Matching Items (6)
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- All Subjects: Wireless communication systems
- Creators: Cochran, Douglas
- Creators: Bakkaloglu, Bertan
Description
The cyber-physical systems (CPS) are emerging as the underpinning technology for major industries in the 21-th century. This dissertation is focused on two fundamental issues in cyber-physical systems: network interdependence and information dynamics. It consists of the following two main thrusts. The first thrust is targeted at understanding the impact of network interdependence. It is shown that a cyber-physical system built upon multiple interdependent networks are more vulnerable to attacks since node failures in one network may result in failures in the other network, causing a cascade of failures that would potentially lead to the collapse of the entire infrastructure. There is thus a need to develop a new network science for modeling and quantifying cascading failures in multiple interdependent networks, and to develop network management algorithms that improve network robustness and ensure overall network reliability against cascading failures. To enhance the system robustness, a "regular" allocation strategy is proposed that yields better resistance against cascading failures compared to all possible existing strategies. Furthermore, in view of the load redistribution feature in many physical infrastructure networks, e.g., power grids, a CPS model is developed where the threshold model and the giant connected component model are used to capture the node failures in the physical infrastructure network and the cyber network, respectively. The second thrust is centered around the information dynamics in the CPS. One speculation is that the interconnections over multiple networks can facilitate information diffusion since information propagation in one network can trigger further spread in the other network. With this insight, a theoretical framework is developed to analyze information epidemic across multiple interconnecting networks. It is shown that the conjoining among networks can dramatically speed up message diffusion. Along a different avenue, many cyber-physical systems rely on wireless networks which offer platforms for information exchanges. To optimize the QoS of wireless networks, there is a need to develop a high-throughput and low-complexity scheduling algorithm to control link dynamics. To that end, distributed link scheduling algorithms are explored for multi-hop MIMO networks and two CSMA algorithms under the continuous-time model and the discrete-time model are devised, respectively.
ContributorsQian, Dajun (Author) / Zhang, Junshan (Thesis advisor) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Cochran, Douglas (Committee member) / Arizona State University (Publisher)
Created2012
Description
Modern measurement schemes for linear dynamical systems are typically designed so that different sensors can be scheduled to be used at each time step. To determine which sensors to use, various metrics have been suggested. One possible such metric is the observability of the system. Observability is a binary condition determining whether a finite number of measurements suffice to recover the initial state. However to employ observability for sensor scheduling, the binary definition needs to be expanded so that one can measure how observable a system is with a particular measurement scheme, i.e. one needs a metric of observability. Most methods utilizing an observability metric are about sensor selection and not for sensor scheduling. In this dissertation we present a new approach to utilize the observability for sensor scheduling by employing the condition number of the observability matrix as the metric and using column subset selection to create an algorithm to choose which sensors to use at each time step. To this end we use a rank revealing QR factorization algorithm to select sensors. Several numerical experiments are used to demonstrate the performance of the proposed scheme.
ContributorsIlkturk, Utku (Author) / Gelb, Anne (Thesis advisor) / Platte, Rodrigo (Thesis advisor) / Cochran, Douglas (Committee member) / Renaut, Rosemary (Committee member) / Armbruster, Dieter (Committee member) / Arizona State University (Publisher)
Created2015
Description
The explosive growth of data generated from different services has opened a new vein of research commonly called ``big data.'' The sheer volume of the information in this data has yielded new applications in a wide range of fields, but the difficulties inherent in processing the enormous amount of data, as well as the rate at which it is generated, also give rise to significant challenges. In particular, processing, modeling, and understanding the structure of online social networks is computationally difficult due to these challenges. The goal of this study is twofold: first to present a new networked data processing framework to model this social structure, and second to highlight the wireless networking gains possible by using this social structure.
The first part of the dissertation considers a new method for modeling social networks via probabilistic graphical models. Specifically, this new method employs the t-cherry junction tree, a recent advancement in probabilistic graphical models, to develop a compact representation and good approximation of an otherwise intractable probabilistic model. There are a number of advantages in this approach: 1) the best approximation possible via junction trees belongs to the class of t-cherry junction trees; 2) constructing a t-cherry junction tree can be largely parallelized; and 3) inference can be performed using distributed computation. To improve the quality of approximation, an algorithm to build a higher order tree gracefully from an existing one, without constructing it from scratch, is developed. this approach is applied to Twitter data containing 100,000 nodes to study the problem of recommending connections to new users.
Next, the t-cherry junction tree framework is extended by considering the impact of estimating the distributions involved from a training data set. Understanding this impact is vital to real-world applications as distributions are not known perfectly, but rather generated from training data. First, the fidelity of the t-cherry junction tree approximation due to this estimation is quantified. Then the scaling behavior, in terms of the size of the t-cherry junction tree, is approximated to show that higher-order t-cherry junction trees, which with perfect information are higher fidelity approximations, may actually result in decreased fidelity due to the difficulties in accurately estimating higher-dimensional distributions. Finally, this part concludes by demonstrating these findings by considering a distributed detection situation in which the sensors' measurements are correlated.
