I have broad interests in wireless communications. While my master's topic was more related with routing algorithms (Network layer), starting from 2014, I was more into PHY (physical layer) researches.

I have a very broad interest on wireless technology and its applications. For example, 5G and beyond, signal processing and sensing. My strength is the ability to combine theoretic research with system design and prototyping.

You can also check my Google Scholar or ResearchGate

I am always looking for highly motivated undergraduate and graduate students to work with me, if you are interested with my research, please send me an email with your C.V.

Funded Projects

CRII:CNS: Towards Spectrum and Energy Efficient Large-scale IoT Communications: A Cross-layer Optimization Approach
Sole-PI (100%), NSF, total: $175,000. Estimated from Sept. 2022 - Aug. 2024.

ERI: Toward mmWave Vehicular Communication: A Multisensor Multimodal Deep Data Fusion Approach
Sole-PI (100%), NSF, total: $199,955. Estimated from Sept. 2022 - Aug. 2024.

Privacy Protection among Community-Dwelling Older Wisconsinites in the Age of Internet of Things
co-PI, Sponsor: Thompson Center, University of Wisconsin System, total: $105,757, Jul. 2021 - Jun. 2022.

A Multimodal Deep Sensor Fusion System for Reliable and Faster Next-Gen Wireless Vehicle Communications
PI, Sponsor: UW System Regent Scholar Program, total: $50,000. Jan. 2021 - Aug. 2022.

Dual-Antenna RFID Reader Prototype Development For RFID Chip-Based Vehicle Localization
UW-W PI, Sponsor: Tokef LLC (a Nikola Motor Company), total: $112,051.00. Jul. 2020 - Aug. 2021.

Developing Prototype Multimodal Sensors and Wireless Network System for Land- slide Monitoring and Early Warning
co-PI, Sponsor: UW System Regent Scholar Program, total: $50,000. Jan. 2020 - Aug. 2021.

Research Catalyst Grant
PI, Sponsor: UW-W Office of Sponsored and Research Program (OSRP), total: $10,000, Jan. 2021 - Dec. 2021, awarded as two course release.

Research Topics

mmWave Communication for CAVs

Millimeter wave (mmWave) communication generally requires massive antennas to compensate for higher transmission loss, by beamforming. This technique needs TX and RX to sense the channel then steer beams which is quite inefficient in high mobility scenarios.

In this project, I, together with several faculty members, will use side informaion from connected and automated vehicles (CAVs), such as image, LiDAR, radar, etc., to facilitate mmWave communications.

We will also work with UW-Madison's TOPS lab for future road test.

This project is recently funded by the University of Wisconsin System, and I am appointed as the Regent Scholar of 2021.

Update (Dec. 2021): Extension of this project has been funded by NSF ERI grant.

Extremely Low-Power IoT Communication at Large-Scale

By 2030, we will expect tens of billions of IoT devices demanding for Internet connection. However, both spectrum and energy resources are not sufficient to support such densified networks. In this research, we seek to integrate active and passive communication, to achieve better resource allocation and user coordination through a cross-layer optimization. The goal is to seek for higher energy and spectrum efficiency.

C H. Sun, Q. Wang, S. Ahmed and R. Q. Hu, “Non-Orthogonal Multiple Access in a mmWave Based IoT Wireless System with SWIPT,” 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), 2017, pp. 1-5, doi: 10.1109/VTCSpring.2017.8108186.

I have submitted a NSF CRII proposal in this topic.

New Radio Access for 5G and Beyond

Continuing from my PhD research, I am still active in 5G wireless technologies, especially in the PHY layer, where mainly solve hard problems on how to improve energy, spectral, and computing efficiency.
Most of my previous publications are in this area, below is just a few selections.

J H. Sun, F. Zhou, R. Q. Hu and L. Hanzo, “Robust Beamforming Design in a NOMA Cognitive Radio Network Relying on SWIPT”, IEEE Journals on Selected Areas in Communications, vol. 37, no. 1, Jan. 2019. ESI Highly Cited Paper!
J H. Sun, Z. Zhang, R. Q. Hu and Y. Qian, “Wearable Communications in 5G: Challenges and Enabling Technologies”, IEEE Vehicular Technology Magazine, vol. 13, no. 3, Sept. 2018.
J Z. Zhang, H. Sun and R. Q. Hu, “Downlink and Uplink Non-Orthogonal Multiple Access in a Dense Wireless Network”, IEEE Journal on Selected Areas in Communications, vol. 35, no. 12, Dec. 2017.
C H. Sun, F. Zhou, and Z. Zhang, “Robust Beamforming Design in a NOMA Cognitive Radio Network Relying on SWIPT” accepted in Proc. IEEE ICC 2018, Kansas City, MO, USA.
C F. Zhou, Z. Chu, H. Sun, and V. C. M. Leung, “Resource Allocation for Secure MISO-NOMA Cognitive Radios Relying on SWIPT”, in Proc. IEEE ICC 2018, Kansas City, MO, USA.

