Localization of LTE measurement records with missing information

Ray, Avik; Deb, Suash; Monogioudis, P.

doi:10.1109/infocom.2016.7524370

Cited by 59 publications

(58 citation statements)

References 17 publications

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“…To address this challenge, authors employ information such as the distance to the BS, location of neighboring BSs, and levels of interference and noise into a Bayesian-based method that improves the standardized Cell-ID method (enhanced with RTT measurements) by 20%. The low number of BSs is confirmed by researchers at Alcatel-Lucent, U.S.A. who report that most of the observations in a 4G LTE commercial network contain only signal strength information from the serving cell and in some cases (depending on the network event that generated the measurement) one additional signal strength value from the strongest neighbor cell [30]. A machine learning solution is presented based on supervised training of Random Forest with labeled drive-test data to learn the signal strength values at different locations, combined with particle filter-based HMM to perform user tracking with network Measurement Reports (MR).…”

Section: A Academic Solutionsmentioning

confidence: 80%

“…This phenomenon is known to be incurred by the changes of environmental factors (e.g., humidity, people movement, door/window open/close, etc. ), heterogeneous device types, and device statues (e.g., [28] without converting them to geographic coordinates Commercial Sprint Bayesian method with distance to the base station, location 20% better than Cell-ID CDMA2000 [29] of neighboring base stations, and SNR levels Alcatel-Lucent Supervised training of Random Forest with labeled driveMedian error 20 m to 25 m 4G/LTE [30] test data, combined with HMM Comtech Xypoint end-to-end solution offers hybridization of indoor N/A 2G, 3G, 4G/LTE [31] and outdoor positioning techniques Ericsson Mobile Positioning System supports complementary N/A 2G, 3G, 4G/LTE [32] positioning methods Viavi ariesoGEO multi-vendor and multi-positioning platform Error 100 m…”

Section: B Fingerprint Matchingmentioning

confidence: 99%

“…This is an interesting approach because the majority of network MRs, especially in LTE networks, usually contain information only from the serving cell and sometimes the strongest neighboring cell [29], [30]. MonoSense extracts features from both the time and frequency domain information available from the serving cell tower over different sliding window sizes.…”

Section: B Signal Power Measurementsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

354

171

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Abstract-Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches.The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

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Section: A Academic Solutionsmentioning

confidence: 80%

Section: B Fingerprint Matchingmentioning

confidence: 99%

Section: B Signal Power Measurementsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

354

171

View full text Add to dashboard Cite

show abstract

“…AT&T researchers recently studied the fingerprinting-based outdoor localization problems [23], [19]. In particular, the authors in NBL [19] extended CellSense [13] similarly using two stages.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Suppose that we are using a RaF regression model to recover the outdoor locations L(s) and L(s ) for the samples s and s , respectively. The outdoor locations are frequently represented by GPS coordinates [12], [19], [23], [37]. Given the two distributed domains D = D , the MR samples s and s within the two domains indicate that the corresponding RNC/CellID and GPS positions are different, indicating s = s and L(s) = L(s ).…”

Section: System Design a General Ideamentioning

confidence: 99%

Transfer Learning-Based Outdoor Position Recovery With Cellular Data

Zhang

Ding

Ott

et al. 2021

IEEE Trans. on Mobile Comput.

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Telecommunication (Telco) outdoor position recovery aims to localize outdoor mobile devices by leveraging measurement report (MR) data. Unfortunately, Telco position recovery requires sufficient amount of MR samples across different areas and suffers from high data collection cost. For an area with scarce MR samples, it is hard to achieve good accuracy. In this paper, by leveraging the recently developed transfer learning techniques, we design a novel Telco position recovery framework, called TLoc, to transfer good models in the carefully selected source domains (those fine-grained small subareas) to a target one which originally suffers from poor localization accuracy. Specifically, TLoc introduces three dedicated components: 1) a new coordinate space to divide an area of interest into smaller domains, 2) a similarity measurement to select best source domains, and 3) an adaptation of an existing transfer learning approach. To the best of our knowledge, TLoc is the first framework that demonstrates the efficacy of applying transfer learning in the Telco outdoor position recovery. To exemplify, on the 2G GSM and 4G LTE MR datasets in Shanghai, TLoc outperforms a nontransfer approach by 27.58% and 26.12% less median errors, and further leads to 47.77% and 49.22% less median errors than a recent fingerprinting approach NBL.

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BSLoc: Base Station ID-Based Telco Outdoor Localization

Zhao

et al. 2019

Algorithms for Sensor Systems

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Localization of LTE measurement records with missing information

Cited by 59 publications

References 17 publications

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Transfer Learning-Based Outdoor Position Recovery With Cellular Data

BSLoc: Base Station ID-Based Telco Outdoor Localization

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