Characterizing throughput and convergence time in dynamic multi-connectivity 5G deployments Article Swipe
Rustam Pirmagomedov
,
Dmitri Moltchanov
,
Andrey Samuylov
,
Antonino Orsino
,
Johan Torsner
,
Sergey Andreev
,
Yevgeni Koucheryavy
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.comcom.2022.01.015
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.comcom.2022.01.015
Fifth-generation (5G) mobile communications are expected to integrate multiple radio access technologies (RATs) within a unified access network by allowing the user equipment (UE) to utilize them concurrently. As a consequence, mobile users face even more heterogeneous connectivity options, which creates challenges for efficient decision-making when selecting a network dynamically. In this work, with the tools of queuing theory, integral geometry, and optimization theory, we develop a novel mobility-centric analytical methodology for multi-RAT deployments. Particularly, we first contribute a framework for optimal data rate allocation in the network-assisted regime. Then, we characterize the convergence time of the distributed optimization algorithms based on reinforcement learning to reduce the signaling overheads. Our findings suggest that network-assisted strategies may improve the UE throughput by up to 60% depending on the considered deployment, where the gains increase with a higher density of millimeter-wave New Radio (NR) base stations. A user-centric solution based on reinforcement learning mechanisms is capable of approaching the performance of the network-assisted scheme. However, the associated convergence time may be prohibitive, on the order of several minutes. To improve the latter, we further propose and evaluate a transfer learning-based algorithm that allows to decrease the convergence time by up to 10 times, thus becoming a simple solution for rate-optimized operation in future 5G NR deployments.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.comcom.2022.01.015
- OA Status
- hybrid
- Cited By
- 13
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4210736944
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4210736944Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.comcom.2022.01.015Digital Object Identifier
- Title
-
Characterizing throughput and convergence time in dynamic multi-connectivity 5G deploymentsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-02-01Full publication date if available
- Authors
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Rustam Pirmagomedov, Dmitri Moltchanov, Andrey Samuylov, Antonino Orsino, Johan Torsner, Sergey Andreev, Yevgeni KoucheryavyList of authors in order
- Landing page
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https://doi.org/10.1016/j.comcom.2022.01.015Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.comcom.2022.01.015Direct OA link when available
- Concepts
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Computer science, Reinforcement learning, Throughput, Cellular network, Convergence (economics), Base station, Distributed computing, User equipment, Computer network, Wireless, Telecommunications, Machine learning, Economic growth, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 5, 2023: 5Per-year citation counts (last 5 years)
- References (count)
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57Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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