Device Localization Using mmWave Ranging with Sub-6-Assisted Angle of Arrival Estimation

Maletić, Nebojša; Sark, Vladica; Gutiérrez, Jesús; Grass, Eckhard

doi:10.1109/bmsb.2018.8436861

Cited by 18 publications

(21 citation statements)

References 22 publications

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“…An earlier work [25] demonstrated that using angle-of-arrival estimates from low-frequency (2.4/5.8-GHz) antenna arrays to reduce the overhead of mmWave beam training is in fact a promising approach, both for the inference of the optimal steering angles and for the detection of LoS paths, which are subject to lower attenuation than NLoS paths and thus yield higher throughput. More recently, independent measurements carried out in an anechoic chamber confirm that sub-6 GHz angle measurements can inform mmWave beam pointing to a degree that achieves near-ideal range measurements [53].…”

Section: The Importance Of Location Informationmentioning

confidence: 91%

Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments

et al. 2019

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Millimeter wave (mmWave) communications have emerged as one of the most promising options to vastly increase wireless data rates due to the high bandwidth they offer. Given the high path loss at mmWave frequencies, such systems require directional antennas to achieve a good communication range. The communicating devices thus need to align the beam directions of their mmWave antennas. Due to the high penetration loss, the paths between the antennas also need to be free of blocking obstacles. This makes efficient and reliable operation of mmWave networks in dynamic environments very challenging. At the same time, the directionality reduces interference and allows to scale these networks to much higher access point and device densities. In this article, we discuss the above challenges and present techniques that allow mmWave networks to scale to high-density deployments, to adapt to dynamic and mobile environments, and to consistently achieve high data rates. This includes learning the environment to find different propagation paths, reacting timely to channel impairments such as blockage, and integrating mmWave networks with networks operating at lower-frequency for robustness. A key ingredient to enable these forms of adaptivity is the use of location information. Such mechanisms then turn a collection of very-high-speed but brittle mmWave links into an efficient, low-latency, and reliable network.

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Section: The Importance Of Location Informationmentioning

confidence: 91%

Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments

et al. 2019

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“…Because a user's position in a cell can be localized by the BS [46,47], we assume that the BS can observe the positions of users in the system. With a group of users adjacent to each other, the BS can form a narrower beam, which can provide a higher beam gain than a wider beam can [9]; this boosts the performance of the mmWave-NOMA system.…”

Section: St-dbscan Based User Clusteringmentioning

confidence: 99%

Spatial-Temporal-DBSCAN-Based User Clustering and Power Allocation for Sum Rate Maximization in Millimeter-Wave NOMA Systems

2020

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The combination of millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA) systems exploits the capability to serve multiple user devices simultaneously in one resource block. User clustering, power allocation (PA), and hybrid beamforming problems in mmWave-NOMA systems can utilize the network setting’s potential to enhance the system performance. Based on similar characteristics of the spatial distributions of users in real life, we propose a novel spatial-temporal density-based spatial clustering of applications with noise (ST-DBSCAN)-based unsupervised user clustering in order to enhance the system sum-rate. ST-DBSCAN is a state-of-the-art density-based clustering algorithm for solving spatial and non-spatial problems. Moreover, instead of symmetric PA, we propose an inter-cluster PA algorithm. Next, we apply boundary-compressed particle swarm optimization in order to reduce inter-cluster interference and enhance system performance. The simulation results reveal that our proposed solution improves the sum-rate of mmWave-NOMA-based systems when compared with that of mmWave-OMA-based systems. In addition, we compare our proposed algorithm with other benchmark user clustering algorithms in order to investigate the performance of our ST-DBSCAN-based user clustering algorithm. The results also illustrate that our proposed approach outperforms the state-of-the-art user clustering algorithms in mmWave-NOMA systems.

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“…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. Also, estimating the maximum direction of angle of arrival (AoA) is feasible with ±5 ∘ of precision, according to [25], and is more reliable than Time Difference of Arrival (TDoA) and Received Signal Strength (RSS), as evaluated in [26].…”

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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Device Localization Using mmWave Ranging with Sub-6-Assisted Angle of Arrival Estimation

Cited by 18 publications

References 22 publications

Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments

Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments

Spatial-Temporal-DBSCAN-Based User Clustering and Power Allocation for Sum Rate Maximization in Millimeter-Wave NOMA Systems

A Clustering Approach for Multiband Neighbor Discovery on 60 GHz WLAN

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