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Dissecting Carrier Aggregation in 5G Networks: Measurement, QoE Implications and Prediction

Published: 04 August 2024 Publication History

Abstract

By aggregating multiple channels, Carrier Aggregation (CA) is an important technology for boosting cellular network bandwidth. Given diverse radio bands made available in 5G networks, CA plays a particularly critical role in achieving the goal of multi-Gbps throughput performance. In this paper, we carry out a timely comprehensive measurement study of CA deployment in commercial 5G networks (as well as 4G networks). We identify the key factors that influence whether CA is deployed and when, as well as which band combinations are used. Thus, we reveal the challenges posed by CA in 5G performance analysis and prediction as well as their implications in application quality-of-experience (QoE). We argue for and develop a novel CA-aware deep learning framework, dubbed Prism5G, which explicitly accounts for the complexity introduced by CA to more effectively predict 5G network throughput performance. Through extensive evaluations, we demonstrate the superiority of Prism5G over existing throughput prediction algorithms. Prism5G improves 5G throughput prediction accuracy by over 14% on average and a maximum of 22%. Using two use cases as examples, we further illustrate how Prism5G can aid applications in optimizing QoE performance.

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  • (2024)Unveiling the 5G Mid-Band Landscape: From Network Deployment to Performance and Application QoEProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672269(358-372)Online publication date: 4-Aug-2024
  • (2024)Bayesian Active Learning for Sample Efficient 5G Radio Map ReconstructionIEEE Transactions on Wireless Communications10.1109/TWC.2024.348311223:12(19382-19396)Online publication date: Dec-2024

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  1. Dissecting Carrier Aggregation in 5G Networks: Measurement, QoE Implications and Prediction

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          cover image ACM Conferences
          ACM SIGCOMM '24: Proceedings of the ACM SIGCOMM 2024 Conference
          August 2024
          1033 pages
          ISBN:9798400706141
          DOI:10.1145/3651890
          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 August 2024

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          Author Tags

          1. carrier aggregation
          2. 4G
          3. 5G
          4. network measurement
          5. mobile network throughput prediction
          6. deep learning

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          ACM SIGCOMM '24: ACM SIGCOMM 2024 Conference
          August 4 - 8, 2024
          NSW, Sydney, Australia

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          • (2024)Unveiling the 5G Mid-Band Landscape: From Network Deployment to Performance and Application QoEProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672269(358-372)Online publication date: 4-Aug-2024
          • (2024)Bayesian Active Learning for Sample Efficient 5G Radio Map ReconstructionIEEE Transactions on Wireless Communications10.1109/TWC.2024.348311223:12(19382-19396)Online publication date: Dec-2024

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