Defense Notices

This PhD research tackles two critical challenges in NextG wireless networks: hybrid precoder design for wideband sub-Terahertz (sub-THz) massive multiple-input multiple-output (MIMO) communications and privacy-preserving federated learning (FL) over wireless networks.

In the first part, we propose a novel hybrid precoding framework that integrates true-time delay (TTD) devices and phase shifters (PS) to counteract the beam squint effect - a significant challenge in the wideband sub-THz massive MIMO systems that leads to considerable loss in array gain. Unlike previous methods that only designed TTD values while fixed PS values and assuming unbounded time delay values, our approach jointly optimizes TTD and PS values under realistic time delays constraint. We determine the minimum number of TTD devices required to achieve a target array gain using our proposed approach. Then, we extend the framework to multi-user wideband systems and formulate a hybrid array optimization problem aiming to maximize the minimum data rate across users. This problem is decomposed into two sub-problems: fair subarray allocation, solved via continuous domain relaxation, and subarray gain maximization, addressed via a phase-domain transformation.

The second part focuses on preserving privacy in FL over wireless networks. First, we design a differentially-private FL algorithm that applies time-varying noise variance perturbation. Taking advantage of existing wireless channel noise, we jointly design differential privacy (DP) noise variances and users transmit power to resolve the tradeoffs between privacy and learning utility. Next, we tackle two critical challenges within FL networks: (i) privacy risks arising from model updates and (ii) reduced learning utility due to quantization heterogeneity. Prior work typically addresses only one of these challenges because maintaining learning utility under both privacy risks and quantization heterogeneity is a non-trivial task. We approach to improve the learning utility of a privacy-preserving FL that allows clusters of devices with different quantization resolutions to participate in each FL round. Specifically, we introduce a novel stochastic quantizer (SQ) that ensures a DP guarantee and minimal quantization distortion. To address quantization heterogeneity, we introduce a cluster size optimization technique combined with a linear fusion approach to enhance model aggregation accuracy. Lastly, inspired by the information-theoretic rate-distortion framework, a privacy-distortion tradeoff problem is formulated to minimize privacy loss under a given maximum allowable quantization distortion. The optimal solution to this problem is identified, revealing that the privacy loss decreases as the maximum allowable quantization distortion increases, and vice versa.

This research advances hybrid array optimization for wideband sub-THz massive MIMO and introduces novel algorithms for privacy-preserving quantized FL with diverse precision. These contributions enable high-throughput wideband MIMO communication systems and privacy-preserving AI-native designs, aligning with the performance and privacy protection demands of NextG networks.

As internet services like high-definition videos, cloud computing, and artificial intelligence keep growing, optical networks need to keep up with the demand for more capacity. Optical amplifiers play a crucial role in offsetting fiber loss and enabling long-distance wavelength division multiplexing (WDM) transmission in high-capacity systems. Various methods have been proposed to enhance the capacity and reach of fiber communication systems, including advanced modulation formats, dense wavelength division multiplexing (DWDM) over ultra-wide bands, space-division multiplexing, and high-performance digital signal processing (DSP) technologies. To maintain higher data rates along with maximizing the spectral efficiency of multi-level modulated signals, a higher Optical Signal-to-Noise Ratio (OSNR) is necessary. Despite advancements in coherent optical communication systems, the spectral efficiency of multi-level modulated signals is ultimately constrained by fiber nonlinearity. Raman amplification is an attractive solution for wide-band amplification with low noise figures in multi-band systems.

Distributed Raman Amplification (DRA) have been deployed in recent high-capacity transmission experiments to achieve a relatively flat signal power distribution along the optical path and offers the unique advantage of using conventional low-loss silica fibers as the gain medium, effectively transforming passive optical fibers into active or amplifying waveguides. Also, DRA provides gain at any wavelength by selecting the appropriate pump wavelength, enabling operation in signal bands outside the Erbium doped fiber amplifier (EDFA) bands. Forward (FW) Raman pumping configuration in DRA can be adopted to further improve the DRA performance as it is more efficient in OSNR improvement because the optical noise is generated near the beginning of the fiber span and attenuated along the fiber. Dual-order FW pumping scheme helps to reduce the non-linear effect of the optical signal and improves OSNR by more uniformly distributing the Raman gain along the transmission span.

