Mobile Device Localization in 5G Wireless Networks

Wang, Dandan; Hosangadi, Gurudutt; Monogioudis, P.; Rao, A.

doi:10.1109/iccnc.2019.8685597

Cited by 9 publications

(4 citation statements)

References 2 publications

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“…Therefore, instead of estimating the mobile positioning information, we obtain it directly by a GPS software on the mobile phone, with a positioning error range of about 10 meters in our final dataset. Such error range is close to that of existing positioning methods [15,34] via WiFi or 5G networks. Finally, the wireless signals of 29 pedestrians are recorded and these 29 pedestrians are captured by at least two cameras.…”

Section: Wp-reid Datasetsupporting

confidence: 79%

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Vision Meets Wireless Positioning: Effective Person Re-identification with Recurrent Context Propagation

Liu

Zhou

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Existing person re-identification methods rely on the visual sensor to capture the pedestrians. The image or video data from visual sensor inevitably suffers the occlusion and dramatic variations of pedestrian postures, which degrades the re-identification performance and further limits its application to the open environment. On the other hand, for most people, one of the most important carry-on items is the mobile phone, which can be sensed by WiFi and cellular networks in the form of a wireless positioning signal. Such signal is robust to the pedestrian occlusion and visual appearance change, but suffers some positioning error. In this work, we approach person re-identification with the sensing data from both vision and wireless positioning. To take advantage of such cross-modality cues, we propose a novel recurrent context propagation module that enables information to propagate between visual data and wireless positioning data and finally improves the matching accuracy. To evaluate our approach, we contribute a new Wireless Positioning Person Re-identification (WP-ReID) dataset. Extensive experiments are conducted and demonstrate the effectiveness of the proposed algorithm. Code will be released at https://github.com/yolomax/WP-ReID. CCS CONCEPTS• Computing methodologies → Matching.

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Section: Wp-reid Datasetsupporting

confidence: 79%

“…Different methods such as trilateration, triangulation, and fingerprinting have been adopted to obtain position information. In [34], by utilizing the beam information of the 5G wireless network, the authors achieve a positioning accuracy of about 12 meters.…”

Section: Wireless Positioningmentioning

confidence: 99%

Vision Meets Wireless Positioning: Effective Person Re-identification with Recurrent Context Propagation

Liu

Zhou

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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“…The theoretical complexity depends only on the second one since the first one is executed off-line and the complexity of our solution will be O(n 2 ). The Algorithm described in [22] is based on SS-RSRP measurements and a step using Random forest classifier with the machine learning Library Sklearn. We are interested here in the Algorithm without considering the learning part which is also executed off-line.…”

Section: Complexity and Performance Analysismentioning

confidence: 99%

“…A comparative study of complexity is also performed with two recent works Wang in [22] and Kim in [23].…”

mentioning

confidence: 99%

Connected Objects Geo-Localization Based on SS-RSRP of 5G Networks

2021

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The Global Positioning System (GPS) is not the only way to solve connected objects’ geo-localization problems; it is also possible to use the mobile network infrastructure to geo-locate objects connected to the network, using antennas and signals designed for voice and data transfer, such as the 5th generation network. 5G is considered as a least expensive solution because there is no specific equipment to set up. As long as the object is in an area covered by the network, it connects to the nearest 5G Micro-Cell (MC). Through exchange of signals with the MC node we can locate the object. Currently, this location is very fast with less than 5 s but not very precise because it depends on the number of MC antennas of the operator in question and their distance. This paper presents a novel technique to geo-locate connected object in a covered 5G area. We exploit the 5G SS-RSRP used for signal quality measurement, to estimate the distance between two Connected Objects (COs) in move and in a dense urban area. The overall goal is to present a new concept laying on the 5G SS-RSRP signalling. The proposed solution takes into consideration the Deterministic and the Stochastic effect of the received signals which is not treated by the previous works. The accuracy is optimum even after approaching to the distance of one meter which is not reached in previous works too. Our method can also be deployed in the upcoming 5G network since it relies on 5G signals itself. This work and that of Wang are both based on RSRP and give comparable theoretical complexities therefore comparable theoretical execution times as well. However, to obtain a reliable learning Wang requires a huge amount of data which makes it difficult to get a real time aspect of this algorithm. The use of RSRP and the elimination of the learning phase will give more chance to our work to achieve desired performances. Numerical results show the appropriateness of the proposed algorithms and good location accuracy of around one meter. The Cramer Rao Lower Bound derivations shows the robustness of the proposed estimator and consolidate the work.

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Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches

Afifi¹,

Gadallah

2021

IEEE Access

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Mobile Device Localization in 5G Wireless Networks

Cited by 9 publications

References 2 publications

Vision Meets Wireless Positioning: Effective Person Re-identification with Recurrent Context Propagation

Vision Meets Wireless Positioning: Effective Person Re-identification with Recurrent Context Propagation

Connected Objects Geo-Localization Based on SS-RSRP of 5G Networks

Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches

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