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- Genre: Doctoral Dissertation
Description
Unmanned aerial systems (UASs) have recently enabled novel applications such as passenger transport and package delivery, but are increasingly vulnerable to cyberattack and therefore difficult to certify. Legacy systems such as GPS provide these capabilities extremely well, but are sensitive to spoofing and hijacking. An alternative intelligent transport system (ITS) was developed that provides highly secure communications, positioning, and timing synchronization services to networks of cooperative RF users, termed Communications and High-Precision Positioning (CHP2) system. This technology was implemented on consumer-off-the-shelf (COTS) hardware and it offers rapid (<100 ms) and precise (<5 cm) positioning capabilities in over-the-air experiments using flexible ground stations and UAS platforms using limited bandwidth (10 MHz). In this study, CHP2 is considered in the context of safety-critical and resource limited transport applications and urban air mobility. The two-way ranging (TWR) protocol over a joint positioning-communications waveform enables distributed coherence and time-of-flight(ToF) estimation. In a multi-antenna setup, the cross-platform ranging on participating nodes in the network translate to precise target location and orientation. In the current form, CHP2 necessitates a cooperative timing exchange at regular intervals. Dynamic resource management supports higher user densities by constantly renegotiating spectral access depending on need and opportunity. With these novel contributions to the field of integrated positioning and communications, CHP2 is a suitable candidate to provide both communications, navigation, and surveillance (CNS) and alternative positioning, navigation, and timing (APNT) services for high density safety-critical transport applications on a variety of vehicular platforms.
ContributorsSrinivas, Sharanya (Author) / Bliss, Daniel W. (Thesis advisor) / Richmond, Christ D. (Committee member) / Chakrabarti, Chaitali (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2020
Description
In conventional radar signal processing, a structured model for the target response is used, while clutter and interference are characterized by the covariance matrix of the data distribution. In contrast, the channel matrix-based model represents both target and clutter returns as responses to corresponding channels, resulting in a more versatile model that can incorporate various scenarios. Optimal receive architectures for target detection within a channel matrix-based model are explored using likelihood ratio tests (LRT) and average LRT (ALRT) tests. Generalized likelihood ratio test (GLRT) statistics are derived for the channel matrix-based MIMO radar data model under the assumption of complex multivariate elliptically symmetric (CMES) data distribution, considering both known and unknown covariance matrices of the waveform-independent colored noise (WICN). For the known covariance case, the GLRT statistic follows a chi-square distribution, while for the unknown covariance case, it aligns with Wilks' lambda distribution. The GLRT statistic for the known WICN covariance case, when the maximum likelihood estimate of the covariance matrix replaces the true covariance matrix, matches the Bartlett-Nanda-Pillai trace statistic under the null hypothesis and follows a non-central Lawley-Hotelling $T_0^2$ distribution under the alternative hypothesis. Asymptotically, all derived statistics converge to the known covariance case. Monte Carlo simulations and the saddle point approximation method are employed to generate receiver operating characteristic (ROC) curves for a simple numerical example, supplemented by experimental results and high-fidelity simulations. The potential of deep learning techniques for radar target detection is investigated, with a proposed deep neural network (DNN) architecture benefiting from both model-based and data-driven approaches. The asymptotic distribution of the GLRT statistic for adaptive target detection is non-central chi-squared with a non-centrality parameter that depends on the waveform information. This provides a basis for the design of optimal waveforms fortarget detection. The waveform optimization problem is formulated as a semidefinite programming instance, and an algorithm is proposed to maximize the non-centrality parameter, thereby enhancing the probability of target detection. This algorithm also incorporates power and peak-to-average power ratio (PAPR) constraints, essential for ensuring practical and efficient radar operation.
ContributorsAli, Touseef (Author) / Richmond, Christ D (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Guerci, Joseph R (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2024
Description
Reconfigurable metasurfaces (RMSs) are promising solutions for beamforming and sensing applications including 5G and beyond wireless communications, satellite and radar systems, and biomarker sensing. In this work, three distinct RMS architectures – reconfigurable intelligent surfaces (RISs), meta-transmission lines (meta-TLs), and substrate integrated waveguide leaky-wave antennas (SIW-LWAs) are developed and characterized. The ever-increasing demand for higher data rates and lower latencies has propelled the telecommunications industry to adopt higher frequencies for 5G and beyond wireless communications. However, this transition to higher frequencies introduces challenges in terms of signal coverage and path loss. Many base stations would be necessary to ensure signal fidelity in such a setting, making bulky phased array-based solutions impractical. Consequently, to meet the unique needs of 5G and beyond wireless communication networks, this work proposes the use of RISs characterized by low-profile, low-RF losses, low-power consumption, and high-gain capabilities, making them excellent candidates for future wireless communication applications. Specifically, RISs at sub-6GHz, mmWave and sub-THz frequencies are analyzed to demonstrate their ability to improve signal strength and coverage.
