Full Text:   <4799>

Summary:  <1928>

CLC number: TN928

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2021-03-03

Cited: 0

Clicked: 5729

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jie Yang

https://orcid.org/0000-0002-7452-8102

Shi Jin

https://orcid.org/0000-0003-0271-6021

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.4 P.457-470

http://doi.org/10.1631/FITEE.2000505


Integrated communication and localization in millimeter-wave systems


Author(s):  Jie Yang, Jing Xu, Xiao Li, Shi Jin, Bo Gao

Affiliation(s):  National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China; more

Corresponding email(s):   yangjie@seu.edu.cn, shadowaccountxj@foxmail.com, li_xiao@seu.edu.cn, jinshi@seu.edu.cn, gao.bo1@zte.com.cn

Key Words:  Millimeter-wave, Integrated communication and localization, Location-assisted communication, Extremely large antenna array, Reconfigurable intelligent surface, Artificial intelligence, Neural networks


Jie Yang, Jing Xu, Xiao Li, Shi Jin, Bo Gao. Integrated communication and localization in millimeter-wave systems[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(4): 457-470.

@article{title="Integrated communication and localization in millimeter-wave systems",
author="Jie Yang, Jing Xu, Xiao Li, Shi Jin, Bo Gao",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="4",
pages="457-470",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000505"
}

%0 Journal Article
%T Integrated communication and localization in millimeter-wave systems
%A Jie Yang
%A Jing Xu
%A Xiao Li
%A Shi Jin
%A Bo Gao
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 4
%P 457-470
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000505

TY - JOUR
T1 - Integrated communication and localization in millimeter-wave systems
A1 - Jie Yang
A1 - Jing Xu
A1 - Xiao Li
A1 - Shi Jin
A1 - Bo Gao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 4
SP - 457
EP - 470
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000505


Abstract: 
As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation (6G) mobile communication systems have been conducted. Future mobile communication systems are evidently evolving toward a more intelligent and software-reconfigurable functionality paradigm that can provide ubiquitous communication, as well as sense, control, and optimize wireless environments. Thus, integrating communication and localization using the highly directional transmission characteristics of millimeter waves (mmWaves) is a promising route. This approach not only expands the localization capabilities of a communication system but also provides new concepts and opportunities to enhance communication. In this paper, we explain the integrated communication and localization in mmWave systems, in which these processes share the same set of hardware architecture and algorithms. We also provide an overview of the key enabling technologies and the basic knowledge on localization. Then, we provide two promising directions for studies on localization with an extremely large antenna array and model-based (or model-driven) neural networks. We also discuss a comprehensive guidance for location-assisted mmWave communications in terms of channel estimation, channel state information feedback, beam tracking, synchronization, interference control, resource allocation, and user selection. Finally, we outline the future trends on the mutual assistance and enhancement of communication and localization in integrated systems.

