Title: "Cellular Data-Based Indoor Localization for Smart Health Applications"  

Authors: Eric Forbes, Ting Liao, Ying Wang, 

Date: Jun 2025

Accepted: 2025 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

In this study, we present an LTE-based indoor localization framework that leverages key LTE performance indicators to predict grid locations within an indoor environment. Using a multi-class classification approach, our model achieved an accuracy of 95.78% by analyzing signal strength variations and propagation characteristics.Our findings demonstrate that LTE- based localization offers a scalable and infrastructure-independent alternative to conventional methods, enabling real-time tracking with minimal deployment effort.

Title: "LOS/NLOS Classification for UAV Communications: A Time-Frequency Multimodal Learning Approach"  

Authors: Mingze Pan, Ying Wang

Date: Feb 2025

Accepted: 59th Annual Conference on Information Science and Systems (CISS) 

In communication networks, distinguishing between line-of-sight (LOS) and non-line-of-sight (NLOS) is critical for optimizing signal quality, coverage and network reliability. It influences network design, frequency band selection, and technology choices to ensure efficient and reliable communications. In this paper, we design an noval multimodel detection method to distinguish LOS and NLOS communication scenarios. This method combines time-domain channel characterization with frequency-domain spectrogram analysis, Time-Frequency Multimodal Detection (TFMD). 

Title: "FiCo: A Fingerprinting-based Two-step Learning-to-learn Approach Combing Vibration and 5G Communication for UAV Classification"  

Authors: Xinyi Li, Yifeng Peng, Ying Wang

Date: January 2025

Publication: 2025 IEEE International Conference on Communications (ICC) 

Unmanned Aerial Vehicles (UAVs) are widely utilized across industries, necessitating robust security measures. RF fingerprinting based UAV identification can enhance UAV authentication and authorization by identifying unique RF signals, bypassing vulnerabilities of software-based methods. However, its effectiveness can be limited by environmental factors such as distance, interference, and multi-path propagation. To address the challenges of UAV identification, we propose a novel lightweight two-step learning-to-learn classification approach, FiCo, which integrates multiple data sources from diverse domains, including mechanical vibration and 5G communications. In the first step, we employ two Extreme Gradient Boosting (XGBoost) models to separately analyze communication and vibration data from UAVs. In the second step, a Logistic Regression meta-network is utilized to jointly learn from the predictions of these two XGBoost models. Experimental results show that the FiCo method boosts the AUC to 0.9792 and the accuracy to 92.59%. This represents a 2% accuracy increase over the Data Combined method and it improves accuracy by 9.3% with communication data alone and by 5.6% with vibration data alone, raising the AUC by 0.088 and 0.029, respectively. This approach reduces computational complexity and requires fewer training samples, enabling faster and more agile UAV identification in practice.

Title: "Qsco: A Quantum Scoring Module for Open-Set Supervised Anomaly Detection"  

Authors: Yifeng Peng, Xinyi Li, Zhiding Liang, Ying Wang

Date: December 2024

Publication: The Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25) 

Open set anomaly detection (OSAD) is a crucial task that aims to identify abnormal patterns or behaviors in data sets, especially when the anomalies observed during training do not represent all possible classes of anomalies. The recent advances in quantum computing in handling complex data structures and improving machine learning models herald a paradigm shift in anomaly detection methodologies. This study proposes a Quantum Scoring Module (Qsco), embedding quantum variational circuits into neural networks to enhance the model's processing capabilities in handling uncertainty and unlabeled data. Extensive experiments conducted across eight real-world anomaly detection datasets demonstrate our model's superior performance in detecting anomalies across varied settings and reveal that integrating quantum simulators does not result in prohibitive time complexities. At the same time, the experimental results under different noise models also prove that Qsco is a noise-resilient algorithm. Our study validates the feasibility of quantum-enhanced anomaly detection methods in practical applications.

Title: "Quantum-Position-Locked Loop: New Concept for Collaborative Beam forming for UAV Swarm"  

Authors: Suhanshu Arya,Ying Wang

Date: December 2024

Download: Paper Download 

Publication:  IEEE Wireless Communication Letters 

We introduce a novel quantum optimization method combining Grover's algorithm and Nelder-Mead for real-time UAV positioning and precise beamforming. This work opens exciting opportunities in aerial communication infrastructures, enabling synchronized UAV swarms to perform high-precision tasks like distributed Multiple Input Multiple Output (MIMO) systems. While many areas remain unexplored, we look forward to advancing this field further in collaboration with the broader community.