Edge Computing and Learning

Recent years have witnessed the transformation from computing moves from cloud to edge, due to increasing computation capacity from mobile devices and the demand for low latency applications. In this research, I mainly study the performance gain using mobile offloading, where edge user and server can share resources, wirelessly. I also proposed the idea on “computation capacity”, which received extensive followup.

J H. Sun, F. Zhou, and R. Q. Hu, “Joint Offloading and Computation Energy Efficiency Maximization in a Mobile Edge Computing System” accepted, IEEE Transactions on Vehicular Technology, vol. 68, no. 3, Mar. 2019. ESI Highly Cited Paper!
J Y. Ye, L. Shi, H. Sun, R. Q. Hu, and G. Lu, “System-Centric Computation Energy Eciency for Distributed NOMA-Based MEC Networks”, IEEE Transactions on Vehicular Technology, vol. 69, no. 8, Aug. 2020.
J H. Sun, X. Ma, and R. Q. Hu, “Adaptive Federated Learning With Gradient Compression in Uplink NOMA”, IEEE Transactions on Vehicular Technology, 2020.
C X. Ma, H. Sun, and R. Q. Hu, “Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC”, IEEE Globecom 2021.
C F. Zhou, H. Sun, Z. Chu, and R. Q. Hu, “Computation Efficiency Maximization for Wireless-Powered Mobile Edge Computing”, IEEE Globecomm 2019.
C F. Zhou, Y. Wu, H. Sun, and Z. Chu, “UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design”, accepted in IEEE ICC 2018, Kansas City, MO, USA.

Wireless Sensing

This research focuses on utilizing wireless signals to sense environment dynamics. Typical applications include: localization, guesture and emotion detection, home security, and industry automotion.
I have worked on this field since my internship in Mitsubishi Laboratory, where I developed a mmWave commerical Wi-Fi platform for 60GHz localization. Also, I have received a funding from a private company in developing RFID accurate localization system (sub-10 cm) for electrical vehicle with software defined radio (SDR).

J T. Koike-Akino, P. Wang, M Pajovic, H. Sun, and P. V. Orlik, “Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach”, IEEE Access, 2020.
C H. Sun, P.Wang, M. Pajovic, T. Koike-Akino, P. V Orlik, A. Taira, and K. Nakagawa, “Fingerprinting-Based Outdoor Localization with 28-GHz Channel Measurement: A Field Study”, in Proc. IEEE SPAWC 2020.
C J. Yu, P. Wang, T. Koike-Akino, Y. Wang, P. V. Orlik, and H. Sun, “Human Pose and Seat Occupancy Classification with Commercial mmWave WiFi”, in Workshop Globecom 2020.
C P. Wang, M. Pajovic, T. Koike-Akino, H. Sun, and P. Orlik, “Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi – Part I: RSS and Beam Indices”, in Proc. IEEE Globecom 2019.
C P. Wang, M. Pajovic, T. Koike-Akino, H. Sun, and P. Orlik, “Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi – Part II: Spatial Beam SNRs”, in Proc. IEEE Globecom 2019.

Embedded System Security

As more cyber-physical systems become available, their security aspect is a major concern. In this reaserch, I started with wireless communication security, utilizing my experiences on system optimization and considering the factor from wireless transmission security. Now, a transformation to embedded system security, include one course I created, and mentoring a few students.

C F. Zhou, Z. Chu, H. Sun, and V. C. M. Leung, “Resource Allocation for Secure MISO-NOMA Cognitive Radios Relying on SWIPT”, in Proc. IEEE ICC 2018, Kansas City, MO, USA.
C J. Yang, Y. Xu, Q. Liu, Q. Li, and H. Sun, “Energy-ecient Resource Allocation for Secure IRS Networks with Active Eavesdropper,” , IEEE ICCC 2020.
J F. Zhou, Z. Chu, H. Sun, et. al, “Articial Noise Aided Secure Cognitive Beamforming for Cooperative MISO-NOMA Using SWIPT”, in IEEE Journal on Selected Areas in Communications, vol. 36, no 4, pp. 918-931. Apr. 2018. . ESI Highly Cited Paper!
C Q. Wang, H. Hu, H. Sun, and R. Q. Hu, “Secure and Energy-Ecient offloading and Resource Allocation in a NOMA-Based MEC Network”, in ACM EdgeCom 2020.

Acknowledgement: I would like to thank generous supports from National Science Foundation, University of Wisconsin System, Utah State University College of Engineering, UW-W Office of Research, Tokef LLC. Reserch during my Ph.D. was supported by Intel, NSF, etc.