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dc.contributor.authorWilhelmi Roca, Francesc-
dc.contributor.authorBarrachina Muñoz, Sergio-
dc.contributor.authorBellalta, Boris-
dc.contributor.authorCano Sandín, Cristina-
dc.contributor.authorJonsson, Anders-
dc.contributor.authorNeu, Gergely-
dc.identifier.citationWilhelmi, F., Barrachina-Muñoz, S., Cano, C., Bellalta, B., Jonsson, A., & Neu, G. (2018). Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs. Journal of Network and Computer Applications, 127(), 26-42. doi: 10.1016/j.jnca.2018.11.006-
dc.description.abstractSpatial Reuse (SR) has recently gained attention to maximize the performance of IEEE 802.11 Wireless Local Area Networks (WLANs). Decentralized mechanisms are expected to be key in the development of SR solutions for next-generation WLANs, since many deployments are characterized by being uncoordinated by nature. However, the potential of decentralized mechanisms is limited by the significant lack of knowledge with respect to the overall wireless environment. To shed some light on this subject, we show the main considerations and possibilities of applying online learning to address the SR problem in uncoordinated WLANs. In particular, we provide a solution based on Multi-Armed Bandits (MABs) whereby independent WLANs dynamically adjust their frequency channel, transmit power and sensitivity threshold. To that purpose, we provide two different strategies, which refer to selfish and environment-aware learning. While the former stands for pure individual behavior, the second one considers the performance experienced by surrounding networks, thus taking into account the impact of individual actions on the environment. Through these two strategies we delve into practical issues of applying MABs in wireless networks, such as convergence guarantees or adversarial effects. Our simulation results illustrate the potential of the proposed solutions for enabling SR in future WLANs. We show that substantial improvements on network performance can be achieved regarding throughput and fairness.en
dc.publisherJournal of Network and Computer Applications-
dc.relation.ispartofJournal of Network and Computer Applications, 2019, 127()-
dc.subjectspatial reuseen
dc.subjectIEEE 802.11 WLANen
dc.subjectreinforcement learningen
dc.subjectmulti-armed banditsen
dc.subjectdecentralized learningen
dc.subjectreutilización espaciales
dc.subjectIEEE 802.11 WLANes
dc.subjectaprendizaje por refuerzoes
dc.subjectbandido multibrazoes
dc.subjectaprendizaje descentralizadoes
dc.subjectreutilització espacialca
dc.subjectIEEE 802.11 WLANca
dc.subjectaprenentatge per reforçca
dc.subjectproblema de la màquina escurabutxaquesca
dc.subjectaprenentatge descentralitzatca
dc.subject.lcshWireless LANsen
dc.titlePotential and pitfalls of multi-armed bandits for decentralized spatial reuse in WLANs-
dc.subject.lemacXarxes locals sense fil Wi-Fica
dc.subject.lcshesRedes locales inalámbricas Wi-Fies
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