Exploration of Physical Layer Security in Underwater Acoustic Communications

136 Pages·2017·2.12 MB·English

by  Yi Huang

University of Connecticut
OpenCommons@UConn
Doctoral Dissertations University of Connecticut Graduate School
5-24-2016
Exploration of Physical Layer Security in
Underwater Acoustic Communications
Yi Huang
University of Connecticut - Storrs, [email protected]
Follow this and additional works at:https://opencommons.uconn.edu/dissertations
Recommended Citation
Huang, Yi, "Exploration of Physical Layer Security in Underwater Acoustic Communications" (2016).Doctoral Dissertations. 1169.
https://opencommons.uconn.edu/dissertations/1169
ExplorationofPhysicalLayerSecurityinUnderwaterAcoustic
Communications
YiHuang,Ph.D.
UniversityofConnecticut,2016
Physical layer security has been under extensive investigation in recent years in
wirelessradiocommunications. However,itsstudyinthecontextofunderwateracous-
tic (UWA) communications is very limited. This dissertation will explore the funda-
mentalpropertiesofUWAchannelstoachievephysicallayersecurity. Itincludesthree
research topics: 1) Channel estimation in UWA systems leveraging the inherent chan-
nel sparsity; 2) Secret key generation through the reciprocity of UWA channels; 3)
Self-protectionjamminginhalf-duplexsystemsleveraginglargepropagationdelays.
The first part of the dissertation deals with sparse channel estimation in UWA or-
thogonal frequency division multiplexing (OFDM) systems. By exploiting the sparse
nature of UWA channels, compressed sensing (CS) based channel estimation meth-
ods have demonstrated superior performance compared to conventional least-squares
(LS) methods. However, a priori information of channel sparsity level is required to
set the regularization parameter properly. We propose a data-driven sparsity learning
approach based on a linear minimum mean squared error (LMMSE) equalizer to tune
theregularizationparameterforOFDMtransmissions.
YiHuang––UniversityofConnecticut,2016
ThesecondpartofthedissertationfocusesonsecretkeygenerationinUWAchan-
nels. Predefined secret keys are often used to encrypt information. However, they
could be leaked to eavesdroppers. A key generation protocol is presented where se-
cretkeysaredynamicallygeneratedbyquantizingthemeasuredamplitudesonOFDM
subcarriers, and then using error correction codes for secret bits extraction according
to the Slepian-Wolf coding principle. By analyzing the performance based on col-
lected field data, an improved key generation protocol is proposed by incorporating
twomodulestoincreasethechannelcorrelationanddealwithchanneldynamics.
The last part of the dissertation presents a self-protection jamming approach for
blocktransmissionsinhalf-duplexUWAsystems. Differentfromexistingapproaches,
where additional helpers (e.g., relays) are needed to transmit jamming signals, the
proposedprotocoldoesnotneedanyhelperbutinsteadreliesonthelegitimatereceiver
itself. This approach exploits the half-duplex nature of underwater transceivers and
the block-based transmission structure, by taking advantage of the large propagation
delays to create interferenceat the eavesdropper without affecting the reception of the
intendeduser.
ExplorationofPhysicalLayerSecurityinUnderwaterAcoustic
Communications
YiHuang
B.S.,NorthwesternPolytechnicalUniversity,Xi’an,China,2008
M.S.,NorthwesternPolytechnicalUniversity,Xi’an,China,2011
ADissertation
SubmittedinPartialFulfillmentofthe
RequirementsfortheDegreeofDoctorofPhilosophy
atthe
UniversityofConnecticut
2016
Copyrightby
YiHuang
2016
APPROVAL PAGE
DoctorofPhilosophyDissertation
ExplorationofPhysicalLayerSecurityinUnderwaterAcoustic
Communications
Presentedby
YiHuang, B.S.,M.S.
MajorAdvisor
RajeevBansal
AssociateAdvisor
PeterWillett
AssociateAdvisor
ShalabhGupta
UniversityofConnecticut
2016
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Tomyfamily
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ACKNOWLEDGEMENTS
First, I want to express my heartfelt gratitude to Professor Shengli Zhou for his
excellent guidance, constant support, and providing me the precious opportunity to
pursuemyPh.D.inUCONN.Icaneverexpresswhattheexperiencemeanstomeand
how much it shapes me. He encouraged me to collaborate with others in teamwork,
andtrainedmetobeacoordinatorandmentorinprojectswheneverpossible. Hissharp
insight,broadknowledge,creativeandcriticalthinking,anddown-to-earthattitudeset
me an outstanding example of academic perfection. I attribute this dissertation to his
effortandassistance.
I am also extremely grateful to my major advisor Professor Rajeev Bansal for his
help and advice. The acknowledgement also goes to Professor Zhijie Shi for his su-
pervision in my research. They are so nice and professional, and sets a good example
tome,whichwillcontinuetobenefittherestofmylife.
I would like to thank Professor Peter Willett, Professor Shalabh Gupta, Professor
Yaakov Bar-Shalom, Professor Song Han, Professor Bing Wang, Professor Pattipati,
Professor Marten and Dr. Athanasios Bamis for their broad knowledge and heuris-
tic advice. It is fabulously intriguing to get inspired by their cool achievements and
exceptionalcreativity.
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I want to thank my colleagues and lab mates Lei Wan, Zhaohui Wang, Jianzhong
Huang, Xiufeng Song, Hao Zhou, Xiaoka Xu, Huizhong Gao, Qin Lu, Sharief Abdel-
Razeq, Patrick Carroll, Katherine Domrese, Yuzhi Zhang, Yougan Chen, Xiaoyan
Kuai, Lu Ma, Wei Li, Xingyu Cai, Peng Xiao, and my lab mates/neighbors Mingyang
Li, Wenbo Dou, Djedjiga Belfadel, Ting Yuan for their helpful discussions in my re-
search,andadviceonmycareer.
I also would like to thank my friends in UCONN, Junlin Chen, Guoxian Huang,
Dongqiao Zhang and Dong Zhao, etc, for their thoughtful support and companion in
my hard time. The happiness and woe we shared make the life here an amazing and
unforgettablejourney.
Lastandforemost,Igivemydeepestandsincerestgratitudetomyparents,Youkang
Huang,andQunyingZhang,fortheirendlesslove,understanding,encouragement,and
beliefinmylife. Theirunconditionalgenerosityandsupporthavebeenmyinspiration
toaccomplishmydoctoralprogram,andraisemetomorethanIcanbe. Tomyfamily,
Idedicatethisdissertation.
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TABLE OF CONTENTS
Chapter1: Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 OutlineoftheDissertation . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Chapter2: Comparison of Sparse Recovery Algorithms for Channel Es-
timation in Underwater Acoustic OFDM with Data-Driven
SparsityLearning 7
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 SystemModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.1 ChannelModel . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 ReceiverModel . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 ChannelEstimation . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.1 Sparse Channel Representation Based on Overcomplete Dic-
tionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.2 SparseRecoveryAlgorithms . . . . . . . . . . . . . . . . . . 16
2.3.2.1 L Algorithms-LSQR . . . . . . . . . . . . . . . . . . . 16
2
2.3.2.2 L Algorithms-OMP . . . . . . . . . . . . . . . . . . . . 17
0
2.3.2.3 L Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 17
1
2.3.2.4 L Algorithm . . . . . . . . . . . . . . . . . . . . . . . 19
1/2
2.4 Data-drivenSparsityLearning . . . . . . . . . . . . . . . . . . . . . 20
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