毫米波系统中的通信定位一体化技术

杨杰1,徐靖1,李潇1,金石1,高波2,3
1东南大学移动通信国家重点实验室,中国南京市,210096
2中兴通讯股份有限公司,中国深圳市,518001
3移动网络和移动多媒体技术国家重点实验室,中国深圳市,518001
概要:随着第五代(5G)移动通信系统的商业化,关于5G和6G移动通信系统的演进也已经展开了广泛的研究。未来的移动通信系统显然正朝着更加智能化和软件可重新配置的方向发展,它可以提供万物互联能力,并且能感知、控制和优化无线环境。因此,通过利用毫米波的高定向传输特性来整合通信与定位是一个很有前景的方法。这种方法不仅扩展了通信系统的定位能力,而且提供了增强通信的新概念和新机会。本文解释了毫米波系统中的通信定位一体化技术,该技术共享同一套硬件架构和算法体系;阐述了基于超大规模天线阵列和模型驱动神经网络的通信定位一体化技术;并为定位辅助的毫米微波通信提出全面的指导。
通信定位一体化技术通过共享无线通信的基础设施和时间-频率-空间资源,来实现通信和定位的先进技术在硬件架构和算法系统层面上的高度整合。通信和定位的协同能通过高速率、低延时的毫米波通信系统的信息交互能力来实现。通信和定位的共同设计打破了二者单独运行的传统模式,并在一个系统中实现了高吞吐量的通信和高精度的定位。因此,通过信道估计获得的信道状态信息(CSI)不仅是通信的基础,也含有发射端、接收端和周围散射体的位移和移动的附带信息。毫米波系统中的通信定位一体化是基于CSI或CSI相关参数的。然后,更加可靠的通信能提供定位所需要的更加精确的测量,通信和定位的相互辅助与增强就能以迭代的方式实现。此外,更加精确的位置估计减少了通信开销。
毫米波通信系统中的定位旨在基于基站或锚节点发送或接收的一组无线参考信号,估计用户设备或代理节点的位置、速度和方向以及可能的散射体。通过复用毫米波通信的基础设施来部署定位既方便又划算。该过程重复利用了通信系统接收端已有的实时信道状态信息。
(1)基于超大天线阵的定位
随着天线维度逐渐上升,天线阵列辐射的近场区的距离也逐渐增大,因此用户和重要的散射体就更可能会被定位在阵列的近场区。基于球面波的大均匀线性阵列的标准响应模型,允许使用新参数表征路径,即在平面波的假设之下,除了常规参数之外,源和参考点之间的距离也可以用来表征路径。因此,近场效应有利于用波前曲率共同估计源的距离和方向。这个过程可以提高定位的准确性,并可能取消参考锚之间显式同步的需求。
(2)基于模型驱动神经网络的定位
为克服基于纯数据或纯模型的定位方法的缺点,提出一种基于混合数据和模型的定位方法,即基于模型的神经网络定位。使用这个技术,就能够设计出带有理论定位基础的神经网络拓扑,网络的结构可以被解释和预测。目前,将神经网络与几何模型相结合的定位方法很少被提及。基于模型的神经网络方法很明显保留了基于模型方法的优点(确定性和理论合理性)和基于数据方法的强大学习能力。它还克服了精准建模的困难,避免了时间和计算资源的大量需求。基于模型的神经网络定位包括3个部分:测量模型、定位算法和神经网络。
毫米波频段的电磁特性决定了毫米波通信的高方向性。因此,位置信息(包括速度)与毫米波通信的各个方面有关,例如自由空间路径损耗、多普勒频移、信道质量、波束方向、阻塞和干扰水平。传统的毫米波通信完全基于估计的CSI运行,因此要求非常频繁的波束训练和信道估计过程,以克服毫米波信号所经受的大路径损耗和高阻塞概率。受通信系统获得的高精度位置信息的激励,传统的基于CSI的通信方法可以转变为基于CSI和基于位置的混合方法。
当前的毫米波通信系统是为无线通信而非定位应用而设计的。因此,通过毫米波通信和定位一体化系统的高吞吐量通信和高精度定位需要额外的研究,包括硬件不理想特性、通信和定位层的交互设计、跨设备信息的融合等。

关键词:毫米波;通信定位一体化;位置辅助通信;超大规模天线阵列;可重构智能表面;人工智能;神经网络

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

Reference

[1]Abu-Shaban Z, Zhou XY, Abhayapala T, et al., 2018. Error bounds for uplink and downlink 3D localization in 5G millimeter wave systems. IEEE Trans Wirel Commun, 17(8):4939-4954.

[2]Akdeniz MR, Liu YP, Samimi MK, et al., 2014. Millimeter wave channel modeling and cellular capacity evaluation. IEEE J Sel Areas Commun, 32(6):1164-1179.

[3]Akyildiz IF, Han C, Nie S, 2018. Combating the distance problem in the millimeter wave and terahertz frequency bands. IEEE Commun Mag, 56(6):102-108.

[4]Ali A, Gonzalez-Prelcic N, Heath RW, et al., 2020. Leveraging sensing at the infrastructure for mmWave communication. IEEE Commun Mag, 58(7):84-89.

[5]Amiri A, Angjelichinoski M, de Carvalho E, et al., 2018. Extremely large aperture massive MIMO: low complexity receiver architectures. IEEE Globecom Workshops, p.1-6.

[6]Amiri R, Behnia F, Zamani H, 2017a. Asymptotically efficient target localization from bistatic range measurements in distributed MIMO radars. IEEE Signal Process Lett, 24(3):299-303.

[7]Amiri R, Behnia F, Zamani H, 2017b. Efficient 3-D positioning using time-delay and AOA measurements in MIMO radar systems. IEEE Commun Lett, 21(12):2614-2617.

[8]Andrews JG, Buzzi S, Choi W, et al., 2014. What will 5G be? IEEE J Sel Areas Commun, 32(6):1065-1082.

[9]Badiu MA, Hansen TL, Fleury BH, 2017. Variational Bayesian inference of line spectra. IEEE Trans Signal Process, 65(9):2247-2261.