The major concern with Forward Distributed Raman Amplification (FW DRA) is the fluctuation in pump power, known as relative intensity noise (RIN), which transfers from the pump laser to both the intensity and phase of the transmitted optical signal as they propagate in the same direction. Additionally, another concern of FW DRA is the rise in signal optical power near the start of the fiber span, leading to an increase in the non-linear phase shift of the signal. These factors, including RIN transfer-induced noise and non-linear noise, contribute to the degradation of system performance in FW DRA systems at the receiver.

As the performance of DRA with backward pumping is well understood with relatively low impact of RIN transfer, our research  is focused on the FW pumping configuration, and is intended to provide a comprehensive analysis on the system performance impact of dual order FW Raman pumping, including signal intensity and phase noise induced by the RINs of both 1st and the 2nd order pump lasers, as well as the impacts of linear and nonlinear noise. The efficiencies of pump RIN to signal intensity and phase noise transfer are theoretically analyzed and experimentally verified by applying a shallow intensity modulation to the pump laser to mimic the RIN. The results indicate that the efficiency of the 2nd order pump RIN to signal phase noise transfer can be more than 2 orders of magnitude higher than that from the 1st order pump. Then the performance of the dual order FW Raman configurations is compared with that of single order Raman pumping to understand trade-offs of system parameters. The nonlinear interference (NLI) noise is analyzed to study the overall OSNR improvement when employing a 2nd order Raman pump. Finally, a DWDM system with 16-QAM modulation is used as an example to investigate the benefit of DRA with dual order Raman pumping and with different pump RIN levels. We also consider a DRA system using a 1st order incoherent pump together with a 2nd order coherent pump. Although dual order FW pumping corresponds to a slight increase of linear amplified spontaneous emission (ASE) compared to using only a 1st order pump, its major advantage comes from the reduction of nonlinear interference noise in a DWDM system. Because the RIN of the 2nd order pump has much higher impact than that of the 1st order pump, there should be more stringent requirement on the RIN of the 2nd order pump laser when dual order FW pumping scheme is used for DRA for efficient fiber-optic communication. Also, the result of system performance analysis reveals that higher baud rate systems, like those operating at 100Gbaud, are less affected by pump laser RIN due to the low-pass characteristics of the transfer of pump RIN to signal phase noise.

As machine learning (ML), artificial intelligence (AI), and deep learning continue to advance, their applications become more diverse – one such application is synthetic aperture radar (SAR) automatic target recognition (ATR). These SAR ATR networks use different forms of deep learning such as convolutional neural networks (CNN) to classify targets in SAR imagery. An emerging research area of SAR is dual function radar communication (DFRC) which performs both radar and communications functions using a single co-designed modulation. The utilization of DFRC emissions for SAR imaging impacts image quality, thereby influencing SAR ATR network training. Here, using the Civilian Vehicle Data Dome dataset from the AFRL, SAR ATR networks are trained and evaluated with simulated data generated using Gaussian Minimum Shift Keying (GMSK) and Linear Frequency Modulation (LFM) waveforms. The networks are used to compare how the target classification accuracy of the ATR network differ between DFRC (i.e., GMSK) and baseline (i.e., LFM) emissions. Furthermore, as is common in pulse-agile transmission structures, an effect known as ’range sidelobe modulation’ is examined, along with its impact on SAR ATR. Finally, it is shown that SAR ATR network can be trained for GMSK emissions using existing LFM datasets via two types of data augmentation.

The next generation of digital communications systems aims to operate in high-Doppler environments such as high-speed trains and non-terrestrial networks that utilize satellites in low-Earth orbit. Current generation systems use Orthogonal Frequency Division Multiplexing modulation which is known to suffer from inter-carrier interference (ICI) when different channel paths have dissimilar Doppler shifts.

