The extremely large reconfigurable intelligent surface (XL-RIS) is an architecture that shows potential in expanding the transmission region to meet high requirements of sixth-generation communication systems. In XL-RIS-assisted scenarios, users transmit signal through spherical-wavefront near-field channel, where the channel estimation presents great challenges. Additionally, XL-RIS elements are prone to failure due to accidental damages or blockages, which further complicates the channel state. To address these issues, we propose a three-stage signal-assisted sparse representation-based channel estimation (SA-SRCE) scheme to robustly recover near-field channel and diagnose RIS failure state with low pilot overhead. The first stage utilizes a double domain filter (DDF) to eliminate noise based on sparse representation in the angle and polar domains. Then, based on the filtered signal, a failure-aware orthogonal matching pursuit (FA-OMP) algorithm is proposed to iteratively reconstruct the channel, as well as eliminate the perturbation invoked by RIS element failures. Moreover, considering the limited availability of pilot, we further exploit the information of compressed signal and propose the signal-assisted failure-aware double-sparsity OMP (FADS-OMP) algorithm as the third stage to improve the estimation performance in FA-OMP. The effectiveness and robustness of our proposed scheme is validated through theoretical analysis and extensive simulations.

The extremely large reconfigurable intelligent surface (XL-RIS) is an architecture that shows potential in expanding the transmission region and meeting the high transmission requirements. With the deployment of XL-RIS, users transmit signals through the spherical-wavefront near-field channel, which presents challenges in channel estimation with low pilot overhead. Additionally, XL-RIS is a non-stationary system whose elements are prone to failure due to accidental damages or blockages, thereby further complicating the channel state. To address these issues, this paper proposes a two-stage sparse representation-based channel estimation (SRCE) scheme to jointly recover the near-field channel state and diagnose RIS element failures with low pilot overhead. The first stage utilizes a proposed double-domain filter (DDF) method to eliminate part of the received noise based on the sparse representation in the angle and polar domain. Then, a robust failure-aware double sparsity orthogonal matching pursuit (FA-OMP) algorithm is proposed by iteratively reconstructing the channel state, as well as eliminating the perturbation invoked by RIS element failures. The efficiency and robustness of our proposed scheme are validated through simulations.

Compressive Sensing (CS) technology, which handles large amounts of data in its low-dimensional form, has been shown to enjoy excellent transmission and storage efficiency. To further improve the stability and robustness of CS-based wireless communication system, Reconfigurable Intelligent Surface (RIS) technology has been introduced to enhance the transmission link connectivity. Current researches generally assume perfect RIS; however, some of its elements may be damaged and fail to work, which degrades the sparse signal recovery. Therefore, this paper establishes a double-sparsity optimization model to jointly recover the sparse signal and diagnose the RIS element failure. For the scenarios with and without Channel State Information (CSI) at the receiver, Double-Sparsity based algorithm (DS), and Atomic norm and Double-Sparsity based algorithm (ADS) exploiting Alternating Direction Method of Multipliers (ADMM) framework are proposed respectively. Additionally, a novel ℓB,B norm is incorporated to further constrain the block and binary characteristics of RIS failures, and the resulting improved versions of DS and ADS are termed as Binary and Block-Sparsity based algorithm (BBS) and Atomic norm, Binary and Block-Sparsity based algorithm (ABBS). Simulations verify the effectiveness and robustness of the proposed algorithms, and show the improved performance of the BBS and ABBS algorithms compared with the DS and ADS algorithms.

Compressive sensing (CS) technology, which handles large amounts of data in its low-dimensional form, enjoys excellent transmission and storage efficiency. To further improve the stability and robustness of CS-based wireless communication system, reconfigurable intelligent surface (RIS) technology has been introduced to enhance the transmission link connectivity. Current researches generally assume perfect RIS; however, some of its elements may fail to work, which degrades the sparse signal recovery. Therefore, this paper establishes a double-sparsity optimization model to jointly recover the sparse signal and diagnose the RIS element state. A double-sparsity based algorithm (DS) exploiting Alternating Direction Method of Multiplier framework is proposed as the solution. Additionally, a novel$\ell_{B,B}$norm is incorporated to further constrain the block and binary characteristics of RIS failures, and the resulting improved version of DS is termed as the binary and block-sparsity based algorithm (BBS). Simulations verify the effectiveness and robustness of the proposed algorithms, and show the improved performance of the BBS algorithm compared with the DS algorithm.

In practical compressive sensing (CS) communication system, Analog to Digital Converter (ADC) is a necessary component to convert analog signals into digital ones. However, nonlinear distortion in ADC is unavoidable and definitely affects the reception accuracy. Though recent works have studied various methods to combat the negative effect of ADC nonlinear distortion, few of them discuss the solution in communication system with CS. This paper studies the CS recovery method when compressive measurements suffer from ADC nonlinear distortion. A double-sparsity model is first formulated, where the original signal and the ADC nonlinear distortion are both sparse. Then, for the type of clipping ADC, we propose a corresponding algorithm based on the Alternating Direction Method of Multipliers (ADMM) strategy to solve the double-sparsity (DS) problem, named as DS-ADMM. For the type of self-reset (SR) ADC, we explore its essence of rounding operation to design an integer constraint based feedback updating (ICFU) strategy, and accordingly propose the DS-ADMM-ICFU recovery algorithm. Experiment results show that the DS-ADMM algorithm for the double-sparsity problem improves the recovery performance compared with the existing counterpart, and DS-ADMM-ICFU for SR ADC exhibits preferable advantage in typical communication systems.