research-article

Public Access

Environment-Aware Link Quality Prediction for Millimeter-Wave Wireless LANs

Authors:

Douglas M. BloughAuthors Info & Claims

MobiWac '22: Proceedings of the 20th ACM International Symposium on Mobility Management and Wireless Access

Pages 1 - 10

https://doi.org/10.1145/3551660.3560912

Published: 24 October 2022 Publication History

Abstract

Millimeter-wave (mmWave) communications have been regarded as one of the most promising solutions to deliver ultra-high data rates in wireless local-area networks. A significant barrier to delivering consistently high rate performance is the rapid variation in quality of mmWave links due to blockages and small changes in user locations. If link quality can be predicted in advance, proactive resource allocation techniques such as link-quality-aware scheduling can be used to mitigate this problem. In this paper, we propose a link quality prediction scheme based on knowledge of the environment. We use geometric analysis to identify the shadowed regions that separate LoS and NLoS scenarios, and build LoS and NLoS link-quality predictors based on an analytical model and a regression-based approach, respectively. For the more challenging NLoS case, we use a synthetic dataset generator with accurate ray tracing analysis to train a deep neural network (DNN) to learn the mapping between environment features and link quality. We then use the DNN to efficiently construct a map of link quality predictions within given environments. Extensive evaluations with additional synthetically generated scenarios show a very high prediction accuracy for our solution. We also experimentally verify the scheme by applying it to predict link quality in an actual 802.11ad environment, and the results show a close agreement between predicted values and measurements of link quality.

References

[1]

IEEE Standard 802.11ad-2012. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6392842

[2]

IEEE Standard 802.11ay/Draft 7.0: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Speci!cations- Amendment 2: Enhanced Throughput for Operation in License-Exempt Bands Above 45 GHz, Dec. 2020.

[3]

F. Firyaguna, A. Bonfante, J. Kibilda, and N. Marchetti, ''Performance evaluation of scheduling in 5G-mmWave networks under human blockage", arXiv preprint arXiv:2007.13112, 2020.

[4]

L. Shen, T. Wang, and S. Wang, ''Proactive proportional fair: A novel scheduling algorithm based on future channel information in OFDMA systems", Proc. of IEEE/CIC Int'l Conf. on Communications in China, 2019.

[5]

Y. Liu, Q. Hu, and D. Blough, ''Joint link-level and network-level recon!guration for mmWave backhaul survivability in urban environments", Proc. of Int'l ACM Conference on Modeling, Analysis & Simulation of Wireless & Mobile Systems, 2019.

Digital Library

[6]

B. Neekzad, K. Sayra!an-Pour, J. Perez, and J. S. Baras, ''Comparison of ray tracing simulations and millimeter wave channel sounding measurements", Proc. of IEEE Int'l Symposium on Personal, Indoor, and Mobile Radio Communications, 2007.

[7]

A. Alkhateeb, I. Beltagy, and S. Alex, ''Machine learning for reliable mmwave systems: Blockage prediction and proactive handoff", Proc. of IEEE Global Signal Inf. Process, 2018.

[8]

S. Shah, M. Sharma, and S. Rangan, "LSTM-based multi-link prediction for mmwave and sub-THz wireless systems", Proc. of IEEE Conf. on Commu., 2020.

[9]

A. Kalor, O. Simeone, and P. Popovski, ''Latency-constrained prediction of mmWave/THz link blockages through meta-learning", arXiv preprint arXiv:2106.07442, 2021.

[10]

Z. Ali, A. Duel-Hallen, and H. Hallen, ''Early warning of mmWave signal blockage and AOA transition using sub-6 GHz observations", IEEE Commun. Letters, 2019.

[11]

D. Burghal, R. Wang, and A. Molisch, ''Deep learning and Gaussian process based band assignment in dual band systems," arXiv preprint:1902.10890, 2019.

[12]

M. Hussain, M. Scalabrin, M. Rossi, and N. Michelusi, "Mobility and blockage-aware communications in millimeter-wave vehicular networks", IEEE Transaction on Vehicular Technology, 2020.

[13]

G. Charan, M. Alrabeiah, and A. Alkhateeb, ''Vision-aided 6G wireless communications: Blockage prediction and proactive handoff", arXiv preprint arXiv:2102.09527, 2021.

[14]

M. Alrabeiah, A. Hredzak, and A. Alkhateeb, ''Millimeter wave base stations with cameras: Vision-aided beam and blockage prediction", Proc. of IEEE VTC, 2020.

