Channel Estimation Method and Phase Shift Design for Reconfigurable Intelligent Surface Assisted MIMO Networks

Mirza, Jawad; Ali, Bakhtiar

doi:10.1109/tccn.2021.3072895

Cited by 82 publications

(49 citation statements)

References 39 publications

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“…The cascaded channel can be inferred through Bayesian posterior estimation, and the approximate message passing (AMP) algorithm is used to reduce the complexity of estimation approximation. Moreover, [153] considers the illconditioned low-rank channel where RIS is deployed near the UE, transforming the RIS CE problem into a dictionary learning problem. The reliable and robust bilinear adaptive vector AMP algorithm is used to estimate the BS-RIS-UE channel, and the sparsity of the RIS phase shift matrix is utilized in the training phase to eliminate the restored channel permutation ambiguity.…”

Section: Channel Estimation In Ris-empowered Systemsmentioning

confidence: 99%

Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

Jian¹,

Alexandropoulos²,

Başar³

et al. 2022

Preprint

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The demanding objectives for the future sixth generation (6G) of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity, as well as revolutionary communication and computing paradigms. Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces (RISs), which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities. Incorporating RISs in wireless networks has been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one, intended for various networking objectives, such as coverage extension and capacity boosting, spatiotemporal focusing with benefits in energy efficiency and secrecy, and low electromagnetic field exposure.Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments, in this paper, we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation. We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems, which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks. Finally, we discuss the relevance of the RIS technology in the latest wireless communication standards, and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

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Section: Channel Estimation In Ris-empowered Systemsmentioning

confidence: 99%

Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

Jian¹,

Alexandropoulos²,

Başar³

et al. 2022

Preprint

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“…The work [17] proposes an uplink channel estimation protocol for an IRS aided multi-user MIMO system applying compressing sensing (CS) methods. In [18], an IRS-aided MIMO system is considered, and channel arXiv:2008.04766v1 [eess.SP] 10 Aug 2020 estimation is carried out in a two-stage approach, and the IRS-assisted link is estimated by means of an approximate message-passing method. Considering an IRS-assisted internet of things scenario, [19] formulates a joint active detection and channel estimation based on sparse matrix factorization, matrix completion, and multiple measurement vector problems.…”

Section: Introductionmentioning

confidence: 99%

Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach

Araújo

Almeida

Boyer

2021

IEEE J. Sel. Top. Signal Process.

149

165

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Intelligent reflecting surface (IRS) is an emerging technology for future wireless communications including 5G and especially 6G. It consists of a large 2D array of (semi-)passive scattering elements that control the electromagnetic properties of radio-frequency waves so that the reflected signals add coherently at the intended receiver or destructively to reduce co-channel interference. The promised gains of IRS-assisted communications depend on the accuracy of the channel state information. In this paper, we address the receiver design for an IRS-assisted multiple-input multiple-output (MIMO) communication system via a tensor modeling approach aiming at the channel estimation problem using supervised (pilot-assisted) methods. Considering a structured time-domain pattern of pilots and IRS phase shifts, we present two channel estimation methods that rely on a parallel factor (PARAFAC) tensor modeling of the received signals. The first one has a closed-form solution by solving rank-1 matrix approximation problems, while the second one is based on an alternating estimation scheme. Uniqueness issues are discussed and design requirements that guide the choice of the system parameters as well as the structures of the pilot signals and IRS phase shifts are discussed. A performance analysis is also carried out by means of the Cramr-Rao lower bound. Numerical results show the effectiveness of the proposed receivers and highlight the involved tradeoffs, while corroborating their superior performance compared to competing solutions.

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“…We propose a two-stage CE to recover the channel parameters from the received signals in (10) and (11). To be specific, in the first stage, we extract the channel parameters in the H M,R from the received signals at the RIS (10).…”

Section: Hybrid Rismentioning

confidence: 99%

“…After that, the RIS sends the estimates of the channel parameters to the BS via the error-free backhaul link, and the BS reconstructs H M,R based on the estimates with the reconstructed one denoted as ĤM,R . In the second stage, we recover the channel parameters in H R,B from the received signals at the BS (11) by assuming H M,R = ĤM,R . More details will be provided in the sequel.…”

Section: Hybrid Rismentioning

confidence: 99%

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Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization

Schroeder¹,

He²,

Juntti³

2021

Preprint

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Reconfigurable intelligent surfaces (RISs) have been introduced as a remedy for mitigating frequent blockages in millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication networks. However, perfect or nearly perfect channel state information (CSI) is fundamental in order to achieve their full potential. Traditionally, an RIS is fully passive without any baseband processing capabilities, which poses great challenges for CSI acquisition. Thus, we focus on the hybrid RIS architecture, where a small portion of RIS elements are active and able to processing the received pilot signals for estimating the corresponding channel. The channel estimation (CE) is done by resorting to off-the-grid compressive sensing technique, i.e., atomic norm minimization, for exacting channel parameters through two stages. Simulation results show that the proposed scheme outperforms the passive RIS CE under the same training overhead.

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Channel Estimation Method and Phase Shift Design for Reconfigurable Intelligent Surface Assisted MIMO Networks

Cited by 82 publications

References 39 publications

Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach

Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization

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