micron length. The Newton-Poisson-Brillouin (NPB) model uses

Newtonian physics to model the interaction of a population of

thermally excited quasi-particles. The NPB model is self-consistent

with Poisson’s equation, and the quasi-particles are confined to the

CNT’s band structure. In this work, we explore the parameter space of

the model.

]]>This dissertation considers signal processing approaches for exploiting or mitigating the effects of non-ideal components in wireless communication systems. In the first part of the dissertation, we introduce and study a novel, model-based approach to wireless device identification that exploits imperfections in the transmitter caused by manufacturing process nonidealities. Previous approaches to device identification based on hardware imperfections vary from transient analysis to machine learning but have not provided verifiable accuracy. Here, we detail a model-based approach, that uses statistical models of RF transmitter components: digital-to-analog converter, power amplifier and RF oscillator, which are amenable for analysis. Our proposed approach examines the key device characteristics that cause anonymity loss, countermeasures that can be applied by the nodes to regain the anonymity, and ways of thwarting such countermeasures. We develop identification algorithms based on statistical signal processing methods and address the challenging scenario when the units that need to be distinguished from one another are of the same model and from the same manufacturer. Using simulations and measurements of components that are commonly used in commercial communications systems, we show that our anonymity breaking techniques are effective.

In the second part of the dissertation, we consider innovative approaches for the acquisition of frequency-sparse signals with wide-band receivers when a weak signal of interest is received in the presence of a very strong interference, and the effects of the nonlinearities in the low-noise amplifier at the receiver must be mitigated. All samples with amplitude above a given threshold, dictated by the linear input range of the receiver, are discarded to avoid the distortion caused by saturation of the low noise amplifier. Such a sampling scheme, while avoiding nonlinear distortion that cannot be corrected in the digital domain, poses challenges for signal reconstruction techniques, as the samples are taken non-uniformly, but also non-randomly. The considered approaches fall into the field of compressive sensing (CS); however, what differentiates them from conventional CS is that a structure is forced upon the measurement scheme. Such a structure causes a violation of the core CS assumption of the measurements' randomness. We consider two different types of structured acquisition: signal independent and signal dependent structured acquisition. For the first case, we derive bounds on the number of samples needed for successful CS recovery when samples are drawn at random in predefined groups. For the second case, we consider enhancements of CS recovery methods when only small-amplitude samples of the signal that needs to be recovered are available for the recovery. Finally, we address a problem of spectral leakage due to the limited processing block size of block processing, wide-band receivers and propose an adaptive block size adjustment method, which leads to significant dynamic range improvements.

]]>Broadcasting, i.e., delivering a message from a single node to the entire network in a wireless ad hoc network can be achieved by nodes acting as relays. However, due to the random placement of nodes, broadcasting gets more difficult as the network size increases. We study how a stronger form of cooperation where nodes coordinate and transmit at the same time to increase their collective transmit range can improve broadcast ability. We show that, in this case, broadcast performance strongly depends on the type of wireless medium, in particular how fast the signal strength decays with distance. Specifically, we establish that, with increasing network size, broadcast probability goes to zero unless the attenuation in the medium is lower than a certain critical threshold. We consider the case of a wireless ad hoc network that is supported by base stations to improve data rate, which is referred to as a hybrid network. Although the availability of base stations may improve the throughput between the wireless nodes by providing access to an overlaid high-speed wired network, this improvement does not necessarily bring a scaling advantage as the network gets larger. Motivated by work which suggests the capacity increase depends on at what rate the number of base stations scales in comparison to the number of wireless nodes, we study the ultimate constraints on the capacity of hybrid networks. In particular, we prove upper bounds on the capacity scaling benefit the base stations can provide and also show constructions that achieve these bounds in some cases.

We study secret communication capabilities of nodes in a large wireless ad hoc network that also includes eavesdropper nodes. Under an information-theoretic secrecy framework, we investigate whether nodes can exchange data while keeping bits secret from eavesdropper nodes without sacrificing on the data rate, and, most importantly, without location information about the eavesdroppers. We show that this is indeed possible by employing a combination of secret sharing, two-way communications and network coding, where nodes perform simple coding operations on messages instead of simply forwarding them.

Finally, motivated by the results in the theory of random graphs that facilitate the understanding of the behavior of large wireless networks, we study connectivity in general random graphs in more detail. In particular, we study the percolation phenomenon, which refers to the abrupt transition of connectivity in large random graphs from a combination of disconnected islands to a large cluster spanning the whole graph when a critical threshold on the randomness parameter is exceeded. We study the extension of this percolation behavior to the case of a multilayer graph, which is formed by merging different graphs on the same vertex set, each representing a different type of connection between vertices. A multilayer graph, in general, is better connected than its individual layers, as vertices can be connected through paths traversing many layers. We numerically calculate the critical connectivity level on each layer such that the multilayer graph transitions to a well-connected state, i.e., percolates. Furthermore, we study the exact asymptotic behavior of this critical percolation threshold as the number of layers increases.

]]>Specifically, the thesis focuses on three concrete areas. The first one is on the development of a versatile low-cost beam steering system that can enable dual-polarimetric phased array radars to operate with high-frequency repetition pulses, difference pulsing schemes, and modern scanning strategies. In particular, the dissertation will present the development of critical components and describes the concept of operations of the beam steering system.

The second area is to develop a calibration technique for small phased arrays. The work focused in finding the calibration settings for the array that best fit to the desired excitation. The technique provides lower random errors than conventional approaches, enabling the implementation of radiation patterns with sidelobes closer to the desired level. Additionally, the technique is extended to solve the gain-drift problem occurring in the two-way antenna pattern due to the temperature changes.

The third area studies the use of mutual coupling as signal injection technique to maintain the calibration of both array and radar. Future air-cooled phased array radars will require the use internal circuitry to calibrate the aspect of the radar that tends to change over time. In particular, this work is focused on developing low-cost calibration techniques to correct the antenna gain and radar constant from effects of temperature changes and element failures.

]]>The first one is a multiple target localization and tracking problem in a wireless sensor network comprising binary proximity sensors [38]. We analyze this problem using the geometry of sensing of the individual sensors, and apply graph theoretical concepts to develop a fully-distributed multiple, interfering, target localization and tracking algorithm. Our distributed algorithm demonstrates the power of the use of localized information by sensors to make decisions that contribute to the inference about phenomena, in this case target movement, that are essentially global in nature. The distributed implementation of information interpretation also lends efficiency advantages, such as more efficient energy consumption due to reduced communication requirements, as shown in our simulations.

While the first two problems in this dissertation, as described above, deal with sensor information in one domain, target tracking in one case and weather sensing in the other, the third problem we investigate is cross-domain [36]. Here, parameters of one domain affect parameters of another domain, but only the affected domain parameters are measured, and tracked, to ultimately control these parameters in the affected domain. Specifically, we develop methods of network configuration based on distributed estimation and prediction of network performance degradataion parameters, where this performance degradation is originally affected by external environmental parameters such as weather conditions. We take "Routing in Wirelss Mesh Networks in the Face of Adverse Weather Conditions" as an example application to demonstrate our ideas of predictive network configuration. Through the simulations generated using real-world weather data, we are able to show that localized estimation and prediction of wireless link quality, as affected by the extreme weather events, results in remarkable improvements in network routing performance, and performs equally well, or even better, than routing that uses predictions of the affecting weather itself.

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