Guoyi Xu (许郭译)

I'm currently a postdoctoral research scientist at the Department of Electrical Engineering at Columbia University, advised by Prof. Harish Krishnaswamy.

I did my Ph.D. at the School of Electrical and Computer Engineering at Cornell University, advised by Prof. Edwin C. Kan, in Aug. 2023.

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Education

Ph.D., School of Electrical and Computer Engineering (ECE)

Cornell University

Ithaca, NY, USA

Advisor: Prof. Edwin C. Kan

B. Eng., School of Electronic Science and Engineering (ESE)

University of Electronic Science and Technology of China (UESTC)

Chengdu, Sichuan Province, China

Concurrent Enrollment, Department of Electrical Engineering and Computer Science (EECS)

University of California, Berkeley

Berkeley, CA, USA

Work Experience

Postoctoral Research Scientist, Department of Electrical Engineering (EE)

Columbia University in the City of New York

New York, NY, USA

Advisor: Prof. Harish Krishnaswamy

Software Engineer Intern, Engineering Development Group (EDG)

The MathWorks Inc.

Natick, MA, USA

Managers: Ms. Nitya Jay and Mr. Mukesh Chugh

Undergraduate Research Intern, Department of Electrical Engineering and Computer Science

University of California, Berkeley

Berkeley, CA, USA

Advisor: Prof. Ali M. Niknejad

Research

My research interests are joint communication and sensing (JCAS), indoor localization, radio-frequency identification (RFID), RF/mmWave systems and IoT.

This work presents a novel precision 3D loclization framework that leverages spatially diverse redundant channels to resolve ambiguities under conditions of near-field, inhomogeneous medium and heavy multi-path. It does not rely on a broad bandwidth, and achieves millimeter-precision at sub-1GHz carrier frequency. The multiple-input multiple-output (MIMO) system was implemented on Universal Software Radio Pheripheral (USRP) and harmonic radio-frequency identification (RFID) tag.

We present an indoor occupant counting system using ambient radio-frequency identification (RFID) sensors and deep learning models, without requiring on-person tags or movement. Effects of wall and furniture tags and significant reduction of number of tags were studied for accurate counting. We achieved counting accuracies above 90% with 80 tags, and above 85% with 16 - 30 tags in room sizes from 100 to 600 square feet. Different room layouts, tag deployment and occupants postures were tested.

In multi-channel radio transceivers, random phase offsets are present due to non-repeatable initial phases of individual phase-locked loops (PLL). To address this issue, this paper proposes to directly measure both random and systematic time-invariant phase offsets and calibrate them in real time, made possible by additional connections based on splitters and combiners. It achieves repeatable phase calibration to within 2 degrees of errors without relying on bandwidth resources and optimization-based signal processing, is scalable from sub-GHz to mmWave frequencies, and can be extended to distributed systems. The proposed method is designed for applications requiring phase synchronization but without PLL design flexibility.

Publications to be updated ...

Independent Reviewer Activities

• IEEE Internet of Things Journal (2024)
• IEEE Communications Magazine (2024)
• IEEE Journal on Selected Areas in Sensors (2024)
• IEEE Transactions on Intelligent Vehicles (2024)
• IEEE Transactions on Instrumentation and Measurement (2024)
• IEEE Microwave and Wireless Technology Letters (2023, 2024)
• IEEE Sensors Conference (2024)
• IEEE Sensors Journal (2023)
• IEEE Sensors Conference (2023)
• IEEE Transactions on Microwave Theory and Techniques (2022, 2023)
• IEEE Journal of Radio Frequency Identification (2021)
• IEEE Instrumentation & Measurement Magazine (2021)
• IET Renewable Power Generation (2021)

Teaching

To be updated ...

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