Approximate Message Passing for Channel Estimation in Reconfigurable Intelligent Surface Aided MIMO Multiuser Systems

Ruan, Chengyao; Zhang, Zaichen; Jiang, Hao; Dang, Jian; Wu, Liang; Zhang, Hongming

doi:10.1109/tcomm.2022.3182369

Cited by 36 publications

(13 citation statements)

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“…The sparse channel vector λ can be reconstructed from the measurements in (26) using standard sparse recovery algorithms such as orthogonal matching pursuit (OMP) [25] and subspace pursuit [27]. Alternating direction method of multipliers (ADMM) [28] and approximate message passing [29] have also been explored for sparse channel estimation in RIS. Noh et al [30] show that, for an RIS-aided single antenna system employing J pilots (J < N ) for sparse channel estimation, using the J equi-spaced columns of the N × N DFT matrix as training states produce lower mean squared error compared with canonical training states and the first J columns of the DFT matrix.…”

Section: Sparse Channel Estimationmentioning

confidence: 99%

Channel Training & Estimation for Reconfigurable Intelligent Surfaces: Exposition of Principles, Approaches, and Open Problems

2023

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Reconfigurable intelligent surfaces (RIS) are passive controllable arrays of small reflectors that direct electromagnetic energy towards or away from the target nodes, thereby allowing better management of signals and interference in a wireless network. The RIS has the potential for significantly improving the performance of wireless networks. Unfortunately, RIS also multiplies the number of Channel State Information (CSI) coefficients between the transmitter and receiver, which magnifies the challenges in estimating and communicating the channel state information. Furthermore, the simplicity and cost-effectiveness of the passive RIS also implies that the incoming links are not locally estimated at the RIS, and fresh pilots are not inserted into outgoing RIS links. This introduces new challenges for training and estimation of channel state information. The rapid growth of the literature on CSI acquisition in RIS-aided systems has been accompanied by variations in the underlying assumptions, models, and notation, which can obscure the similarities and differences of various techniques, and their relative merits. This paper presents a comprehensive exposition of principles and approaches in RIS channel estimation. The basic ideas underlying each class of techniques are reduced to their simplest form under a unified model and notation, and various approaches within each class are discussed. Several open problems in this area are identified and highlighted.INDEX TERMS Reconfigurable intelligent surfaces, channel estimation, overhead reduction, spectral efficiency, sparse recovery, machine learning techniques, channel modeling.

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Section: Sparse Channel Estimationmentioning

confidence: 99%

Channel Training & Estimation for Reconfigurable Intelligent Surfaces: Exposition of Principles, Approaches, and Open Problems

2023

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“…Therefore, channel estimation is frequently conducted by considering that perfect synchronization is established, although imperfect synchronization may degrade estimation performance, and vice versa. This is a reasonable assumption used widely in many practical applications such as RIS-aided MIMO multiuser systems [31] and MIMO systems applied to microwave backhaul [32]. Hence, we also assume that perfect synchronization is established in our concerned systems.…”

Section: Optimal Length Of Training Sequencesmentioning

confidence: 99%

Spatially correlated channel estimation for RIS‐assisted MIMO systems with correlated gaussian perturbation

Chu

Zhang

2023

IET Communications

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This paper studies the problem of spatially correlated channel estimation for reconfigurable intelligent surface (RIS)‐assisted multiple‐input multiple‐output (MIMO) systems with arbitrarily correlated Gaussian perturbation. In particular, composite RIS channel estimation is first explored based on minimum mean square error (MMSE) method, and optimal training sequence design of pilots with MSE minimization. Applying the Kronecker‐structured model, correlated composite RIS‐related channels and correlated Gaussian perturbation are then characterized. The optimal training sequence structure and spatial training power allocation conditions are derived, and the choice of the optimal training sequence length is also analyzed. Finally, numerical results are provided to assess the performance of the proposed estimate method under different training sequence structures, the optimal length of the training sequence, and information statistics. Extensive numerical results show that the proposed estimate method has supreme performance compared to state of‐the‐art baseline methods (e.g. maximum likelihood and two‐sided linear channel estimation methods).

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“…The need for faster data rates has increased dramatically and exponentially during the past few years 1 . While transferring more data, the network capacity is lowered, and the pilot overhead problem increases, resulting in high signal sparsity/scattering 2,3 . As a result, the millimeter Wave (mmWave) frequency bands are utilized for transmitting data at higher frequencies, offering bandwidths ranging from multiple GHz to multiple Gbps, enabling high‐speed data transmission 4 .…”

Section: Introductionmentioning

confidence: 99%

Channel estimation for reconfigurable intelligent surface‐assisted mmWave using multidimensional Runge Kutta orthogonal matching pursuit algorithm in MIMO systems

Panda

2024

Int J Communication

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SummaryIn the context of Reconfigurable Intelligent Surface (RIS) assisted millimeter wave communications within the Multiple Input Multiple Output (MIMO) system, Channel Estimation (CE) poses a significant challenge due to signal sparsity and blockages between transmitters and receivers. Consequently, communication faces difficulties without a direct line‐of‐sight path, leading to increased pilot overhead. To address these obstacles, this research introduces a channel estimation approach for RIS‐aided millimeter wave communication employing the Multidimensional Runge Kutta Orthogonal Matching Pursuit (MRKOMP) algorithm in MIMO systems. The proposed method aims to enhance network coverage and channel capacity for efficient data transmission. By utilizing the multiscale cumulative residual distribution entropy function, the method establishes an indirect path when the line‐of‐sight is obstructed. Furthermore, the multidimensional orthogonal matching pursuit methodology calculates sparsity levels and facilitates sparse recovery. Optimization of the weight parameter through the Runge Kutta optimizer effectively eliminates sparsity, resulting in improved spectral efficiency and RIS gain. Simulation results demonstrate a significant 99.9% enhancement in network capacity and spectral efficiency compared to existing methods.

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Approximate Message Passing for Channel Estimation in Reconfigurable Intelligent Surface Aided MIMO Multiuser Systems

Cited by 36 publications

References 50 publications

Channel Training & Estimation for Reconfigurable Intelligent Surfaces: Exposition of Principles, Approaches, and Open Problems

Channel Training & Estimation for Reconfigurable Intelligent Surfaces: Exposition of Principles, Approaches, and Open Problems

Spatially correlated channel estimation for RIS‐assisted MIMO systems with correlated gaussian perturbation

Channel estimation for reconfigurable intelligent surface‐assisted mmWave using multidimensional Runge Kutta orthogonal matching pursuit algorithm in MIMO systems

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