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Contextual multi-armed bandits for link adaptation in cellular networks
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0001-6630-243X
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0002-3599-5584
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2019 (English)In: NetAI 2019 - Proceedings of the 2019 ACM SIGCOMM Workshop on Network Meets AI and ML, Part of SIGCOMM 2019, Association for Computing Machinery (ACM), 2019, p. 44-49Conference paper, Published paper (Refereed)
Abstract [en]

Cellular networks dynamically adjust the transmission parameters for a wireless link in response to its time-varying channel state. This is known as link adaptation, where the typical goal is to maximize the link throughput. State-of-the-art outer loop link adaptation (OLLA) selects the optimal transmission parameters based on an approximate, offline, model of the wireless link. Further, OLLA refines the offline model by dynamically compensating any deviations from the observed link performance. However, in practice, OLLA suffers from slow convergence and a sub-optimal link throughput. In this paper, we propose a link adaptation approach that overcomes the shortcomings of OLLA through a novel learning scheme. Our approach relies on contextual multi-armed bandits (MAB), where the context vector is composed of the instantaneous wireless channel state along with side information about the link. For a given context, our approach learns the success probability for each of the available transmission parameters, which is then exploited to select the throughput-maximizing parameters. Through numerical experiments, we show that our approach converges faster than OLLA and achieves a higher steady-state link throughput. For frequent and infrequent channel reports respectively, our scheme outperforms OLLA by 15% and 25% in terms of the steady-state link throughpu.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019. p. 44-49
Keywords [en]
Artificial neural networks, Cellular networks, Contextual multiarmed bandits, Outer loop link adaptation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-262526DOI: 10.1145/3341216.3342212Scopus ID: 2-s2.0-85072036655ISBN: 9781450368728 (print)OAI: oai:DiVA.org:kth-262526DiVA, id: diva2:1366093
Conference
2019 ACM SIGCOMM Workshop on Network Meets AI and ML, NetAI 2019, Part of SIGCOMM 2019, 23 August 2019
Note

QC 20191028

Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2019-10-28Bibliographically approved

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Saxena, ViditJaldén, JoakimBengtsson, Mats

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