000211115 001__ 211115
000211115 005__ 20190317000247.0
000211115 0247_ $$2doi$$a10.5075/epfl-thesis-6736
000211115 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis6736-4
000211115 02471 $$2nebis$$a10514060
000211115 037__ $$aTHESIS 000211115 041__$$aeng
000211115 088__ $$a6736 000211115 245__$$aFlexible Spectrum Assignment for Local Wireless Networks
000211115 269__ $$a2015 000211115 260__$$aLausanne$$bEPFL$$c2015
000211115 300__ $$a130 000211115 336__$$aTheses
000211115 502__ $$aProf. Jean-Pierre Hubaux (président) ; Prof. Patrick Thiran (directeur de thèse) ; Prof. Bixio Rimoldi, Prof. Albert Banchs, Prof. Giuseppe Bianchi (rapporteurs) 000211115 520__$$aIn this dissertation, we consider the problem of assigning spectrum to wireless local-area networks (WLANs). In line with recent IEEE 802.11 amendments and newer hardware capabilities, we consider situations where wireless nodes have the ability to adapt not only their channel center-frequency but also their channel width. This capability brings an important additional degree of freedom, which adds more granularity and potentially enables more efficient spectrum assignments. However, it also comes with new challenges; when consuming a varying amount of spectrum, the nodes should not only seek to reduce interference, but they should also seek to efficiently fill the available spectrum. Furthermore, the performances obtained in practice are especially difficult to predict when nodes employ variable bandwidths. We first propose an algorithm that acts in a decentralized way, with no communication among the neighboring access points (APs). Despite being decentralized, this algorithm is self-organizing and solves an explicit tradeoff between interference mitigation and efficient spectrum usage. In order for the APs to continuously adapt their spectrum to neighboring conditions while using only one network interface, this algorithm relies on a new kind of measurement, during which the APs monitor their surrounding networks for short durations. We implement this algorithm on a testbed and observe drastic performance gains compared to default spectrum assignments, or compared to efficient assignments using a fixed channel width. Next, we propose a procedure to explicitly predict the performance achievable in practice, when nodes operate with arbitrary spectrum configurations, traffic intensities, transmit powers, etc. This problem is notoriously difficult, as it requires capturing several complex interactions that take place at the MAC and PHY layers. Rather than trying to find an explicit model acting at this level of generality, we explore a different point in the design space. Using a limited number of real-world measurements, we use supervised machine-learning techniques to learn implicit performance models. We observe that these models largely outperform other measurement-based models based on SINR, and that they perform well, even when they are used to predict performance in contexts very different from the context prevailing during the initial set of measurements used for learning. We then build a second algorithm that uses the above-mentioned learned models to assign the spectrum. This algorithm is distributed and collaborative, meaning that neighboring APs have to exchange a limited amount of control traffic. It is also utility-optimal -- a feature enabled both by the presence of a model for predicting performance and the ability of APs to collaboratively take decisions. We implement this algorithm on a testbed, and we design a simple scheme that enables neighboring APs to discover themselves and to implement collaboration using their wired backbone network. We observe that it is possible to effectively gear the performance obtained in practice towards different objectives (in terms of efficiency and/or fairness), depending on the utility functions optimized by the nodes. Finally, we study the problem of scheduling packets both in time and frequency domains. Such ways of scheduling packets have been made possible by recent progress in system design, which make it possible to dynamically tune and negotiate the spectrum band [...]
000211115 6531_ $$aSpectrum allocation 000211115 6531_$$aresource allocation
000211115 6531_ $$awireless networks 000211115 6531_$$aalgorithms
000211115 6531_ $$aWLANs 000211115 6531_$$aperformance modeling
000211115 6531_ $$aself-organization 000211115 6531_$$apacket scheduling
000211115 700__ $$0244094$$aHerzen, Julien$$g167320 000211115 720_2$$0240373$$aThiran, Patrick$$edir.$$g103925 000211115 8564_$$s3259962$$uhttps://infoscience.epfl.ch/record/211115/files/EPFL_TH6736.pdf$$yn/a$$zn/a 000211115 909C0$$0252454$$pLCA3$$xU10431
000211115 909CO $$ooai:infoscience.tind.io:211115$$pthesis-bn2018$$pDOI$$pIC$$pthesis$$qDOI2$$qGLOBAL_SET 000211115 917Z8$$x108898
000211115 917Z8 $$x108898 000211115 918__$$aIC$$cISC$$dEDIC2005-2015
000211115 919__ $$aLCA3 000211115 920__$$a2015-9-25$$b2015 000211115 970__$$a6736/THESES
000211115 973__ $$aEPFL$$sPUBLISHED
000211115 980__ aTHESIS