Personal Profile
Wang Shengchu, Associate Professor, PhD/Master's Supervisor, earned his PhD from the Department of Electronic Engineering at Tsinghua University. His research focuses on embodied intelligent swarm navigation and positioning, as well as intelligent wireless communication. He is dedicated to integrating modern artificial intelligence with classical signal processing theories to achieve collaborative pose estimation of embodied intelligent agents without navigation infrastructure, and to develop nonlinear MIMO physical layer signal processing technologies for next-generation cellular communications. His related research has been supported by more than ten projects, including the National Natural Science Foundation General/Youth Projects, the National Key R&D Program, National Defense Research Funds, and enterprise-sponsored projects. His research findings have been published in over forty first/corresponding author papers in top-tier journals such as IEEE Transactions on Wireless Communications, IEEE Transactions on Transportation Systems, IEEE Transactions on Communications, and international conferences (see the publication list at https://dblp.org/pid/136/5328.html). He has also been granted seven national invention patents and has one pending. In terms of student mentorship, he has guided three master's students to win National Scholarships, led undergraduate students to achieve second and third prizes in the University Student Innovation and Entrepreneurship Competition, and guided graduate students to secure a second prize in the Innovation and Entrepreneurship Competition.
Educational Experience
2004.9
2008.6
- The Beijing Institute of Technology
- Information and communication engineering
- Bachelor's Degree
- Undergraduate
- Department of Electronic Engineering
2008.9
2011.7
- Tsinghua University
- Information and communication engineering
- Master's Degree
- Postgraduate
- Department of Electronic Engineering
Engaged in research related to image processing.
2011.9
2015.11
- Tsinghua University
- Information and communication engineering
- Doctoral Degree
- Postgraduate
- Department of Electronic Engineering
Engaged in large-scale MIMO, compressed sensing signal processing research.
Work ExperienceMore>>
2019.11
2019.12
- Beijing University of Posts and Telecommunications
- School of Information and Communication Engineering
- Associate Professor
2020.1
Now
- Beijing University of Posts and Telecommunications
- Artificial Intelligence Academy
- Associate Professor
- On the job
2017.9
2019.11
- Beijing University of Posts and Telecommunications
- School of Information and Communication Engineering
- Lecturer
2015.9
2017.7
- Beijing University of Posts and Telecommunications
- School of Information and Communication Engineering
- Faculty postdoctoral fellow
Social Affiliations
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Research Group
Name of Research Group: IPC TEAM
Description of Research Group: Our team is led by Associate Professor Wang Shengchu and currently consists of 1 doctoral student and 6 master's students. The team's main research directions include embodied intelligent agent collaborative pose estimation (aimed at achieving high-precision positioning and navigation in infrastructure-free environments) and nonlinear MIMO signal processing (focused on physical layer technologies for next-generation communication systems). In the field of embodied collaborative pose estimation, we primarily explore the design of front-end multi-sensor fusion-based intelligent SLAM odometry and back-end six-degree-of-freedom pose recovery algorithms. For nonlinear MIMO signal processing, we investigate the design of radio frequency receiver chains using low-power, low-cost amplitude/phase detectors and other nonlinear devices, as well as the development of dedicated baseband signal processing algorithms, including channel estimation and multi-user detection, based on Bayesian approximate message passing algorithms. In addition to theoretical exploration, the team emphasizes hardware system implementation. We build collaborative pose estimation hardware platforms using monocular/binocular/RGBD cameras, LiDAR, UWB, IMU, and other sensors to validate the performance of related algorithms. We also develop RF receivers using ADI nonlinear devices to collect multi-channel observations for nonlinear MIMO, enabling the verification of baseband algorithms.
