Localization in Cellular and Heterogeneous Networks for 5G and Beyond: A Review
DOI:
https://doi.org/10.13052/jmm1550-4646.1913Keywords:
5G, cellular networks, fingerprinting, Het-Net, localization, positioning, triangulation, trilateration, wireless networksAbstract
Localization in modern and future wireless networks has been established as an important field of research work due to the requirements of location-based applications and services with variety of accuracy requirements. These are driven by the strong heterogeneity in terms of processing power, size and range of the nodes in beyond Fifth Generation (5G) telecommunications. Thus, localization methods in cellular and heterogeneous networks (Het-Nets) diversify in their application scenario (terrestrial and based on aerial platforms) and bands (licensed and unlicensed). They are categorized, according to the methodology used to perform the positioning, into three groups – fingerprinting (learning-based location estimation), trilateration and triangulation (distance or angular based), and hybrid (combining two geometric features of the received signals) methods. For each category, a summary of the methods’ design features and achieved accuracy is presented in tabular form. On the basis of the review, directions for future research are outlined, that will facilitate the further advancements in the design and application of localization methods for wireless communications.
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M. Giordani, M. Polese, M. Mezzavilla, C. Rangan, M. Zorzi, Toward 6G networks: Use cases and technologies‘, IEEE Communications Magazine, 58(3), pp. 55–61, 2020.
W. Jiang, B. Han, M.A. Habibi, H.D. Schotten, ‘The road towards 6G: A comprehensive survey’, IEEE Open Journal of the Communications Society, 2, pp. 334–366, 2021.
J.A. del Peral-Rosado, R. Raulefs, J.A. López-Salcedo, G. Seco-Granados, ‘Survey of cellular mobile radio localization methods: From 1G to 5G’, IEEE Communications Surveys & Tutorials, 20(2), pp. 1124–1148, 2017.
Z. Xiao, Y. Zeng, ‘An overview on integrated localization and communication towards 6G’, Science China Information Sciences, 65(3), pp. 1–46, 2022.
G. Pecoraro, S. Di Domenico, E. Cianca, M. De Sanctis, ‘LTE signal fingerprinting localization based on CSI’, In 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 1–8). IEEE, 2017.
G. Pecoraro, S. Di Domenico, E. Cianca, M. De Sanctis, ‘CSI-based fingerprinting for indoor localization using LTE signals’, EURASIP Journal on Advances in Signal Processing, 2018(1), pp. 1–18, 2018.
D. Li, Y. Lei, ‘Deep learning for fingerprint-based outdoor positioning via LTE networks’, Sensors, 19(23), p. 5180, 2019.
H. Zhang, Z. Zhang, S. Zhang, S. Xu, S. Cao, ‘Fingerprint-based localization using commercial LTE signals: A field-trial study’, In 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) (pp. 1–5). IEEE, 2019.
J.H. Lee, B. Shin, D. Shin, J. Park, Y.S. Ryu, D.H. Woo, T. Lee, ‘Surface correlation-based fingerprinting method using LTE signal for localization in Urban Canyon’, Sensors, 19(15), p. 3325, 2019.
W. Fang, C. Xie, B. Ran, ‘An Accurate and Real-Time Commercial Indoor Localization System in LTE Networks’, IEEE Access, 9, pp. 21167–21179, 2020.
X. Ye, X. Yin, X. Cai, A.P. Yuste, H. Xu, ‘Neural-network-assisted UE localization using radio-channel fingerprints in LTE networks’, IEEE Access, 5, pp. 12071–12087, 2017.
J.H. Lee, B. Shin, D. Shin, J. Kim, J. Park, T. Lee, ‘Precise indoor localization: rapidly-converging 2d surface correlation-based fingerprinting technology using LTE signal’, IEEE Access, 8, pp. 172829–172838, 2020.
J.X. Liao, S.K. Ting, H.Y. Hsieh, ‘AI-Assisted Indoor Localization and Tracking for 5G/B5G Applications’.
S. Huang, K. Zhao, Z. Zheng, W. Ji, T. Li, X. Liao, ‘An Optimized Fingerprinting-Based Indoor Positioning with Kalman Filter and Universal Kriging for 5G Internet of Things’, Wireless Communications and Mobile Computing, 2021.
R. Klus, J. Talvitie, M. Valkama, ‘Neural Network Fingerprinting and GNSS Data Fusion for Improved Localization in 5G’, In 2021 International Conference on Localization and GNSS (ICL-GNSS) (pp. 1–6). IEEE, 2021.
X. Wang, L. Gao, S. Mao, S. Pandey, 2016. ‘CSI-based fingerprinting for indoor localization: A deep learning approach’, IEEE Transactions on Vehicular Technology, 66(1), pp. 763–776, 2016.
