Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems

Abstract

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 $8J-2+(K-1)\lceil (8J-2)/L\rceil$, 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.

arXiv commentIntelligent reflecting surface (IRS), reconfigurable intelligent surface (RIS), Millimeter wave, massive MIMO, AoA/AoD estimation, channel estimation

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• STAR-RIS-Assisted Hybrid NOMA mmWave Communication: Optimization and Performance Analysis
• One-Bit Channel Estimation for IRS-aided Millimeter-Wave Massive MU-MISO System
• Structure-aware Sparse Bayesian Learning-based Channel Estimation for Intelligent Reflecting Surface-aided MIMO
• A Radio Map Approach for Reduced Pilot CSI Tracking in Massive MIMO Networks
• Joint Admission Control and Power Minimization in IRS-assisted Networks
• Artificial Intelligence Driven Channel Coding and Resource Optimization for Wireless Networks: A Systematic Survey
• SORIS: A Self-Organized Reconfigurable Intelligent Surface Architecture for Wireless Communications