Biography

I am currently a Ph.D. student at NYU WIRELESS, New York University, under the supervision of Prof. Theodore S. Rappaport. I hold a B.Eng. degree with Honors from Chongqing University of Posts and Telecommunications, where I was under the guidance of Prof. Yongjun Xu during my undergraduate studies.

Research

My current research interests lie in the fields of green communication, channel modeling, reconfigurable intelligent surfaces, resource allocation and backscatter communication, etc.

Projects

Waste Factor: A New Metric for Evaluating Power Efficiency in any Cascade (Jan. 2023 - Now)

• Supervisor: Prof. Theodore S. Rappaport (Member of US National Academy of Engineering, Founding Director of NYU WIRELESS, IEEE Fellow)
• Waste factor (W) is a novel metric to evaluate the power efficiency of any device or cascade.
• Enhanced W for data centers using power usage effectiveness (PUE), which won best paper.
• Simulated how component efficiency in a cascaded communication system influences the W at 28 GHz and 142 GH in a 1 km2 cellular cell.
• Extending W to analyze parallel (MIMO) cascade systems, and radio access network (RAN).

FR3 Radio Propagation Measurement and Modeling in 6.75 GHz and 16.95 GHz (Nov. 2023 - Now)

• Supervisor: Prof. Theodore S. Rappaport (Member of US National Academy of Engineering, Founding Director of NYU WIRELESS, IEEE Fellow)
• Operating and conducting a comprehensive FR3 measurement campaign at NYU.
• Creating a detailed statistical spatial channel model based on radio propagation data from outdoor, indoor, and factory environments together with penetration loss and cross-polarization discrimination (XPD) measurement at 6.75 GHz and 16.95 GHz.

Capacity of a Binary Channel with a Time-Bounded Adversary (Feb. 2024 - Now)

• Supervisor: Prof. Sundeep Rangan (Director of NYU WIRELESS, IEEE Fellow)
• This project explores ensuring reliable communication over a binary input memoryless channel under a time-bounded adversary's influence, which can overwrite a fraction of received symbols.
• Derived the worst-case adversarial capacity for the binary channel.
• Introduced a method using a random interleaver and a thresholded log-likelihood ratio (LLR) decoder to achieve this capacity.
• Demonstrated that this method can be integrated into standard decoder architectures.

Using AI and NYURay for Integrated Sensing and Communication (Sept. 2023 - Now)

• Supervisor: Prof. Theodore S. Rappaport (Member of US National Academy of Engineering, Founding Director of NYU WIRELESS, IEEE Fellow)
• Calibrating 3D ray-tracing tool NYURay to NYU campus for precise mapping and sensing.
• Developing a multi-task prediction model using AI for object positioning and classification.

Power Allocation and Frequency Reuse Scheme for 5G Networks (Jul. 2021 - Aug. 2021)

• Supervisor: Prof. Kai-Kit Wong (IEEE Fellow), UCL
• Deployed strict frequency reuse and soft frequency reuse with various novel power allocation algorithms in a multi-cell cellular network capacity simulation.
• Proposed a game theory-based forward-looking water-filling (WF) algorithm to largely decrease the computing complexity and self-optimize the WF algorithm to obtain higher capacity.

Undergraduate Thesis (Sept. 2022 -Jun. 2023)

• Supervisor: Prof. Yongjun Xu (IEEE Senior Member), CQUPT
• Developed and optimized an energy-efficient, wireless-powered backscatter communication network using OFDMA and robust optimization techniques, significantly improving network throughput and reducing outage probability under imperfect CSI.
• Utilized the Dinkelbach method and variable substitution to transform the resource allocation problem into a convex optimization problem, solved using the Lagrange dual method.
• Demonstrated superior performance and robustness through MATLAB simulations, outperforming traditional backscatter and harvest-then-transmit algorithms.

Research Training Program of CQUPT (Apr. 2021 - Apr. 2022)

• Supervisor: Prof. Yongjun Xu (IEEE Senior Member), CQUPT
• Proposed a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless powered communication network with a harvest-then-transmit protocol to improve the system energy efficiency (EE).
• Jointly optimized the active beamforming of the PS and the passive beamforming of the RIS.

Publications

Conference Paper

• Mingjun Ying, Dipankar Shakya, Hitesh Poddar, and Theodore S. Rappaport. "Waste Factor: A New Metric for Evaluating Power Efficiency in any Cascade." IEEE Global Communications Conference 2023 (Best Paper Award).
Arxiv  
• Mingjun Ying and Shuyu Wang. "Self-Optimizing Water-Filling Power Allocation: A Hybrid Fractional Frequency Reuse Way." The 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP). IEEE, 2022: 208-213.
IEEE   Paper   Slides  
• Mingjun Ying and Shuyu Wang. "Capacity Analysis and Hybrid Power Allocation for Mul-ti-cell 5G Networks." The 11th International Conference on Communications, Circuits and Systems (ICCCAS). IEEE, 2022: 192-197.
IEEE   Paper   Slides  
• Yuhao Lian, Mingjun Ying, Shuyu Wang and Yuhua Wang. "An Efficient Fast Walsh-Hadamard Transform Based OFDM-IM Scheme with Lower PAPR." 2023 Wireless Telecommunications Symposium (WTS). IEEE, 2023: 1-6.
IEEE   Paper   Slides  
• Yuhao Lian, Mingjun Ying, Shuyu Wang and Yuhua Wang. "An Efficient Maximum Subcarrier Power Detection Scheme for OFDM-IM Systems." 2023 International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, 2023: 258-263.
IWCMC   Paper  

Patents

• Yongjun Xu, Mingjun Ying, Jihua Zhou, etc. "A Robust Recourses Allocation Algorithm for RIS-Enhanced WPCNs." Chinese patent, CN113825159A, 2021-12-21. CNKI Index
• Yongjun Xu, Mingjun Ying, Qianbin Chen, etc. "A Robust Energy Efficiency Optimization method for RIS-Assisted WPCNs." Chinese patent, CN113613273A, 2021-11-05. CNKI Index

Awards

National Scholarship
First-Class Scholarship of CQUPT
GLOBECOM 2023 Best Paper Award
Li Publication Award from NYU
Meritorious Winner of the MCM/ICM
Second Prize of the CUMCM
...

Useful Stuff

Best Readings in Reconfigurable Intelligent Surfaces
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