Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems

Abstract

Reconfigurable intelligent surface (RIS) is a promising technique that can reconfigure the electromagnetic propagation environment by adjusting the phase shifts of the reflecting elements on the RIS. However, channel estimation in the RIS-aided massive multiuser multiple-input single-output (MU-MISO) wireless communication systems is challenging due to the passive feature of RIS and the large number of reflecting elements that incur high channel estimation overhead. To address this issue, we propose a novel cascaded channel estimation strategy with low pilot overhead by exploiting the sparsity and the correlation of multiuser cascaded channels in millimeter-wave massive MISO systems. Based on the fact that the phsical positions of the BS, the RIS and users may not change in several or even tens of consecutive channel coherence blocks, we first estimate the full channel state information (CSI) including all the angle and gain information in the first coherence block, and then only re-estimate the channel gains in the remaining coherence blocks with much less pilot overhead. In the first coherence block, we propose a two-phase channel estimation method, in which the cascaded channel of one typical user is estimated in Phase I based on the linear correlation among cascaded paths, while the cascaded channels of other users are estimated in Phase II by utilizing the partial CSI of the common base station (BS)-RIS channel obtained in Phase I. The total theoretical minimum pilot overhead in the first coherence block is 8J − 2 + (K − 1) ⌈(8J − 2)/L⌉, where K, L and J denote the numbers of users, paths in the BS-RIS channel and paths in the RIS-user channel, respectively. In each of the remaining coherence blocks, the minimum pilot overhead is JK. Moreover, the training phase shift matrices at the RIS are optimized to improve the estimation performance. Simulation results show that the performance

Channel Estimation for IRS-Assisted Millimeter-Wave MIMO Systems: Sparsity-Inspired Approaches

“…Hence, H e can be directly composed according to (41). On the other hand, with the CS-EST algorithm, the cascaded channel, H c , is estimated via (39), and therefore, H e can be formed according to (41). In the following, we show how to design the beamformer and the IRS reflection matrix for downlink data transmission based on H e , or, in other words, the CSI estimated by the two proposed algorithms.…”

“…. , K [39], [40], where τ BI and τ IU are given by [23] τ BI = 10 −6.14−2 log 10 (d BI ) and τ IU = 10 −6.14−2 log 10 (d IU ) . , while the downlink transmission signal-to-noise-ratio (SNR) is defined as…”