Yanjun Pan
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
Also affiliated: University of Nebraska–Lincoln (2022); University of Arizona (2017–2023); Carnegie Mellon University (2020–2021)
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Yanjun Pan's research interests encompass cybersecurity, wireless sensing, wireless communications, and network optimization. Pan is an Assistant Professor at the University of Arkansas at Fayetteville. Their work has been supported by two federal grants from the National Science Foundation (NSF), totaling $551,903. One grant, for $94,998, supports "Collaborative Research: CyberTraining: Pilot: A Zero Trust-Based Security Training Program in Open RAN Cyberinfrastructure." The second NSF grant, for $456,905, is for "SaTC: CORE: Small: Towards Untraceable Communications in a Ubiquitous Environment with RF Fingerprint Anonymization."
Pan leads a research group and has published on topics including adaptive constrained iLQR for self-driving vehicle planning, message integrity protection over wireless channels, and vulnerability detection in 5G RRC protocols. Additional publications explore online learning for reconfigurable antenna mode selection, cross-modality user authentication using respiratory patterns, proof-of-following for vehicle platoons, and physiological motion sensing via channel state information. Most recently, Pan has investigated deep reinforcement learning for scheduling photovoltaic systems with battery storage.
Metrics
- h-index: 9
- Publications: 33
- Citations: 202
Positions
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Assistant Professor 2023–presentUniversity of Arkansas Department of Electrical Engineering and Computer Science ORCID
Selected Publications
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Delay-Doppler Integrated Sensing and Communications (DD-ISAC) with Predictive Beamforming (2026)Journal of the Arkansas Academy of Science OpenAlex
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CP-Free ODDM: Modeling and Design (2026)
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An Adversarial-Driven Experimental Study on Deep Learning for RF Fingerprinting (2025)
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CP-Free ODDM Over General Doubly-Selective Fading Channels (2025)
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Optimum Scheduling of Truck-Based Mobile Energy Couriers (MEC) Using Deep Deterministic Policy Gradient (2025)
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SideSense: Robust Physiological Motion Detection via mmWave Joint Communication and Sensing Systems With Multiple Beams (2025)
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Harvesting Physical-Layer Randomness in Millimeter Wave Bands (2024)
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Detection of Overshadowing Attack in 4G and 5G Networks (2024)
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Low Complexity OTFS Detection with a Delay-Doppler Domain CMC-MMSE Receiver (2024)
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Deep Reinforcement Learning for Online Scheduling of Photovoltaic Systems with Battery Energy Storage Systems (2024)
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Fuzzing for Power Grids: A Comparative Study of Existing Frameworks and a New Method for Detecting Silent Crashes in Control Devices (2023)
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Low-Latency Attack Detection With Dynamic Watermarking for Grid-Connected Photovoltaic Systems (2023)
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On the Performance of Practical Pulse-Shaped OTFS with Analog Receivers (2023)
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Critical Element First: Enhance C-V2X Signal Coverage using Power-Efficient Liquid Metal-Based Intelligent Reflective Surfaces (2023)
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Cross-Modality Continuous User Authentication and Device Pairing With Respiratory Patterns (2023)
Federal Grants 2 $551,903 total
Collaboration Network
Top Collaborators
- On the Performance of Practical Pulse-Shaped OTFS with Analog Receivers
- Deep Reinforcement Learning for Online Scheduling of Photovoltaic Systems with Battery Energy Storage Systems
- Low-Latency Attack Detection With Dynamic Watermarking for Grid-Connected Photovoltaic Systems
- Low Complexity OTFS Detection with a Delay-Doppler Domain CMC-MMSE Receiver
- Optimum Scheduling of Truck-Based Mobile Energy Couriers (MEC) Using Deep Deterministic Policy Gradient
- Cross-Modality Continuous User Authentication and Device Pairing With Respiratory Patterns
- Physiological Motion Sensing via Channel State Information in NextG Millimeter-Wave Communications Systems
- Critical Element First: Enhance C-V2X Signal Coverage using Power-Efficient Liquid Metal-Based Intelligent Reflective Surfaces
- SideSense: Robust Physiological Motion Detection via mmWave Joint Communication and Sensing Systems With Multiple Beams
- Cross-Modality Continuous User Authentication and Device Pairing With Respiratory Patterns
- Physiological Motion Sensing via Channel State Information in NextG Millimeter-Wave Communications Systems
- SideSense: Robust Physiological Motion Detection via mmWave Joint Communication and Sensing Systems With Multiple Beams
- Cross-Modality Continuous User Authentication and Device Pairing With Respiratory Patterns
- Physiological Motion Sensing via Channel State Information in NextG Millimeter-Wave Communications Systems
- SideSense: Robust Physiological Motion Detection via mmWave Joint Communication and Sensing Systems With Multiple Beams
- Deep Reinforcement Learning for Online Scheduling of Photovoltaic Systems with Battery Energy Storage Systems
- Low-Latency Attack Detection With Dynamic Watermarking for Grid-Connected Photovoltaic Systems
- Optimum Scheduling of Truck-Based Mobile Energy Couriers (MEC) Using Deep Deterministic Policy Gradient
- On the Performance of Practical Pulse-Shaped OTFS with Analog Receivers
- Low Complexity OTFS Detection with a Delay-Doppler Domain CMC-MMSE Receiver
- CP-Free ODDM Over General Doubly-Selective Fading Channels
- An Adversarial-Driven Experimental Study on Deep Learning for RF Fingerprinting
- Physiological Motion Sensing via Channel State Information in NextG Millimeter-Wave Communications Systems
- Physiological Motion Sensing via Channel State Information in NextG Millimeter-Wave Communications Systems
- 5G RRC Protocol and Stack Vulnerabilities Detection via Listen-and-Learn
- 5G RRC Protocol and Stack Vulnerabilities Detection via Listen-and-Learn
- 5G RRC Protocol and Stack Vulnerabilities Detection via Listen-and-Learn
- Cross-Modality Continuous User Authentication and Device Pairing With Respiratory Patterns
- Cross-Modality Continuous User Authentication and Device Pairing With Respiratory Patterns
- Cross-Modality Continuous User Authentication and Device Pairing With Respiratory Patterns
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