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Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Published: 28 October 2015 Publication History

Abstract

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.

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Cited By

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  • (2024)Traffic-Aware Intelligent Association and Task Offloading for Multi-Access Edge ComputingElectronics10.3390/electronics1316313013:16(3130)Online publication date: 7-Aug-2024
  • (2024)AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network SlicingAmerican Journal of Artificial Intelligence10.11648/j.ajai.20240802.148:2(55-62)Online publication date: 28-Nov-2024
  • (2024)KGDA: A Knowledge Graph Driven Decomposition Approach for Cellular Traffic PredictionACM Transactions on Intelligent Systems and Technology10.1145/369065015:6(1-22)Online publication date: 20-Nov-2024
  • Show More Cited By

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Published In

cover image ACM Conferences
IMC '15: Proceedings of the 2015 Internet Measurement Conference
October 2015
550 pages
ISBN:9781450338486
DOI:10.1145/2815675
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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Publication History

Published: 28 October 2015

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Author Tags

  1. geographical location
  2. measurement study
  3. mobile data traffic
  4. traffic patterns

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  • Research-article

Funding Sources

  • National Basic Research Program of China (973 Program)
  • National Nature Science Foundation of China

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IMC '15
Sponsor:
IMC '15: Internet Measurement Conference
October 28 - 30, 2015
Tokyo, Japan

Acceptance Rates

IMC '15 Paper Acceptance Rate 31 of 96 submissions, 32%;
Overall Acceptance Rate 277 of 1,083 submissions, 26%

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Cited By

View all
  • (2024)Traffic-Aware Intelligent Association and Task Offloading for Multi-Access Edge ComputingElectronics10.3390/electronics1316313013:16(3130)Online publication date: 7-Aug-2024
  • (2024)AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network SlicingAmerican Journal of Artificial Intelligence10.11648/j.ajai.20240802.148:2(55-62)Online publication date: 28-Nov-2024
  • (2024)KGDA: A Knowledge Graph Driven Decomposition Approach for Cellular Traffic PredictionACM Transactions on Intelligent Systems and Technology10.1145/369065015:6(1-22)Online publication date: 20-Nov-2024
  • (2024)Improving Network Robustness via Cellular Infrastructure Sharing: An Empirical Study of Infrastructure Failure with All Cellular Operators in a CityProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691307(617-620)Online publication date: 29-Oct-2024
  • (2024)Toward Massive Distribution of Intelligence for 6G Network Management Using Double Deep Q-NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2023.333387521:2(2077-2094)Online publication date: Apr-2024
  • (2023)Large-scale Urban Cellular Traffic Generation via Knowledge-Enhanced GANs with Multi-Periodic PatternsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599853(4195-4206)Online publication date: 6-Aug-2023
  • (2023)Deep Transfer Learning for City-scale Cellular Traffic Generation through Urban Knowledge GraphProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599801(4842-4851)Online publication date: 6-Aug-2023
  • (2023)EMS: Erasure-Coded Multi-Source Streaming for UHD Videos Within Cloud Native 5G NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2023.3238356(1-15)Online publication date: 2023
  • (2022)A Comprehensive Evaluation of Generating a Mobile Traffic Data Scheme without a Coarse-Grained Process Using CSR-GANSensors10.3390/s2205193022:5(1930)Online publication date: 1-Mar-2022
  • (2022)Fine-Grained Urban Functional Region Identification via Mobile App Usage DataMobile Information Systems10.1155/2022/64345982022Online publication date: 1-Jan-2022
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