[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120850-en":3,"doc-seo-120850-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120850,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","THE TOA ESTIMATION OF CELLULAR NETWORK SIGNALS BASED ON MACHINE LEARNING IN COMPLEX URBAN ENVIRONMENTS","Precision location-based services in complex environments remain a key challenge for navigation and positioning. With the evolution of wireless communications, cellular network signals such as LTE and 5G offer distinct advantages for ranging and location. This work proposes a time-of-arrival (TOA) estimation approach driven by machine learning, designed to provide accurate ranging under low signal-to-noise conditions. Vehicular experiments in an urban environment validate feasibility and demonstrate metre-level ranging accuracy.","THE TOA ESTIMATION OF CELLULAR NETWORK SIGNALS BASED ON MACHINE LEARNING IN COMPLEX URBAN ENVIRONMENTS  \nZhaoliang Liu, Liang Chen*, Zhenhang Jiao, Xiangcheng Lu, Yanlin Ruan  \nState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS),  \nWuhan University, Hubei Province, China.  \nEmail: [l.chen@whu.edu.cn](l.chen@whu.edu.cn)  \nKEY WORDS: Location-based service (LBS), Cellular Network, Machine Learning (ML), Time-of-Arrival (TOA), Support Vector Machine (SVM) .  \nABSTRACT:  \nThe precision location-based services in complex environment is a challenge in the field of navigation and positioning. With the continuous development of wireless communication technology in recent years, cellular network signals such as LTE and 5G have emerged as unique advantages in navigation and positioning applications. This paper presents a time-of-arrival (TOA) estimation method based on machine learning, which can use cellular network signals to obtain accurate ranging results in low signal-to-noise ratio conditions. For this purpose, we first present the cellular network signals that can be applied in navigation and positioning. Then, we describe in detail the process of TOA estimation based on machine learning. Finally, we carried out vehicular experiments in an urban environment to test the performance of the proposed method. The test results demonstrate the feasibility of the proposed method and achieve metre-level ranging accuracy.  \n1. INTRODUCTION  \nLocation-based services (LBSs) in complex scenarios are the key focus of scholars and research institutions in recent years. Accurate location information has irreplaceable value in areas such as autonomous driving, precision marketing and emergency rescue. Traditional location services mostly provide location information to users through global navigation satellite system (GNSS) in outdoor open scenes. However, in complex scenarios such as cities, canyons and indoors, the performance of LBSs will be affected by the fading and refraction of GNSS signals.  \nThe acquisition of high-precision navigation observation information through signal of opportunity (SOP) is a method to assist GNSS for high precision positioning. At present, WiFi (Shu et al., 2016, Yan et al., 2018, Gao et al., 2021), Bluetooth (Chenet al., 2013, Faragher and Harle, 2015, Zhuang et al., 2018) and cellular network signals (Driusso et al., 2017, Liu et al., 2023b, Shamaei and Kassas, 2021, Chen et al., 2022, Liu et al., 2023a, Ruan et al., 2022, Liu et al., 2022) are the widely used signals of opportunity in wireless positioning technology. Although WiFi and Bluetooth have the advantages of low-cost and lowpower consumption, they cannot provide high-precision LBSsto a large number of users under a wide area because of the limited coverage area of the base station (BS) .  \nWith the emergence and commercial application of the latest generation of cellular network technology, the introduction of multiple-input multiple-output (MIMO) and ultra-dense network (UDN) has enabled 5G signals to show unique advantages in wireless positioning technology. Researchers are gradually shifting their focus to positioning technologies based on cellular network signals. In (Driusso et al., 2017, Liu et al., 2023b), the researchers have developed several high-precision softwaredefined receivers (SDRs) for time-of-arrival (TOA) estimation that can be used in complex environments based on LTE signals. In (Shamaei and Kassas, 2021), Kimia Shamaei [et al. de-](et al. de-)  \nFigure 1 . Schematic of TOA estimation from cellular network signals based on ML methods.  \nveloped an SDR for TOA estimation by jointly applying delay lock loop (DLL) and phase lock loop (PLL) to achieve a continuous tracking of 5G signals. In (Chen et al., 2022), L. Chenet al. developed an SDR for TOA estimation based on the carrier phase, which achieved tracking and ranging of 5G signals in indoor environments.  \nAt present, wireless sign","cbCaiqWVvI7I0gzu","https://ap.wps.com/l/cbCaiqWVvI7I0gzu","pdf",5893707,1,6,"English","en",105,"# Introduction\n## Location-based services in complex scenarios\n## Signals of opportunity for wireless positioning\n## Cellular network advances (5G, MIMO, UDN) for TOA estimation\n## Challenges of traditional SDR tracking methods\n## Machine learning for signal tracking and TOA estimation\n## Contributions and paper overview","[{\"question\":\"Why are location-based services difficult in complex urban environments?\",\"answer\":\"In cities, GNSS performance is degraded by fading and refraction, reducing the reliability of location information.\"},{\"question\":\"What problem does this paper address in TOA estimation?\",\"answer\":\"Traditional SDR-based wireless tracking can produce large errors under low SNR and severe multipath effects.\"},{\"question\":\"How does the proposed method improve TOA estimation accuracy?\",\"answer\":\"It integrates machine learning into TOA estimation and uses SVM regression in an SDR framework to reduce the impact of noise, achieving metre-level ranging in experiments.\"}]","THE TOA ESTIMATION OF CELLULAR NETWORK SIGNALS BASED ON MACHINE LEARNING IN COMPLEX URBAN ENVIRONMENTS | 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are location-based services difficult in complex urban environments?","Question",{"text":75,"@type":76},"In cities, GNSS performance is degraded by fading and refraction, reducing the reliability of location information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does this paper address in TOA estimation?",{"text":80,"@type":76},"Traditional SDR-based wireless tracking can produce large errors under low SNR and severe multipath effects.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed method improve TOA estimation accuracy?",{"text":84,"@type":76},"It integrates machine learning into TOA estimation and uses SVM regression in an SDR framework to reduce the impact of noise, achieving metre-level ranging in 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