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  5. Near-Field Modelling and Performance Analysis of Modular Extremely Large-Scale Array Communications

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Preprint
English
2022

Near-Field Modelling and Performance Analysis of Modular Extremely Large-Scale Array Communications

0 Datasets

0 Files

English
2022
arXiv (Cornell University)
DOI: 10.48550/arxiv.2201.09082

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Rui Zhang
Rui Zhang

The Chinese University of Hong Kong

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Xinrui Li
Haiquan Lu
Yong Zeng
+2 more

Abstract

This letter studies a new array architecture, termed as modular extremely large-scale array (XL-array), for which a large number of array elements are arranged in a modular manner. Each module consists of a moderate number of array elements and the modules are regularly arranged with the inter-module space typically much larger than signal wavelength to cater to the actual mounting structure. We study the mathematical modelling and conduct the performance analysis for modular XL-array communications, by considering the non-uniform spherical wave (NUSW) characteristic that is more suitable than the conventional uniform plane wave (UPW) assumption for physically large arrays. A closed-form expression is derived for the maximum signal-to-noise ratio (SNR) in terms of the geometries of the modular XL-array, including the total array size and module separation, as well as the user's location. The asymptotic SNR scaling law is revealed as the size of modular array goes to infinity. Furthermore, we show that the developed modelling and performance analysis include the existing results for collocated XL-array or far-field UPW assumption as special cases. Numerical results demonstrate the importance of near-field modelling for modular XL-array communications since it leads to significantly different results from the conventional far-field UPW modelling.

How to cite this publication

Xinrui Li, Haiquan Lu, Yong Zeng, Shi Jin, Rui Zhang (2022). Near-Field Modelling and Performance Analysis of Modular Extremely Large-Scale Array Communications. arXiv (Cornell University), DOI: 10.48550/arxiv.2201.09082.

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Publication Details

Type

Preprint

Year

2022

Authors

5

Datasets

0

Total Files

0

Language

English

Journal

arXiv (Cornell University)

DOI

10.48550/arxiv.2201.09082

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