Positioning Technology Trends and Solutions Toward 6G

Säily, Mikko; Yilmaz, Osman N. C.; Michalopoulos, Diomidis S.; Perez, Eva; Keating, Ryan; Schaepperle, Joerg

doi:10.1109/pimrc50174.2021.9569341

Cited by 22 publications

(18 citation statements)

References 4 publications

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“…The evolution of expectations, and thus solutions, in cellular positioning remains unbroken-just as the emergence of commercial use cases such as factory automation, transportation, and logistics have shaped 5G positioning, advanced commercial applications of the future such as extended reality (XR) gaming, telemedicine, driverless vehicles, and autonomous industrial systems will largely influence the evolution of cellular networks beyond 5G [25,[69][70][71][72][73].…”

Section: Beyond 5gmentioning

confidence: 99%

“…The 6G network will aim to satisfy the challenging requirements of these new applications based on its unprecedented technological advancements. These advancements include even higher frequency ranges, wider bandwidths, massive antenna arrays, intelligent surfaces, intelligent beam-space processing, AI and machine-learning-based techniques, sidelink solutions, architecture evolution, and beyond connectivity [25,[69][70][71][72][73][74][75]. These new technologies (as illustrated by Figure 6) will enhance legacy solutions through efficiency and cost optimization [25,74], and open up new possibilities for 6G localization [25,[69][70][71][72][73].…”

Section: Beyond 5gmentioning

confidence: 99%

“…These advancements include even higher frequency ranges, wider bandwidths, massive antenna arrays, intelligent surfaces, intelligent beam-space processing, AI and machine-learning-based techniques, sidelink solutions, architecture evolution, and beyond connectivity [25,[69][70][71][72][73][74][75]. These new technologies (as illustrated by Figure 6) will enhance legacy solutions through efficiency and cost optimization [25,74], and open up new possibilities for 6G localization [25,[69][70][71][72][73]. In order to satisfy the requirements of new technologies, networks beyond 5G will be forced to conform to even higher standards related to positioning, translating to possibly under 0.5 ms latency, up to eight nines reliability and down to centimeter-level positioning accuracy [72,75,76], or even sub-centimeter level relative-positioning accuracy [73].…”

Section: Beyond 5gmentioning

confidence: 99%

“…The possible capabilities of 6G networks as opposed to 5G are summarized in Table 3. In current 5G NR positioning, position estimation is based on three components: signal strength, which is often inaccurate because both the path loss of the radio channel and the exact gain of the transmitter and receiver chains are unknown, time-of-arrival, which is limited by the bandwidth of the Positioning Reference Signal (PRS), or angle measurements whose accuracy is limited by the size of the antenna array or the aperture [25].…”

Section: Beyond 5gmentioning

confidence: 99%

“…Ref. [25] discusses the technical trends and opportunities in 6G networks to achieve latency and accuracy improvements and low-cost positioning.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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