[15]

Y. Koda, K. Nakashima, K. Yamamoto, T. Nishio, and M. Morikura, ''Handover management for mmWave networks with proactive performance prediction using camera images and deep reinforcement learning", IEEE Trans. on Cognitive Commu. and Netw., 2019, 6(2), 802--816.

[16]

J. Joo, M. Park, D. Han, and V. Pejovic, ''Deep learning-based channel prediction in realistic vehicular communications", IEEE Access, 2019.

[17]

C. Luo, J. Ji, Q. Wang, X. Chen, and P. Li, ''Channel state information prediction for 5G wireless communications: A deep learning approach", IEEE Transactions on Network Science and Engineering, 2018.

[18]

M. Piacentini and F. Rinaldi,?Path loss prediction in urban environment using learning machines and dimensionality reduction techniques", Computational Management Science, 2014, 8(1), 371--384.

[19]

M. Kasparick, R. Cavalcante, S. Valentin, S. Stanczak, and M. Yukawa, ''Kernel-based adaptive online reconstruction of coverage maps with side information", IEEE Transactions on Vehicular Technology, 2015.

[20]

E. DallAnese, S. Kim, and G. Giannakis, ''Channel gain map tracking via distributed kriging", IEEE Trans. on Vehicular Technology, 2011.

[21]

L. Muppirisetty, T. Svensson, and H. Wymeersch, "Spatial wireless channel prediction under location uncertainty?, IEEE Trans. on Wireless Commun., 2016.

Digital Library

[22]

N. Bui and J. Widmer, ''Mobile network resource optimization under imperfect prediction", IEEE World of Wireless, Mobile and Multimedia Networks, 2015.

[23]

J. Palacios, P. Casari, H. Assasa, and J Widmer, ''LEAP: Location estimation and predictive handover with consumer-grade mmWave devices", Proc. of IEEE Int'l. Conference on Computer Communications, 2019.

Digital Library

[24]

M. Neema and E. S. Gopi, ''Data driven approach for mmWave channel characteristics prediction using deep neural network", Wireless Personal Communications, 2021, 120.3, 2161--2177.

[25]

N. Bui, M. Cesana, S. Hosseini, Q. Liao, I. Malanchini, and J. Widmer, ''A survey of anticipatory mobile networking: Context-based classi!cation, prediction methodologies, and optimization techniques", IEEE Communications Surveys & Tutorials, 2017, 19(3), 1790--1821.

Digital Library

[26]

Y. Liu, Y. Jian, et al., ''Maximizing line-of-sight coverage for mmWave wireless LANs with multiple access points", IEEE/ACM Transactions on Networking, 2021.

[27]

3GPP TR, ''Study on channel model for frequencies from 0.5 to 100 GHz", 2019.

[28]

?MmWave ray tracer for WLAN data generation", https://github.com/yuchen-sh/mmWave_ray_tracer.

[29]

J. Lu, D. Steinbach, P. Cabrol, P. Pietraski, and R. Pragada, ''Propagation characterization of an o"ce building in the 60 GHz band", Proc. of IEEE European Conference on Antennas and Propagation, 2014.

[30]

K. Sato, T. Manabe, T. Ihara, et al. ?Measurements of re$ection and transmission characteristics of interior structures of o"ce building in the 60-GHz band", IEEE Transactions on Antennas and Propagation, 1997, 45(12), 1783--1792.

[31]

M. Samimi, and T. Rappaport, ''Characterization of the 28 GHz millimeter-wave dense urban channel for future 5G mobile cellular", NYU Wireless TR 1, 2014.

[32]

H. Assasa, J. Widmer, et al., ''Enhancing the ns-3 IEEE 802.11 ad model !delity: Beam codebooks, multi-antenna beamforming training, and quasi-deterministic mmWave channel", Proc. of ACM Workshop on ns-3, 2019.

[33]

D. Kingma and J. Ba, ''Adam: A method for stochastic optimization", 2014, arXiv preprint arXiv:1412.6980.

[34]

J. Heaton, S. McElwee, J. Fraley, and J. Cannady, ''Early stabilizing feature importance for TensorFlow deep neural networks", Proc. of IEEE International Joint Conference on Neural Networks, 2017.

[35]

A. Deng, Y. Liu and D. Blough, ''Maximizing coverage for mmWave WLANs with dedicated re$ectors", Proc. of IEEE Int'l Conference on Communications, 2021.

[36]

Y. Liu, and D. Blough, ''Blockage robustness in access point association for mmWave wireless LANs with mobility", Proc. of Local Computer Networks, 2020.

