Overview of Positioning in 5G New Radio

Keating, Ryan; Säily, Mikko; Hulkkonen, Jari; Karjalainen, Juha

doi:10.1109/iswcs.2019.8877160

Cited by 105 publications

(49 citation statements)

References 7 publications

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“…User requirements and key performance indicator (KPI) defined for 5G positioning services are equally important and have been under active discussion in the 3GPP releases 16 and 17. However, it is worth noticing that there is a significant gap between the most stringent requirements of the use cases, as described in [61], [62], and what has been effectively achieved in the current 3GPP standards, in particular, in Release 16 [9], [63]. This gap between the requirements of the envisaged use cases and the performance targets in Release 17 may become a clear objective to be attained by the future 6G systems.…”

Section: Localization and Sensing Opportunities For Future 6g Systemsmentioning

confidence: 99%

“…Within RAN1 discussions, the relative time of arrival is also considered per beam basis in the NR configuration. Finally, the available reference signals, namely channel state information reference signal (CSI-RS) for beam management, CSI-RS for time-frequency tracking, CSI-RS for CSI acquisition, CSI-RS for mobility measurements, phase tracking reference signal (PTRS), demodulation reference signal (DMRS) need to be enhanced so as to support high accuracy positioning in the Rel-16 and upcoming releases [9]. Recently, the technical specification group started discussing the remaining issues for the uplink-/downlink-based positioning.…”

Section: Introductionmentioning

confidence: 99%

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Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges

et al. 2021

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Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.The associate editor coordinating the review of this manuscript and approving it for publication was Ahmed Farouk .

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Section: Localization and Sensing Opportunities For Future 6g Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges

et al. 2021

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“…Although 5G ranging is still under research [8], it promises a very high availability thanks to the omnipresence of 5G base stations. Long term evolution (LTE) has also been used for obtaining location, although its precision is not as high as UWB or WiFi FTM [9].…”

Section: Introductionmentioning

confidence: 99%

WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning

Álvarez‐Merino

Luo-Chen

Khatib

et al. 2021

Sensors

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High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of several different technologies, such as ultra-wide band (UWB) and Wi-Fi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and Wi-Fi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required.

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“…In addition to communication, high-resolution sensing functions are envisioned to be integrated in such RANs, which opens up new opportunities for high-precision positioning and tracking [1]- [4]. Along these lines, 3GPP Rel-16 has specified positioning support for 5G New Radio (NR) [5]. However, current network-side NR positioning techniques are based on triangulation with Angle-of-Arrival (AOA) or trilateration with Time-Difference-of-Arrival (TDOA) measurements at multiple BSs, which require rigorous calibration or accurate synchronization among network elements.…”

Section: Introductionmentioning

confidence: 99%

Network-side Localization via Semi-Supervised Multi-point Channel Charting

Deng

Tirkkonen

Zhang

et al. 2021

2021 International Wireless Communications and Mobile Computing (IWCMC)

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We consider the network-side mobile localization problem in future 5G and beyond wireless networks with distributed multi-antenna base stations (BSs). For this application, we propose a semi-supervised multi-point channel charting (SS-MPCC) framework, which consists of (i) collaborative collection of channel state information (CSI) and other sideinformation by distributed BSs; (ii) local CSI feature extraction and self-learning of a dissimilarity metric, and (iii) global graph construction and constrained manifold learning. We show that side-information from routine network operations, including timestamps, channel qualities, and a small set of labeled samples, can be exploited to construct a consistent global graph. The graph is then mapped to a 2D channel chart using constrained manifold learning for localization purposes. We evaluate the performance of SS-MPCC in a simulated urban outdoor scenario with realistic user motion. Our results show that SS-MPCC achieves a mean localization error of 5.6 m with only 10% of labeled CSI samples. SS-MPCC does not require accurate synchronization among multiple BSs and is promising for future cellular localization.

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Overview of Positioning in 5G New Radio

Cited by 105 publications

References 7 publications

Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges

Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges

WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning

Network-side Localization via Semi-Supervised Multi-point Channel Charting

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