Zero Overhead Device Tracking in 60 GHz Wireless Networks using Multi-Lobe Beam Patterns

Loch, Adrian; Assasa, Hany; Palacios, Joan; Widmer, Joerg; Suys, Hans; Debaillie, Björn

doi:10.1145/3143361.3143395

Cited by 29 publications

(13 citation statements)

References 90 publications

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“…A "beam sounding" mechanism is introduced to estimate the link quality for selected beams, and identify and adapt to link impairments. In [31] the authors develop a zero overhead beam tracking mechanism that uses two different beam patterns during the preamble of the frames. This allows to estimate user rotation and movement.…”

Section: Related Workmentioning

confidence: 99%

Beam Search Strategy for MillimeterWave Networks with Out-of-Band Input Data

Rea

Giustiniano

Bielsa

et al. 2020

2020 Mediterranean Communication and Computer Networking Conference (MedComNet)

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Beam training in dynamic millimeter-wave (mmwave) networks with mobile devices is highly challenging as devices must scan a large angular domain to maintain alignment of their directional antennas under mobility. Exploiting the fact that mobile devices will typically integrate multiple chipsets, we study a set of non-mmwave input data that can be leveraged jointly to provide faster beam search and better data rate. We leverage these findings to introduce SLASH, an algorithm that adaptively narrows the sector search space and accelerates link establishment, link maintenance and handover between mmwave devices. We evaluate SLASH both with simulations and experiments in a 60-GHz testbed. SLASH can increase the data rate by more than 64% for link establishment and 67% for link maintenance with respect to prior work.

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Section: Related Workmentioning

confidence: 99%

Beam Search Strategy for MillimeterWave Networks with Out-of-Band Input Data

Rea

Giustiniano

Bielsa

et al. 2020

2020 Mediterranean Communication and Computer Networking Conference (MedComNet)

Self Cite

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“…By default, the IEEE 802.11ad protocol resorts to costly beamtraining when nodes move. Related work presents improvements on this behavior, such as short frames preceding data packets to assess the current state of the channel and react accordingly [48], or using the data packet preamble to slightly steer the main lobe to the sides [49]. Cacciapuoti [50] has designed a mobility-aware user association strategy for 5G-mmWave networks based on association strategies.…”

Section: Beam Steeringmentioning

confidence: 99%

Machine Learning Based Network Analysis Using Millimeter-Wave Narrow-Band Energy Traces

Scalabrin

Bielsa

Loch

et al. 2020

IEEE Trans. on Mobile Comput.

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“…Rather than carrying out a full beam training, more sophisticated solutions can reduce such overhead to nearly zero by performing tracking on a per-packet basis [23]. This approach uses beam patterns with two strong adjacent lobes during part of the packet preamble, where the lobes have a 180°phase shift with respect to each other.…”

Section: B the Impact Of Dynamicsmentioning

confidence: 99%

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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Zero Overhead Device Tracking in 60 GHz Wireless Networks using Multi-Lobe Beam Patterns

Cited by 29 publications

References 90 publications

Beam Search Strategy for MillimeterWave Networks with Out-of-Band Input Data

Beam Search Strategy for MillimeterWave Networks with Out-of-Band Input Data

Machine Learning Based Network Analysis Using Millimeter-Wave Narrow-Band Energy Traces

Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments

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