Lightweight Indoor Localization for 60-GHz Millimeter Wave Systems

Olivier, Alain; Bielsa, Guillermo; Tejado, Irene; Zorzi, Michele; Widmer, Joerg; Casari, Paolo

doi:10.1109/sahcn.2016.7732999

Cited by 50 publications

(43 citation statements)

References 27 publications

(35 reference statements)

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“…This paper substantially extends several aspects of our previous work [1]. First, while [1] is concerned with singleanchor localization, this work focuses on realistic mmWave deployments with multiple APs and extends all localization algorithms to work with multiple anchors. In this sense, the algorithms in [1] are a special case of those presented in this paper.…”

Section: Introductionmentioning

confidence: 77%

“…The second one is based on ADoA information, and has the advantage that it is inherently immune to orientation biases, but requires a larger number of anchors than TV. These algorithms will be compared against two benchmark approaches from the literature: the first is based on fingerprinting (FP) [1], [36], which is lightweight for the client, but requires the setup of a fingerprint database, typically implying a very significant measurement collection effort; the second one is named CCAL and is based on the solution of ADoA localization as a convex quadratic problem [62]. All algorithms are designed to leverage the sparse AoA spectrum that results from the quasi-optical mmWave propagation, and can manage the multiple APs that are typically encountered in indoor mmWave deployments.…”

Section: Millimeter Wave Localization Schemesmentioning

confidence: 99%

“…First, while [1] is concerned with singleanchor localization, this work focuses on realistic mmWave deployments with multiple APs and extends all localization algorithms to work with multiple anchors. In this sense, the algorithms in [1] are a special case of those presented in this paper. When designing these improved algorithms, we adhere to the initial design requirement that localization should be achieved using only angle information as provided by the mmWave hardware (e.g., through sector information) and that the algorithms should have limited computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Single- and Multiple-Access Point Indoor Localization for Millimeter-Wave Networks

Palacios

Bielsa

Casari

et al. 2019

IEEE Trans. Wireless Commun.

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Millimeter wave (mmWave) location systems not only provide accurate positioning for location-based services, but can also help optimize network operations, for example through location-driven beam steering and access point association. In this paper, we design and evaluate localization schemes that exploit the characteristics of mmWave communication systems. We propose two range-free algorithms belonging to the broad classes of triangulation and angle difference-of-arrival. The schemes work both with multiple anchors and with as few as a single anchor, under the only assumption that the floor plan and the positions of the mmWave access points are known. Moreover, they are designed to be lightweight, so that even computationally-constrained devices can run them. We evaluate our proposed algorithms against two benchmark approaches based on fingerprinting and angles of arrival, respectively. Our results, obtained both by means of simulations and through measurements involving commercial 60-GHz mmWave devices, show that sub-meter accuracy is achieved in most of the cases, even in the presence of only a single access point. The availability of multiple access points substantially improves the localization accuracy, especially for large indoor spaces.

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Section: Introductionmentioning

confidence: 77%

Section: Millimeter Wave Localization Schemesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Single- and Multiple-Access Point Indoor Localization for Millimeter-Wave Networks

Palacios

Bielsa

Casari

et al. 2019

IEEE Trans. Wireless Commun.

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“…where P r (d 0 ) represents the power received at reference distance d 0 and η is the path loss exponent. 4) Fingerprinting: Fingerprinting approach is based on the fact that radio waves emitted from the base stations leave a unique radio fingerprint at a given location that can be used for localization [107]. The radio fingerprint is obtained by creating a database of the average values of RSS from various anchors at different locations.…”

