Full Text:   <94>

Summary:  <20>

CLC number: TN95

On-line Access: 2025-01-24

Received: 2024-06-02

Revision Accepted: 2024-09-30

Crosschecked: 2025-01-24

Cited: 0

Clicked: 148

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Lingxiang LI

https://orcid.org/0000-0002-8600-4461

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.12 P.1708-1722

http://doi.org/10.1631/FITEE.2400472


Near-field joint estimation of multi-targets’ position and velocity in a terahertz MIMO-OFDM system based on tensor decomposition


Author(s):  Ke LIU, Shengfu ZHAO, Weixin CHEN, Zhen WANG, Lingxiang LI, Zhi CHEN, Qiang XU

Affiliation(s):  National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China; more

Corresponding email(s):   lingxiang.li@uestc.edu.cn

Key Words:  Terahertz, Multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM), Near-field localization (NFL), Velocity estimation, Tensor decomposition


Ke LIU, Shengfu ZHAO, Weixin CHEN, Zhen WANG, Lingxiang LI, Zhi CHEN, Qiang XU. Near-field joint estimation of multi-targets’ position and velocity in a terahertz MIMO-OFDM system based on tensor decomposition[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(12): 1708-1722.

@article{title="Near-field joint estimation of multi-targets’ position and velocity in a terahertz MIMO-OFDM system based on tensor decomposition",
author="Ke LIU, Shengfu ZHAO, Weixin CHEN, Zhen WANG, Lingxiang LI, Zhi CHEN, Qiang XU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="25",
number="12",
pages="1708-1722",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400472"
}

%0 Journal Article
%T Near-field joint estimation of multi-targets’ position and velocity in a terahertz MIMO-OFDM system based on tensor decomposition
%A Ke LIU
%A Shengfu ZHAO
%A Weixin CHEN
%A Zhen WANG
%A Lingxiang LI
%A Zhi CHEN
%A Qiang XU
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 12
%P 1708-1722
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400472

TY - JOUR
T1 - Near-field joint estimation of multi-targets’ position and velocity in a terahertz MIMO-OFDM system based on tensor decomposition
A1 - Ke LIU
A1 - Shengfu ZHAO
A1 - Weixin CHEN
A1 - Zhen WANG
A1 - Lingxiang LI
A1 - Zhi CHEN
A1 - Qiang XU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 12
SP - 1708
EP - 1722
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400472


Abstract: 
This paper investigates the joint estimation of multi-targets’ position and velocity for a terahertz multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system operating in the near field based on tensor decomposition. The waveforms transmitted from shared antennas carry communication messages and are orthogonal to each other in the frequency domain. The estimation of the position and velocity of multiple targets in the considered near-field scenario is challenging because it involves spherical wavefronts. A signal model based on spherical wavefronts enables higher resolution on spatial position, which, if properly designed, can be used to improve the estimation accuracy. In this paper, we propose a CANDE-COMP/PARAFAC (CP) decomposition-based near-field localization (CP-NFL) algorithm for the joint estimation of the position and velocity of multiple targets. In our proposed method, the received signal is expressed as a third-order tensor; based on its factor matrices we convert the original non-convex optimization problem into a convex one and solve it with CVX tools. Our analysis reveals that the uniqueness in CP decomposition can be guaranteed and the computational complexity of our proposed method is linear to the sum of the third powers of the number of sub-carriers, OFDM symbols, antennas, and targets. Numerical results show that our proposed method has a clear advantage over the existing method in terms of estimation accuracy and computational complexity.