[37]

TP-Link Talon AD7200 Wi-Fi Router, URL: https://www.pcmag.com/review/347405/tp-link-talon-ad7200-multi-band-wi-!-router

[38]

Acer TravelMate P648, URL: https://www.acer.com/ac/en/US/press/2016/175243

Cited By

Almutairi AKeshavarz-Haddad AAryafar E(2024)A deep learning framework for blockage mitigation and duration prediction in mmWave wireless networksAd Hoc Networks10.1016/j.adhoc.2024.103562162(103562)Online publication date: Sep-2024
https://doi.org/10.1016/j.adhoc.2024.103562
Li ZChen MLi GLiu Y(2023)Spatial-Temporal Attention-Based mmWave Link Quality Prediction Under Dynamic BlockagesGLOBECOM 2023 - 2023 IEEE Global Communications Conference10.1109/GLOBECOM54140.2023.10437103(6279-6284)Online publication date: 4-Dec-2023
https://doi.org/10.1109/GLOBECOM54140.2023.10437103
Maciel FDe Souza ABittencourt LVillas L(2023)Resource Aware Client Selection for Federated Learning in IoT Scenarios2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)10.1109/DCOSS-IoT58021.2023.00010(1-8)Online publication date: Jun-2023
https://doi.org/10.1109/DCOSS-IoT58021.2023.00010

Index Terms

Environment-Aware Link Quality Prediction for Millimeter-Wave Wireless LANs
1. Networks
  1. Network performance evaluation
    1. Network performance analysis
    2. Network performance modeling

Recommendations

Design of 60 GHz millimeter‐wave SIW antenna for 5G WLAN/WPAN applications
Abstract
Broadband millimeter Wave (mmWave) transmission at the 60 GHz band is a great prospect to meet the demanding high data rate requirements of future wireless personal area network (WPAN) and wireless local area network (WLAN) 5G networks. This paper ...
In this paper, a single layer H‐plane sectoral horn mmWave antenna is proposed at 60GHz using substrate integrated waveguide (SIW) technology for the future high‐speed short range WPAN and WLAN networks. The benefits of the proposed antenna are high gain, ...
Characterization of millimeter-wave antenna using photonic measurement techniques
Recent Advances in RF and Microwave Computer-aided Engineering in Japan

This article describes an electric-field measurement system that is based on electro-optic sampling (EOS). This system is able to map near- and far-field patterns of millimeter waves radiated from antennas by scanning the electric-field sensor probe. ...
Miniaturized Multiband Slotted Microstrip Antenna for Wireless Applications

A miniaturized multiband slotted Microstrip antenna for wireless applications is designed and its characteristics have been investigated. The proposed antenna consists of a substrate, one side of substrate is having rectangular radiating patch ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MobiWac '22: Proceedings of the 20th ACM International Symposium on Mobility Management and Wireless Access

October 2022

134 pages

ISBN:9781450394802

DOI:10.1145/3551660

General Chair:
Frank Li
University of Agder, Norway
,
Program Chair:
Rodolfo W.L. Coutinho
Concordia University, Canada

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

MSWiM '22

Sponsor:

SIGSIM

MSWiM '22: Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems

October 24 - 28, 2022

Quebec, Montreal, Canada

Acceptance Rates

MobiWac '22 Paper Acceptance Rate 16 of 50 submissions, 32%;

Overall Acceptance Rate 83 of 272 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
219
Total Downloads

Downloads (Last 12 months)108
Downloads (Last 6 weeks)17

Reflects downloads up to 29 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Almutairi AKeshavarz-Haddad AAryafar E(2024)A deep learning framework for blockage mitigation and duration prediction in mmWave wireless networksAd Hoc Networks10.1016/j.adhoc.2024.103562162(103562)Online publication date: Sep-2024
https://doi.org/10.1016/j.adhoc.2024.103562
Li ZChen MLi GLiu Y(2023)Spatial-Temporal Attention-Based mmWave Link Quality Prediction Under Dynamic BlockagesGLOBECOM 2023 - 2023 IEEE Global Communications Conference10.1109/GLOBECOM54140.2023.10437103(6279-6284)Online publication date: 4-Dec-2023
https://doi.org/10.1109/GLOBECOM54140.2023.10437103
Maciel FDe Souza ABittencourt LVillas L(2023)Resource Aware Client Selection for Federated Learning in IoT Scenarios2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)10.1109/DCOSS-IoT58021.2023.00010(1-8)Online publication date: Jun-2023
https://doi.org/10.1109/DCOSS-IoT58021.2023.00010

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents