| dc.contributor.advisor |
Heath , Robert W . , Ph . D . |
|
| dc.contributor.committeeMember |
Andrews , Jeffrey |
|
| dc.contributor.committeeMember |
Nettles , Scott |
|
| dc.contributor.committeeMember |
Caramanis , Constantine |
|
| dc.contributor.committeeMember |
Qiu , Lili |
|
| 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.description.department |
Electrical and Computer Engineering |
|
| dc.type.genre |
thesis |
* |
| dc.type.material |
text |
* |
| thesis.degree.name |
Doctor of Philosophy |
|
| thesis.degree.level |
Doctoral |
|
| thesis.degree.discipline |
Electrical and Computer Engineering |
|
| thesis.degree.grantor |
University of Texas at Austin |
|
| thesis.degree.department |
Electrical and Computer Engineering |
|
| dc.date.updated |
2012 -01 -30T18 :20 :49Z |
|
| dc.identifier.slug |
2152 /ETD -UT -2011 -12 -4509 |
|