Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion

Xhafa, Alda; Peral-Rosado, José A. del; López-Salcedo, José A.; Seco‐Granados, Gonzalo

doi:10.3390/s22010101

Cited by 24 publications

(10 citation statements)

References 16 publications

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“…Additionally, fast Fourier transform is used in this work to speed up the IAA method's computation of the spectrum [79]. The AOA and UTDOA based joint 5G positioning method proposed in [80] utilizes the antenna arrays to combine UTDOA and AOA measurements to considerably enhance the location accuracy. The study also suggests a selective exclusion strategy that omits NLOS observations and evaluates the performances of proposed method using a deep urban map and SRS from the 5G NR in the centimeter-wave band through simulation.…”

Section: Joint Estimation-based 5gmentioning

confidence: 99%

5G Positioning: Advantageous Aspects, Taxonomy, State-of-the-art Review and Future Trends

Sadhukhan,

Mukherjee,

Das

2024

Preprint

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5G positioning is an essential part of many applications, such as driverless vehicles, drone tracking, emergency services, location-based services, etc. Such positioning can attain an accuracy on the order of 1 meter or even lower in both indoor and outdoor settings due to its several advantageous aspects, which include millimeter wave signal, multiple input multiple output antennas, device-to-device communication, etc. Hence, many researchers have paid attention to the development of 5G positioning systems over the past decade. This paper, therefore, presents a state-of-the-art review of the existing 5G positioning methods along with their taxonomy and comparative analysis in several metrics including their merits and demerits. Moreover, the architecture and advantageous aspects of 5G positioning are also presented in this paper. Similar to the satellite signals, 5G signals, however, can be impeded by various obstructions, further declining the accuracy of such positioning. Moreover, many security concerns and privacy issues are also associated with 5G positioning. Thus, the development of a precise and secure 5G positioning system faces a number of challenges. Therefore, various ways to improve the 5G positioning accuracy and several solutions to address the security concerns and privacy issues of such positioning are pointed out in this study.

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Section: Joint Estimation-based 5gmentioning

confidence: 99%

5G Positioning: Advantageous Aspects, Taxonomy, State-of-the-art Review and Future Trends

Sadhukhan,

Mukherjee,

Das

2024

Preprint

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“…The presence of NLOS in the wireless channel path deteriorates both the ToA and AoA estimates if the NLOS paths cannot be detected and eliminated from the final positioning solution [ 24 , 25 ]. Recent research in [ 26 ] has also proposed ways to harness information from NLOS paths in order to enhance the UE position and orientation information.…”

Section: Signal Modelmentioning

confidence: 99%

Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

Jayawardana

Obaid

Yesilyurt

et al. 2023

Sensors

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The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors’ knowledge. This paper focuses on LOS/NLOS detection methods for 5G signals by using both statistical/model-driven and data-driven/machine learning (ML) approaches and three challenging channel model classes widely used in 5G: namely Tapped Delay Line (TDL), Clustered Delay Line (CDL) and Winner II channel models. We show that, with simulated data, the ML-based detection can reach between 80% and 98% detection accuracy for TDL, CDL and Winner II channel models and that TDL is the most challenging in terms of LOS detection capabilities, as its richness of features is the lowest compared to CDL and Winner II channels. We also validate the findings through in-lab measurements with 5G signals and Yagi and 3D-vector antenna and show that measurement-based detection probabilities can reach 99–100% with a sufficient amount of training data and XGBoost or Random Forest classifiers.

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“…Even though angle-based methods are one of the main winners of the massive MIMO introduced in 5G, the positioning in the LOS scenario does not require an ML approach [47,48]. On the other hand, even if the angle information cannot be used for accurate positioning (e.g., in a NLOS scenario), it can be utilized to enhance the accuracy of other approaches [49][50][51]. In most cases, the AoA/AoD is used as a fingerprint feature, which can seriously decrease the positioning error in an LOS scenario.…”

Section: Advances In Positioning Aided By Machine Learningmentioning

confidence: 99%

Positioning in 5G and 6G Networks—A Survey

Mogyorósi

Revisnyei

Pašić

et al. 2022

Sensors

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Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios.

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Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion

Cited by 24 publications

References 16 publications

5G Positioning: Advantageous Aspects, Taxonomy, State-of-the-art Review and Future Trends

5G Positioning: Advantageous Aspects, Taxonomy, State-of-the-art Review and Future Trends

Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

Positioning in 5G and 6G Networks—A Survey

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