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Posts

Military and Amateur HF Radios – the Basics

12 minute read

Published:

Since the escalation of the Russo-Ukrainian War on Feb. 24, 2022, the high frequency (HF) radio has made headlines and sparked some discussions in the amateur radio community. With low bandwidth (2.5kHz-10kHz per HF channel), HF radio can only be used for digital or analog voice and E-mail/chat/text messaging. However, if the Internet, satellites, and telecom networks were all blocked in a war or natural disaster, HF radio is the only way of long distance communications without relying on any infrastructure.

news

Lincoln Named Smart Gigabit Community

less than 1 minute read

Published:

This morning, Lincoln Mayor Chris Beutler announced Lincoln’s new designation as a Smart Gigabit Community (SGC), making it part of a network of SGCs connected by shared high-speed technology infrastructure. Dec. 11, 2017

Nebraska Experimental Testbed of Things, known as NEXTT, named finalist for NSF program to expand rural broadband

less than 1 minute read

Published:

A team of researchers and wireless technology experts from the University of Nebraska–Lincoln and the city of Lincoln, along with community, industry and university partners, has been selected as a finalist to lead a prestigious National Science Foundation research program focused on studying novel ways to reduce the cost of broadband delivery to rural communities.

Our paper “A City-Wide Experimental Testbed for The Next Generation Wireless Networks” is published on Journal of Ad Hoc Networks

less than 1 minute read

Published:

Our journal paper “A city-wide experimental testbed for the next generation wireless networks” is published in the journal of Ad Hoc Networks. In this paper, we present a city-wide wireless testbed providing researchers and students with realistic radio environments, standardized experimental configurations, reusable datasets, and advanced computational resources. The testbed contains 5 cognitive radio sites deployed on two campuses of the University of Nebraska-Lincoln and a public street in the city of Lincoln, Nebraska. The testbed is equipped with flagship software-defined radio transceivers, over-the-air and underground antenna arrays, and 20Gbps fronthaul connectivity to cloud facility on campus, and provides remote access to sandbox and live setups of wireless experiments. The development is in collaboration with departments of the university, city of Lincoln, and industrial partners. The goal of this testbed is to improve the accessibility and reproducability of wireless experiments in dynamic spectrum access, 5G/6G, vehicular networks, underground wireless communications, and radio frequency machine learning.

Our manuscript “Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks” is revised and submitted to IEEE JSAC

less than 1 minute read

Published:

“Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks” is revised and submitted to IEEE Journal on Selected Areas in Communications. The revised version includes significant improvement in the design, training approach, and performance of the complex-valued deep neural networks.

Our paper “Distributed Scheduling using Graph Neural Networks” is accepted by IEEE ICASSP 2021

less than 1 minute read

Published:

In our latest paper “Distributed Scheduling using Graph Neural Networks” accepted by IEEE ICASSP 2021, we augment the distributed greedy scheduler with topology-aware node embeddings generated by Graph Convolutional Networks. Our approach can close the sub-optimality gap by half with minimal increase in the local communication complexity (as low as only one additional round of message passing). The deployment of GCN can be distributed while the training is centralized.

Our manuscript entitled “Link Scheduling Using Graph Neural Networks” is submitted to IEEE Journal on Selected Topics in Signal Processing.

less than 1 minute read

Published:

In this work arXiv, we address the link scheduling problem in wireless multi-hop networks with orthogonal access, by incorporating machine learning over graphs into conventional algorithmic frameworks. Our work proposes both centralized and distributed solutions, which can improve the bandwidth efficiency of wireless multi-hop networks at low computational and communication costs.

portfolio

publications

Design of Motor Soft Starter based on DSP and CPLD

Published in University of Electronic Science and Technology of China, 2006

This undergraduate thesis is about design and implementation of a soft starter for 3-phase AC motor to reduce the electrical and mechanical impacts during its starting. It is awarded as Outstanding Undergraduate Thesis in 2006.