Further, a linear meta-TL wave space is designed to achieve miniaturization of true-time delay beamforming structures such as Rotman lenses which are traditionally bulky. To address this challenge, a modified lumped element TL model is proposed. A meta-TL is created by including the mutual coupling effects and can be used to slow down the electromagnetic signal and realize miniaturized lenses. A proof-of-concept 1D meta-TL is developed to demonstrate about 90% size reduction and 40% bandwidth improvement.
Furthermore, a conformable antenna design for radio frequency-based tracking of hand gestures is also detailed. SIW-LWA is employed as the radiating element to couple RF signals into the human hand. The antenna is envisaged to be integrated in a wristband topology and capture the changes in the electric field caused by various movements of the hand. The scattering parameters are used to track the changes in the wrist anatomy. Sensor characterization showed significant sensitivity suppression due to lossy multi-dielectric nature tissues in the wrist. However, the sensor demonstrates good coupling of electromagnetic energy making it suitable for on-body wireless communications and magnetic resonance imaging applications.
ContributorsKashyap, Bharath Gundappa (Author) / Trichopoulos, Georgios C (Thesis advisor) / Balanis, Constantine A (Committee member) / Aberle, James T (Committee member) / Alkhateeb, Ahmed (Committee member) / Imani, Seyedmohammedreza F (Committee member) / Arizona State University (Publisher)
Created2023
Description
In the past half century, low-power wireless signals from portable radar sensors, initially continuous-wave (CW) radars and more recently ultra-wideband (UWB) radar systems, have been successfully used to detect physiological movements of stationary human beings.
The thesis starts with a careful review of existing signal processing techniques and state of the art methods possible for vital signs monitoring using UWB impulse systems. Then an in-depth analysis of various approaches is presented.
Robust heart-rate monitoring methods are proposed based on a novel result: spectrally the fundamental heartbeat frequency is respiration-interference-limited while its higher-order harmonics are noise-limited. The higher-order statistics related to heartbeat can be a robust indication when the fundamental heartbeat is masked by the strong lower-order harmonics of respiration or when phase calibration is not accurate if phase-based method is used. Analytical spectral analysis is performed to validate that the higher-order harmonics of heartbeat is almost respiration-interference free. Extensive experiments have been conducted to justify an adaptive heart-rate monitoring algorithm. The scenarios of interest are, 1) single subject, 2) multiple subjects at different ranges, 3) multiple subjects at same range, and 4) through wall monitoring.
A remote sensing radar system implemented using the proposed adaptive heart-rate estimation algorithm is compared to the competing remote sensing technology, a remote imaging photoplethysmography system, showing promising results.
State of the art methods for vital signs monitoring are fundamentally related to process the phase variation due to vital signs motions. Their performance are determined by a phase calibration procedure. Existing methods fail to consider the time-varying nature of phase noise. There is no prior knowledge about which of the corrupted complex signals, in-phase component (I) and quadrature component (Q), need to be corrected. A precise phase calibration routine is proposed based on the respiration pattern. The I/Q samples from every breath are more likely to experience similar motion noise and therefore they should be corrected independently. High slow-time sampling rate is used to ensure phase calibration accuracy. Occasionally, a 180-degree phase shift error occurs after the initial calibration step and should be corrected as well. All phase trajectories in the I/Q plot are only allowed in certain angular spaces. This precise phase calibration routine is validated through computer simulations incorporating a time-varying phase noise model, controlled mechanic system, and human subject experiment.
The thesis starts with a careful review of existing signal processing techniques and state of the art methods possible for vital signs monitoring using UWB impulse systems. Then an in-depth analysis of various approaches is presented.
Robust heart-rate monitoring methods are proposed based on a novel result: spectrally the fundamental heartbeat frequency is respiration-interference-limited while its higher-order harmonics are noise-limited. The higher-order statistics related to heartbeat can be a robust indication when the fundamental heartbeat is masked by the strong lower-order harmonics of respiration or when phase calibration is not accurate if phase-based method is used. Analytical spectral analysis is performed to validate that the higher-order harmonics of heartbeat is almost respiration-interference free. Extensive experiments have been conducted to justify an adaptive heart-rate monitoring algorithm. The scenarios of interest are, 1) single subject, 2) multiple subjects at different ranges, 3) multiple subjects at same range, and 4) through wall monitoring.