Having developed a framework to model social structure in online social networks, the study then highlights two approaches for utilizing this social network data in existing wireless communication networks. The first approach is a novel application: using social networks to enhance device-to-device wireless communication. It is well known that wireless communication can be significantly improved by utilizing relays to aid in transmission. Rather than deploying dedicated relays, a system is designed in which users can relay traffic for other users if there is a shared social trust between them, e.g., they are ``friends'' on Facebook, and for users that do not share social trust, implements a coalitional game framework to motivate users to relay traffic for each other. This framework guarantees that all users improve their throughput via relaying while ensuring that each user will function as a relay only if there is a social trust relationship or, if there is no social trust, a cycle of reciprocity is established in which a set of users will agree to relay for each other. This new system shows significant throughput gain in simulated networks that utilize real-world social network traces.
The second application of social structure to wireless communication is an approach to reduce the congestion in cellular networks during peak times. This is achieved by two means: preloading and offloading. Preloading refers to the process of using social network data to predict user demand and serve some users early, before the cellular network traffic peaks. Offloading allows users that have already obtained a copy of the content to opportunistically serve other users using device-to-device communication, thus eliminating the need for some cellular traffic. These two methods work especially well in tandem, as preloading creates a base of users that can serve later users via offloading. These two processes can greatly reduce the peak cellular traffic under ideal conditions, and in a more realistic situation, the impact of uncertainty in human mobility and the social network structure is analyzed. Even with the randomness inherent in these processes, both preloading and offloading offer substantial improvement. Finally, potential difficulties in preloading multiple pieces of content simultaneously are highlighted, and a heuristic method to solve these challenges is developed.
The first part of the dissertation considers a new method for modeling social networks via probabilistic graphical models. Specifically, this new method employs the t-cherry junction tree, a recent advancement in probabilistic graphical models, to develop a compact representation and good approximation of an otherwise intractable probabilistic model. There are a number of advantages in this approach: 1) the best approximation possible via junction trees belongs to the class of t-cherry junction trees; 2) constructing a t-cherry junction tree can be largely parallelized; and 3) inference can be performed using distributed computation. To improve the quality of approximation, an algorithm to build a higher order tree gracefully from an existing one, without constructing it from scratch, is developed. this approach is applied to Twitter data containing 100,000 nodes to study the problem of recommending connections to new users.
Next, the t-cherry junction tree framework is extended by considering the impact of estimating the distributions involved from a training data set. Understanding this impact is vital to real-world applications as distributions are not known perfectly, but rather generated from training data. First, the fidelity of the t-cherry junction tree approximation due to this estimation is quantified. Then the scaling behavior, in terms of the size of the t-cherry junction tree, is approximated to show that higher-order t-cherry junction trees, which with perfect information are higher fidelity approximations, may actually result in decreased fidelity due to the difficulties in accurately estimating higher-dimensional distributions. Finally, this part concludes by demonstrating these findings by considering a distributed detection situation in which the sensors' measurements are correlated.
Having developed a framework to model social structure in online social networks, the study then highlights two approaches for utilizing this social network data in existing wireless communication networks. The first approach is a novel application: using social networks to enhance device-to-device wireless communication. It is well known that wireless communication can be significantly improved by utilizing relays to aid in transmission. Rather than deploying dedicated relays, a system is designed in which users can relay traffic for other users if there is a shared social trust between them, e.g., they are ``friends'' on Facebook, and for users that do not share social trust, implements a coalitional game framework to motivate users to relay traffic for each other. This framework guarantees that all users improve their throughput via relaying while ensuring that each user will function as a relay only if there is a social trust relationship or, if there is no social trust, a cycle of reciprocity is established in which a set of users will agree to relay for each other. This new system shows significant throughput gain in simulated networks that utilize real-world social network traces.
The second application of social structure to wireless communication is an approach to reduce the congestion in cellular networks during peak times. This is achieved by two means: preloading and offloading. Preloading refers to the process of using social network data to predict user demand and serve some users early, before the cellular network traffic peaks. Offloading allows users that have already obtained a copy of the content to opportunistically serve other users using device-to-device communication, thus eliminating the need for some cellular traffic. These two methods work especially well in tandem, as preloading creates a base of users that can serve later users via offloading. These two processes can greatly reduce the peak cellular traffic under ideal conditions, and in a more realistic situation, the impact of uncertainty in human mobility and the social network structure is analyzed. Even with the randomness inherent in these processes, both preloading and offloading offer substantial improvement. Finally, potential difficulties in preloading multiple pieces of content simultaneously are highlighted, and a heuristic method to solve these challenges is developed.
ContributorsProulx, Brian (Author) / Zhang, Junshan (Thesis advisor) / Cochran, Douglas (Committee member) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
A principal goal of this dissertation is to study wireless network design and optimization with the focus on two perspectives: 1) socially-aware mobile networking and computing; 2) security and privacy in wireless networking. Under this common theme, this dissertation can be broadly organized into three parts.
The first part studies socially-aware mobile networking and computing. First, it studies random access control and power control under a social group utility maximization (SGUM) framework. The socially-aware Nash equilibria (SNEs) are derived and analyzed. Then, it studies mobile crowdsensing under an incentive mechanism that exploits social trust assisted reciprocity (STAR). The efficacy of the STAR mechanism is thoroughly investigated. Next, it studies mobile users' data usage behaviors under the impact of social services and the wireless operator's pricing. Based on a two-stage Stackelberg game formulation, the user demand equilibrium (UDE) is analyzed in Stage II and the optimal pricing strategy is developed in Stage I. Last, it studies opportunistic cooperative networking under an optimal stopping framework with two-level decision-making. For both cases with or without dedicated relays, the optimal relaying strategies are derived and analyzed.
The second part studies radar sensor network coverage for physical security. First, it studies placement of bistatic radar (BR) sensor networks for barrier coverage. The optimality of line-based placement is analyzed, and the optimal placement of BRs on a line segment is characterized. Then, it studies the coverage of radar sensor networks that exploits the Doppler effect. Based on a Doppler coverage model, an efficient method is devised to characterize Doppler-covered regions and an algorithm is developed to find the minimum radar density required for Doppler coverage.
The third part studies cyber security and privacy in socially-aware networking and computing. First, it studies random access control, cooperative jamming, and spectrum access under an extended SGUM framework that incorporates negative social ties. The SNEs are derived and analyzed. Then, it studies pseudonym change for personalized location privacy under the SGUM framework. The SNEs are analyzed and an efficient algorithm is developed to find an SNE with desirable properties.
The first part studies socially-aware mobile networking and computing. First, it studies random access control and power control under a social group utility maximization (SGUM) framework. The socially-aware Nash equilibria (SNEs) are derived and analyzed. Then, it studies mobile crowdsensing under an incentive mechanism that exploits social trust assisted reciprocity (STAR). The efficacy of the STAR mechanism is thoroughly investigated. Next, it studies mobile users' data usage behaviors under the impact of social services and the wireless operator's pricing. Based on a two-stage Stackelberg game formulation, the user demand equilibrium (UDE) is analyzed in Stage II and the optimal pricing strategy is developed in Stage I. Last, it studies opportunistic cooperative networking under an optimal stopping framework with two-level decision-making. For both cases with or without dedicated relays, the optimal relaying strategies are derived and analyzed.
The second part studies radar sensor network coverage for physical security. First, it studies placement of bistatic radar (BR) sensor networks for barrier coverage. The optimality of line-based placement is analyzed, and the optimal placement of BRs on a line segment is characterized. Then, it studies the coverage of radar sensor networks that exploits the Doppler effect. Based on a Doppler coverage model, an efficient method is devised to characterize Doppler-covered regions and an algorithm is developed to find the minimum radar density required for Doppler coverage.
The third part studies cyber security and privacy in socially-aware networking and computing. First, it studies random access control, cooperative jamming, and spectrum access under an extended SGUM framework that incorporates negative social ties. The SNEs are derived and analyzed. Then, it studies pseudonym change for personalized location privacy under the SGUM framework. The SNEs are analyzed and an efficient algorithm is developed to find an SNE with desirable properties.
ContributorsGong, Xiaowen (Author) / Zhang, Junshan (Thesis advisor) / Cochran, Douglas (Committee member) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
Traditional wireless communication systems operate in duplexed modes i.e. using time division duplexing or frequency division duplexing. These methods can respectively emulate full duplex mode operation or realize full duplex mode operation with decreased spectral efficiency. This thesis presents a novel method of achieving full duplex operation by actively cancelling out the transmitted signal in pseudo-real time. With appropriate hardware, the algorithms and techniques used in this work can be implemented in real time without any knowledge of the channel or any training sequence. Convergence times of down to 1 ms can be achieved which is adequate for the coherence bandwidths associated with an indoor environment. By utilizing adaptive cancellation, additional overhead for re-calibrating the system in other open-loop methods is not needed.
