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

Palacios, Joan; Casari, Paolo; Assasa, Hany; Widmer, Joerg

doi:10.1109/infocom.2019.8737434

Cited by 27 publications

(20 citation statements)

References 27 publications

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“…In order to bring down this latency to the order of 1 ms, there is a need to perform predictive management of traffic flows based on accurate context information about the device and environment and exploit multiple forms of diversity and redundancy simultaneously. An interesting approach in this direction is the use of location information, such as studied by Palacios et al [20]. Future research in this direction should go beyond pure throughput maximization and study how the location and movement of users and potential obstacles can be used for proactive handovers that guarantee a bounded latency.…”

Section: Discussion and Open Challengesmentioning

confidence: 99%

“…An interesting research direction to tackle this is to use context information about the devices and their environment (e.g., location information). Palacios et al recently evaluated the use of estimated location information to perform inter-AP handovers in IEEE 802.11ad mmWave networks [20]. However, their approach focused on throughput maximization and did not consider latency optimization.…”

Section: State Of the Artmentioning

confidence: 99%

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Immersive Interconnected Virtual and Augmented Reality: A 5G and IoT Perspective

et al. 2020

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In this article, we articulate the technical challenges to enable a future AR/VR end-to-end architecture, that combines 5G URLLC and Tactile IoT technology to support this next generation of interconnected AR/VR applications. Through the use of IoT sensors and actuators, AR/VR applications will be aware of the environmental and user context, supporting human-centric adaptations of the application logic, and lifelike interactions with the virtual environment. We present potential use cases and the required technological building blocks. For each of them, we delve into the current state of the art and challenges that need to be addressed before the dream of remote AR/VR interaction can become reality.

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Section: Discussion and Open Challengesmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Immersive Interconnected Virtual and Augmented Reality: A 5G and IoT Perspective

et al. 2020

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“…Commercial devices typically have codebooks with up to 64 predefined sectors [5]. Beam training for such codebook sizes typically takes several milliseconds, but can take up to seconds in dense AP deployments [17,32,54]. It also incurs unacceptable latency and a very high beam training overhead in mobile scenarios [55,56,57].…”

Section: Related Workmentioning

confidence: 99%

“…Based on the estimated Angle of Arrival (AoA) to the AP, the station then determines which of its own beam patterns to use for the current channel and also gives feedback to the AP to indicate which AP beam pattern provided the highest SNR. Beam switching on a per symbol level is a prerequisite for 802.11ad beam refinement and for in-packet training mechanisms in 802.11ay systems [31] and current devices already support this [32]. Our mechanism requires only minor changes in the signal processing at the stations.…”

Section: Introductionmentioning

confidence: 96%

Scalable Phase-Coherent Beam-Training for Dense Millimeter-Wave Networks

Martí

Lacruz

Mateo

et al. 2023

IEEE Trans. on Mobile Comput.

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mm-wave communications use analog beamforming techniques, which steer the signal energy in a desired direction, to overcome the high path-loss at such frequencies. To determine the direction in which to steer, mm-wave standards such as IEEE 802.11ad specify beam training mechanisms for both access points as well as client stations. However, the overhead of the beam training limits scalability as the density of network deployments increases and mobile devices that require constant re-training are supported. We design SPIDER, a low-overhead beam-training mechanism where only access points actively participate in the training and stations perform passive compressive estimation of the angle-of-arrival. To this end, stations carry out phase-coherent measurements by switching through multiple receive beam patterns on a time-scale of tens of nanoseconds when receiving a packet preamble. Since no suitable testbed platforms exist that support such fast antenna reconfiguration, we design a high-performance, full-bandwidth FPGA-based testbed platform for flexible mm-wave experimentation, that we make available as open source. The performance analysis with this testbed shows that our algorithm achieves highly accurate angle estimation used to drive the beam steering decisions and reduces overhead by an order of magnitude compared to IEEE 802.11ad beam training.

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“…AP-centric localization algorithms such as [11] exploit CSI measurements to infer angle information from mmWave signals sent by a client. A map-assisted positioning technique is proposed in [12] to estimate the location of the user.…”

Section: A Classical Mmwave Localization Systemsmentioning

confidence: 99%

Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks

Shastri¹,

Palacios²,

Casari³

2021

Preprint

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Millimeter wave (mmWave) localization algorithms exploit the quasi-optical propagation of mmWave signals, which yields sparse angular spectra at the receiver. Geometric approaches to angle-based localization typically require to know the map of the environment and the location of the access points. Thus, several works have resorted to automated learning in order to infer a device's location from the properties of received mmWave signals. However, collecting training data for such models is a significant burden. In this work, we propose a shallow neural network model to localize mmWave devices indoors. This model requires significantly fewer weights than those proposed in the literature. Therefore, it is amenable for implementation in resource-constrained hardware, and needs fewer training samples to converge. We also propose to relieve training data collection efforts by retrieving (inherently imperfect) location estimates from geometry-based mmWave localization algorithms. Even in this case, our results show that the proposed neural networks perform as good as or better than state-of-the-art algorithms.

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

Cited by 27 publications

References 27 publications

Immersive Interconnected Virtual and Augmented Reality: A 5G and IoT Perspective

Immersive Interconnected Virtual and Augmented Reality: A 5G and IoT Perspective

Scalable Phase-Coherent Beam-Training for Dense Millimeter-Wave Networks

Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks

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