A new Orthogonal Time Frequency Space (OTFS) modulation (also known as Delay-Doppler modulation) is proposed as a candidate modulation for 6G networks that is resilient to ICI. To date, OTFS demodulation designs have focused on the use cases of popular urban terrestrial channel models where path delay spread is a fraction of the OTFS symbol duration. However, wireless wide-area networks that operate in the aeronautical mobile telemetry (AMT) space can have large path delay spreads due to reflections from distant geographic features. This presents problems for existing channel estimation techniques which assume a small maximum expected channel delay, since data transmission is paused to sound the channel by an amount equal to twice the maximum channel delay. The dropout in data contributes to a reduction in spectral efficiency.

Our research addresses OTFS limitations in the AMT use case. We start with an exemplary OTFS framework with parameters optimized for AMT. Following system design, we focus on two distinct areas to improve OTFS performance in the AMT environment. First we propose a new channel estimation technique using a pilot signal superimposed over data that can measure large delay spread channels with no penalty in spectral efficiency. A successive interference cancellation algorithm is used to iteratively improve channel estimates and jointly decode data. A second aspect of our research aims to equalize in delay-Doppler space. In the delay-Doppler paradigm, the rapid channel variations seen in the time-frequency domain is transformed into a sparse quasi-stationary channel in the delay-Doppler domain. We propose to use machine learning using Gaussian Process Regression to take advantage of the sparse and stationary channel and learn the channel parameters to compensate for the effects of fractional Doppler in which simpler channel estimation techniques cannot mitigate. Both areas of research can advance the robustness of OTFS across all communications systems.

This project presents AAFIYA (Antenna Analysis in Frequency-domain for Impedance and Yield Assessment), a modular Python toolkit developed to automate and streamline the characterization and analysis of radiofrequency (RF) antennas using both measurement and simulation data. Motivated by the need for reproducible, flexible, and publication-ready workflows in modern antenna research, AAFIYA provides comprehensive support for all major antenna metrics, including S-parameters, impedance, gain and beam patterns, polarization purity, and calibration-based yield estimation. The toolkit features robust data ingestion from standard formats (such as Touchstone files and beam pattern text files), vectorized computation of RF metrics, and high-quality plotting utilities suitable for scientific publication.

Validation was carried out using measurements from industry-standard electromagnetic anechoic chamber setups involving both Log Periodic Dipole Array (LPDA) reference antennas and Askaryan Radio Array (ARA) Bottom Vertically Polarized (BVPol) antennas, covering a frequency range of 50–1500 MHz. Key performance metrics, such as broadband impedance matching, S11 and S21 related calculations, 3D realized gain patterns, vector effective lengths,  and cross-polarization ratio, were extracted and compared against full-wave electromagnetic simulations (using HFSS and WIPL-D). The results demonstrate close agreement between measurement and simulation, confirming the reliability of the workflow and calibration methodology.

AAFIYA’s open-source, extensible design enables rapid adaptation to new experiments and provides a foundation for future integration with machine learning and evolutionary optimization algorithms. This work not only delivers a validated toolkit for antenna research and pedagogy but also sets the stage for next-generation approaches in automated antenna design, optimization, and performance analysis.

With the exponential growth of user-generated content, online platforms face unprecedented challenges in moderating toxic and harmful comments. Due to this, Automated content moderation has emerged as a critical application of machine learning, enabling platforms to ensure user safety and maintain community standards. Despite its importance, challenges such as severe class imbalance, contextual ambiguity, and the diverse nature of toxic language often compromise moderation accuracy, leading to biased classification performance.

This project presents a comparative analysis of machine learning approaches for a Multi-Label Toxic Comment Classification System using the Toxic Comment Classification dataset from Kaggle.  The study examines the performance of traditional algorithms, such as Logistic Regression, Random Forest, and XGBoost, alongside deep architectures, including Bi-LSTM, CNN-Bi-LSTM, and DistilBERT. The proposed approach utilizes word-level embeddings across all models and examines the effects of architectural enhancements, hyperparameter optimization, and advanced training strategies on model robustness and predictive accuracy.