ContributorsAvasarala, Sanjay (Author) / Kiaei, Sayfe (Thesis advisor) / Kitchen, Jennifer (Committee member) / Bakkaloglu, Bertan (Committee member) / Arizona State University (Publisher)
Created2016
Description
This dissertation focuses on three different efficiency enhancement methods that are applicable to handset applications. These proposed designs are based on three critical requirements for handset application: 1) Small form factor, 2) CMOS compatibility and 3) high power handling. The three presented methodologies are listed below:
1) A transformer-based power combiner architecture for out-phasing transmitters
2) A current steering DAC-based average power tracking circuit for on-chip power amplifiers (PA)
3) A CMOS-based driver stage for GaN-based switched-mode power amplifiers applicable to fully digital transmitters
This thesis highlights the trends in wireless handsets, the motivates the need for fully-integrated CMOS power amplifier solutions and presents the three novel techniques for reconfigurable and digital CMOS-based PAs. Chapter 3, presents the transformer-based power combiner for out-phasing transmitters. The simulation results reveal that this technique is able to shrink the power combiner area, which is one of the largest parts of the transmitter, by about 50% and as a result, enhances the output power density by 3dB.
The average power tracking technique (APT) integrated with an on-chip CMOS-based power amplifier is explained in Chapter 4. This system is able to achieve up to 32dBm saturated output power with a linear power gain of 20dB in a 45nm CMOS SOI process. The maximum efficiency improvement is about ∆η=15% compared to the same PA without APT. Measurement results show that the proposed method is able to amplify an enhanced-EDGE modulated input signal with a data rate of 70.83kb/sec and generate more than 27dBm of average output power with EVM<5%.
Although small form factor, high battery lifetime, and high volume integration motivate the need for fully digital CMOS transmitters, the output power generated by this type of transmitter is not high enough to satisfy the communication standards. As a result, compound materials such as GaN or GaAs are usually being used in handset applications to increase the output power. Chapter 5 focuses on the analysis and design of two CMOS based driver architectures (cascode and house of cards) for driving a GaN power amplifier. The presented results show that the drivers are able to generate ∆Vout=5V, which is required by the compound transistor, and operate up to 2GHz. Since the CMOS driver is expected to drive an off-chip capacitive load, the interface components, such as bond wires, and decoupling and pad capacitors, play a critical role in the output transient response. Therefore, extensive analysis and simulation results have been done on the interface circuits to investigate their effects on RF transmitter performance. The presented results show that the maximum operating frequency when the driver is connected to a 4pF capacitive load is about 2GHz, which is perfectly matched with the reported values in prior literature.
1) A transformer-based power combiner architecture for out-phasing transmitters
2) A current steering DAC-based average power tracking circuit for on-chip power amplifiers (PA)
3) A CMOS-based driver stage for GaN-based switched-mode power amplifiers applicable to fully digital transmitters
This thesis highlights the trends in wireless handsets, the motivates the need for fully-integrated CMOS power amplifier solutions and presents the three novel techniques for reconfigurable and digital CMOS-based PAs. Chapter 3, presents the transformer-based power combiner for out-phasing transmitters. The simulation results reveal that this technique is able to shrink the power combiner area, which is one of the largest parts of the transmitter, by about 50% and as a result, enhances the output power density by 3dB.
The average power tracking technique (APT) integrated with an on-chip CMOS-based power amplifier is explained in Chapter 4. This system is able to achieve up to 32dBm saturated output power with a linear power gain of 20dB in a 45nm CMOS SOI process. The maximum efficiency improvement is about ∆η=15% compared to the same PA without APT. Measurement results show that the proposed method is able to amplify an enhanced-EDGE modulated input signal with a data rate of 70.83kb/sec and generate more than 27dBm of average output power with EVM<5%.
Although small form factor, high battery lifetime, and high volume integration motivate the need for fully digital CMOS transmitters, the output power generated by this type of transmitter is not high enough to satisfy the communication standards. As a result, compound materials such as GaN or GaAs are usually being used in handset applications to increase the output power. Chapter 5 focuses on the analysis and design of two CMOS based driver architectures (cascode and house of cards) for driving a GaN power amplifier. The presented results show that the drivers are able to generate ∆Vout=5V, which is required by the compound transistor, and operate up to 2GHz. Since the CMOS driver is expected to drive an off-chip capacitive load, the interface components, such as bond wires, and decoupling and pad capacitors, play a critical role in the output transient response. Therefore, extensive analysis and simulation results have been done on the interface circuits to investigate their effects on RF transmitter performance. The presented results show that the maximum operating frequency when the driver is connected to a 4pF capacitive load is about 2GHz, which is perfectly matched with the reported values in prior literature.
ContributorsMoallemi, Soroush (Author) / Kitchen, Jennifer (Thesis advisor) / Kiaei, Sayfe (Committee member) / Bakkaloglu, Bertan (Committee member) / Thornton, Trevor (Committee member) / Arizona State University (Publisher)
Created2019