X. Wang, S. Mao, ‘Deep learning for indoor localization based on bi-modal CSI data’, Appl. Mach. Learn. Wirel. Commun, 81, p. 343, 2021.
R. Zhou, Y. Yang, P. Chen, ‘An RSS transform-Based WKNN for indoor positioning’, Sensors, 21(17), p. 5685, 2021.
M. Kolakowski, ‘Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning’, Sensors, 21(18), p. 6270, 2021.
Y. Hou, X. Yang, Q.H. Abbasi, ‘Efficient AoA-based wireless indoor localization for hospital outpatients using mobile devices’, Sensors, 18(11), p. 3698, 2018.
S.Y. Zhuk, I.O. Tovkach, O. Neuimin, V. Vasyliev, ‘Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements’, Journal of Aerospace Technology and Management, 13, 2021.
F. Watanabe, ‘Wireless sensor network localization using AoA measurements with two-step error variance-weighted least squares’, IEEE Access, 9, pp. 10820–10828, 2021.
K. Zhu, J. Liu, X. Song, W. Wang, H. Chen, ‘Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling’, Journal of Advanced Transportation, 2021.
G. Çelik, H. Çelebi, G. Tuna, ‘A novel RSRP-based E-CID positioning for LTE networks’, In 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC) (pp. 1689–1692). IEEE, 2017.
S.M. Kim, S. Seo, J. Kim, ‘Improved positioning reference signal pattern for indoor positioning in LTE-advanced system’, Int. J. Appl. Eng. Res, 12(5), pp. 664–670, 2017.
M.R. Danaee, ‘One-to-one non-linear transformation for RSS-based localization with unknown transmit power’, IET Communications, 15(4), pp. 627–641, 2021.
A. Bannour, A. Harbaoui, F. Alsolami, ‘Connected Objects Geo-Localization Based on SS-RSRP of 5G Networks’, Electronics, 10(22), p.2750, 2021.
Y. Li, F. Shu, B. Shi, X. Cheng, Y. Song, J. Wang, ‘Enhanced RSS-based UAV localization via trajectory and multi-base stations’, IEEE Communications Letters, 25(6), pp. 1881–1885, 2021.
L. Wielandner, E. Leitinger, K. Witrisal, ‘RSS-based Cooperative Localization and Orientation Estimation Exploiting Directive Antenna Patterns’, arXiv preprint arXiv:2103.13181, 2021.
H. Ryden, A.A. Zaidi, S.M. Razavi, F. Gunnarsson, I. Siomina, ‘Enhanced time of arrival estimation and quantization for positioning in LTE networks’, In 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (pp. 1–6). IEEE, 2016.
M. Driusso, C. Marshall, M. Sabathy, F. Knutti, H. Mathis, F. Babich, ‘Vehicular position tracking using LTE signals’, IEEE Transactions on Vehicular Technology, 66(4), pp. 3376–3391, 2021.
F. Pérez-Cruz, P.M. Olmos, M.M. Zhang, H. Huang, ‘Probabilistic time of arrival localization’, IEEE Signal Processing Letters, 26(11), pp. 1683–1687, 2019.
K. Shamaei, J. Khalife, Z.M. Kassas, ‘Exploiting LTE signals for navigation: Theory to implementation’, IEEE Transactions on Wireless Communications, 17(4), pp. 2173–2189, 2018.
C.Y. Chen, I.H. Li, ‘Time-of-arrival estimation algorithm for positioning in NB-IoT physical layer’, IET Communications, 14(11), pp. 1822–1826, 2020.
Y. Xue, W. Su, H. Wang, D. Yang, J. Ma, ‘A model on indoor localization system based on the time difference without synchronization’, IEEE Access, 6, pp. 34179–34189, 2018.
I. Sobron, I. Landa, I. Eizmendi, M. Velez, ‘Adaptive TDOA Estimation for Positioning in NB-IoT’ In 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) (pp. 149–152). IEEE, 2019.
X. Ye, J. Rodríguez-Piñeiro, Y. Liu, X. Yin, A. Pérez Yuste, ‘A novel experiment-free site-specific TDOA localization performance-evaluation approach’, Sensors, 20(4), p. 1035, 2020.
J. Pospisil, R. Fujdiak, K. Mikhaylov, ‘Investigation of the Performance of TDoA-Based Localization Over LoRaWAN in Theory and Practice’, Sensors, 20(19), p. 5464, 2020.
N. Xia, M.A. Weitnauer, ‘TDOA-based mobile localization using particle filter with multiple motion and channel models’, IEEE Access, 7, pp. 21057–21066, 2019.
