Optimal Grouping Strategy for Reconfigurable Intelligent Surface Assisted Wireless Communications

Kundu, Neel Kanth; Li, Zan; Rao, Junhui; Shen, Shanpu; McKay, Matthew R.; Murch, R.D.

doi:10.1109/lwc.2022.3156978

Cited by 21 publications

(16 citation statements)

References 21 publications

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“…Under some scenarios, the estimation of all channel gain parameters induced by the RIS may be prohibitive in terms of time, power, or both. A remedy has been proposed [16]- [23] that constrains groups of RIS elements to have the same reflection coefficient. Then, it is not difficult to see that the relevant estimation parameter is an aggregate channel gain corresponding to the total reflection produced by the RIS elements in each group (that have the identical reflection coefficient).…”

Section: Remark 3 (Grouping the Ris Elements)mentioning

confidence: 99%

“…To further reduce the estimation overhead, one may group the neighboring RIS elements and use the same reflection coefficient for all the elements in each group. This solution has been suggested for both flat and frequency selective channels [16]- [23]. If the N RIS elements are divided into N g groups (N g < N ) with each group containing N/N g elements, then the channel estimation requires estimating only N g aggregated RIS-aided channels corresponding to each group, instead of estimating all the N cascaded channels corresponding to each RIS element.…”

Section: Wideband Channel Estimationmentioning

confidence: 99%

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

Shamasundar¹,

Daryanavardan²,

Nosratinia³

2023

Preprint

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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.

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Section: Remark 3 (Grouping the Ris Elements)mentioning

confidence: 99%

Section: Wideband Channel Estimationmentioning

confidence: 99%

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

Shamasundar¹,

Daryanavardan²,

Nosratinia³

2023

Preprint

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“…The y i can be selected to provide whatever desired characteristics required [11], [24]. In this work we demonstrate the RIS performance by using the situation in which an incident plane wave is reflected towards a specified beam angle Ω beam = (θ, φ) beam .…”

Section: A Passive Beamforming Designmentioning

confidence: 99%

“…As a compromise, the number of reconfigurable states of the meta-atom can be reduced [22] to only 2 to 4. Furthermore, another solution that groups some meta-atoms together as a single element has also been proposed to reduce the complexity of the controlling circuits by tradeoffs in reconfigurability and performance [23], [24]. Despite solutions to reduce the complexity, the remaining complexity of this approach due to the high density of metaatoms still sets significant requirements on the controlling system in practical applications of RIS.…”

mentioning

confidence: 99%

A Novel Reconfigurable Intelligent Surface for Wide-Angle Passive Beamforming

Rao

Zhang

Tang

et al. 2022

IEEE Trans. Microwave Theory Techn.

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A design for a highly reconfigurable intelligent surface (RIS) that can provide three-dimensional passive reflective beamforming suitable for sub-6 GHz frequency bands is proposed. The RIS is based on a novel double-layer structure consisting of independently controllable and highly reconfigurable elements on top of a ground plane. The novelty of the structure is the use of reconfigurable RIS elements, whose size is of the order of a wavelength, that are optimized to function over wide incident and reflected angles. In addition, the individual elements each have four RF switches, in a 5 × 5 sub-element structure, providing 16 different reflective phases, from 0 • to 360 • relative to the incident waves. To quantify the performance of the RIS, the performance metric of phase entropy is defined and it is also utilized as an objective function for optimizing the design. An efficient analytical method for predicting the reflected waves from the RIS elements, as well as phase entropy, is also provided. For verification, a prototype of a 4 × 4-element RIS, with a total size of 0.32 m × 0.34 m, is fabricated and an experimental setup for measuring the scattered pattern is described. It is shown that waves incident from various angles can be passively beam steered to any angle within 0 − 360 • azimuth and ±70 • elevation angles. The high reconfigurability to manipulate incident waves makes the proposed RIS a promising surface for use in future high-capacity communication systems.

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“…To reduce the pilot overhead for channel estimation introduced by massive RIS reflecting elements, [16]- [21] have proposed novel CSI estimation frameworks in which they grouped those adjacent RIS elements into several sub-surfaces. Within every sub-surface, the RIS elements adopt the same configuration.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Super-Resolution Channel Estimation in Reconfigurable Intelligent Surface Aided Systems

Shen

Qin

Nallanathan

2023

IEEE Trans. Commun.

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Reconfigurable intelligent surface (RIS) enables the configuration of the propagation environment. Channel estimation is an essential task in realizing the RIS-aided communication system. A RIS-aided multi-user multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication system involves cascaded channels with high dimensions and sophisticated statistics. Thus, implementing the optimal minimum mean square error (MMSE) with the integration computation is infeasible in practice. To accurately estimate channels with high accuracy in a RIS-aided multi-user MIMO-OFDM system, we model the channel state information (CSI) estimation as an image super-resolution (SR) problem to recover and denoise the channel matrix. Particularly, a convolutional neural network based on a super-resolution convolutional neural network (SRCNN) and denoising convolutional neural network (DnCNN), named SRDnNet, is then proposed. By taking estimated channels at pilot positions as a low-resolution image, the enhanced SRCNN can fully exploit the features of inputs to learn a suitable interpolation method and generate the coarse estimation of the channel matrix. The denoising model DnCNN with an element-wise subtraction structure can exploit features of the additive noise and recover channel coefficients from the coarse channel matrix. The simulation results demonstrate the effectiveness and excellent performance of the proposed SRDnNet.

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Optimal Grouping Strategy for Reconfigurable Intelligent Surface Assisted Wireless Communications

Cited by 21 publications

References 21 publications

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

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

A Novel Reconfigurable Intelligent Surface for Wide-Angle Passive Beamforming

Deep Learning for Super-Resolution Channel Estimation in Reconfigurable Intelligent Surface Aided Systems

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