Fast learning for dynamic resource allocation in AI-Enabled radio networks

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buir.contributor.authorQureshi, Muhammad Anjum
buir.contributor.authorTekin, Cem
dc.citation.epage110en_US
dc.citation.issueNumber1en_US
dc.citation.spage95en_US
dc.citation.volumeNumber6en_US
dc.contributor.authorQureshi, Muhammad Anjum
dc.contributor.authorTekin, Cem
dc.date.accessioned2021-02-18T09:05:24Z
dc.date.available2021-02-18T09:05:24Z
dc.date.issued2020
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractArtificial Intelligence (AI)-enabled radios are expected to enhance the spectral efficiency of 5th generation (5G) millimeter wave (mmWave) networks by learning to optimize network resources. However, allocating resources over the mmWave band is extremely challenging due to rapidly-varying channel conditions. We consider several resource allocation problems for mmWave radio networks under unknown channel statistics and without any channel state information (CSI) feedback: i) dynamic rate selection for an energy harvesting transmitter, ii) dynamic power allocation for heterogeneous applications, and iii) distributed resource allocation in a multi-user network. All of these problems exhibit structured payoffs which are unimodal functions over partially ordered arms (transmission parameters) as well as over partially ordered contexts (side-information). Unimodality over arms helps in reducing the number of arms to be explored, while unimodality over contexts helps in using past information from nearby contexts to make better selections. We model this as a structured reinforcement learning problem, called contextual unimodal multi-armed bandit (MAB), and propose an online learning algorithm that exploits unimodality to optimize the resource allocation over time, and prove that it achieves logarithmic in time regret. Our algorithm's regret scales sublinearly both in the number of arms and contexts for a wide range of scenarios. We also show via simulations that our algorithm significantly improves the performance in the aforementioned resource allocation problems.en_US
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 116E229en_US
dc.identifier.doi10.1109/TCCN.2019.2953607en_US
dc.identifier.issn2332-7731
dc.identifier.urihttp://hdl.handle.net/11693/75437
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/TCCN.2019.2953607en_US
dc.source.titleIEEE Transactions on Cognitive Communications and Networkingen_US
dc.subjectAI-enabled radioen_US
dc.subjectmmWaveen_US
dc.subjectResource allocationen_US
dc.subjectContextual MABen_US
dc.subjectUnimodal MABen_US
dc.subjectRegret boundsen_US
dc.titleFast learning for dynamic resource allocation in AI-Enabled radio networksen_US
dc.typeArticleen_US

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