Near-Field Beamforming for Large Intelligent Surfaces

Hu, Sha; Ilter, Mehmet Cagri; Wang, Hao

doi:10.1109/pimrc54779.2022.9977582

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…In the far-field region, the changes in amplitude, angle, and phase can be ignored, and the path loss effect is dominant in determining the received signal strength. In the near-field, there are obvious changes in amplitude, angle, and phase according to the distance from the user equipment (UE) to the antenna surface [7] [8]. In the far-field region, the plane wave model is used, and the signals on the antenna array are parallel, each antenna has the same arrival angle, and the phase difference between different array elements is only related to the arrival angle.…”

Section: Basic Concepts Of Electromagnetic Wave Near-fieldmentioning

confidence: 99%

Reconfigurable Intelligent Surface Constructing 6G Near-field Networks

Zhao

2024

Preprint

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Near-field propagation, especially when facilitated by reconfigurable intelligent surface (RIS), has become a significant field of inquiry in recent times. Despite this, there remains a noticeable absence of an exhaustive literature review concerning RIS-empowered near-field technologies. This paper seeks to bridge this knowledge gap by presenting a concise overview of near-field principles, coupled with a systematic examination of the latest advancements in RIS-driven near-field technologies. It specifically concentrates on three pivotal areas: the establishment of pervasive near-field wireless propagation environments through RIS, the introduction of novel near-field frameworks for 6G networks via RIS, and the array of challenges inherent in RIS-based near-field technologies. The objective of this technical review is to bolster the progression and innovative exploration in the realm of RIS-oriented near-field technologies.

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Section: Basic Concepts Of Electromagnetic Wave Near-fieldmentioning

confidence: 99%

Reconfigurable Intelligent Surface Constructing 6G Near-field Networks

Zhao

2024

Preprint

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“…However, significant quantization errors exist in quantizing the channel in real space. Besides, [22] derived a one-dimensional (1D) near-field beamforming codebook design, which locates the codeword quantization point based on the Lloyd-Max algorithm. The scheme in [22] improved the near-field beamforming gain, but this scheme still needed to give a theoretical analysis of the codebook quantization performance.…”

Section: A Related Workmentioning

confidence: 99%

Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A Model-Based Deep Learning Approach

Zhang

Wang²,

Zhang

et al. 2023

IEEE Commun. Lett.

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Extremely large-scale Array (ELAA) promises to deliver ultra-high data rates with more antenna elements. Meanwhile, the increase of antenna elements leads to a wider realm of near-field, which challenges the traditional design of codebooks. In this paper, we propose novel codebook design schemes which provide better quantized correlation with limited overhead. First, we analyze the correlation between codewords and channel vectors uniform linear array (ULA) and uniform planar array (UPA). The correlation formula for the ULA channel can be expressed as an elliptic function, and the correlation formula for the UPA channel can be represented as an ellipsoid formula. Based on the analysis, we design a uniform sampling codebook to maximize the minimum quantized correlation and a dislocation ULA codebook to reduce the number of quantized bits further. Besides, we give a better sampling interval for the codebook of the UPA channel. Numerical results demonstrate the appealing advantages of the proposed codebook over existing methods in quantization bit number and quantization accuracy.

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“…The LIS-array 1 is assumed to contain M × N antenna-elements and forms a 2D uniform 1 Other shapes such as a disk or spherical LIS [4] can follow similar analysis, and so are cases when the antenna-spacing is different from λ/2 as what is assumed in this paper for simplicity. planar-array (UPA).…”

Section: A Model Assumptionsmentioning

confidence: 99%

Design of Near-Field Beamforming for Large Intelligent Surfaces

Hu¹,

Wang²,

ilter³

2023

Preprint

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<p>In this paper, we propose a novel three-dimensional (3D) near-field beamforming (BF) design for Large Intelligent Surface (LIS). We firstly investigate the definitions of near-field and far-field of LIS, and derive the Fresnel near-field region where amplitudes variations are negligible and only phase variations worsen the harvested array-gains. We show that the Fresnel region which covers the majority part of near-field, can be enlarged by a factor of four when considering possible imperfectness from a conventional two-dimensional (2D) far-field BF. Therefore, it is of interest to design analog 3D-BF than can recover array-gain losses in this region. Secondly, with the proved decomposition theorem we show that the optimal 3D-BF can be decomposed into a 2D far-field BF and a one-dimensional (1D) near-field BF. The 2D far-field BF compensates phase variations from mismatches in the azimuth and elevation angles, while the 1D near-field BF compensates remaining phases variations caused by distance differences from the UE to different antenna-elements on LIS. Such a proposed “2D+1D” BF design reduces codebook size significantly and is fully compatible with the existing far-field BF in the fifth-generation new-radio (5G-NR) system. Thirdly, we analyze an optimal codebook design for the 1D near-field BF, and show that with a small codebook it can perform close to optimal. Numerical results further verify that our proposal is effective and robust to recover array-gains in the near-field of LIS. </p>

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Near-Field Beamforming for Large Intelligent Surfaces

Cited by 8 publications

References 17 publications

Reconfigurable Intelligent Surface Constructing 6G Near-field Networks

Reconfigurable Intelligent Surface Constructing 6G Near-field Networks

Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A Model-Based Deep Learning Approach

Design of Near-Field Beamforming for Large Intelligent Surfaces

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