A remote sensing radar system implemented using the proposed adaptive heart-rate estimation algorithm is compared to the competing remote sensing technology, a remote imaging photoplethysmography system, showing promising results.
State of the art methods for vital signs monitoring are fundamentally related to process the phase variation due to vital signs motions. Their performance are determined by a phase calibration procedure. Existing methods fail to consider the time-varying nature of phase noise. There is no prior knowledge about which of the corrupted complex signals, in-phase component (I) and quadrature component (Q), need to be corrected. A precise phase calibration routine is proposed based on the respiration pattern. The I/Q samples from every breath are more likely to experience similar motion noise and therefore they should be corrected independently. High slow-time sampling rate is used to ensure phase calibration accuracy. Occasionally, a 180-degree phase shift error occurs after the initial calibration step and should be corrected as well. All phase trajectories in the I/Q plot are only allowed in certain angular spaces. This precise phase calibration routine is validated through computer simulations incorporating a time-varying phase noise model, controlled mechanic system, and human subject experiment.
ContributorsRong, Yu (Author) / Bliss, Daniel W (Thesis advisor) / Richmond, Christ D (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2018
Description
Predicting nonlinear dynamical systems has been a long-standing challenge in science. This field is currently witnessing a revolution with the advent of machine learning methods. Concurrently, the analysis of dynamics in various nonlinear complex systems continues to be crucial. Guided by these directions, I conduct the following studies. Predicting critical transitions and transient states in nonlinear dynamics is a complex problem. I developed a solution called parameter-aware reservoir computing, which uses machine learning to track how system dynamics change with a driving parameter. I show that the transition point can be accurately predicted while trained in a sustained functioning regime before the transition. Notably, it can also predict if the system will enter a transient state, the distribution of transient lifetimes, and their average before a final collapse, which are crucial for management. I introduce a machine-learning-based digital twin for monitoring and predicting the evolution of externally driven nonlinear dynamical systems, where reservoir computing is exploited. Extensive tests on various models, encompassing optics, ecology, and climate, verify the approach’s effectiveness. The digital twins can extrapolate unknown system dynamics, continually forecast and monitor under non-stationary external driving, infer hidden variables, adapt to different driving waveforms, and extrapolate bifurcation behaviors across varying system sizes. Integrating engineered gene circuits into host cells poses a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback. I conducted systematic studies on hundreds of circuit structures exhibiting various functionalities, and identified a comprehensive categorization of growth-induced failures. I discerned three dynamical mechanisms behind these circuit failures. Moreover, my comprehensive computations reveal a scaling law between the circuit robustness and the intensity of growth feedback. A class of circuits with optimal robustness is also identified. Chimera states, a phenomenon of symmetry-breaking in oscillator networks, traditionally have transient lifetimes that grow exponentially with system size. However, my research on high-dimensional oscillators leads to the discovery of ’short-lived’ chimera states. Their lifetime increases logarithmically with system size and decreases logarithmically with random perturbations, indicating a unique fragility. To understand these states, I use a transverse stability analysis supported by simulations.
ContributorsKong, Lingwei (Author) / Lai, Ying-Cheng (Thesis advisor) / Tian, Xiaojun (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2023
Description
Non-terrestrial networks constitute a vertical scaling of conventional radio ecosystems wherein the optimized hierarchical interplay among unmanned aerial vehicles, high altitude platforms, and satellites is envisioned to augment the coverage and service capabilities of terrestrial networks. The 3D mobility and maneuverability of Unmanned Aerial Vehicles (UAVs) render them principally important non-terrestrial assets. Ergo, this work primarily investigates the functional paradigms of UAV augmented networks across numerous application domains while being supplemented with contemporary radio access technologies, viz., MIMO and Millimeter Wave (mmWave). Specifically, coupled with accurate mmWave air-to-ground channel modeling to statistically characterize signal propagation behavior in a plethora of radio environments, this work leverages tools from dynamic programming and artificial intelligence to develop distributed optimization frameworks intended to enhance the operational potential of UAV assisted networks. This work first elucidates a mmWave propagation modeling campaign on the NSF POWDER experimental testbed, wherein a measurement prototype involving a unique fully autonomous antenna alignment & tracking platform combined with a custom broadband sliding correlator channel sounder facilitates continuous beam-steered measurements in vehicular communication scenarios (emulating air-to-ground links in UAV aided settings), thereby enabling exhaustive signal propagation investigations. Upon establishing the propagation characteristics of mmWave signals in UAV augmented applications and further enhancing UAV link performance via rate adaptation, the subsequent discussions in this work highlight “use-inspired” research efforts spanning a diverse set of potential applications. In particular, the intelligent spectrum sharing problem in UAV aided military deployments is formulated as a Partially Observable Markov Decision Process and solved via randomized point based value iteration; the distributed orchestration of UAV relays in precision agriculture is facilitated by a Semi Markov Decision Process construction, solved using value iteration and subgradient methods; lastly, the decentralized orchestration of MIMO UAVs for data harvesting in industrial automation is enabled via a multiple traveling salesman problem setup, solved via mixed integer combinatorial optimization, i.e., a graphical branch-and-bound algorithm. Across these “use-inspired” efforts, the proposed solution addresses specific problems relevant to each application, alleviates the drawbacks observed in the current literature, and demonstrates superior performance over state-of-the-art algorithms vis-à-vis computational tractability, user quality-of-service, and UAV energy efficiency.
