Deep Learning for Optimal Phase-Shift and Beamforming Based on
Individual and Cascaded Channels Estimation in the RIS-MIMO System
Abstract
Reconfigurable Intelligent Surfaces (RIS) represent an advanced
technology reshaping wireless communication networks. Through
intelligent configuration of wireless propagation environments using
both cost-effective passive and active elements, RIS has the potential
to bring about considerable performance improvements. In RIS-MIMO
systems, precise control of passive RIS elements is crucial in
optimizing reflected signal phases. This control necessitates intricate
algorithms, given that inaccurate phase optimization can result in
suboptimal signal focus and decreased data transmission accuracy.
Obtaining accurate channel state information (CSI) is vital for
achieving optimal phase control and high data rates; however, estimating
channels between the transmitter, RIS, and receiver poses challenges.
This paper investigates deep learning methodologies for channel
estimation, explicitly addressing the distinctive challenges associated
with phase shifts and beamforming. We present tailored deep-learning
algorithms for each estimation technique, showcasing notable
improvements in estimation accuracy, computational efficiency, and
adaptability in dynamic environments.