S. Hu, A. Berg, X. Li, F. Rusek, ‘Improving the performance of OTDOA based positioning in NB-IoT systems’, In GLOBECOM 2017–2017 IEEE Global Communications Conference (pp. 1–7). IEEE, 2017.
K. Radnosrati, G. Hendeby, C. Fritsche, F. Gunnarsson, F. Gustafsson, ‘Performance of OTDOA positioning in narrowband IoT systems’, In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (pp. 1–7). IEEE, 2017.
A.K. Alhafid, S. Younis, ‘Observed Time Difference of Arrival Based Position Estimation for LTE Systems: Simulation Framework and Performance Evaluation’, Eastern-European Journal of Enterprise Technologies, 3(9–105), pp. 20–28, 2020.
G. Pan, T. Wang, X. Jiang, S. Zhang, ‘Deep Learning based OTDOA Positioning for NB-IoT Communication Systems’, arXiv preprint arXiv:2004.05008, 2020.
Y. Pan, J. Kim, ‘Optimize OTDOA-based Positioning Accuracy by Utilizing Multiple Linear Regression Model under NB-IoT Technology’, In Proceedings of the Korean Society of Computer Information Conference (pp. 139–142). Korean Society of Computer Information, 2020.
K. McDermott, R.M. Vaghefi, R.M. Buehrer, ‘Cooperative UTDOA positioning in LTE cellular systems’, In 2015 IEEE Globecom Workshops (GC Wkshps) (pp. 1–6). IEEE, 2015.
X. Li, L. Xing, ‘Optimal deployment of drone base stations for cellular communication by network-based localization’, In 2018 37th Chinese Control Conference (CCC) (pp. 7282–7287). IEEE, 2018.
Y. Zou, H. Liu, Q. Wan, ‘An iterative method for moving target localization using TDOA and FDOA measurements’, IEEE Access, 6, pp. 2746–2754, 2017.
F. Ma, F. Guo, L. Yang, ‘Low-complexity TDOA and FDOA localization: a compromise between two-step and DPD methods’, Digital Signal Processing, 96, p. 102600, 2020.
Y. Li, C. Hao, M. Li, L. He, P. Li, Q. Wan, ‘Moving Target Tracking Using TDOA and FDOA Measurements from Two UAVs with Varying Baseline’, In Journal of Physics: Conference Series (Vol. 1169, No. 1, p. 012013). IOP Publishing, 2019.
L. Congfeng, Y. Jinwei, ‘A Joint TDOA/FDOA Localization Algorithm Using Bi-iterative Method with Optimal Step Length’, Chinese Journal of Electronics, 30(1), pp. 119–126, 2021.
H. Zhang, Z. Zheng, W.Q. Wang, S. Zhang, ‘Source localisation using TDOA and FDOA measurements under unknown noise power knowledge’, IET Signal Processing, 14(7), pp. 435–439, 2020.
A. Elgamoudi, H. Benzerrouk, G.A. Elango, R. Landry, ‘Gauss Hermite H8 Filter for UAV Tracking Using LEO Satellites TDOA/FDOA Measurement-Part I’, IEEE Access, 8, pp. 201428–201440, 2020.
W. Ding, S. Chang, J. Li, ‘A Novel Weighted Localization Method in Wireless Sensor Networks Based on Hybrid RSS/AoA Measurements’, IEEE Access, 9, pp. 150677–150685, 2021.
M.S. Costa, S. Tomic, M. Beko, ‘An SOCP estimator for hybrid RSS and AOA target localization in sensor networks’, Sensors, 21(5), p. 1731, 2021.
F. Liu, Y. Cui, C. Masouros, J. Xu, T.X. Han, Y.C. Eldar, S. Buzzi, ‘Integrated sensing and communications: Towards dual-functional wireless networks for 6G and beyond’, arXiv preprint arXiv:2108.07165, 2021.
T. Wild, V. Braun, H. Viswanathan, ‘Joint design of communication and sensing for beyond 5G and 6G systems’, IEEE Access, 9, pp. 30845–30857, 2021.
G. Geraci, A. Garcia-Rodriguez, M.M. Azari, A. Lozano, M. Mezzavilla, S. Chatzinotas, Y. Chen, S. Rangan, M. Di Renzo, ‘What will the future of UAV cellular communications be? A flight from 5G to 6G’, arXiv preprint arXiv:2105.04842, 2021.
L. Stankovic, D.P. Mandic, M. Dakovic, I. Kisil, E. Sejdic, A.G. Constantinides, ‘Understanding the basis of graph signal processing via an intuitive example-driven approach [lecture notes]’, IEEE Signal Processing Magazine, 36(6), pp. 133–145, 2019.
