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.
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。
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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. https://doi.org/10.1109/TWC.2018.2832134
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. https://doi.org/10.1109/JSAC.2014.2328154
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. https://doi.org/10.1109/MCOM.2018.1700928
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. https://doi.org/10.1109/MCOM.001.1900700
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. https://doi.org/10.1109/GLOCOMW.2018.8644126
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. https://doi.org/10.1109/LSP.2017.2660545
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. https://doi.org/10.1109/LCOMM.2017.2742945
Andrews JG, Buzzi S, Choi W, et al., 2014. What will 5G be?. IEEE J Sel Areas Commun, 32(6):1065–1082. https://doi.org/10.1109/JSAC.2014.2328098
Badiu MA, Hansen TL, Fleury BH, 2017. Variational Bayesian inference of line spectra. IEEE Trans Signal Process, 65(9):2247–2261. https://doi.org/10.1109/TSP.2017.2655489
Bi Q, 2019. Ten trends in the cellular industry and an outlook on 6G. IEEE Commun Mag, 57(12):31–36. https://doi.org/10.1109/MCOM.001.1900315
Boccardi F, Heath RW, Lozano A, et al., 2014. Five disruptive technology directions for 5G. IEEE Commun Mag, 52(2):74–80. https://doi.org/10.1109/MCOM.2014.6736746
Bölcskei H, Gesbert D, Papadias CB, et al., 2006. Space-Time Wireless Systems: from Array Processing to MIMO Communications. Cambridge University Press, Cambridge.
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. https://doi.org/10.1109/TAP.2013.2254442
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. https://doi.org/10.1109/ICL-GNSS.2018.8543121
Dardari D, Conti A, Ferner U, et al., 2009. Ranging with ultrawide bandwidth signals in multipath environments. Proc IEEE, 97(2):404–426. https://doi.org/10.1109/JPROC.2008.2008846
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. https://doi.org/10.1109/ISWCS.2018.8491210
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. https://doi.org/10.1109/COMST.2017.2785181
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. https://doi.org/10.1109/MSP.2014.2332611
Einemo M, So HC, 2015. Weighted least squares algorithm for target localization in distributed MIMO radar. Signal Process, 115:144–150. https://doi.org/10.1016/j.sigpro.2015.04.004
Ferrand P, Decurninge A, Guillaud M, 2020. DNN-based localization from channel estimates: feature design and experimental results. https://arxiv.org/abs/2004.00363
Friedlander B, 2019. Localization of signals in the near-field of an antenna array. IEEE Trans Signal Process, 67(15):3885–3893. https://doi.org/10.1109/TSP.2019.2923164
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. https://doi.org/10.1109/SPAWC.2016.7536855
Garcia N, Wymeersch H, Larsson EG, et al., 2017. Direct localization for massive MIMO. IEEE Trans Signal Process, 65(10):2475–2487. https://doi.org/10.1109/TSP.2017.2666779
Ge XH, Tu S, Mao GQ, et al., 2016. 5G ultra-dense cellular networks. IEEE Wirel Commun, 23(1):72–79. https://doi.org/10.1109/MWC.2016.7422408
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. https://doi.org/10.1109/JIOT.2018.2810601
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. https://doi.org/10.1109/TWC.2019.2911497
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. https://doi.org/10.1109/TVT.2019.2923997
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. https://doi.org/10.1109/LWC.2019.2963877
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. https://doi.org/10.1109/TIT.2015.2511778
He HT, Jin S, Wen CK, et al., 2019. Model-driven deep learning for physical layer communications. IEEE Wirel Commun, 26(5):77–83. https://doi.org/10.1109/MWC.2019.1800447
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. https://doi.org/10.1109/WCNCW48565.2020.9124848
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. https://doi.org/10.1109/JSTSP.2016.2523924
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. https://doi.org/10.1109/TSP.2004.831921
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. https://doi.org/10.1109/TSP.2018.2795547
Jeong S, Simeone O, Haimovich A, et al., 2016. Positioning via direct localisation in C-RAN systems. IET Commun, 10(16):2238–2244. https://doi.org/10.1049/iet-com.2016.0403
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. https://doi.org/10.1109/WOCN.2010.5587342
Kraus JD, Marhefka RJ, 2002. Antennas for All Applications (3rd Ed.). McGraw Hill, Upper Saddle River, NJ, USA.
Latva-Aho M, Leppänen K, 2019. Key Drivers and Research Challenges for 6G Ubiquitous Wireless Intelligence. Oulun yliopisto, Finland.
LeCun Y, Bengio Y, Hinton G, 2015. Deep learning. Nature, 521(7553):436–444. https://doi.org/10.1038/nature14539
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. https://doi.org/10.1109/IWCMC.2016.7577201
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. https://doi.org/10.1109/JIOT.2018.2889303
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
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. https://doi.org/10.1109/IPIN.2019.8911774
Ma YS, Zhou G, Wang SQ, 2019. WiFi sensing with channel state information: a survey. ACM Comput Surv, 52(3):46. https://doi.org/10.1145/3310194
Mamandipoor B, Ramasamy D, Madhow U, 2016. Newtonized orthogonal matching pursuit: frequency estimation over the continuum. IEEE Trans Signal Process, 64(19):5066–5081. https://doi.org/10.1109/TSP.2016.2580523
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. https://doi.org/10.1109/GLOCOM.2017.8254901
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. https://doi.org/10.1016/B978-0-12-374483-8.00001-7
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. https://doi.org/10.1109/JSTSP.2019.2933142
Molisch AF, 2005. Wireless Communications. Wiley, Chichester, UK.
