Joint Channel Estimation and Beamforming for Reconfigurable Intelligent Surface Aided MIMO Systems: Sparsity-Based Approach

Abstract

Continuous efforts have been devoted to integrate millimeter wave (mmWave) and terahertz (THz) bands into future communication standards in order to overcome the bandwidth shortage problem and achieve high data rates, primarily through developing accompanying technologies that can overcome the severe propagation loss and blockage associated with increased carrier frequency. One of the most notable accompanying technologies is reconfigurable intelligent surface (RIS), which uses a large number of low-cost passive reflecting elements to reconfigure the propagation environments for improved communication performance and coverage. Despite its numerous benefits, RIS can make channel estimation more difficult due to its lack of radio frequency (RF) chains that can perform baseband signal processing. In addition, the cascaded channel structure of RIS-aided communication systems, which differs from that in conventional systems, brings about significant challenges in both channel estimation and beamforming. In this paper, we propose the joint channel estimation and beamforming optimization algorithm for RIS-aided multiple-input multipleoutput (MIMO) communication systems. By carefully exploiting the angular sparsity of mmWave/THz channels, our proposed algorithm successfully designs the RIS matrices that not only facilitate the channel estimation process but also achieve the passive beamforming gain through increased channel capacity. Simulation results demonstrate that our proposed algorithm provides the systems of interest with significant improvement in spectral efficiency.

Figures (4)

• Figure 1: Block diagram of an RIS-aided MIMO system. The RIS enables the communication between the TX and RX despite the blockage of the direct link (represented by the gray signal) between them.
• Figure 2: Bayesian network of the joint channel estimation and beamforming optimization problem in RIS-aided MIMO system. Gray (blue) circles represent the latent (observable) variables, while black (red) arrows indicate the conditional dependencies associated with channel estimation (beamforming optimization). Unlike previous works, our algorithm directly exploits the red dotted arrows by incorporating ${\mathbf{P}}$ and ${\mathbf{Y}}_p$ into the design of ${\mathbf{A}}^\star$.
• Figure 3: Capacity and spectral efficiency achieved in RIS-aided mmWave MIMO systems versus the transmit power constraint $P_\text{TX}$.
• Figure 4: Capacity and spectral efficiency achieved in RIS-aided THz MIMO systems versus the transmit power constraint $P_\text{TX}$.