[10]Bi Q, 2019. Ten trends in the cellular industry and an outlook on 6G. IEEE Commun Mag, 57(12):31-36.

[11]Boccardi F, Heath RW, Lozano A, et al., 2014. Five disruptive technology directions for 5G. IEEE Commun Mag, 52(2):74-80.

[12]Bölcskei H, Gesbert D, Papadias CB, et al., 2006. Space-Time Wireless Systems: from Array Processing to MIMO Communications. Cambridge University Press, Cambridge.

[13]Brady J, Behdad N, Sayeed AM, 2013. Beamspace MIMO for millimeter-wave communications: system architecture, modeling, analysis, and measurements. IEEE Trans Antenn Propag, 61(7):3814-3827.

[14]Dardari D, Guidi F, 2018. Direct position estimation from wavefront curvature with single antenna array. Proc 8th Int Conf on Localization and GNSS, p.1-5.

[15]Dardari D, Conti A, Ferner U, et al., 2009. Ranging with ultrawide bandwidth signals in multipath environments. Proc IEEE, 97(2):404-426.

[16]Decurninge A, Ordóñez LG, Ferrand P, et al., 2018. CSI-based outdoor localization for massive MIMO: experiments with a learning approach. Proc 15th Int Symp on Wireless Communication Systems, p.1-6.

[17]del Peral-Rosado JA, Raulefs R, López-Salcedo JA, et al., 2018. Survey of cellular mobile radio localization methods: from 1G to 5G. IEEE Commun Surv Tutor, 20(2):1124-1148.

[18]di Taranto R, Muppirisetty S, Raulefs R, et al., 2014. Location-aware communications for 5G networks: how location information can improve scalability, latency, and robustness of 5G. IEEE Signal Process Mag, 31(6):102-112.

[19]Einemo M, So HC, 2015. Weighted least squares algorithm for target localization in distributed MIMO radar. Signal Process, 115:144-150.

[20]Ferrand P, Decurninge A, Guillaud M, 2020. DNN-based localization from channel estimates: feature design and experimental results. https://arxiv.org/abs/2004.00363

[21]Friedlander B, 2019. Localization of signals in the near-field of an antenna array. IEEE Trans Signal Process, 67(15):3885-3893.

[22]Garcia N, Wymeersch H, Ström EG, et al., 2016. Location-aided mm-wave channel estimation for vehicular communication. Proc IEEE 17th Int Workshop on Signal Processing Advances in Wireless Communications, p.1-5.

[23]Garcia N, Wymeersch H, Larsson EG, et al., 2017. Direct localization for massive MIMO. IEEE Trans Signal Process, 65(10):2475-2487.

[24]Ge XH, Tu S, Mao GQ, et al., 2016. 5G ultra-dense cellular networks. IEEE Wirel Commun, 23(1):72-79.

[25]Guo XS, Ansari N, Li L, et al., 2018. Indoor localization by fusing a group of fingerprints based on random forests. IEEE Int Things J, 5(6):4686-4698.

[26]Han Y, Hsu TH, Wen CK, et al., 2019a. Efficient downlink channel reconstruction for FDD multi-antenna systems. IEEE Trans Wirel Commun, 18(6):3161-3176.

[27]Han Y, Tang WK, Jin S, et al., 2019b. Large intelligent surface-assisted wireless communication exploiting statistical CSI. IEEE Trans Veh Technol, 68(8):8238-8242.

[28]Han Y, Jin S, Wen CK, et al., 2020. Channel estimation for extremely large-scale massive MIMO systems. IEEE Wirel Commun Lett, 9(5):633-637.

[29]Han YJ, Shen Y, Zhang XP, et al., 2016. Performance limits and geometric properties of array localization. IEEE Trans Inform Theory, 62(2):1054-1075.

[30]He HT, Jin S, Wen CK, et al., 2019. Model-driven deep learning for physical layer communications. IEEE Wirel Commun, 26(5):77-83.

[31]He JG, Wymeersch H, Sanguanpuak T, et al., 2020. Adaptive beamforming design for mmWave RIS-aided joint localization and communication. IEEE Wireless Communications and Networking Conf Workshops, p.1-6.

[32]Heath RW, González-Prelcic N, Rangan S, et al., 2016. An overview of signal processing techniques for millimeter wave MIMO systems. IEEE J Sel Top Signal Process, 10(3):436-453.

[33]Ho KC, Xu WW, 2004. An accurate algebraic solution for moving source location using TDOA and FDOA measurements. IEEE Trans Signal Process, 52(9):2453-2463.

