Elsevier

Computer Networks

Volume 198, 24 October 2021, 108344
Computer Networks

Beam searching for mmWave networks with sub-6 GHz WiFi and inertial sensors inputs: An experimental study

https://doi.org/10.1016/j.comnet.2021.108344Get rights and content

Abstract

Beam training in dynamic millimeter-wave (mm-wave) networks with mobile devices is highly challenging as devices must scan a large angular domain to maintain alignment of their directional beams under mobility. In this work, we exploit the trend of multiple chipsets integrated in the same mobile device to 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 mm-wave devices. We experimentally evaluate SLASH with commodity hardware, including a 60 GHz testbed, commercial sub-6 GHz WiFi APs and smartphones. SLASH can increase the median data rate by more than 22% for link establishment and 25% for link maintenance with respect to prior work.

Introduction

Fine beam alignment of the highly directional antennas of mm-wave communication systems is necessary to achieve high data rates or even just a sufficient link margin for communication. The need for fast and efficient beam training strategies has stimulated a variety of research studies, both theoretical and experimental [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]. There has been substantial progress in terms of beam training efficiency compared to the original brute force or (optionally) hierarchical training of IEEE 802.11ad. For instance, the compressive beam training approaches only need to scan a subspace of the available antenna beams [9], [11]. Nevertheless, dense deployments with many devices or networks with high mobility remain a challenge. In dense networks with small cell sizes, handovers occur frequently, and a device may need to beam train with potentially many Access Points (APs) to determine to which APs it has the best link quality. In this case, it has been shown that using contextual information can help provide beam steering information to speed up the link establishment without the need for explicit beam training [10], [12].

However, prior works have mainly considered in isolation non mm-wave inputs. Motivated by the availability of multiple chipsets in the same mobile device, we investigate how to jointly use contextual input data, in particular the orientation of the mobile device and its distance and angle to the AP as measured with sub-6 GHz legacy WiFi, for speeding up the beam search at mm-wave frequencies. With these inputs, the problems that we aim to address are how to (i) maintain beam alignment under device rotation over a very short period of time, (ii) reduce beam training delay exploiting angle information with sub-6 GHz legacy WiFi and channel proprieties at mmwave frequency, and (iii) increase the data rate during the handover process to other APs in range using knowledge of the estimated distance with time measurements extracted with sub-6, GHz legacy WiFi.

We design SLASH, a beam search strategy that exploits out-of-mmwave band contextual input easily available in today’s devices. Our contributions are as follows:

  • For link establishment we use angle of arrival extracted from channel state information in sub-6 GHz legacy WiFi as input to narrow down the sector search space. We further exploit the relationship between the quasi-reciprocity of the mm-wave channel to further speed up the link establishment;

  • For link maintenance, we propose a fast strategy to maintain the mm-wave link by tracking the device orientation under user mobility through inertial measurements of the mobile device;

  • For handover, we use the distance measured with Fine Time Measurements (FTM) integrated in sub-6 GHz legacy WiFi to proactively select the AP to connect to;

  • We conduct our studies with experiments with a 60 GHz testbed to validate our approach, and we compare SLASH both to the IEEE 802.11ad standard and prior work. Our study indicates that SLASH is very effective in increasing the data rate with respect to prior work.

Section snippets

Motivation

mm-wave communication supports physical data rates of several Gb/s using highly-directional phased antenna arrays [13]. Examples of technology using mm-wave are the IEEE 802.11ad standard for Wireless Local Area Networks (WLANs) in the 60 GHz band [14] and 5G cellular networks for licensed mm-wave bands [15]. The communication between AP and User Equipment (UE) in mm-wave requires:

Link establishment: beam training is needed to find the Angle of Departure (AoD) at the transmitter and the Angle

SLASH with ideal directional antennas

In this section, we present the ideal working principle of SLASH for mm-wave link establishment and maintenance. SLASH is a beam search strategy which exploits input from inertial sensors, channel properties at mm-wave and sub-6 GHz angular information obtained from CSI and ranging estimations from FTM measurements.

Deployment and experimental platforms

In this section we first introduce the deployment scenario and then the experimental platforms for the evaluation of SLASH during link establishment and link maintenance.

System evaluation

In this section, we first introduce the real working principle of SLASH, using real beam patterns extracted from the hardware described in Section 4.2.1. We then evaluate the performance of our proposed algorithm in static and mobile scenarios, comparing it against existing solutions in the literature.

