Newtonized Near-Field Channel Estimation for Ultra-Massive MIMO Systems

Ruoxiao Cao, Wentao Yu, Hengtao He, Xianghao Yu, Shenghui Song, Jun Zhang, Yi Gong, Khaled B. Letaief

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

5 Citations (Scopus)

Abstract

To meet the stringent requirements of future communication systems, ultra-massive multiple-input and multiple-output (UM-MIMO) technology has garnered significant attention as a key enabling technology for 6G. However, the deployment of UM-MIMO introduces new challenges, particularly the near-field effect. In this paper, by leveraging the unique characteristics of near-field channels, we propose a novel near-field channel estimation algorithm based on the Newton's method. We also design a near-field codebook that meets the requirements for convergence guarantee. Our algorithm overcomes the limitations of existing approaches by offering a low-complexity, tuning-free, and convergence-guaranteed solution. Simulation results show that our proposed algorithm outperforms state-of-the-art baselines in terms of estimation accuracy, establishing its effectiveness in near-field channel estimation for UM-MIMO systems. © 2024 IEEE.
Original languageEnglish
Title of host publication2024 IEEE Wireless Communications and Networking Conference (WCNC) - Proceedings
PublisherIEEE
ISBN (Print)979-8-3503-0358-2
DOIs
Publication statusPublished - 2024
Event25th IEEE Wireless Communications and Networking Conference (WCNC 2024) - Conrad Dubai Hotel, Dubai, United Arab Emirates
Duration: 21 Apr 202424 Apr 2024
https://wcnc2024.ieee-wcnc.org/

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
ISSN (Print)1525-3511

Conference

Conference25th IEEE Wireless Communications and Networking Conference (WCNC 2024)
PlaceUnited Arab Emirates
CityDubai
Period21/04/2424/04/24
Internet address

Funding

This work was supported in part by the Research Grants Council under the Areas of Excellence scheme grant AoE/E-601/22-R and the Shenzhen Science and Technology Innovation Committee under Grant SGDX20210823103201006. The work of Xianghao Yu was supported in part by the Hong Kong Research Grants Council under Grant No. 16212922 and in part by the National Natural Science Foundation of China for Young Scientists under Grant No. 62301468.

RGC Funding Information

  • RGC-funded

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