[34]Hu S, Rusek F, Edfors O, 2018. Beyond massive MIMO: the potential of positioning with large intelligent surfaces. IEEE Trans Signal Process, 66(7):1761-1774.

[35]Jeong S, Simeone O, Haimovich A, et al., 2016. Positioning via direct localisation in C-RAN systems. IET Commun, 10(16):2238-2244.

[36]Kodippili NS, Dias D, 2010. Integration of fingerprinting and trilateration techniques for improved indoor localization. Proc 7th Int Conf on Wireless and Optical Communications Networks, p.1-6.

[37]Kraus JD, Marhefka RJ, 2002. Antennas for All Applications (3rd Ed.). McGraw Hill, Upper Saddle River, NJ, USA.

[38]Latva-Aho M, Leppänen K, 2019. Key Drivers and Research Challenges for 6G Ubiquitous Wireless Intelligence. Oulun yliopisto, Finland.

[39]LeCun Y, Bengio Y, Hinton G, 2015. Deep learning. Nature, 521(7553):436-444.

[40]Lemic F, Martin J, Yarp C, et al., 2016. Localization as a feature of mmWave communication. Proc Int Wireless Communications and Mobile Computing Conf, p.1033-1038.

[41]Li Y, He Z, Gao ZZ, et al., 2019. Toward robust crowdsourcing-based localization: a fingerprinting accuracy indicator enhanced wireless/magnetic/inertial integration approach. IEEE Int Things J, 6(2):3585-3600.

[42]Li Y, Zhuang Y, Hu X, et al., 2020. Location-enabled IoT (LE-IoT): a survey of positioning techniques, error sources, and mitigation. https://arxiv.org/abs/2004.03738

[43]Liu W, Cheng QQ, Deng ZL, et al., 2019. Survey on CSI-based indoor positioning systems and recent advances. Proc Int Conf on Indoor Positioning and Indoor Navigation, p.1-8.

[44]Ma YS, Zhou G, Wang SQ, 2019. WiFi sensing with channel state information: a survey. ACM Comput Surv, 52(3):46.

[45]Mamandipoor B, Ramasamy D, Madhow U, 2016. Newtonized orthogonal matching pursuit: frequency estimation over the continuum. IEEE Trans Signal Process, 64(19):5066-5081.

[46]Maschietti F, Gesbert D, de Kerret P, et al., 2017. Robust location-aided beam alignment in millimeter wave massive MIMO. IEEE Global Communications Conf, p.1-6.

[47]Matz G, Hlawatsch F, 2011. Fundamentals of time-varying communication channels. In: Hlawatsch F, Matz G (Eds.), Wireless Communications over Rapidly Time-Varying Channels. Academic Press, Orlando, FL, USA, p.1-63.

[48]Mendrzik R, Meyer F, Bauch G, et al., 2019. Enabling situational awareness in millimeter wave massive MIMO systems. IEEE J Sel Top Signal Process, 13(5):1196-1211.

[49]Molisch AF, 2005. Wireless Communications. Wiley, Chichester, UK.

[50]Muppirisetty LS, Charalambous T, Karout J, et al., 2018. Location-aided pilot contamination avoidance for massive MIMO systems. IEEE Trans Wirel Commun, 17(4):2662-2674.

[51]Niu JW, Wang BW, Shu L, et al., 2015. ZIL: an energy-efficient indoor localization system using ZigBee radio to detect WiFi fingerprints. IEEE J Sel Areas Commun, 33(7):1431-1442.

[52]Rappaport TS, Xing YC, Kanhere O, et al., 2019. Wireless communications and applications above 100 GHz: opportunities and challenges for 6G and beyond. IEEE Access, 7:78729-78757.

[53]Rezaie S, Manchón CN, de Carvalho E, 2020. Location- and orientation-aided millimeter wave beam selection using deep learning. Proc IEEE Int Conf on Communications, p.1-6.

[54]Rizk H, Torki M, Youssef M, 2019. CellinDeep: robust and accurate cellular-based indoor localization via deep learning. IEEE Sens J, 19(6):2305-2312.

[55]Sallouha H, Chiumento A, Pollin S, 2017. Localization in long-range ultra narrow band IoT networks using RSSI. Proc IEEE Int Conf on Communications, p.1-6.

[56]Shahmansoori A, Garcia GE, Destino G, et al., 2018. Position and orientation estimation through millimeter-wave MIMO in 5G systems. IEEE Trans Wirel Commun, 17(3):1822-1835.