Recommended citation: Zhongyuan Zhao, (2006). "Design of Motor Soft Starter based on DSP and CPLD," B.S. Thesis. University of Electronic Science and Technology of China, P.R.China.

DSP and CPLD-based Digital AC Soft Starter

Published in Automation Information, 自动化信息, ISSN 1817-0633, 2007

This paper is about design and implementation of a soft starter for 3-phase AC motor to reduce the electrical and mechanical impacts during its starting.

Recommended citation: Haihong Tang, Zhongyuan Zhao, "DSP and CPLD-based Digital AC Soft Starter." Automation Information, 2007 (5), pp53-55, May 2007. http://lib.cqvip.com/qk/91378X/200705/24490287.html

Design and Implementation of Channelized Digital Receiver based on PCI-Express

Published in University of Electronic Science and Technology of China, 2009

This paper is about design and implementation of a Channelized Receiver with Xilinx Virtex 5 FPGA.

Recommended citation: Zhongyuan Zhao, (2009). "Design and Implementation of Channelized Digital Receiver based on PCI-Express," M.S. Thesis. University of Electronic Science and Technology of China, P.R.China.

A Real-Time High Resolution Image Compression System Based on ADV212

Published in 2nd International Congress on Image and Signal Processing, 2009

This paper is about design and implementation of a soft starter for 3-phase AC motor to reduce the electrical and mechanical impacts during its starting.

Recommended citation: Hongping Hu, Zhongyuan Zhao, "A Real-Time High Resolution Image Compression System Based on ADV212," in Proc. 2nd International Congress on Image and Signal Processing (CISP 09) , Tianjin, China, pp.1-4, Oct. 2009. https://doi.org/10.1109/CISP.2009.5302779

Method and Apparatus for An Implementation of Polyphase Filter Structure

Published in NIPA, 2012

An implementation method for polyphase filter structure that is capable of real-time synchronous processing and with number of sub-filters online reconfigurable.

Recommended citation: Zishu He, Zhongyuan Zhao, Jianzhong Zhang, Ting Chen, and Kexin Jia, "Method and Apparatus for An Implementation of Polyphase Filter Structure," NIPA, CN101958697B, Issued Date: Nov. 14, 2012. https://patents.google.com/patent/CN101958697A/en?oq=CN201010297382

Ratings for Spectrum: Impacts of TV Viewership on TV Whitespace

Published in IEEE GLOBECOM, 2014

This paper is to propose a new spectrum sharing paradigm in Television (TV) spectrum based on the studies on population dynamics and TV usages. It aims to address the urban spectrum crisis left by current TV white space regulations, which mainly benefit rural areas.

Recommended citation: Zhongyuan Zhao, Mehmet C. Vuran, Demet Batur, and Eylem Ekici, "Ratings for Spectrum: Impacts of TV Viewership on TV Whitespace," in Proc. IEEE Global Communications Conference (GlobeCom), pp.941-947, Austin, TX, Dec. 2014 https://doi.org/10.1109/GLOCOM.2014.7036930

PLL and Adaptive Compensation Method in PLL

Published in NIPA, USPTO, EPO, 2016

A method for mitigating the vibration-induced phase noise of an phase locked loop with an acceleration sensitive voltage controlled oscillator.

Recommended citation: Zhongyuan Zhao, Weixu Wang, Luping Pan, "PLL and Adaptive Compensation Method in PLL," International Patent, US9496881 B2, EP3047573 A4, CN105580278A, Issued Date: May. 11, 2016. https://patents.google.com/patent/US9496881B2/en

A Cognitive Radio TV Prototype For Effective TV Spectrum Sharing

Published in IEEE DySPAN, 2017

This paper demonstrated a prototype of Wireless Local Area Network with dynamic spectrum access in TV spectrum under Cog-TV framework. It shows that with TV set feedback enabled in next generation TV standard, ATSC 3.0, secondary users could evacuate wirelss channels in real-time without significant degradation of TV user experience.