Title: "Enhanced UAV Classification: Gaining Deeper Insights from Mechanical Vibration Over RF Characteristics"  

Authors: Joshua Meharg,Thomas Byrnes,Jessica Sabatino,Spruha Paradkar,Ella Crabtree,Aman Khandelwal,Ying Wang

Date: November 2024

Download:

Publication:  IEEE Aerospace Conference 2025 

Congrats to the undergraudate senior deisng team's paper acccpeted by IEEE Aerospace Conference 2025. The paper presents a novel approach to UAV classification by combining RF characteristics and mechanical vibration data to strengthen the security of UAV systems. 

Undergraduate Senior Design Group selected as Best Senior Design Project "Mechanical Vibration vs RF Characteristics: A Meta Fingerprinting Approach for UAV Classifiction"

May, 2024

Mechanical Vibration vs RF Characteristics: A Meta Fingerprinting Approach for UAV Classifiction by Joshua Meharg,Thomas Byrnes,Jessica Sabatino,Aman Khandelwal,Spruha Paradkar,and Ella Crabtree, adviced by Ying Wang have won Computer Science Departments Best Senior Design Project.

Title: "Trustworthy Formal Verification in 5G Protocols: Evaluating Generative and Classification Models for Relation Extraction"  

Authors: Jingda Yang, Ying Wang

Date: October 2024

Download:Slides, Poster  

Publication:  IEEE MILCOM

To ensure the security and reliability of 5G wireless communication protocols, formal verification is employed to guarantee protocol correctness. Given the complexity and scale of 3GPP protocols, automating the formal modeling process is essential, as it can help security experts extract formal properties and dependencies and subsequently build the formal code. This paper introduces a novel Cross-Attention Large Language Model (CAL), a unique classification model specifically designed for this purpose. The study highlights the strengths, weaknesses, and optimal application scenarios for each model. The results demonstrate the feasibility of automating formal verification for complex communication protocols, ensuring their trustworthiness. This research marks a significant step towards the automation of formal verification, enhancing the robustness and reliability of 5G protocol design.

Title: "5G Specifications Formal Verification with Over the Air Validation: Prompting is All You Need"  

Authors: Tom Wray, Ying Wang

Date: October 2024

Download: PDF, Slides, Poster  

Publication:  IEEE MILCOM

The critical role of 5G and other complex systems in infrastructure necessitates rigorous protocol verification and system validation to ensure security and reliability. This paper explores the application of applying Large Language Model enabled auto Formal Verification with Real-world Prompting on Large Language Models (LLMs) for 5G and NextG protocols, addressing ambiguities and security concerns in network infrastructure protocol and specification design. By leveraging generative transformer-based LLMs, we present a formal approach to prompt engineering that validates complex specifications in a closed-loop framework. The Prompt-based Inspector Aided Formal VErification, named PAVE, was utilized to formally verify 5G/NextG specifications at the RRC layer. 

Title: "HyQ2: A Hybrid Quantum Neural Network for NextG Vulnerability Detection "  

Authors: Yifeng Peng, Xinyi Li,  Zhiding Liang, Ying Wang

Date: October 2024

Download: link

Publication:  IEEE Transactions on Quantum Engineering

As 5G and next-generation communication systems advance and find widespread application in critical infrastructures, the importance of vulnerability detection becomes increasingly critical. The growing complexity of these systems necessitates rigorous testing and analysis, with stringent requirements for both accuracy and speed. In this paper, we present a state-of-the-art supervised hybrid quantum neural network named HyQ2 for vulnerability detection in next-generation wireless communication systems. The proposed HyQ2 is integrated with graph-embedded and quantum variational circuits to validate and detect vulnerabilities from the 5G system’s state transitions based on graphs extracted from log files.

Title: "CoCo: A CBOW-Based Framework for Synergistic Vulnerability Detection in Partial and Discontinuous Logs for NextG Communications "  

Authors: Yifeng Peng, Xinyi Li,  Sudhanshu Arya, Ying Wang

Date: October 2024

Download: link

Publication:  IEEE Open Journal of the Communications Society

With the development of communication technology, protocol design, and infrastructure implementation have become more complex, bringing significant security challenges to 5G and NextG systems. Fuzz testing is widely used to detect system vulnerabilities and the health status under the condition of abnormal input. In this paper, we generate fuzz testing via the Man In The Middle Model (MITM) at various locations of the time sequence in the 5G authentication and authorization process and analyze the communication state transitions, which are recorded in the log files of fuzz testing cases. 

Title: "Quantum Squeeze-and-Excitation Networks "  

Authors: Yifeng Peng, Xinyi Li, Ying Wang

Date: August 2024

Publication:  IEEE International Conference on Quantum Computing and Engineering (QCE)

In this paper, we introduce Quantum Squeeze-and-Excitation (QSE) Networks, a pioneering approach within the domain of quantum computing designed to enhance the excitation module of classical Squeeze-and-Excitation (SE) networks. Our method significantly enhances performance by leveraging quantum computing techniques while simplifying the model's complexity. Neural network data encoding is performed through quantum amplitude coding, substantially reducing the parameter count of the classical SE network's fully connected layers.

