A Deep Learning-Based Low-Overhead Beam Tracking Scheme for Reconfigurable Intelligent Surface-Aided Multiple-Input and Single-Output Systems with Estimated Channels

The next generation network requires not only an ultra-high data rate, global coverage, and connectivity, but also a reduction in network deployment costs and energy consumption. The emergence of reconfigurable intelligent surface (RIS) technology provides an effective way to improve efficiency and reduce cost, while the passive elements bring new challenges of channel estimation (CE) and beam tracking. For an RIS-aided multiple-input and single-output (MISO) system, in this paper, to obtain the channel state information (CSI), we propose a principle component analysis (PCA)-based staged channel estimation method. Based on the estimated channel, we propose a deep learning (DL)-based beam tracking scheme to realize low-complexity RIS reflection coefficient design, which effectively improves the signal-to-noise ratio (SNR) on the user side. The simulation results verified our proposed channel estimation scheme based on PCA, and the beam tracking scheme based on a deep neural network (DNN) for semi-active RIS-aided MISO systems can obtain approximate performances to traditional hand-crafted convex optimization-based algorithms like semi-definite relaxation (SDR) with much lower computational complexity.