Junting CHEN

Adventure in Scottish Highlands 

B.Sc (NJU), Ph.D (HKUST)
Post-doctoral Research Fellow

Ming Hsieh Department of Electrical Engineering
University of Southern California
Los Angeles, CA 90089, USA

Contact: juntingc@usc.edu

“I know of no better life purpose than to perish in attempting the great and the impossible…”  —  Nietzsche, 1873.


Junting is a postdoctoral research associate in Prof. Urbashi Mitra's group in University of Southern California, Los Angeles, USA. He received the B.Sc degree in Electronic Science and Engineering from Nanjing University (NJU), Nanjing, China, in 2009, and the Ph.D. degree in Electronic and Computer Engineering from Hong Kong University of Science and Technology (HKUST) , Hong Kong, in 2015. He was advised by Prof. Vincent Lau. From February to December 2014, he was a visiting student in the Laboratory for Information & Decision Systems (LIDS), Massachusetts Institute of Technology (MIT) , USA, advised by Prof. Moe Z. Win. From 2015 – 2016, he was a post-doctoral research fellow in Prof. David Gesbert's group in EURECOM, France.

Research Highlights

My research encompasses various topics in signal processing, optimizations, nonlinear control, and stochastic processes, with applications to wireless communications and localization. In particular, his recent works include cellular networks assisted by unmanned aerial vehicles (UAVs), precoding and feedback design for MIMO systems, wireless systems aided by device-to-device (D2D) communications, and game approaches for cooperative network localization.

3D Communication Network Formation

  • Can we optimally place a flying relay base station (BS) to help the communication between ground terminals in arbitrary obstructive environments?

  • Can we find the optimal relay position in limited search trajectory, if we have no prior information of the signal-blocking obstacles?

To answer these questions, we need to start from modeling, and then incorporate the techniques from learning to optimization.
energy samples for source localization 

Somewhat surprisingly, we find affirmative answers from our preliminary result! We are able to find the optimal relay position in 3D to relay the signal from a BS to an obstructive ground user, no matter how complex the obstructive environment is! Moreover, the search trajectory length is only linear to the BS-user distance, even though we do not have any prior information of the environment.

I am interested in optimal 3D communication network formation to both avoiding propagation blockage to intended ground users and, at the same time, exploiting propagation blockage to isolate the signal to unintended users. I believe that such a new frontier will also be a promising enabler to other fast developing technologies, such as millimeter-wave (mmWave) which highly prefers line-of-sight (LOS) propagation conditions. [See more …]

Machine Learning for Network Localization and Communication

  • How to localize a signal source without knowing the propagation model?

  • How to localize an attacker in a sensor network by analyzing the packet delivery ratio?

I believe that the solution concepts to the above two questions are related, and there are common structural properties to be learned and exploited. In general, I am interested in the learning and estimation (e.g., a source location, propagation environment) with the help of an ample of (yet, low resolution) sensing nodes, while the underlying source model is unknown (or too complicated to acquire). This will lead to important applications in future IoT (Internet-of-things) networks powered by low-cost long-duration nodes.

In particular, in underwater source localization, we have developed matrix/tensor observation models and used matrix/tensor decomposition techniques to extract the source signature vectors for localization. While we make no assumption on the energy propagation model, the method is also robust to various imperfection such as sensor location uncertainty. [See more …]


3740 McClintock Ave, EEB 508
Los Angeles, CA 90089

Email: juntingc@usc.edu