Adaptive Bandwidth Allocation via Uncertainty-Constrained Deep Reinforcement Learning.
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| Title: | Adaptive Bandwidth Allocation via Uncertainty-Constrained Deep Reinforcement Learning. |
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| Authors: | Wei, Li1 liweisz@js.sgcc.com.cn, Yong, Wu1 wuy11@js.sgcc.com.cn, Dong, Yan1 yandongsz@js.sgcc.com.cn |
| Source: | Computer Science & Information Systems. Jan2026, Vol. 23 Issue 1, p277-297. 21p. |
| Subjects: | Bandwidth allocation, Reinforcement learning, Telecommunications services, Resource allocation, Machine learning |
| Abstract: | With the rapid growth of network services, traditional static bandwidth allocation schemes can no longer meet the demands of multi-user, dynamic, and QoS-sensitive applications. Ensuring both efficiency and stability in bandwidth allocation remains a significant challenge, especially under high variability and uncertainty conditions. To address this, we propose a novel algorithm named UncertaintyConstrained Stability-aware Deep Reinforcement Learning (UCS-DRL) for dynamic bandwidth allocation. UCS-DRL adopts a dual-policy architecture: a task policy that learns optimal bandwidth allocation decisions, and a stability policy guided by uncertainty-aware value estimation to identify and mitigate potential risky or unstable behaviors during deployment. Furthermore, the framework incorporates a curiosity-driven exploration mechanism based on Random Network Distillation, which enhances exploration efficiency by encouraging the agent to visit informative and under-explored states. Experimental results show that UCS-DRL achieves high bandwidth utilization and service quality while reducing policy volatility and risky actions, balancing performance and robustness in dynamic bandwidth allocation. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192054647 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive Bandwidth Allocation via Uncertainty-Constrained Deep Reinforcement Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wei%2C+Li%22">Wei, Li</searchLink><relatesTo>1</relatesTo><i> liweisz@js.sgcc.com.cn</i><br /><searchLink fieldCode="AR" term="%22Yong%2C+Wu%22">Yong, Wu</searchLink><relatesTo>1</relatesTo><i> wuy11@js.sgcc.com.cn</i><br /><searchLink fieldCode="AR" term="%22Dong%2C+Yan%22">Dong, Yan</searchLink><relatesTo>1</relatesTo><i> yandongsz@js.sgcc.com.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Science+%26+Information+Systems%22">Computer Science & Information Systems</searchLink>. Jan2026, Vol. 23 Issue 1, p277-297. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bandwidth+allocation%22">Bandwidth allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunications+services%22">Telecommunications services</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the rapid growth of network services, traditional static bandwidth allocation schemes can no longer meet the demands of multi-user, dynamic, and QoS-sensitive applications. Ensuring both efficiency and stability in bandwidth allocation remains a significant challenge, especially under high variability and uncertainty conditions. To address this, we propose a novel algorithm named UncertaintyConstrained Stability-aware Deep Reinforcement Learning (UCS-DRL) for dynamic bandwidth allocation. UCS-DRL adopts a dual-policy architecture: a task policy that learns optimal bandwidth allocation decisions, and a stability policy guided by uncertainty-aware value estimation to identify and mitigate potential risky or unstable behaviors during deployment. Furthermore, the framework incorporates a curiosity-driven exploration mechanism based on Random Network Distillation, which enhances exploration efficiency by encouraging the agent to visit informative and under-explored states. Experimental results show that UCS-DRL achieves high bandwidth utilization and service quality while reducing policy volatility and risky actions, balancing performance and robustness in dynamic bandwidth allocation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2298/CSIS250923008L Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 277 Subjects: – SubjectFull: Bandwidth allocation Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Telecommunications services Type: general – SubjectFull: Resource allocation Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Adaptive Bandwidth Allocation via Uncertainty-Constrained Deep Reinforcement Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wei, Li – PersonEntity: Name: NameFull: Yong, Wu – PersonEntity: Name: NameFull: Dong, Yan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18200214 Numbering: – Type: volume Value: 23 – Type: issue Value: 1 Titles: – TitleFull: Computer Science & Information Systems Type: main |
| ResultId | 1 |