Section: Fundamental Ranging Schemesmentioning

confidence: 99%

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

Saeed

Nam

Al-Naffouri

et al. 2019

IEEE Commun. Surv. Tutorials

146

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Current and future wireless applications strongly rely on precise real-time localization. A number of applications such as smart cities, Internet of Things (IoT), medical services, automotive industry, underwater exploration, public safety, and military systems require reliable and accurate localization techniques. Generally, the most popular localization/ positioning system is the Global Positioning System (GPS). GPS works well for outdoor environments but fails in indoor and harsh environments. Therefore, a number of other wireless local localization techniques are developed based on terrestrial wireless networks, wireless sensor networks (WSNs) and wireless local area networks (WLANs). Also, there exist localization techniques which fuse two or more technologies to find out the location of the user, also called signal of opportunity based localization. Most of the localization techniques require ranging measurements such as time of arrival (ToA), time difference of arrival (TDoA), direction of arrival (DoA) and received signal strength (RSS). There are also range-free localization techniques which consider the proximity information and do not require the actual ranging measurements. Dimensionality reduction techniques are famous among the range free localization schemes. Multidimensional scaling (MDS) is one of the dimensionality reduction technique which has been used extensively in the recent past for wireless networks localization. In this paper, a comprehensive survey is presented for MDS and MDS based localization techniques in WSNs, Internet of Things (IoT), cognitive radio networks, and 5G networks. 2 MDS is much popular among all these techniques because of its simplicity and many application areas. MDS analysis finds the spatial map for objects given that the similarity or dissimilarity information between the objects is available [16].In the recent past, MDS is widely used for localization and mapping of wireless sensor networks (WSNs) and the internet of things (IoT). In [17] a proximity information based sensor network localization is proposed, where the main idea is to construct a local configuration of sensor nodes by using classical MDS (CMDS). The MDS based localization algorithms in [17] and [18] are centralized with higher computational complexity [7]. Semi-centralized (or clustered) MDS techniques are developed to compute local coordinates of nodes, which then are refined to find the final position of the nodes [19], [20]. In [21], [22] and [23] the authors proposed manifold learning to estimate the sensor nodes position in wireless sensor networks. In [24] the authors proposed Nystrom approximation for the proximity information matrix in MDS to reduce its size for better localization accuracy in sensor networks. Distributed MDS based localization algorithm is proposed in [25] with noisy range measurements, where the authors assume that the distances are corrupted with independent Gaussian random noise. MDS methods with different refinement schemes have also been proposed in the literature t...

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“…The estimation of the distance among two nodes with a high accuracy is possible in the mmWave band due to low scattering characteristics in this region of spectrum. In [22,23], indoor localization mechanisms are proposed based on the exploitation of limited scattering, achieving submeter precision in distance measurement with high probability. Maletic et al [24] obtain precision enhancements in real experiments which result in 16 cm error in distance measurements.…”

Section: Transmission Angle Calculationmentioning

confidence: 99%

A Clustering Approach for Multiband Neighbor Discovery on 60 GHz WLAN

Brilhante

Rezende

2019

Wireless Communications and Mobile Computing

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The 60 GHz mmWave unlicensed band has a very large spectrum available, divided into four orthogonal channels, which allows up to 7 Gbps data rate. On the other side, the propagation in the 60 GHz band is subject to severe path loss attenuation, which can be mitigated using highly directional antennas. This high directionality brings a new challenge to neighbor discovery; the devices now need to know the exact neighbors’ physical location to successfully communicate with them. In order to expedite the neighbor discovery process, multiband protocols have been proposed in the literature in which a separate band is used for the exchange of control messages in an omnidirectional mode. Nonetheless, these proposals suffer from the control channel bottleneck problem due to the numerous messages that need to be exchanged in this channel. In this work, we propose a scheme that divides the network nodes in clusters and for each cluster we allocate one separate control channel and also a separate mmWave channel for beamforming only. The former separation allows decreasing the number of control messages exchanged in each control channel, and the latter allows the simultaneously execution of multiple beamforming. In conjunction to this clustering scheme, we propose a multiband protocol in which only the cluster leader performs beamforming and uses the control channel to propagate the information obtained during this process. We compare the existing protocols with our proposed clustering protocol in terms of average transmission time, overhead, and accuracy of neighbor discovery information.

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Lightweight Indoor Localization for 60-GHz Millimeter Wave Systems

Cited by 50 publications

References 27 publications

Single- and Multiple-Access Point Indoor Localization for Millimeter-Wave Networks

Single- and Multiple-Access Point Indoor Localization for Millimeter-Wave Networks

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

A Clustering Approach for Multiband Neighbor Discovery on 60 GHz WLAN

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