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Aurora: conformity-based configuration recommendation to improve LTE/5G service

Published: 25 October 2022 Publication History

Abstract

Cellular service operators frequently tune the network configuration to optimize coverage, support seamless handovers, minimize channel interference, and improve the service performance experience to the end-users. Tuning such a complicated network is highly challenging because of the many configuration parameters, evolving complexity of cellular networks, and diverse requirements across voice, video, and data applications. Any misconfigurations or even poor settings can significantly negatively impact service quality. In this paper, we propose a new approach Aurora that derives best practices knowledge from exploration of the massive existing configuration in the network and uses conformity-based recommendation with performance-based filtering to improve cellular service. We implemented and evaluated Aurora using data from a very large LTE and 5G cellular service provider. Our operational experience over the last one year highlights the benefits of Aurora and exposes exciting research opportunities and challenges in configuration tuning and performance management.

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  • (2024)NetCR: Knowledge-Graph-Based Recommendation Framework for Manual Network ConfigurationIEEE Internet of Things Journal10.1109/JIOT.2023.333701711:7(12941-12952)Online publication date: 1-Apr-2024
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cover image ACM Conferences
IMC '22: Proceedings of the 22nd ACM Internet Measurement Conference
October 2022
796 pages
ISBN:9781450392594
DOI:10.1145/3517745
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]

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Published: 25 October 2022

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Author Tags

  1. collaborative filtering
  2. network configuration conformity
  3. performance impact assessment
  4. recommendation algorithms

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IMC '22
IMC '22: ACM Internet Measurement Conference
October 25 - 27, 2022
Nice, France

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Overall Acceptance Rate 277 of 1,083 submissions, 26%

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Cited By

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  • (2024)Supervised Fine-Tuning for Unsupervised KPI Anomaly Detection for Mobile Web SystemsProceedings of the ACM on Web Conference 202410.1145/3589334.3645392(2859-2869)Online publication date: 13-May-2024
  • (2024)Modeling configuration-performance relation in a mobile network: a data-driven approach2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)10.1109/PIMRC59610.2024.10817303(1-6)Online publication date: 2-Sep-2024
  • (2024)NetCR: Knowledge-Graph-Based Recommendation Framework for Manual Network ConfigurationIEEE Internet of Things Journal10.1109/JIOT.2023.333701711:7(12941-12952)Online publication date: 1-Apr-2024
  • (2024)Temporal and Multivariate Similarity Clustering of 5G Performance DataIEEE Access10.1109/ACCESS.2024.344470412(114137-114145)Online publication date: 2024
  • (2023)Dependent Misconfigurations in 5G/4.5G Radio Resource ControlProceedings of the ACM on Networking10.1145/35952881:CoNEXT1(1-20)Online publication date: 5-Jul-2023
  • (2023)ConfigReco: Network Configuration Recommendation With Graph Neural NetworksIEEE Network: The Magazine of Global Internetworking10.1109/MNET.2023.333623938:1(7-14)Online publication date: 23-Nov-2023
  • (2023)Human-Intent-Driven Cellular Configuration Generation Using Program SynthesisIEEE Journal on Selected Areas in Communications10.1109/JSAC.2023.334538742:3(658-668)Online publication date: 21-Dec-2023

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