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. https://doi.org/10.1109/TWC.2018.2800038
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. https://doi.org/10.1109/JSAC.2015.2430171
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. https://doi.org/10.1109/ACCESS.2019.2921522
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. https://doi.org/10.1109/ICC40277.2020.9149272
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. https://doi.org/10.1109/JSEN.2018.2885958
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. https://doi.org/10.1109/ICC.2017.7997195
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. https://doi.org/10.1109/TWC.2017.2785788
Studer C, Medjkouh S, Gonultaş E, et al., 2018. Channel charting: locating users within the radio environment using channel state information. IEEE Access, 6:47682–47698. https://doi.org/10.1109/ACCESS.2018.2866979
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.
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. https://doi.org/10.1109/TSP.2018.2858190
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. https://doi.org/10.1109/TWC.2019.2961892
Wang TQ, Wen CK, Wang HQ, et al., 2017. Deep learning for wireless physical layer: opportunities and challenges. China Commun, 14(11):92–111. https://doi.org/10.1109/CC.2017.8233654
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. https://doi.org/10.1109/WCNC.2015.7127718
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. https://doi.org/10.1109/TWC.2015.2456057
Wen FX, Wymeersch H, Peng BL, et al., 2019. A survey on 5G massive MIMO localization. Digit Signal Process, 94:21–28. https://doi.org/10.1016/j.dsp.2019.05.005
Wu QQ, Zhang R, 2020. Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network. IEEE Commun Mag, 58(1):106–112. https://doi.org/10.1109/MCOM.001.1900107
Wymeersch H, 2020. A Fisher information analysis of joint localization and synchronization in near field. IEEE Int Conf on Communications Workshops, p.1–6. https://doi.org/10.1109/ICCWorkshops49005.2020.9145059
Wymeersch H, Seco-Granados G, Destino G, et al., 2017. 5G mmWave positioning for vehicular networks. IEEE Wirel Commun, 24(6):80–86. https://doi.org/10.1109/MWC.2017.1600374
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. https://doi.org/10.1109/JSAC.2017.2719924
Xiao ZQ, Zeng Y, 2020. An overview on integrated localization and communication towards 6G. https://arxiv.org/abs/2006.01535v1
Xu ZB, Sun J, 2018. Model-driven deep-learning. Natl Sci Rev, 5(1):22–24. https://doi.org/10.1093/nsr/nwx099
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. https://doi.org/10.1109/TCOMM.2018.2805359
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
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. https://doi.org/10.1109/TVT.2020.2963847
Yang J, Zeng Y, Jin S, et al., 2021. Communication and localization with extremely large lens antenna array. IEEE Trans Wirel Commun, in press. https://doi.org/10.1109/TWC.2020.3046766
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. https://doi.org/10.1109/MCOM.2018.1701050
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. https://doi.org/10.1109/TWC.2017.2725260
Zekavat R, Buehrer RM, 2011. Handbook of Position Location: Theory, Practice, and Advances. John Wiley and Sons, Hoboken, NJ, USA.
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. https://doi.org/10.1109/TCOMM.2016.2533490
Zhao HY, Zhang N, Shen Y, 2020. Beamspace direct localization for large-scale antenna array systems. IEEE Trans Signal Process, 68:3529–3544. https://doi.org/10.1109/TSP.2020.2996155
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. https://doi.org/10.1109/TSP.2019.2894789
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. https://doi.org/10.1109/GLOCOMW.2015.7414041
Author information
Authors and Affiliations
Contributions
Jie YANG designed the research and drafted the manuscript. Jie YANG, Jing XU, Xiao LI, Shi JIN, and Bo GAO revised and finalized the paper.
Corresponding author
Additional information
Compliance with ethics guidelines
Jie YANG, Jing XU, Xiao LI, Shi JIN, and Bo GAO declare that they have no conflict of interest.
Project supported by the National Natural Science Foundation of China for Distinguished Young Scholars (No. 61625106), the National Natural Science Foundation of China (No. 61941104), and the Scientific Research Foundation of Graduate School of Southeast University, China (No. YBPY2015)
Rights and permissions
About this article
Cite this article
Yang, J., Xu, J., Li, X. et al. Integrated communication and localization in millimeter-wave systems. Front Inform Technol Electron Eng 22, 457–470 (2021). https://doi.org/10.1631/FITEE.2000505
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2000505
Key words
- Millimeter-wave
- Integrated communication and localization
- Location-assisted communication
- Extremely large antenna array
- Reconfigurable intelligent surface
- Artificial intelligence
- Neural networks