基于张量分解的太赫兹MIMO-OFDM系统中多目标位置和速度近场联合估计

刘轲1,赵胜福1,陈伟鑫1,王珍1,2,李玲香1,陈智1,徐强1
1电子科技大学通信抗干扰全国重点实验室,中国成都市,611731
2西南石油大学电气工程与信息学院,中国成都市,610500
摘要:本文基于张量分解研究了近场多输入多输出(MIMO)正交频分复用(OFDM)系统中多目标位置和速度的联合估计问题。考虑各天线发送携带有通信消息且在频域中彼此正交的OFDM波形,此时的近场多目标位置和速度估计问题涉及到球面波前信号模型,其求解是极具挑战的。然而,基于球面波前的信号模型具有更高的空间位置分辨率,如果设计得当,可以用于提高参数估计精度。本文提出了一种基于CANDECOMP/PARAFAC(CP)分解的近场定位(CP-NFL)算法,用于多目标位置和速度的联合估计。该方法将接收到的信号表示为一个三阶张量;根据其因子矩阵,在此基础上将原非凸优化问题转化为凸优化问题,并使用CVX工具求解。我们的分析表明,所提出的方法可以保证CP分解的唯一性,并且计算复杂度与子载波数、OFDM符号数、天线数和目标数的三次方之和呈线性关系。仿真结果表明,相比现有方法,该方法在估计精度和计算复杂度方面都具有明显优势。

关键词:太赫兹;多输入多输出-正交频分复用(MIMO-OFDM);近场定位(NFL);速度估计;张量分解

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Akyildiz IF, Kak A, Nie S, 2020. 6G and beyond: the future of wireless communications systems. IEEE Access, 8:133995-134030.

[2]Akyildiz IF, Han C, Hu ZF, et al., 2022. Terahertz band communication: an old problem revisited and research directions for the next decade. IEEE Trans Commun, 70(6):4250-4285.

[3]Alhafid AK, Younis S, Ali YEM, 2024. Enhanced far-field localization scheme using multi-RIS and efficient beam sweeping. Prog Electromagn Res C, 140:163-175.

[4]Chen H, Sarieddeen H, Ballal T, et al., 2022. A tutorial on terahertz-band localization for 6G communication systems. IEEE Commun Surv Tutor, 24(3):1780-1815.

[5]Chen HY, Ahmad F, Vorobyov S, et al., 2021. Tensor decompositions in wireless communications and MIMO radar. IEEE J Sel Top Signal Process, 15(3):438-453.

[6]Cohen D, Cohen D, Eldar YC, et al., 2018. SUMMeR: sub-Nyquist MIMO radar. IEEE Trans Signal Process, 66(16):4315-4330.

[7]Cui MY, Wu ZD, Lu Y, et al., 2023. Near-field MIMO communications for 6G: fundamentals, challenges, potentials, and future directions. IEEE Commun Mag, 61(1):40-46.

[8]Da Rosa Zanatta M, De Mendonça FLL, Antreich F, et al., 2019. Tensor-based time-delay estimation for second and third generation global positioning system. Dig Signal Process, 92:1-19.

[9]De Lathauwer L, 2006. A link between the canonical decomposition in multilinear algebra and simultaneous matrix diagonalization. SIAM J Matrix Anal Appl, 28(3):‍642-666.

[10]Gao JB, Chen XM, Li GY, 2024. Deep unfolding based channel estimation for wideband terahertz near-field massive mimo systems. Front of Inform Technol Electron Eng, 25(8):1162-1172.

[11]Gaudio L, Kobayashi M, Bissinger B, et al., 2019. Performance analysis of joint radar and communication using OFDM and OTFS. IEEE Int Conf on Communications Workshops, p.1-6.

[12]Knill C, Roos F, Schweizer B, et al., 2019. Random multiplexing for an MIMO-OFDM radar with compressed sensing-based reconstruction. IEEE Microw Wirel Compon Lett, 29(4):300-302.

[13]Li X, Chang B, Chen Z, 2023. Tensor decomposition based THz channel estimation in OTFS for integrated sensing and communications. IEEE Global Communications Conf, p.3996-4001.

[14]Liu YJ, Liao GS, Yang ZW, et al., 2017. Design of integrated radar and communication system based on MIMO-OFDM waveform. J Syst Eng Electron, 28(4):669-680.

[15]Pan YJ, De Bast S, Pollin S, 2021. Indoor direct positioning with imperfect massive MIMO array using measured near-field channels. IEEE Trans Instrum Meas, 70:1-11.