Recommended citation: Davis Rempe, Mitchell Snyder, Andrew Pracht, Andrew Schwarz, Tri Nguyen, Mitchel Vostrez, Zhongyuan Zhao, and Mehmet C. Vuran, "A Cognitive Radio TV Prototype For Effective TV Spectrum Sharing," in Proceedings of IEEE DySPAN, Baltimore, MD, May. 2017, pp.1-2 https://doi.org/10.1109/DySPAN.2017.7920765

Modeling Aggregate Interference with Heterogeneous Secondary Users and Passive Primary Users for Dynamic Admission and Power Control in TV Spectrum

Published in IEEE BalkanCom 18, 2018

Recommended citation: Zhongyuan Zhao and Mehmet C. Vuran, "Modeling Aggregate Interference with Heterogeneous Secondary Users and Passive Primary Users for Dynamic Admission and Power Control in TV Spectrum," in Proc. Int. Balkan Conference on Communications and Networking (BalkanCom 18), Podgorica, Montenegro, Jun. 2018.

Vehicle-to-Barrier Communication During Real-World Vehicle Crash Tests

Published in Computer Communications, 2018

This paper is about characterizing wireless channel for Vehicle-to-Barrier (V2B) communication through experiments in real-world crash tests.

Recommended citation: S. Tamel, M. C. Vuran, M.M.R. Lunar, Z. Zhao, A. Salam, R. K. Faller, and C. Stolle, "Vehicle-to-Barrier Communication During Real-World Vehicle Crash Tests," Computer Communications, Vol 127, Sep. 2018, pp. 172-186. https://doi.org/10.1016/j.comcom.2018.05.009

Dynamic Pricing of Wireless Internet Based on Usage and Stochastically Changing Capacity

Published in Manufacturing and Service Operations Management, 2019

This paper is about development of Markov Decision Process-based dynamic pricing model for wireless Internet access in a setting of dynamic spectrum access when demand and capacity (bandwidth) are stochastic.

Recommended citation: Demet Batur, Jennifer Ryan, Zhongyuan Zhao, and Mehmet C. Vuran, "Dynamic Pricing of Wireless Internet Based on Usage and Stochastically Changing Capacity," Manufacturing and Service Operations Management, Vol. 21, No.4, Publised Online, Feb. 2019. https://doi.org/10.1287/msom.2018.0727

Shades of White: Impacts of Population Dynamics and TV Viewership on Available TV Spectrum

Published in IEEE Transactions on Vehicular Technology, 2019

This paper is to propose a new spectrum sharing paradigm in Television (TV) spectrum based on the studies on population dynamics and TV usages. It aims to address the urban spectrum crisis left by current TV white space regulations, which mainly benefit rural areas.

Recommended citation: Zhongyuan Zhao, Mehmet C. Vuran, Demet Batur, and Eylem Ekici, "Shades of White: Impacts of Population Dynamics and TV Viewership on Available TV Spectrum," IEEE Transactions on Vehicular Technology, Vol. 68, No. 3, pp2427-2442, March 2019. https://doi.org/10.1109/TVT.2019.2892867

Improving Spectrum Efficiency by Exploiting User and Channel Behaviors for Next Generation Wireless Networks

Published in University of Nebraska-Lincoln, 2019

This dissertation explores how to make the next generation wireless networks more interactive and intelligent in accessing spectrum resource and wireless channel, including dynamic spectrum sharing, cloud radio access networks, and radio frequency machine learning.

Recommended citation: Zhongyuan Zhao, (2019). "Improving Spectrum Efficiency by Exploiting User and Channel Behaviors for Next Generation Wireless Networks," Ph.D. dissertation, University of Nebraska-Lincoln. https://digitalcommons.unl.edu/dissertations/AAI13862638/

A city-wide experimental testbed for the next generation wireless networks

Published in Ad Hoc Networks, 2020

This paper is to propose a new spectrum sharing paradigm in Television (TV) spectrum based on the studies on population dynamics and TV usages. It aims to address the urban spectrum crisis left by current TV white space regulations, which mainly benefit rural areas.

