Machine learning for link adaptation in wireless networks

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Machine learning for link adaptation in wireless networks

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dc.contributor.advisor Heath, Robert W., Ph. D.
dc.creator Daniels, Robert C.
dc.date.accessioned 2012-01-30T18:20:24Z
dc.date.available 2012-01-30T18:20:24Z
dc.date.created 2011-12
dc.date.issued 2012-01-30
dc.date.submitted December 2011
dc.identifier.uri http://hdl.handle.net/2152/ETD-UT-2011-12-4509
dc.description.abstract Link adaptation is an important component of contemporary wireless networks that require high spectral efficiency and service a variety of network applications/configurations. By exploiting information about the wireless channel, link adaptation strategically selects wireless communication transmission parameters in real-time to optimize performance. Link adaptation in practice has proven challenging due to impairments outside system models and analytical intractability in modern broadband networks with multiple antennas (MIMO), orthogonal frequency division multiplexing (OFDM), forward error correction, and bit-interleaving. The objective of this dissertation is to provide simple and flexible link adaptation algorithms with few link model assumptions that are amenable to modern wireless networks. First, a complete design and analysis of supervised learning for link adaptation in MIMO-OFDM is provided. This includes the construction of a publicly available data set, a new frame error rate bound leading to a new feature set, and IEEE 802.11n performance evaluation to verify that my design outperforms existing link quality metrics. Next, two supervised learning classification algorithms are designed to exploit information collected from packets transmitted and received over standard links in real time: database learning with nearest neighbor classifiers and support vector machines. Algorithms are also proposed to preserve diversity of feature sets in the database and to allow learning algorithms to seek out more information about the network. Finally, link adaptation with supervised learning is applied to MIMO-OFDM systems where the modulation order may be adapted per-stream. This leads to the analysis of the ordered SNR per stream and its connection to the cumulative distribution function of SNR on each stream. Decoupled link adaptation algorithms, which significantly reduce the complexity of non-uniform link adaptation algorithms, are proposed. New analysis of non-uniform link adaptation shows that the performance of decoupled link adaptation algorithms converge to the performance of joint (optimal) link adaptation algorithms as the number of modulation and coding options per-stream increase. This guides the construction of future standards to reduce the complexity of link adaptation in MIMO-OFDM.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subject Wireless Communications
dc.subject Link adaptation
dc.subject Adaptive modulation and coding
dc.subject Machine learning
dc.subject MIMO
dc.subject OFDM
dc.title Machine learning for link adaptation in wireless networks
dc.date.updated 2012-01-30T18:20:49Z
dc.identifier.slug 2152/ETD-UT-2011-12-4509
dc.contributor.committeeMember Andrews, Jeffrey
dc.contributor.committeeMember Nettles, Scott
dc.contributor.committeeMember Caramanis, Constantine
dc.contributor.committeeMember Qiu, Lili
dc.description.department Electrical and Computer Engineering
dc.type.genre thesis
dc.type.material text
thesis.degree.department Electrical and Computer Engineering
thesis.degree.discipline Electrical and Computer Engineering
thesis.degree.grantor University of Texas at Austin
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy

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