[16]Podkurkov I, Hamidullina L, Traikov E, et al., 2018. Tensor-based near-field localization in bistatic MIMO radar systems. 22nd Int ITG Workshop on Smart Antennas, p.1-8.

[17]Podkurkov I, Seidl G, Khamidullina L, et al., 2021. Tensor-based near-field localization using massive antenna arrays. IEEE Trans Signal Process, 69:5830-5845.

[18]Rinchi O, Elzanaty A, Alouini MS, 2022. Compressive near-field localization for multipath RIS-aided environments. IEEE Commun Lett, 26(6):1268-1272.

[19]Sakhnini A, De Bast S, Guenach M, et al., 2022. Near-field coherent radar sensing using a massive MIMO communication testbed. IEEE Trans Wirel Commun, 21(8):‍6256-6270.

[20]Sanson JB, Castanheira D, Gameiro A, et al., 2019. High-resolution DOA estimation of closely-spaced and correlated targets for MIMO OFDM radar-communication system. IEEE Int Symp on Phased Array System & Technology, p.1-5.

[21]Sarieddeen H, Alouini MS, Al-Naffouri TY, 2021. An overview of signal processing techniques for terahertz communications. Proc IEEE, 109(10):1628-1665.

[22]Singh PR, Wang Y, Chargé P, 2016. Bistatic MIMO radar for near field source localisation using PARAFAC. Electron Lett, 52(12):1060-1061.

[23]Singh PR, Wang YD, Chargé P, 2017. Near field targets localization using bistatic MIMO system with spherical wavefront based model. 25th European Signal Processing Conf, p.2408-2412.

[24]Temiz M, Alsusa E, Baidas MW, 2021. Optimized precoders for massive MIMO OFDM dual radar-communication systems. IEEE Trans Commun, 69(7):4781-4794.

[25]Tian TW, Zhang TX, Kong LJ, et al., 2021. Transmit/receive beamforming for MIMO-OFDM based dual-function radar and communication. IEEE Trans Veh Technol, 70(5):‍4693-4708.

[26]Tsujimura K, Mori H, 2022. Near field DoA estimation utilizing a large aperture MIMO array radar with Tx beamforming. 18th European Radar Conf, p.137-140.

[27]Wan T, Liu Q, Du XF, et al., 2023. Performance analysis of joint range and velocity estimator for e-band ISAC. IEEE Wireless Communications and Networking Conf, p.1-5.

[28]Xiao M, Mumtaz S, Huang YM, et al., 2017. Millimeter wave communications for future mobile networks. IEEE J Sel Areas Commun, 35(9):1909-1935.

[29]Zhang HY, Shlezinger N, Guidi F, et al., 2022. Near-field wireless power transfer for 6G Internet of Everything mobile networks: opportunities and challenges. IEEE Commun Mag, 60(3):12-18.

[30]Zhang HY, Shlezinger N, Guidi F, et al., 2023. 6G wireless communications: from far-field beam steering to near-field beam focusing. IEEE Commun Mag, 61(4):72-77.

[31]Zhang JA, Liu F, Masouros C, et al., 2021. An overview of signal processing techniques for joint communication and radar sensing. IEEE J Sel Top Signal Process, 15(6):‍‍1295-1315.

[32]Zhang RY, Cheng L, Wang S, et al., 2023. Target sensing in wideband massive MIMO-ISAC systems in the presence of beam squint. IEEE Int Conf on Communications Workshops, p.931-936.

[33]Zhang XF, Chen WY, Zheng W, et al., 2018. Localization of near-field sources: a reduced-dimension music algorithm. IEEE Commun Lett, 22(7):1422-1425.

[34]Zhao BB, Hu KK, Wen FX, et al., 2023. TDLoc: passive localization for MIMO-OFDM system via tensor decomposition. IEEE Int Things J, 10(23):20819-20833.

[35]Zuo WL, Xin JM, Liu WY, et al., 2019. Localization of near-field sources based on linear prediction and oblique projection operator. IEEE Trans Signal Process, 67(2):‍415-430.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2025 Journal of Zhejiang University-SCIENCE