Title: "QRNG-DDPM: Enhancing Diffusion Models through Fitting Mixture Noise with Quantum Random Number "  

Authors: Yifeng Peng, Xinyi Li, Ying Wang

Date: August 2024

Publication:  IEEE International Conference on Quantum Computing and Engineering (QCE)

Recent research has demonstrated that the denoising diffusion probabilistic model (DDPM) can generate high-quality images in artificial intelligence (AI), showing its distinctive capabilities. However, despite this, the diversity of generated images is often limited by the predictability of traditional pseudo-random number generators in the stochastic process. To address this problem, this paper proposes a new mixed noise model based on quantum random numbers QRNG-DDPM. By operating on single qubits, we generate quantum random numbers and apply a self-developed encoding scheme to convert the quantum random numbers into distributions suitable for noise models. Due to the inherent unpredictability of quantum phenomena, quantum random numbers offer a higher level of randomness and diversity compared to traditional pseudo-random numbers.

Title: "RAIDER: Rapid AI Diagnosis at Edge using Ensemble Models for Radiology"  

Authors: Ishan Aryendu, Ying Wang

Date: Aug 2024

Download: Link

Code Repository: GitHub

Publication: IEEE Access (Accepted)

Chest X-rays have played an indispensable part in medical diagnosis for several decades. However, there is a scarcity of experts who can interpret these images to diagnose critical illnesses, which can lead to preventable fatalities. This paper introduces a novel Rapid AI Diagnosis at Edge using Ensemble Models for Radiology (RAIDER) designed to leverage the advantages of cross-geolocation meta-learning models. We can generate local machine learning models at individual locations and distribute them across other locations for diagnosing diseases at the edge or on-premises if required before they become worldwide pandemics, significantly enhancing the rapid or near-real-time identification of fast-spreading respiratory diseases through online learning. This novel approach allows for geo-distributed multi-fold model training, harnessing the unique strengths of diverse geographical data sources to improve diagnostic accuracy and speed by leveraging edge computing. Using the existing  Convolutional Neural Network (CNN) models and distributed training at the edge, we can enhance the accuracy and cost-effectiveness of diagnosis. The proposed architecture allows for distributed training and independently verified performance metrics on the MIMIC-CXR and COVIDGR chest X-ray datasets with accuracy, sensitivity, specificity, F1-score, and AUC of 97.80%, 97.06%, 98.48%, 96.51%, and 0.9739, respectively. Our proposed RAIDER architecture marks the first implementation of a collaborative framework that facilitates seamless interaction across different geographic locations and edge computing, enabling a more effective and efficient response to emerging health threats. 

Title: "Hybrid Quantum Downsampling Networks "  

Authors: Yifeng Peng, Xinyi Li, Zhiding Liang, Ying Wang

Date: May 2024

Download: link

Publication: Under Review (On Arxiv Now)

Classical max pooling plays a crucial role in reducing data dimensionality among various well-known deep learning models, yet it often leads to the loss of vital information. We proposed a novel hybrid quantum downsampling module (HQD), which is a noise-resilient algorithm. By integrating a substantial number of quantum bits (qubits), our approach ensures the key characteristics of the original image are maximally preserved within the local receptive field. 

Title: "Radar Altimeter Coexistence Design in the 4.2-4.4 GHz Band: Mitigating Multi-Stage Interference Risks in 5G and Beyond"  

Authors: Jarret Rock and Ying Wang

Date: June 2024

Download: link

Publication: IEEE Aerospace and Electronic Systems Magazine, 2024

This paper addresses safety concerns in aviation arising from the 5G network's interference with radar altimeters on 14 CFR Pt 25 category aircraft, predominantly used in passenger travel. This work is the result of Jarret's SYS800 course. Congratulations on his first research work being published in the prestigious IEEE Aerospace and Electronic Systems Magazine!

Title: "Securing the Unprotected: Enhancing Heartbeat Messaging for MAVLink UAV Communications"

Authors: Isabel Hughes, Adriel Pupo, Jenna Wynd, Zachary Thurlow, Connor Ivancik, and Ying Wang

Date: June 2024

Publication:2024 IEEE Pacific Rim Conference on Communications, Computers, and Signal Processing (PACRIM’24)

This is the effort of the 2023-2024 Undergraduate Senior Design Group UAV-Security. Congratulations on reaching their first research experience milestone! Their paper, "Securing the Unprotected: Enhancing Heartbeat Messaging for MAVLink UAV Communications" has been accepted by the 2024 IEEE Pacific Rim Conference on Communications, Computers, and Signal Processing (PACRIM’24).