Related work

The problem of fast mm-wave link establishment and maintenance is widely discussed in the literature. A comparative analysis of initial access techniques in mm-wave networks is presented in [1]. Simultaneous transmissions from multiple direction-coded beams to accelerate the beam search are exploited in [2], [3]. In [25] the authors use multi-lobe antenna patterns with random phase shifts for the beam training, which enables compressive sensing approaches to determine AoA and AoD. This approach

Conclusion

In this work we investigated how context information from various out-of-band inputs such as channel state information and fine time measurements from legacy WiFi devices as well as from the inertial sensors of the smartphone can be used jointly to speed-up the beam training process in mm-wave networks. We have then introduced SLASH, an algorithm to perform beam search for link establishment and maintenance. We have shown through extensive experiments that SLASH can significantly increase the

CRediT authorship contribution statement

Maurizio Rea: Writing – review & editing, Writing – original draft, Conceptualization, Investigation, Methodology, Visualization, Formal analysis. Domenico Giustiniano: Funding acquisition, Supervision, Conceptualization, Project administration, Writing – review & editing. Pablo Jiménez Mateo: Writing – review & editing, Investigation. Yago Lizarribar: Writing – review & editing, Investigation. Joerg Widmer: Supervision, Conceptualization, Project administration, Writing – review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research work was sponsored in part by the European Union’s Horizon 2020 research and innovation programme under Grant No. 871249 (LOCUS), and in part by Ministerio de Ciencia, Innovación y Universidades (MICIU) grant RTI2018-094313-B-I00 (PinPoint5G+), Spain.

Maurizio Rea is Post-Doc Research at IMDEA Networks Institute, Madrid, Spain. He holds a Ph.D. in Telematics Engineering from the University Carlos III of Madrid. He received his M.Sc. in 2015 in Telecommunications Engineering from the University of Palermo, Italy. He also received a M.Sc. from the University Carlos III of Madrid in 2016. Before joining IMDEA, he worked as Researcher at ETH Zurich where he focused his research on indoor localization systems. His interests include data analysis,

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    Maurizio Rea is Post-Doc Research at IMDEA Networks Institute, Madrid, Spain. He holds a Ph.D. in Telematics Engineering from the University Carlos III of Madrid. He received his M.Sc. in 2015 in Telecommunications Engineering from the University of Palermo, Italy. He also received a M.Sc. from the University Carlos III of Madrid in 2016. Before joining IMDEA, he worked as Researcher at ETH Zurich where he focused his research on indoor localization systems. His interests include data analysis, wireless communication, mmWave networks, beamforming algorithms, channel state information, angle of arrival algorithms and context-aware mechanisms.

    Domenico Giustiniano is Research Associate Professor (tenured) at IMDEA Networks, Madrid, Spain. He holds a Ph.D. in Telecommunication Engineering from the University of Rome Tor Vergata (2008). Before joining IMDEA, he was a Senior Researcher and Lecturer at ETH Zurich. He also worked for a total of four years as Post-Doctoral Researcher in industrial research labs (Disney Research Zurich and Telefonica Research Barcelona). He has authored over 100 international papers, he is Leader of the OpenVLC Project and Co-Founder of the non-profit Electrosense Association. His research interests are in the area of Pervasive Wireless Systems, with networking solutions that use technologies such as LiFi systems, large scale spectrum sensing and 5G localization.

    Pablo Jimenez M. is currently pursuing his Ph.D. in Telematics Engineering from the University Carlos III of Madrid, Spain. He holds a B.Sc. in Computational Mathematics and another B.Sc. in Computer Engineering, both from Universitat Jaume I at Castellón de la Plana, Spain. He also received two M.Sc., one in Artificial Systems from the same university and one in Telematics Engineering from the University Carlos III of Madrid.

    Yago Lizarribar is currently a Ph.D. Student at IMDEA Networks working on Distributed Wireless Sensor Networks and Localization. He previously obtained his B.Sc. and M.Sc. in Engineering in 2016 and 2018 respectively at the University of Navarra. He also received a M.Sc. in Telematics Engineering in 2020 at the University Carlos III of Madrid, Spain.

    Joerg Widmer is Research Professor and Research Director of IMDEA Networks in Madrid, Spain. Before, he held positions at DOCOMO Euro-Labs in Munich, Germany and EPFL, Switzerland. He was a visiting researcher at the International Computer Science Institute in Berkeley, USA, University College London, UK, and TU Darmstadt, Germany. His research focuses on wireless networks, ranging from extremely high frequency millimeter-wave communication and MAC layer design to mobile network architectures. Joerg Widmer authored more than 200 conference and journal papers and three IETF RFCs, and holds 14 patents. He was awarded an ERC consolidator grant, the Friedrich Wilhelm Bessel Research Award of the Alexander von Humboldt Foundation, a Mercator Fellowship of the German Research Foundation, a Spanish Ramon y Cajal grant, as well as nine best paper awards. He is an IEEE Fellow and Distinguished Member of the ACM.

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