ContributorsKeshavamurthy, Bharath (Author) / Michelusi, Nicolò (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Alkhateeb, Ahmed (Committee member) / Bliss, Daniel (Committee member) / Arizona State University (Publisher)
Created2024
Description
Power amplifiers (PA) are often operated near saturation to meet the demands of both current and future wireless standards, including 4G, 5G NR, LTE, WCDMA, future 6G, and WiFi 7, leading to non-linear behavior either intentionally or unintentionally. The intentional use of high peak-to-average power ratio (PAPR) modulation schemes in these systems enables higher data rates, and reduced communication latency, but requires digital pre-distortion (DPD) to maintain high linearity and power efficiency. Conversely, in applications such as in-band full-duplex (IBFD) communication, amplifiers may unintentionally saturate due to strong self-interference (SI) between transmit and receive antennas. This saturation necessitates the use of digital SI cancellers, which regenerate the SI in order to recover the signal of interest. Both scenarios require accurate and low-complexity estimation and equalization of the PA channel to maintain linearity and power efficiency. Existing methods equalize using a large number of channel coefficients, posing challenges for low-latency applications with size, weight, power, and cost (SWaP-C) constraints. This dissertation addresses these challenges by proposing a novel two-box channel estimation and DPD architectures that uses basis functions inspired by amplifier physics. The proposed architecture is both theoretically studied and practically demonstrated using Ettus SDR and is at least ten times faster compared to the baseline in-direct learning architecture (ILA) DPD. Additionally, this dissertation addresses the challenge of capturing amplifier data cost-effectively by establishing the use of commercial-off-the-shelf (COTS) SDRs as measurement devices, along with design of novel calibration techniques. Traditional methods are often expensive, inflexible, and typically conducted under laboratory conditions with static waveforms and channels that are unrepresentative of real-world scenarios. The proposed calibration techniques minimize SDR system noise while accurately capturing the amplifier channel behavior. Theoretical and practical aspects of time, frequency, and phase alignment for high-quality data capture are investigated and demonstrated using Ettus N320 SDRs. The calibrated measurements are also used to demonstrate DPD using SDRs. Finally, the dissertation demonstrates IBFD by utilizing the proposed two-box channel architecture and SDR measurement setup in S-band. The combined techniques achieve performance comparable to baseline Hammerstein cancellers but with significantly reduced time complexity.
ContributorsVenkataramani, Adarsh Akkshai (Author) / Bliss, Daniel W (Thesis advisor) / Alkhateeb, Ahmed (Committee member) / Dasarathy, Gautam (Committee member) / Michelusi, Nicolò (Committee member) / Arizona State University (Publisher)
Created2024
Description
This dissertation describes the hardware implementation and analysis of a multi-function wireless receiver system to perform Joint Radar Communication on Domain-focused Advanced Software-reconfigurable Heterogeneous (DASH) system-on-chip (SoC). As the problem of spectral congestion becomes more chronic and widespread, Electromagnetic radio frequency (RF) based systems are posing as a viable solution to this problem. RF Spectral Convergence enables better utilization of available spectrum by reducing wastage and enabling multi-function tasks. There is a need forhigh-performance processors that can operate at low power while being flexible and easy to program.