Minimizing Age of Information: Adaptive Spectrum Sharing in Ultra-Reliable and Low-Latency eVTOL Communications

by

Ishan Aryendu, Sudhanshu Arya, and Ying Wang

Jun, 2024

 This paper has been accepted at the IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (WoWMoM) 2024

Link

Formal-Guided Fuzz Testing: Targeting Security Assurance from Specification to Implementation for 5G and Beyond

by

Jingda Yang and Ying Wang

Feb, 2024

 This paper has been accepted at IEEE Access

Towards Auto-Modeling of Formal Verification for NextG Protocols: A Multimodal cross- and self-attention Large Language Model Approach

by

Jingda Yang and Ying Wang

Feb, 2024

 This paper has been accepted at IEEE Access

link

Dr. Ying Wang, Associate Professor in the School of Systems and Enterprises, has been selected to receive the Tom Ode Richardson Faculty Fellowship

12/06/2024

The Tom Ode Richardson Faculty Fellowship, previously known as the Stevens Presidential Fellowship, recognizes a faculty member’s achievements in research and their potential for future contributions and impact.

Domain Knowledge Powered Machine Learning for the Classification of LOS/NLOS Signals for Dedicated-Spectrum SAGIN Networks

by

Mingze Pan,  Sudhanshu Arya  and Ying Wang

Feb, 2024

 This paper has been accepted at 2024 2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

Dependency-Graph enabled Formal Analysis for 5G AKA Protocols: Assumption Propagation and Verification

by

Jingda Yang,  and Ying Wang

Jan, 2024

 This paper has been accepted at 

2024 IEEE International Conference on Communications (ICC)

Real-Time UAV Collaborative Beam Reforming for Coexistent Satellite-Terrestrial Communications

by

Sudhanshu Arya, Jingda Yang, Paul Grogan and Ying Wang

Dec, 2023

 This paper has been accepted at the 

2024 IEEE Aerospace Conference 

GeTOA: Game-Theoretic Optimization for AOI of Ultra-Reliable eVTOL Collaborative Communication

by

I. Aryendu, S. Arya and Y. Wang

Dec, 2023

 This paper has been accepted at 

2024 IEEE Wireless Communications and Networking Conference (WCNC)

Link

Bayesian Inference-assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)

by

S. Jere, Y. Wang, I.Aryendu, S. Dayek, L.Liu

Nov, 2023

 This paper has been accepted by IEEE Transaction of Wireless Communications.

RAFT: A Real-Time Framework for Root Cause Analysis in 5G and Beyond Vulnerability Detection

by

Y. Peng, X. Li, J. Yang, S. Arya and Y. Wang

Oct, 2023

 By processing the random fragments of the logging profiles captured during fuzz testing with the continuous bag-of-words (CBOW) Model, we extract the information of states and states transitions to identifythe root cause for vulnerability detection in the 5G system.  This paper has been accepted at the IEEE Consumer Communications & Networking Conference (CCNC).

DEFT: A Novel Deep Framework for Fuzz Testing Performance Evaluation in NextG Vulnerability Detection

by

Y. Peng, X. Li, S. Arya and Y. Wang

Oct, 2023

DEFT can be further applied to identifying the type of attacks or abnormal inputs from the partial system profiling for the impacted behaviors.  This paper has been accepted at IEEE Access.

HyFuzz: A NextG Hybrid Testing Platform for Multi-step Deep Fuzzing and Performance Assessment from Virtualization to Over-the-Air

by

J. Yang and Y. Wang 

Oct, 2023

The paper presents HyFuzz, the first-of-its-kind framework that enables multi-step interactive deep fuzzing for NextG cybersecurity assurance.  This paper has been accepted at IEEE CloudNet 2023.

SmiLe Net: A Supervised Graph Embedding-based

Machine Learning Approach for NextG

Vulnerability Detection

by

Y. Peng, J. Yang, S. Arya and Y. Wang 

Aug, 2023

This paper presents a new approach named SmiLe Net for

5G vulnerability detection. We focus primarily on predicting

the information about the fuzzed layer and performing the root

cause analysis in fuzzing test log files.  This paper has been presentd at IEEE Milcom 2023.

Towards the Designing of Low-Latency SAGIN: Ground-to-UAV Communications over Interference Channel

by

S. Arya, J. Yang and Y. Wang 

July, 2023

A novel and first-of-its-kind information-theoretic framework for the implementation of ground-to-UAV communication with an aim to minimize transmission delay has been published in Drones.

Characterization of Low-Latency Next-Generation eVTOL Communications: From Channel Modeling to Performance Evaluation

by

B. Mak, S. Arya, Y. Wang and J. Ashdown  

July, 2023

A nonstationary geometry-based stochastic channel modeling and characterization for next-generation eVTOL communications has been published in Electronics.