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Implications of decentralized Q-learning resource allocation in wireless networks

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dc.contributor.author Wilhelmi Roca, Francesc
dc.contributor.author Bellalta, Boris
dc.contributor.author Cano Bastidas, Cristina
dc.contributor.author Jonsson, Anders, 1973-
dc.date.accessioned 2018-02-26T11:18:00Z
dc.date.available 2018-02-26T11:18:00Z
dc.date.issued 2017
dc.identifier.citation Wilhelmi F, Bellalta B, Cano C, Jonsson A. Implications of decentralized Q-learning resource allocation in wireless networks. In: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC); 2017 Oct 8-13; Montreal, Canada. Piscataway (NJ): IEEE; 2017. [5 p.]. DOI: 10.1109/PIMRC.2017.8292321
dc.identifier.uri http://hdl.handle.net/10230/34001
dc.description Comunicació presentada al 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), celebrat els dies 8 a 13 d'octubre de 2017 a Montreal, Canadà.
dc.description.abstract Reinforcement Learning is gaining attention by the wireless networking community due to its potential to learn good-performing configurations only from the observed results. In this work we propose a stateless variation of Q-learning, which we apply to exploit spatial reuse in a wireless network. In particular, we allow networks to modify both their transmission power and the channel used solely based on the experienced throughput. We concentrate in a completely decentralized scenario in which no information about neighbouring nodes is available to the learners. Our results show that although the algorithm is able to find the best-performing actions to enhance aggregate throughput, there is high variability in the throughput experienced by the individual networks. We identify the cause of this variability as the adversarial setting of our setup, in which the most played actions provide intermittent good/poor performance depending on the neighbouring decisions. We also evaluate the effect of the intrinsic learning parameters of the algorithm on this variability.
dc.description.sponsorship This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502), and by the European Regional Development Fund under grant TEC2015-71303-R (MINECO/FEDER).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC); 2017 Oct 8-13; Montreal, Canada. Piscataway (NJ): IEEE; 2017. [5 p.].
dc.relation.isreferencedby http://github.com/wn-upf/Decentralized_Qlearning_Resource_Allocation_in_WNs
dc.rights © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published article can be found at http://ieeexplore.ieee.org/document/8292321/
dc.title Implications of decentralized Q-learning resource allocation in wireless networks
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1109/PIMRC.2017.8292321
dc.subject.keyword Throughput
dc.subject.keyword Aggregates
dc.subject.keyword Interference
dc.subject.keyword Resource management
dc.subject.keyword Meters
dc.subject.keyword Signal to noise ratio
dc.subject.keyword Wireless networks
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TEC2015-71303-R
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion


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