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Automating Diagnosis of Cellular Radio Access Network Problems

Published: 04 October 2017 Publication History

Abstract

In an increasingly mobile connected world, our user experience of mobile applications more and more depends on the performance of cellular radio access networks (RAN). To achieve high quality of experience for the user, it is imperative that operators identify and diagnose performance problems quickly. In this paper, we describe our experience in understanding the challenges in automating the diagnosis of RAN performance problems. Working with a major cellular network operator on a part of their RAN that services more than 2 million users, we demonstrate that fine-grained modeling and analysis could be the key towards this goal. We describe our methodology in analyzing RAN problems, and highlight a few of our findings, some previously unknown. We also discuss lessons from our attempt at building automated diagnosis solutions.

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Cited By

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  • (2025)GAT-AD: Graph Attention Networks for contextual anomaly detection in network monitoringComputers & Industrial Engineering10.1016/j.cie.2024.110830200(110830)Online publication date: Feb-2025
  • (2024)NetGSR: Towards Efficient and Reliable Network Monitoring with Generative Super ResolutionProceedings of the ACM on Networking10.1145/36964002:CoNEXT4(1-27)Online publication date: 25-Nov-2024
  • (2024)SEEN: ML Assisted Cellular Service DiagnosisProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3690678(1060-1073)Online publication date: 4-Dec-2024
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  1. Automating Diagnosis of Cellular Radio Access Network Problems

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      cover image ACM Conferences
      MobiCom '17: Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking
      October 2017
      628 pages
      ISBN:9781450349161
      DOI:10.1145/3117811
      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 the author(s) 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: 04 October 2017

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      MobiCom '17 Paper Acceptance Rate 35 of 186 submissions, 19%;
      Overall Acceptance Rate 440 of 2,972 submissions, 15%

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      Cited By

      View all
      • (2025)GAT-AD: Graph Attention Networks for contextual anomaly detection in network monitoringComputers & Industrial Engineering10.1016/j.cie.2024.110830200(110830)Online publication date: Feb-2025
      • (2024)NetGSR: Towards Efficient and Reliable Network Monitoring with Generative Super ResolutionProceedings of the ACM on Networking10.1145/36964002:CoNEXT4(1-27)Online publication date: 25-Nov-2024
      • (2024)SEEN: ML Assisted Cellular Service DiagnosisProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3690678(1060-1073)Online publication date: 4-Dec-2024
      • (2024)SpotLight: Accurate, Explainable and Efficient Anomaly Detection for Open RANProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3649380(923-937)Online publication date: 4-Dec-2024
      • (2024)Bottleneck Identification in Cloudified Mobile Networks based on Distributed TelemetryIEEE Transactions on Mobile Computing10.1109/TMC.2023.3312051(1-18)Online publication date: 2024
      • (2024)A Unified Prediction Framework for Signal Maps: Not All Measurements are Created EqualIEEE Transactions on Mobile Computing10.1109/TMC.2022.322177323:1(70-89)Online publication date: Jan-2024
      • (2023)Adapting Foundation Models for Operator Data AnalyticsProceedings of the 22nd ACM Workshop on Hot Topics in Networks10.1145/3626111.3628191(172-179)Online publication date: 28-Nov-2023
      • (2023)AI Anomaly Detection for Cloudified Mobile Core ArchitecturesIEEE Transactions on Network and Service Management10.1109/TNSM.2022.320324620:2(1976-1992)Online publication date: Jun-2023
      • (2022)RCAD: Real-time Collaborative Anomaly Detection System for Mobile Broadband NetworksProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539097(2682-2691)Online publication date: 14-Aug-2022
      • (2022)Towards automatic troubleshooting for user-level performance degradation in cellular servicesProceedings of the 28th Annual International Conference on Mobile Computing And Networking10.1145/3495243.3560535(716-728)Online publication date: 14-Oct-2022
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