Skip to main content
CenXiv.org
This website is in trial operation, support us!
We gratefully acknowledge support from all contributors.
Contribute
Donate
cenxiv logo > cs > arXiv:2306.01458

Help | Advanced Search

Computer Science > Information Theory

arXiv:2306.01458 (cs)
[Submitted on 2 Jun 2023 (v1) , last revised 24 Aug 2023 (this version, v2)]

Title: Extremely Large-scale Array Systems: Near-Field Codebook Design and Performance Analysis

Title: 超大规模阵列系统:近场码本设计与性能分析

Authors:Feng Zheng, Hongkang Yu, Chenchen Wang, Luyang Sun, Qingqing Wu, Yijian Chen
Abstract: Extremely Large-scale Array (ELAA) promises to deliver ultra-high data rates with increased antenna elements. However, increasing antenna elements leads to a wider realm of near-field, which challenges the traditional design of codebooks. In this paper, we propose novel near-field codebook schemes based on the fitting formula of codewords' quantization performance. First, we analyze the quantization performance properties of uniform linear array (ULA) and uniform planar array (UPA) codewords. Our findings reveal an intriguing property: the correlation formula for ULA codewords can be represented by the elliptic formula, while the correlation formula for UPA codewords can be approximated using the ellipsoid formula. Building on this insight, we propose a ULA uniform codebook that maximizes the minimum correlation based on the derived formula. Moreover, we introduce a ULA dislocation codebook to further reduce quantization overhead. Continuing our exploration, we propose UPA uniform and dislocation codebook schemes. Our investigation demonstrates that oversampling in the angular domain offers distinct advantages, achieving heightened accuracy while minimizing overhead in quantifying near-field channels. Numerical results demonstrate the appealing advantages of the proposed codebook over existing methods in decreasing quantization overhead and increasing quantization accuracy.
Abstract: 超大规模阵列(ELAA)有望通过增加天线元素提供超高速数据速率。 然而,增加天线元素会导致近场区域更广,这挑战了传统码本的设计。 在本文中,我们提出基于码字量化性能拟合公式的新型近场码本方案。 首先,我们分析了均匀线性阵列(ULA)和均匀平面阵列(UPA)码字的量化性能特性。 我们的研究发现了一个有趣的特性:ULA码字的相关性公式可以用椭圆公式表示,而UPA码字的相关性公式可以用椭球公式近似。 基于这一见解,我们提出了一种基于推导公式的最大化最小相关性的ULA均匀码本。 此外,我们引入了一种ULA偏移码本以进一步减少量化开销。 继续我们的探索,我们提出了UPA的均匀和偏移码本方案。 我们的研究证明,在角度域进行过采样具有显著优势,在量化近场信道时实现了更高的精度并最小化了开销。 数值结果表明,所提出的码本在减少量化开销和提高量化精度方面相对于现有方法具有明显的优势。
Subjects: Information Theory (cs.IT) ; Signal Processing (eess.SP); Systems and Control (eess.SY)
Cite as: arXiv:2306.01458 [cs.IT]
  (or arXiv:2306.01458v2 [cs.IT] for this version)
  https://doi.org/10.48550/arXiv.2306.01458
arXiv-issued DOI via DataCite

Submission history

From: Feng Zheng [view email]
[v1] Fri, 2 Jun 2023 11:36:02 UTC (3,425 KB)
[v2] Thu, 24 Aug 2023 11:29:48 UTC (3,240 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled
  • View Chinese PDF
  • View PDF
  • TeX Source
license icon view license
Current browse context:
cs.IT
< prev   |   next >
new | recent | 2023-06
Change to browse by:
cs
cs.SY
eess
eess.SP
eess.SY
math
math.IT

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar
a export BibTeX citation Loading...

BibTeX formatted citation

×
Data provided by:

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)
  • Author
  • Venue
  • Institution
  • Topic

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
  • About
  • Help
  • contact arXivClick here to contact arXiv Contact
  • subscribe to arXiv mailingsClick here to subscribe Subscribe
  • Copyright
  • Privacy Policy
  • Web Accessibility Assistance
  • arXiv Operational Status
    Get status notifications via email or slack

京ICP备2025123034号