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 (2017–2022); Carnegie Mellon University (2020–2021)
Faculty Researcher
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Yanjun Pan's research focuses on cybersecurity, wireless sensing, wireless communications, and network optimization. He is an Assistant Professor in the Department of Computer Science and Computer Engineering at the University of Arkansas. Pan leads a research group and has a scholarship profile including an h-index of 9, 33 total publications, and 192 total citations.
His federally funded work includes a $456,905 NSF SaTC grant for research towards untraceable communications through RF fingerprint anonymization. Pan's recent publications explore diverse topics such as 5G protocol vulnerabilities, online learning for reconfigurable antennas, cross-modality user authentication, vehicle platoon following protocols, pulse-shaped OTFS performance, physiological motion sensing using channel state information, and attack detection for photovoltaic systems.
Pan collaborates with several researchers at the University of Arkansas at Fayetteville, including Jingxian Wu (8 shared publications), Jeremiah R. Wimer (2 shared publications), Andong Zhou (1 shared publication), and Qinghua Li (1 shared publication). His work extends to areas like deep reinforcement learning for photovoltaic system scheduling and low-latency attack detection for grid-connected photovoltaic systems.
Metrics
- h-index: 9
- Publications: 33
- Citations: 200
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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