[57]Studer C, Medjkouh S, Gonultacs E, et al., 2018. Channel charting: locating users within the radio environment using channel state information. IEEE Access, 6:47682-47698.

[58]Tang WK, Chen MZ, Chen XY, et al., 2021. Wireless communications with reconfigurable intelligent surface: path loss modeling and experimental measurement. IEEE Trans Wirel Commun, 20(1):421-439.

[59]van der Perre L, Liu L, Larsson EG, 2018. Efficient DSP and circuit architectures for massive MIMO: state of the art and future directions. IEEE Trans Signal Process, 66(18):4717-4736.

[60]Wang HQ, Kosasih A, Wen CK, et al., 2020. Expectation propagation detector for extra-large scale massive MIMO. IEEE Trans Wirel Commun, 19(3):2036-2051.

[61]Wang TQ, Wen CK, Wang HQ, et al., 2017. Deep learning for wireless physical layer: opportunities and challenges. China Commun, 14(11):92-111.

[62]Wang XY, Gao LJ, Mao SW, et al., 2015. DeepFi: deep learning for indoor fingerprinting using channel state information. Proc IEEE Wireless Communications and Networking Conf, p.1666-1671.

[63]Wang Y, Ho KC, 2015. An asymptotically efficient estimator in closed-form for 3-D AOA localization using a sensor network. IEEE Trans Wirel Commun, 14(12):6524-6535.

[64]Wen FX, Wymeersch H, Peng BL, et al., 2019. A survey on 5G massive MIMO localization. Digit Signal Process, 94:21-28.

[65]Wu QQ, Zhang R, 2020. Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network. IEEE Commun Mag, 58(1):106-112.

[66]Wymeersch H, 2020. A Fisher information analysis of joint localization and synchronization in near field. IEEE Int Conf on Communications Workshops, p.1-6.

[67]Wymeersch H, Seco-Granados G, Destino G, et al., 2017. 5G mmWave positioning for vehicular networks. IEEE Wirel Commun, 24(6):80-86.

[68]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.

[69]Xiao ZQ, Zeng Y, 2020. An overview on integrated localization and communication towards 6G. https://arxiv.org/abs/2006.01535v1

[70]Xu ZB, Sun J, 2018. Model-driven deep-learning. Natl Sci Rev, 5(1):22-24.

[71]Yang J, Wen CK, Jin S, et al., 2018. Beamspace channel estimation in mmWave systems via cosparse image reconstruction technique. IEEE Trans Commun, 66(10):4767-4782.

[72]Yang J, Jin S, Wen CK, et al., 2019. 3-D positioning and environment mapping for mmWave communication systems. https://arxiv.org/abs/1908.04142v1

[73]Yang J, Jin S, Wen CK, et al., 2020. Fast beam training architecture for hybrid mmWave transceivers. IEEE Trans Veh Technol, 69(3):2700-2715.

[74]Yang J, Zeng Y, Jin S, et al., 2021. Communication and localization with extremely large lens antenna array. IEEE Trans Wirel Commun, in press.

[75]Yang X, Matthaiou M, Yang J, et al., 2019. Hardware-constrained millimeter-wave systems for 5G: challenges, opportunities, and solutions. IEEE Commun Mag, 57(1):44-50.

[76]Yin XF, Wang S, Zhang N, et al., 2017. Scatterer localization using large-scale antenna arrays based on a spherical wave-front parametric model. IEEE Trans Wirel Commun, 16(10):6543-6556.

[77]Zekavat R, Buehrer RM, 2011. Handbook of Position Location: Theory, Practice, and Advances. John Wiley and Sons, Hoboken, NJ, USA.

[78]Zeng Y, Zhang R, 2016. Millimeter wave MIMO with lens antenna array: a new path division multiplexing paradigm. IEEE Trans Commun, 64(4):1557-1571.

[79]Zhao HY, Zhang N, Shen Y, 2020. Beamspace direct localization for large-scale antenna array systems. IEEE Trans Signal Process, 68:3529-3544.

[80]Zhou BP, Liu A, Lau V, 2019. Successive localization and beamforming in 5G mmWave MIMO communication systems. IEEE Trans Signal Process, 67(6):1620-1635.

[81]Zhou Z, Gao X, Fang J, et al., 2015. Spherical wave channel and analysis for large linear array in LoS conditions. IEEE Globecom Workshops, p.1-6.

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 - 2024 Journal of Zhejiang University-SCIENCE