Location-aware Beam Training and Multi-dimensional ANM-based Channel Estimation for RIS-aided mmWave Systems

Abstract

In this paper, we propose location-aware beam training and multi-dimensional atomic norm minimization (ANM)-based channel estimation for reconfigurable intelligent surface (RIS)-aided millimeter-wave systems. The use of both location information and RIS beamwidth adaptation allows a significant reduction of beam training overhead. However, considering a trade-off between accuracy and beam training overhead, this may induce inaccurate channel estimation. Nevertheless, superior channel estimation performance is achieved by multi-dimensional ANM techniques, which have been shown to be effective in capturing cascaded structures such as the channel in RIS-aided systems. In the proposed work, a cascade of BS-to-RIS channel and RIS-to-BS channel is represented as a linear combination of either steering vectors, 2D steering vectors, or 3D steering vectors, and ANM with appropriate dimension is applied to estimate the channel. From simulation results, it has been demonstrated that location-aware channel estimation via 2D ANM and 3D ANM achieves excellent estimation accuracy along with a reduced beam training overhead.