Big Data-Driven Collaborative Channel Estimation in RIS Communications: A DNN Approach for Optimized Performance

In recent years, the field of wireless communications supported by reconfigurable intelligent surface (RIS) has emerged as a cutting-edge area of research. A primary challenge in this domain is the accurate and efficient channel estimation, especially under conditions of low pilot overhead. This work introduces a system model and a DNN-based channel estimation solution with the goal of improving the efficiency and accuracy of channel estimation under low pilot overhead in RIS-assisted communication systems. A significant highlight is the reduction in pilot overhead required for downlink channel estimation, which was accomplished by leveraging statistical correlation among different users' channels. Mainly, the research emphasizes the collaborative training of the DNN model, where both the Base Station (BS) and users iteratively exchange data and model updates, resulting in a jointly learned model that offers improved performance. The findings show that the proposed approach not only substantially reduces the pilot overhead but also ensures efficient channel state information learning, paving the way for more efficient RIS-assisted wireless communications. Simulation outcomes reveal that, when compared with conventional estimation techniques like least squares (LS) and minimum mean square error (MMSE), the suggested deep neural network (DNN) model attains enhanced estimation performance while reducing the required pilot overhead for all users.