Blockage Effect and Beam Cooperation in Indoor Hotspot Based on 3GPP NR Blockage Model

Wu, Yang; Liu, Kuang-Hao

doi:10.1109/icc.2019.8761104

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…[ [19][20][21][22] Beam cooperation and delegation schemes between UE and APs in indoor mmWave networks.…”

Section: Algorithmmentioning

confidence: 99%

“…Beam blockage for mmWave communications in indoor networks have also been addressed. For example, the authors of [19] investigate a beam cooperation mechanism that configures a beam pair between user equipment (UE) and multiple access points (AP) installed on a ceiling, and is based upon three GPP channel models. However, indoor settings feature increased directivity and rich scattering environment, as compared to sparse outdoor scattering.…”

Section: Beam Recovery Schemes In Indoor Mmwave Networkmentioning

confidence: 99%

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Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications

2021

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Millimeter wave (mmWave) bands formulate the standalone (SA) operation mode in the new radio (NR) access technology of 5G systems. These bands rely on beamforming architectures to aggregate antenna array gains that compensate for dynamic channel fluctuations and propagation impairments. However, beamforming results in directional transmission and reception, thus resulting in beam management challenges, foremost initial access, handover, and beam blockage recovery. Here, beam establishment and maintenance must feature ultra-low latencies in the control and data planes to meet network specifications and standardization. Presently, existing schemes rely on arrays redundancy, multi-connectivity, such as dual-beam and carrier aggregation, and out-of-band information. These schemes still suffer from prolonged recovery times and aggregated power consumption levels. Along these lines, this work proposes a fast beam restoration scheme based on deep learning in SA mmWave networks. Once the primary beam is blocked, it predicts alternative beam directions in the next time frame without any reliance on out-of-band information. The scheme adopts long short-term memory (LSTM) due to the robust memory structure, which uses past best beam observations. The scheme achieves near-instantaneous recovery times, i.e., maintaining communications sessions without resetting beam scanning procedures.

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“…[ [19][20][21][22] Beam cooperation and delegation schemes between UE and APs in indoor mmWave networks.…”

Section: Algorithmmentioning

confidence: 99%

Section: Beam Recovery Schemes In Indoor Mmwave Networkmentioning

confidence: 99%

Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications

2021

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“…2) Dynamic blockage generation: to model the blockage loss, we adopt the Blockage Model B that is incorporated within the 3GPP 3D SCM. Compared to the stochastic Blockage Model A [37], the deterministic Blockage Model B supports spatial consistency, which enables to simulate the smooth and continuous-time evolution of the blockage effect while capturing the correlated behaviour of the blockage loss for UEs located closely [25]. As a case study, we consider the presence of one blocker at a time moving in the network scenario.…”

Section: A Experiments Setupmentioning

confidence: 99%

Performance of Predictive Indoor mmWave Networks with Dynamic Blockers

Bonfante¹,

Giordano²,

Macaluso³

et al. 2021

Preprint

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In this paper, we consider millimeter Wave (mmWave) technology to provide reliable wireless network service within factories where links may experience rapid and temporary fluctuations of the received signal power due to dynamic blockers, such as humans and robots, moving in the environment. We propose a novel beam recovery procedure that leverages Machine Learning (ML) tools to predict the starting and finishing of blockage events. This erases the delay introduced by current 5G New Radio (5G-NR) procedures when switching to an alternative serving base station and beam, and then reestablish the primary connection after the blocker has moved away. Firstly, we generate synthetic data using a detailed systemlevel simulator that integrates the most recent 3GPP 3D Indoor channel models and the geometric blockage Model-B. Then, we use the generated data to train offline a set of beam-specific Deep Neural Network (DNN) models that provide predictions about the beams' blockage states. Finally, we deploy the DNN models online into the system-level simulator to evaluate the benefits of the proposed solution. Our prediction-based beam recovery procedure guarantee higher signal level stability and up to 82% data rate improvement with respect detection-based methods when blockers move at speed of 2 m/s.

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