Coarse-scale heterogeneous processors such as DASH SoC have been developed to demonstrate the feasibility of such architectures. DASH SoC addresses domain requirements of spectral convergence by offering high throughput rates with minimal energy expenditure. It also offers a high degree of programmability which enables adaptability to the domain of real-time joint communications and radar systems for applications such as spectral situational awareness, autonomous vehicular navigation systems, and positioning, navigation, and timing (PNT). The hardware implementation of a joint-radar communications receiver on DASH SoC validates these claims. Task scheduling is optimized by using an imitation learning-based runtime scheduler implemented on the DASH SoC platform. Modern applications such as 5G New
Radio (5GNR) require varying frame processing requirements, which are also implemented to demonstrate compatibility with current applications. General Matrix Multiply (GEMM) is an integral kernel within the Joint Radar and Communications (JRC) receiver; an estimation model is developed to study characteristics of GEMM implementation on domain adaptive processor (DAP) accelerator core within DASH SoC.
ContributorsSiddiqui, Saquib Ahmad (Author) / Bliss, Daniel (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Alkhateeb, Ahmed (Committee member) / Chiriyath, Alex (Committee member) / Akoglu, Ali (Committee member) / Arizona State University (Publisher)
Created2024
Description
Millimeter-wave (mmWave) and sub-terahertz (sub-THz) systems aim to utilize the large bandwidth available at these frequencies. This has the potential to enable several future applications that require high data rates, such as autonomous vehicles and digital twins. These systems, however, have several challenges that need to be addressed to realize their gains in practice. First, they need to deploy large antenna arrays and use narrow beams to guarantee sufficient receive power. Adjusting the narrow beams of the large antenna arrays incurs massive beam training overhead. Second, the sensitivity to blockages is a key challenge for mmWave and THz networks. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle both these challenges lies in leveraging additional side information such as visual, LiDAR, radar, and position data. These sensors provide rich information about the wireless environment, which can be utilized for fast beam and blockage prediction. This dissertation presents a machine-learning framework for sensing-aided beam and blockage prediction. In particular, for beam prediction, this work proposes to utilize visual and positional data to predict the optimal beam indices. For the first time, this work investigates the sensing-aided beam prediction task in a real-world vehicle-to-infrastructure and drone communication scenario. Similarly, for blockage prediction, this dissertation proposes a multi-modal wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. Evaluations on both real-world and synthetic datasets illustrate the promising performance of the proposed solutions and highlight their potential for next-generation communication and sensing systems.
ContributorsCharan, Gouranga (Author) / Alkhateeb, Ahmed (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Turaga, Pavan (Committee member) / Michelusi, Nicolò (Committee member) / Arizona State University (Publisher)
Created2024
Description
With the significant advancements of wireless communication systems that aim to meet exponentially increasing data rate demands, two promising concepts have appeared: (i) Cell-free massive MIMO, which entails the joint transmission and processing of the signals allowing the removal of classical cell boundaries, and (ii) integrated sensing and communication (ISAC), unifying communication and sensing in a single framework. This dissertation aims to take steps toward overcoming the key challenges in each concept and eventually merge them for efficient future communication and sensing networks.Cell-free massive MIMO is a distributed MIMO concept that eliminates classical cell boundaries and provides a robust performance. A significant challenge in realizing the cell-free massive MIMO in practice is its deployment complexity. In particular, connecting its many distributed access points with the central processing unit through wired fronthaul is an expensive and time-consuming approach. To eliminate this problem and enhance scalability, in this dissertation, a cell-free massive MIMO architecture adopting a wireless fronthaul is proposed, and the optimization of achievable rates for the end-to-end system is carried out. The evaluation has shown the strong potential of employing wireless fronthaul in cell-free massive MIMO systems. ISAC merges radar and communication systems, allowing effective sharing of resources, including bandwidth and hardware. The ISAC framework also enables sensing to aid communications, which shows a significant potential in mobile communication applications. Specifically, radar sensing data can address challenges like beamforming overhead and blockages associated with higher frequency, large antenna arrays, and narrow beams. To that end, this dissertation develops radar-aided beamforming and blockage prediction approaches using low-cost radar devices and evaluates them in real-world systems to verify their potential. At the intersection of these two paradigms, the integration of sensing into cell-free massive MIMO systems emerges as an intriguing prospect for future technologies. This integration, however, presents the challenge of considering both sensing and communication objectives within a distributed system. With the motivation of overcoming this challenge, this dissertation investigates diverse beamforming and power allocation solutions. Comprehensive evaluations have shown that the incorporation of sensing objectives into joint beamforming designs offers substantial capabilities for next-generation wireless communication and sensing systems.
ContributorsDemirhan, Umut (Author) / Alkhateeb, Ahmed (Thesis advisor) / Dasarathy, Gautam (Committee member) / Trichopoulos, Georgios (Committee member) / Michelusi, Nicolò (Committee member) / Arizona State University (Publisher)
Created2024