The study emphasizes the significance of loss function optimization and threshold adjustment strategies in improving the detection of minority classes. The comparative results reveal distinct performance trade-offs across model architectures, with transformer models achieving superior contextual understanding at the cost of computational complexity. At the same time, deep learning approaches(LSTM models) offer efficiency advantages. These findings establish evidence-based guidelines for model selection in real-world content moderation systems, striking a balance between accuracy requirements and operational constraints.

Quantum computing has the potential to revolutionize computational problem-solving by leveraging the quantum mechanical phenomena of superposition and entanglement, which allows for processing a large amount of information simultaneously. This capability is significant in the numerical solution of complex and/or multidimensional partial differential equations (PDEs), which are fundamental to modeling various physical phenomena. There are currently many quantum techniques available for solving partial differential equations (PDEs), which are mainly based on variational quantum circuits. However, the existing quantum PDE solvers, particularly those based on variational quantum eigensolver (VQE) techniques, suffer from several limitations. These include low accuracy, high execution times, and low scalability on quantum simulators as well as on noisy intermediate-scale quantum (NISQ) devices, especially for multidimensional PDEs.

 In this work, we propose an efficient and scalable algorithm for solving multidimensional PDEs. We present two variants of our algorithm: the first leverages finite-difference method (FDM), classical-to-quantum (C2Q) encoding, and numerical instantiation, while the second employs FDM, C2Q, and column-by-column decomposition (CCD). Both variants are designed to enhance accuracy and scalability while reducing execution times. We have validated and evaluated our proposed concepts using a number of case studies including multidimensional Poisson equation, multidimensional heat equation, Black Scholes equation, and Navier-Stokes equation for computational fluid dynamics (CFD) achieving promising results. Our results demonstrate higher accuracy, higher scalability, and faster execution times compared to VQE-based solvers on noise-free and noisy quantum simulators from IBM. Additionally, we validated our approach on hardware emulators and actual quantum hardware, employing noise mitigation techniques. This work establishes a practical and effective approach for solving PDEs using quantum computing for engineering and scientific applications.

The escalating complexity inherent in modern computer architecture presents significant challenges for both professional hardware designers and students striving to gain foundational understanding. Historically, the steady improvement of computer systems was driven by transistor scaling, predictable performance increases, and relatively straightforward architectural paradigms. However, with the end of traditional scaling laws and the rise of heterogeneous and parallel architectures, designers now face unprecedented intricacies involving power management, thermal constraints, security considerations, and sophisticated software interactions. Prior tools and methodologies, often reliant on complex, command-line driven simulations, exacerbate these challenges by introducing steep learning curves, creating a critical need for more intuitive, accessible, and efficient solutions. To address these challenges, this thesis introduces two innovative, modern tools.

The first tool, SimScholar, provides an intuitive graphical user interface (GUI) built upon the widely-used gem5 simulator. SimScholar significantly simplifies the simulation process, enabling students and educators to more effectively engage with architectural concepts through a visually guided environment, both reducing complexity and enhancing conceptual understanding. Supporting SimScholar, the gem5 Extended Modules API (gEMA) offers streamlined backend integration with gem5, ensuring efficient communication, modularity, and maintainability.

The second contribution, gem5 Co-Pilot, delivers an advanced framework for architectural design space exploration (DSE). Co-Pilot integrates cycle-accurate simulation via gem5, detailed power and area modeling through McPAT, and intelligent optimization assisted by a large language model (LLM). Central to Co-Pilot is the Design Space Declarative Language (DSDL), a Python-based domain-specific language that facilitates structured, clear specification of design parameters and constraints.

Collectively, these tools constitute a comprehensive approach to taming complexity in computer architecture, offering powerful, user-friendly solutions tailored to both educational and professional settings.

