-known zero-forcing (ZF) and block diagonalization (BD) techniques to achieve efficient and secure solutions in IRS-assisted multi-cast multi-group systems. Furthermore, we consider the system in case of imperfect channel state information (CSI). Adopting the three-phase channel estimation, we propose a two-stage learning framework to enhance the accuracy of the estimated channel. Based on predicted results, we investigate adjustment algorithms to adapt to environmental changes, thus increasing the received QoS and user satisfaction. Our numerical results confirm the efficiency of the proposed design, with the channel estimation error being significantly smaller than that of the three-phase channel estimation algorithm in the literature.