In this paper, we investigate the channel estimation challenge in reconfigurable intelligent surface (RIS)-aided near-field communication systems. Current channel estimation techniques require substantial pilot overhead and computational complexity, especially when the number of RIS elements is extremely large. To address this issue, we introduce a two-timescale channel estimation strategy that leverages the asymmetric coherence times of both the RIS-base station (BS) channel and the User-RIS channel. We derive a time-scaling property indicating that, for any two effective channels within the longer coherence time, one effective channel can be represented as the product of a vector, termed the small-timescale effective channel, and the other effective channel. By integrating the estimated effective channel from the initial time block with observations from our piecewise beam training, we present an efficient method for estimating subsequent small-timescale effective channels. We theoretically verify the efficacy of the proposed RIS design and demonstrate, through simulations, that our channel estimation method outperforms existing methods in pilot overhead and computational complexity across various realistic channel models.

We study the channel estimation problem for a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user multiple-input multiple-output (MU-MIMO) system. In particular, it is assumed that the channel between the RIS and a base station (BS) exhibits a near-field line-of-sight (LoS) channel as a dominant signal path in mm Wave communication system. Due to the high-rankness and non-sparsity of the RIS-BS channel, the existing methods, constructed based on far-field or near-field non-LoS channel, cannot be applied to this system. We for the first time develop an efficient channel estimation method with the idea of a piece-wise low-rank approximation. Via simulations, we demonstrate the effectiveness of our channel estimation method.

In this paper, we investigate the challenges of downlink communication in heavy payload Internet of Things (IoT) networks supported by frequency division duplexing (FDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The substantial overhead required for obtaining channel state information at the transmitter (CSIT) is crucial for achieving high spectral efficiency through conventional massive MIMO techniques; however, it hinders the deployment of ultra-reliable low-latency communications (URLLC) and incurs significant energy expenditure, particularly in dense IoT networks. To address this challenge, we propose an innovative CSIT-Free MIMO precoding method, termed circulant information classification via linear estimation (CIRCLE). Our primary contribution lies in the design of a CSIT-independent (or deterministic) precoding scheme, which is constructed by leveraging the circulant permutation of the discrete Fourier transform (DFT) matrix. This design facilitates interference-free signal combining at the IoT devices. Through theoretical analysis and simulations, we validate the effectiveness of the proposed CIRCLE method.

Reconfigurable intelligent surface (RIS) is an emerging technique for robust millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, we study the channel estimation problem for extremely large-scale RIS (XL-RIS) assisted multi-user XL-MIMO systems with hybrid beamforming structures. In this system, we propose an unified channel estimation method that yields a notable estimation accuracy in the near-field BS-RIS and near-field RIS-User channels (in short, near-near field channels), far-near field channels, and far-far field channels. Our key idea is that the effective channels to be estimated can be each factorized as the product of low-rank matrices (i.e., the product of a common matrix and a user-specific coefficient matrix). The common matrix whose columns are the basis of the column space of the BS-RIS channel is efficiently estimated via a collaborative low-rank approximation (CLRA). Leveraging the hybrid beamforming structures, we develop an efficient iterative algorithm that jointly optimizes the user-specific coefficient matrices. Via experiments and complexity analysis, we verify the effectiveness of the proposed channel estimation method (named CLRA-JO) for the three categories of wireless channels.

We investigate the channel estimation problem in a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user multiple-input multiple-output (MU-MIMO) system. It is posited that the channel between the RIS and the base station (BS) comprises a mixed line-of-sight (LoS) and non-line-of-sight (NLoS) near-field channel. The LoS path component is modeled using the geometric free-space propagation assumption, whereas the NLoS path components are characterized by the near-field array response vectors. Existing channel estimation methods exhibit limited performance due to the lack of sparsity or low-rankness in this mixed channel. For the first time, we propose an efficient near-field LoS/NLoS channel estimation method for RIS-assisted MU-MIMO systems through a piece-wise low-rank approximation. Specifically, the effective channel to be estimated is divided into piece-wise effective channels, each exhibiting a low-rank structure. These channels are then estimated via collaborative low-rank approximation. The proposed method is referred to as PW-CLRA. Simulation results substantiate the effectiveness of PW-CLRA.

We study the joint optimization of a reflection vector and hybrid beamforming matrices for intelligent reflecting surface (IRS) assisted millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems with hybrid beamforming structures. Recently, an efficient and practical method to estimate the so-called effective (or cascaded) channel has been proposed. For the first time, we derive the near-optimal solution of the joint optimization only using the estimated effective channel, in which the modulus constraints of the reflection vector and analog beamforming matrices are satisfied asymptotically. By simply projecting our asymptotic one, we also derive the practical solution. Via simulations, it is demonstrated that our method can outperform the state-of-the-art (SOTA) method. Furthermore, the proposed method can ensure the robustness for inevitable channel estimation errors.