The rapid development of mobile apps on modern smartphone platforms has raised critical concerns regarding user data privacy and the security of app-to-device communications, particularly with companion apps that interface with external IoT or cyber-physical systems (CPS). In this dissertation, we investigate two major aspects of mobile app security: the misuse of permission mechanisms and the security of app to device communication in automotive companion apps.

Mobile apps seek user consent for accessing sensitive information such as location and personal data. However, users often blindly accept these permission requests, allowing apps to abuse this mechanism. As long as a permission is requested, state-of-the-art security mechanisms typically treat it as legitimate. This raises a critical question: Are these permission requests always valid? To explore this, we validate permission requests using statistical analysis on permission sets extracted from groups of functionally similar apps. We identify mobile apps with abusive permission access and quantify the risk of information leakage posed by each app. Through a large-scale statistical analysis of permission sets from over 200,000 Android apps, our findings reveal that approximately 10% of the apps exhibit highly risky permission usage. 

Next, we present a comprehensive study of automotive companion apps, a rapidly growing yet underexplored category of mobile apps. These apps are used for vehicle diagnostics, telemetry, and remote control, and they often interface with in-vehicle networks via OBD-II dongles, exposing users to significant privacy and security risks. Using a hybrid methodology that combines static code analysis, dynamic runtime inspection, and network traffic monitoring, we analyze 154 publicly available Android automotive apps. Our findings uncover a broad range of critical vulnerabilities. Over 74% of the analyzed apps exhibit vulnerabilities that could lead to private information leakage, property theft, or even real-time safety risks while driving. Specifically, 18 apps were found to connect to open OBD-II dongles without requiring any authentication, accept arbitrary CAN bus commands from potentially malicious users, and transmit those commands to the vehicle without validation. 16 apps were found to store driving logs in external storage, enabling attackers to reconstruct trip histories and driving patterns. We demonstrate several real-world attack scenarios that illustrate how insecure data storage and communication practices can compromise user privacy and vehicular safety. Finally, we discuss mitigation strategies and detail the responsible disclosure process undertaken with the affected developers.

Nonlinear Transmission Lines (NLTLs) have gained significant interest due to their ability to generate ultra-short, high-power RF pulses, which are valuable in applications such as ultrawideband radar, space vehicles, and battlefield communication disruption. The waveforms generated by NLTLs offer frequency diversity not typically observed in High-Power Microwave (HPM) sources based on electron beams. Nonlinearity in lumped element transmission lines is usually introduced using voltage-dependent capacitors due to their simplicity and widespread availability. The periodic structure of these lines introduces dispersion, which broadens pulses. In contrast, nonlinearity causes higher-amplitude regions to propagate faster. The interaction of these effects results in the formation of stable, self-localized waveforms known as solitons.
Soliton propagation in NLTLs can be described by the Korteweg-de Vries (KdV) equation. In this thesis, the Bäcklund Transformation (BT) method has been used to derive both single and two-soliton solutions of the KdV equation. This method links two different partial differential equations (PDEs) and their solutions to produce solutions for nonlinear PDEs. The two-soliton solution is obtained from the single soliton solution using a nonlinear superposition principle known as Bianchi’s Permutability Theorem (BPT). Although the KdV model is suitable for NLTLs where the capacitance-voltage relationship follows that of a reverse-biased p-n junction, it cannot generally represent arbitrary nonlinear capacitance characteristics.
To address this limitation, a Finite Difference Time Domain (FDTD) method has been developed to numerically solve the NLTL equation for soliton propagation. To demonstrate the pulse sharpening and RF generation capability of a varactor-loaded NLTL, a 12-section lumped element circuit has been designed and simulated using LTspice and verified with the calculated result. In airborne radar systems, operational constraints such as range, accuracy, data rate, environment, and target type require flexible waveform design, including variation in pulse widths and pulse
repetition frequencies. A gradient descent optimization technique has been employed to generate pulses with varying amplitudes and frequencies by optimizing the NLTL parameters. This work provides a theoretical analysis and numerical simulation to study soliton propagation in NLTLs and demonstrates the generation of tunable RF pulses through optimized circuit design.