Recommended citation: Zhongyuan Zhao, Mehmet C. Vuran, Baofeng Zhou, Mohammad M.R. Lunar, Zahra Aref, David P. Young, Warren Humphrey, Steve Goddard, Garhan Attebury, Blake France, " A city-wide experimental testbed for the next generation wireless networks," Ad Hoc Networks, Vol. 111, pp102305, ISSN 1570-8705, 2021. https://doi.org/10.1016/j.adhoc.2020.102305

Distributed Scheduling using Graph Neural Networks

Published in IEEE ICASSP 2021, 2021

Distributed greedy scheduler is augmented with topology-aware node embeddings generated by Graph Convolutional Networks. Our approach can close the sub-optimality gap by half with minimal increase in the local communication complexity (as low as only one additional round of message passing).

Recommended citation: Zhongyuan Zhao, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra, " Distributed Scheduling Using Graph Neural Networks," IEEE ICASSP 2021, pp. 4720-4724, doi: 10.1109/ICASSP39728.2021.9414098. https://doi.org/10.1109/ICASSP39728.2021.9414098

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-Valued Convolutional Networks

Published in IEEE Journal on Selected Areas in Communications, 2021

This paper is about using complex valued deep neuron networks to implement the lower physical layer of OFDM-based wireless communications. It demonstrates that complex neuron networks could learn the complex waveform used in modern wireless network, and outperforms analytical channel estimation approach in Rayleigh fading under certain conditions.

Recommended citation: Zhongyuan Zhao, Mehmet C. Vuran, Fujuan Guo, and Stephen Scott, "Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-Valued Convolutional Networks," in IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2407-2420, Aug. 2021, doi: 10.1109/JSAC.2021.3087241. https://doi.org/10.1109/JSAC.2021.3087241

Link Scheduling using Graph Neural Networks

Published in IEEE Transactions on Wireless Communications, 2021

We leverage the power of Graph Convolutional Networks in encoding topological information into node embeddings, to enhance the existing algorithmic frameworks of distributed greedy scheduler as well as centralized tree search for solving maximum weighted independent set (MWIS) problem in an efficient and approximate manner. This improves the performance of both distributed and centralized link scheduling in wireless multi-hop networks.

Recommended citation: Zhongyuan Zhao, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra, " Link Scheduling Using Graph Neural Networks," in IEEE Transactions on Wireless Communications, vol. 22, no. 6, pp. 3997-4012, June 2023, doi: 10.1109/TWC.2022.3222781 .

Delay-Oriented Distributed Scheduling using Graph Neural Networks

Published in IEEE ICASSP 2022, 2022

We develop a machine learning method to solve a series of dependent combinatorial optimization problems embedded in a Markov Decision Process. This formulation applies to many problems in wireless networking and operational research. So far, researchers can attack only one of them but their combination.

Recommended citation: Zhongyuan Zhao, Gunjan Verma, Ananthram Swami, Santiago Segarra, " Delay-Oriented Distributed Scheduling Using Graph Neural Networks," IEEE ICASSP 2022, pp. 8902-8906, doi: 10.1109/ICASSP43922.2022.9746926. https://doi.org/10.1109/ICASSP43922.2022.9746926

Delay-aware Backpressure Routing Using Graph Neural Networks

Published in arXiv, 2022

Backpressure routing algorithm based on shortest path bias is augmented with Graph Convolutional Networks, which predict a delay-aware per-hop distance based on link duty cycle in scheduling. Our approach can improve the delay performance of backpressure routing at low signaling overhead.

