Localization as a feature of mmWave communication

Lemić, Filip; Martin, James C.; Yarp, Christopher; Chan, Douglas S.; Handziski, Vlado; Brodersen, R.W.; Fettweis, Gerhard; Wolisz, Adam; Wawrzynek, John

doi:10.1109/iwcmc.2016.7577201

Cited by 112 publications

(77 citation statements)

References 22 publications

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“…Highly directive transmission with mmWave and massive MIMO is also another important feature that will make TOA and AOA more reliable localization measurements [47]. In particular, mmWave transmission is occupying the bandwidth up to 2 GHz with center frequencies around 20 GHz and above.…”

Section: G Localizationmentioning

confidence: 99%

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: G Localizationmentioning

confidence: 99%

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

“…The raw resolution of mmWave systems, defined as the distance light travels between these sampling instants, is the smallest distance difference that can be measured. With available bandwidth B, the raw resolution is given by [20]:…”

Section: Positioning In Future Mmwave Wideband Communication Systemsmentioning

confidence: 99%

Position Locationing for Millimeter Wave Systems

Kanhere¹,

Rappaport²

2018

2018 IEEE Global Communications Conference (GLOBECOM)

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The vast amount of spectrum available for millimeter wave (mmWave) wireless communication systems will support accurate real-time positioning concurrent with communication signaling. This paper demonstrates that accurate estimates of the position of an unknown node can be determined using estimates of time of arrival (ToA), angle of arrival (AoA), as well as data fusion or machine learning. Real-world data at 28 GHz and 73 GHz is used to show that AoA-based localization techniques will need to be augmented with other positioning techniques. The fusion of AoA-based positioning with received power measurements for RXs in an office which has dimensions of 35 m by 65.5 m is shown to provide location accuracies ranging from 16 cm to 3.25 m, indicating promise for accurate positioning capabilities in future networks. Received signal strength intensity (RSSI) based positioning techniques that exploit the ordering of the received power can be used to determine rough estimates of user position. Prediction of received signal characteristics is done using 2-D ray tracing.

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“…In [4] indoor positioning of a device using a single reference device was proposed, by exploiting multipath information in the wideband 5G waveform. The fact that few reference devices are needed was also observed in [13], which conducted an evaluation of the performance of different measurements under LOS and non-LOS (NLOS), also exploiting signal reflections. Finally, the flexibility of using multiple antennas jointly with wideband signals in the mmWave band and sparse signal-processing (compressive and sparse sensing) has been considered for channel parameter estimation, where angles and delays can be estimated jointly [14].…”

Section: Positioning and Tracking Algorithmsmentioning

confidence: 99%

5G mmWave Positioning for Vehicular Networks

Wymeersch

Seco‐Granados

Destino

et al. 2017

IEEE Wireless Commun.

391

247

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AbstrAct5G technologies present a new paradigm to provide connectivity to vehicles, in support of high data-rate services, complementing existing inter-vehicle communication standards based on IEEE 802.11p. As we argue, the specific signal characteristics of 5G communication turn out to be highly conducive for vehicle positioning. Hence, 5G can work in synergy with existing on-vehicle positioning and mapping systems to provide redundancy for certain applications, in particular automated driving. This article provides an overview of the evolution of cellular positioning and discusses the key properties of 5G as they relate to vehicular positioning. Open research challenges are presented. requirements for VehiculAr PositioningWith the increase of automated driving in various forms (highway assistance driving, automatic cruise control, self-parking, up to fully autonomous driving) comes a need for precise positioning information. Positioning of vehicles is achieved through a variety of technologies, as illustrated in Fig. 1, including global navigation satellite-based systems (GNSS), radar, mono and stereo cameras, and laser scanners (lidar), which are fused to give the vehicle an understanding of the environment and its location within this environment. The environment is encoded through a map, which is either stored offline or computed online. The process of learning the environment and building detailed maps using onboard sensors is known as mapping. Different positioning applications have different requirements, which are expressed in terms of accuracy, latency, reliability, and cost. On one hand, standard vehicular navigation applications require only a few meters of absolute positioning accuracy, second-level latency, and low reliability (frequent outages are tolerable), but must rely on low-cost sensors. On the other hand, the safety-critical application of autonomous driving will require centimeter-level absolute and relative positioning accuracy, latencies on the order of tens of milliseconds, and high reliability, but can rely on a more expensive suite of sensors. An overview of the accuracy requirements for several key applications is shown in Table 1.GNSS, which has been the workhorse for vehicular absolute positioning in military, professional, and personal navigation, leads to uncertainties on the order of a few meters. Complemented by dedicated base stations, real-time kinematic GNSS further improves the accuracy down to the centimeter level. However, GNSS fails to work in certain common conditions, such as under tree canopies, in the presence of GNSS jammers, and in dense urban environments, due to the blocking of GNSS signals by buildings. Moreover, GNSS is limited by significant latency and low refresh rate, which are key requirements for guaranteeing safety.For relative positioning, onboard sensors such as cameras, radars, and lidars can generally operate well under these GNSS-challenged conditions, and provide very precise information. However, these sensors are costly in terms of computational ...

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Localization as a feature of mmWave communication

Cited by 112 publications

References 22 publications

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

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

Position Locationing for Millimeter Wave Systems

5G mmWave Positioning for Vehicular Networks

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