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Predictive Impact Analysis for Designing a Resilient Cellular Backhaul Network

Authors:

Jun XuAuthors Info & Claims

Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 1, Issue 2

Article No.: 30, Pages 1 - 33

https://doi.org/10.1145/3154488

Published: 19 December 2017 Publication History

Abstract

Backhaul transport network design and optimization for cellular service providers involve a unique challenge stemming from the fact that an end-user's equipment (UE) is within the radio reach of multiple cellular towers: It is hard to evaluate the impact of the failure of the UE's primary serving tower on the UE, because the UE may simply switch to get service from other nearby cellular towers. To overcome this challenge, one needs to quantify the cellular service redundancy among the cellular towers riding on that transport circuit and their nearby cellular towers, which in turn requires a comprehensive understanding of the radio signal profile in the area of the impacted towers, the spatial distribution of UEs therein, and their expected workload (e.g., calls, data throughput). In this work, we develop a novel methodology for assessing the service impact of any hypothetical cellular tower outage scenario, and implement it in an operational system named Tower Outage Impact Predictor (TOIP). Our evaluations, using both synthetic data and historical real tower outages in a large operational cellular network, show conclusively that TOIP gives an accurate assessment of various tower outage scenarios, and can provide critical input data towards designing a reliable cellular backhaul transport network.

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Cited By

Tipper DBabay APalanisamy BKrishnamurthy P(2024)Network Connectivity Resilience in Next Generation Backhaul Networks: Challenges and Future OpportunitiesIEEE Transactions on Network and Service Management10.1109/TNSM.2024.339285721:5(5321-5334)Online publication date: Oct-2024
https://doi.org/10.1109/TNSM.2024.3392857
Pflughoeft KNemecek GButz N(2022)Prioritizing Cell Tower Site Recommendations outside U.S. Metropolitan AreasAnalytics10.3390/analytics10100061:1(56-71)Online publication date: 8-Sep-2022
https://doi.org/10.3390/analytics1010006
MATSUO YKIMURA TNISHIMATSU K(2021)DeepSIP: A System for Predicting Service Impact of Network Failure by Temporal Multimodal CNNIEICE Transactions on Communications10.1587/transcom.2020EBP3177E104.B:10(1288-1298)Online publication date: 1-Oct-2021
https://doi.org/10.1587/transcom.2020EBP3177
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Index Terms

Predictive Impact Analysis for Designing a Resilient Cellular Backhaul Network
1. Networks

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proceedings of the ACM on Measurement and Analysis of Computing Systems Volume 1, Issue 2

December 2017

480 pages

EISSN:2476-1249

DOI:10.1145/3175501

Editors:
Augustin Chaintreau
Columbia University
,
Aditya Akella
University of Wisconsin-Madison
,
Adam Wierman
California Institute of Technology

Issue’s Table of Contents

Copyright © 2017 ACM.

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Publication History

Published: 19 December 2017

Published in POMACS Volume 1, Issue 2

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Cited By

Tipper DBabay APalanisamy BKrishnamurthy P(2024)Network Connectivity Resilience in Next Generation Backhaul Networks: Challenges and Future OpportunitiesIEEE Transactions on Network and Service Management10.1109/TNSM.2024.339285721:5(5321-5334)Online publication date: Oct-2024
https://doi.org/10.1109/TNSM.2024.3392857
Pflughoeft KNemecek GButz N(2022)Prioritizing Cell Tower Site Recommendations outside U.S. Metropolitan AreasAnalytics10.3390/analytics10100061:1(56-71)Online publication date: 8-Sep-2022
https://doi.org/10.3390/analytics1010006
MATSUO YKIMURA TNISHIMATSU K(2021)DeepSIP: A System for Predicting Service Impact of Network Failure by Temporal Multimodal CNNIEICE Transactions on Communications10.1587/transcom.2020EBP3177E104.B:10(1288-1298)Online publication date: 1-Oct-2021
https://doi.org/10.1587/transcom.2020EBP3177
Fang ZWang GWang SZuo CZhang FZhang D(2020)CellRep: Usage Representativeness Modeling and Correction Based on Multiple City-Scale Cellular NetworksProceedings of The Web Conference 202010.1145/3366423.3380141(584-595)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380141
Matsuo YKimura TNishimatsu K(2020)DeepSIP: A System for Predicting Service Impact of Network Failure by Temporal Multimodal CNNNOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium10.1109/NOMS47738.2020.9110471(1-6)Online publication date: Apr-2020
https://doi.org/10.1109/NOMS47738.2020.9110471
Yang SYan HGe ZWang DXu J(2019)Predictive Impact Analysis for Designing a Resilient Cellular Backhaul NetworkACM SIGMETRICS Performance Evaluation Review10.1145/3308809.330885246:1(84-86)Online publication date: 17-Jan-2019
https://doi.org/10.1145/3308809.3308852
Yang SYan HGe ZWang DXu J(2018)Predictive Impact Analysis for Designing a Resilient Cellular Backhaul NetworkACM SIGMETRICS Performance Evaluation Review10.1145/3292040.321965146:1(84-86)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3292040.3219651
Yang LWong WHajiesmaili M(2018)An Optimal Randomized Online Algorithm for QoS Buffer ManagementACM SIGMETRICS Performance Evaluation Review10.1145/3292040.321962946:1(28-30)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3292040.3219629
Yang SYan HGe ZWang DXu JPsounis KAkella AWierman A(2018)Predictive Impact Analysis for Designing a Resilient Cellular Backhaul NetworkAbstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems10.1145/3219617.3219651(84-86)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3219617.3219651
Yang LWong WHajiesmaili MPsounis KAkella AWierman A(2018)An Optimal Randomized Online Algorithm for QoS Buffer ManagementAbstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems10.1145/3219617.3219629(28-30)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3219617.3219629

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