Deep Learning Based RIS Channel Extrapolation with Element-grouping
This work addresses the pilot overhead problem in RIS channel estimation for wireless communication networks, but it is incremental as it builds on existing element-grouping strategies.
The paper tackles the challenge of acquiring cascaded channels in RIS systems with massive passive elements by using an element-grouping strategy to reduce pilot overhead, and it proposes a deep learning-based scheme that refines partial channels and extrapolates full ones, achieving significant gain over conventional methods.
Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks. In this letter, we consider the acquisition of the cascaded channels, which is a challenging task due to the massive number of passive RIS elements. To reduce the pilot overhead, we adopt the element-grouping strategy, where each element in one group shares the same reflection coefficient and is assumed to have the same channel condition. We analyze the channel interference caused by the element-grouping strategy and further design two deep learning based networks. The first one aims to refine the partial channels by eliminating the interference, while the second one tries to extrapolate the full channels from the refined partial channels. We cascade the two networks and jointly train them. Simulation results show that the proposed scheme provides significant gain compared to the conventional element-grouping method without interference elimination.