Communication-Driven Localization and Mapping for Millimeter Wave Networks

Palacios, Joan; Bielsa, Guillermo; Casari, Paolo; Widmer, Joerg

doi:10.1109/infocom.2018.8485819

Cited by 43 publications

(47 citation statements)

References 22 publications

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“…Multipath propagation can be exploited along with AoA information in order to localize a client [30] even with a single AP [31], although mmWave beamforming upon link establishment may turn out to sparsify the channel and reduce the number of useful multipath components [8]. Given sufficiently many reflected multi-path components, a mmWave location system can also be used to map the environment [32].…”

Section: Related Workmentioning

confidence: 99%

LEAP: Location Estimation and Predictive Handover with Consumer-Grade mmWave Devices

Palacios

Casari

Assasa

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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Future millimeter-wave networks will support very high densities of devices and access points. This vastly increases the overhead required for access point selection and beam training. Fortunately, the quasi-optical properties of millimeterwave channels make location-based network optimization a highly promising technique to reduce control overhead in such millimeter-wave WLANs. In this paper, we extract channel state information from off-the-shelf routers, we use it to design a high accuracy location system, and then show how location information enables the optimization of network operations. The resulting scheme, named LEAP, can predict blockage, optimize access point association, and select the most suitable antenna beam patterns while significantly reducing the beam training overhead. We show that compared to standard state-of-the-art 802.11ad systems, LEAP's location driven management greatly improves network performance and link stability.

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Section: Related Workmentioning

confidence: 99%

LEAP: Location Estimation and Predictive Handover with Consumer-Grade mmWave Devices

Palacios

Casari

Assasa

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

Self Cite

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“…A variety of works have been developed in the context of this paper. We can coarsely categorize these into three classes: methods based on geometry [7]- [10], methods based on message passing [4], [11], [12], and methods based on random finite set (RFS) theory.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [9], [10] develop SLAM methods applicable when the BS location is unknown, however, in their solutions at least three BSs are required.…”

Section: Introductionmentioning

confidence: 99%

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

Kim

Granström

Gao

et al. 2020

IEEE Trans. Wireless Commun.

134

128

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5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station (BS) and vehicles are equipped with large antenna arrays. However, radiobased positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. Building such a map is challenging, due to the inherent data association uncertainty, missed detection, and clutter. We propose a new method for cooperative vehicle positioning and mapping of the radio environment in order to address these challenges. The proposed method comprises a multimodel probability hypothesis density (PHD) filter and a map fusion routine, which is able to consider different types of objects and different fields of views (FoVs). Simulation results demonstrate that the proposed method handles the aforementioned challenges, and improves the vehicle positioning and mapping performance.

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“…Algorithms leveraging Angle Difference of Arrival (ADoA) were developed by first estimating the position of access points then estimate the location of the user by comparing the ADoA from each AP to the user. However, those methods require at least 4 APs [8], [9]. At mmWave frequencies, localization algorithms for LOS and NLOS were developed in [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Leveraging mmWave Imaging and Communications for Simultaneous Localization and Mapping

Aladsani

Alkhateeb

Trichopoulos

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this work, we propose a novel approach for high accuracy user localization by merging tools from both millimeter wave (mmWave) imaging and communications. The key idea of the proposed solution is to leverage mmWave imaging to construct a high-resolution 3D image of the line-of-sight (LOS) and non-line-of-sight (NLOS) objects in the environment at one antenna array. Then, uplink pilot signaling with the user is used to estimate the angle-of-arrival and time-of-arrival of the dominant channel paths. By projecting the AoA and ToA information on the 3D mmWave images of the environment, the proposed solution can locate the user with a sub-centimeter accuracy. This approach has several gains. First, it allows accurate simultaneous localization and mapping (SLAM) from a single standpoint, i.e., using only one antenna array. Second, it does not require any prior knowledge of the surrounding environment. Third, it can locate NLOS users, even if their signals experience more than one reflection and without requiring an antenna array at the user. The approach is evaluated using a hardware setup and its ability to provide sub-centimeter localization accuracy is shown.Index Terms-Millimeter wave communications, simultaneous localization and mapping, millimeter-wave imaging.

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Communication-Driven Localization and Mapping for Millimeter Wave Networks

Cited by 43 publications

References 22 publications

LEAP: Location Estimation and Predictive Handover with Consumer-Grade mmWave Devices

LEAP: Location Estimation and Predictive Handover with Consumer-Grade mmWave Devices

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

Leveraging mmWave Imaging and Communications for Simultaneous Localization and Mapping

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