Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User MIMO Systems

Abstract

Channel acquisition is one of the main challenges for the deployment of reconfigurable intelligent surface (RIS) aided communication system. This is because RIS has a large number of reflective elements, which are passive devices without active transmitting/receiving and signal processing abilities. In this paper, we study the uplink channel estimation for the RIS aided multi-user multi-input multi-output (MIMO) system. Specifically, we propose a novel channel estimation protocol for the above system to estimate the cascade channel, which consists of the channels from the base station (BS) to the RIS and from the RIS to the user. Further, we recognize the cascaded channels are typically sparse, this allows us to formulate the channel estimation problem into a sparse channel matrix recovery problem using the compressive sensing (CS) technique, with which we can achieve robust channel estimation with limited training overhead. In particular, the sparse channel matrixes of the cascaded channels of all users have a common row-columnblock sparsity structure due to the common channel between BS and RIS. By considering such a common sparsity, we further propose a two-step procedure based multi-user joint channel estimator. In the first step, by considering common column-block sparsity, we project the signal into the common column subspace for reducing complexity, quantization error, and noise level. In the second step, by considering common row-block sparsity, we apply all the projected signals to formulate a multi-user joint sparse matrix recovery problem, and we propose an iterative approach to solve this non-convex problem efficiently. Moreover, the optimization of the training reflection sequences at the RIS is studied to improve the estimation performance.

A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications

“…• Straightforward user-by-user (successive) cascaded channel estimation [82]- [84] • Cascaded channel estimation by exploiting common IRS-BS channel [85] and additional channel sparsity [107], [108] • Cascaded channel estimation based on dual-link (BS-IRS-BS) reflection [71] and anchor nodes [72] to resolve common IRS-BS channel (offline) and IRS-user channel (online) sequentially • Cascaded channel estimation based on linear MMSE criterion in the downlink [54] • Cascaded channel estimation based on matrix factorization/decomposition [73], [87] and additional channel sparsity [88]- [90] • Cascaded channel estimation based on deep learning techniques using convolutional neural network [91], [92] • Separate channel estimation based on sparse Bayesian learning [45] Single-user, broadband SISO • Cascaded channel estimation using ON/OFF training reflection pattern and element-grouping strategy at IRS [32] • Cascaded channel estimation using DFT-based training reflection pattern and element-grouping strategy at IRS [33] • Fast cascaded channel estimation using (sampling-wise) full-ON training reflection pattern (using circulant matrix) at IRS [33] • Separate channel estimation based on deep learning/compressed sensing for mmWave channels [48], [49] MISO…”

Positioning with Dual Reconfigurable Intelligent Surfaces in Millimeter-Wave MIMO Systems

“…From the OFDM assumption, the kth subcarrier of the qth BS-RIS channel is [37], [38] G k,q =h R1,q e −i2π kW K τr 1 ,q a R (f R1,q , v R1,q )a H B (g Br,q , v Br,q ),…”

“…However, this approach may result in errors when the SNR is low, as our simulation in Section VII shows. Therefore, we use (38) instead to assign the delays for the direct and reflection paths. Specifically, after estimating {τ i } Q+1 i=1 , denoting ÂD = [a C (τ 1 ), .…”

“…Then, we use the path ordering s t from the sorting of (38) and { [ QD ] :,i delays to path indices. Let the matched estimated delays be {τ d , τr2,1 , .…”

Multiple Residual Dense Networks for Reconfigurable Intelligent Surfaces Cascaded Channel Estimation

“…Furthermore, as the number of antennas of the UE and BS are equipped with more antennas, the channel estimation complexity increases sharply. Using the angular-domain channel sparsity, a CSbased channel estimation scheme is proposed in [7]. However, the difference in structure sparsity between different channels will cause performance loss.…”