Recommended citation: Zhongyuan Zhao, Bojan Radojičić, Gunjan Verma, Ananthram Swami, Santiago Segarra, " Delay-aware Backpressure Routing Using Graph Neural Networks," accepted to IEEE ICASSP 2023, arXiv 2211.10748 . https://arxiv.org/pdf/2211.10748.pdf

Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks

Published in IEEE ICASSP 2024, 2023

Backpressure routing algorithm based on shortest path bias is augmented with Graph Convolutional Networks, which predict a delay-aware per-hop distance based on link duty cycle in scheduling. Our approach can improve the delay performance of backpressure routing at low signaling overhead.

Recommended citation: Zhongyuan Zhao, Jake Perazzone, Gunjan Verma, Santiago Segarra, " Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks," accepted to IEEE ICASSP 2024. http://arxiv.org/abs/2312.02471

Enhanced Backpressure Routing with Wireless Link Features

Published in IEEE CAMSAP, 2023

The latency and practicality of Backpressure routing algorithm based on shortest path bias is improved with three components: optimal bias scaling, low-overhead bias maintenance, and a delay-aware backlog metric.

Recommended citation: Zhongyuan Zhao, Gunjan Verma, Ananthram Swami, Santiago Segarra, " Enhanced Backpressure with Wireless Link Features," accepted to IEEE CAMSAP 2023.

COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery

Published in Briefings in Bioinformatics, 2023

An open source software pipeline for drug repurpose based on multiple modeling methods and heterogeneous biological datasets.

Recommended citation: Brandt Bessell, Josh Loecker, Zhongyuan Zhao, Sara Sadat Aghamiri, Sabyasachi Mohanty, Rada Amin, Tomáš Helikar, Bhanwar Lal Puniya, "COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery," Briefings in Bioinformatics, Volume 24, Issue 6, November 2023, bbad387. https://doi.org/10.1093/bib/bbad387

Distributed Link Sparsification for Scalable Scheduling using Graph Neural Networks

Published in IEEE TWC, 2024

To reduce the scheduling overhead in wireless networks of dense connectivity, a GCN is employed to adjust contention thresholds for individual links based on traffic statistics and network topology, which is trained with an offline constrained reinforcement learning algorithm capable of balancing two competing objectives.

Recommended citation: Zhongyuan Zhao, Gunjan Verma, Ananthram Swami, Santiago Segarra, " Distributed Link Sparsification for Scalable Scheduling using Graph Neural Networks," IEEE Transactions on Wireless Communications, under review

Biased Backpressure Routing using Link Features and Graph Neural Networks

Published in IEEE TWC, 2024

To improve the latency performance of Backpressure routing, we improve shortest path-biased Backpressure routing by introducing a new distance metric for shortest path, principled approaches for optimal bias scaling and bias maintenance, as well as a new delay-based backlog metric that can be integrated into biased Backpressure routing.

Recommended citation: Zhongyuan Zhao, Bojan Radojičić, Gunjan Verma, Ananthram Swami, Santiago Segarra, " Distributed Link Sparsification for Scalable Scheduling using Graph Neural Networks," IEEE Transactions on Wireless Communications, under review

Exploring the Opportunities and Challenges of Graph Neural Networks in Electrical Engineering

Published in Nature Reviews Electrical Engineering, 2024

Review of how Graph Neural Networks are employed to address some selected application questions in Electrical Engineering, including electrical automatic design, modeling and management of wireless communication networks, as well as data and computational challenges in high energy physics, material science and biology.

Recommended citation: Eli Chien, Mufei Li, Anthony Aportela, Kerr Ding, Shuyi Jia, Supriyo Maji, Zhongyuan Zhao, Javier Duarte, Victor Fung, Callie Hao, Yunan Luo, Olgica Milenkovic, David Pan, and Santiago Segarra, Li Pan, " Exploring the Opportunities and Challenges of Graph Neural Networks in Electrical Engineering," Nature Reviews Electrical Engineering, accepted for publication

talks

teaching

Electronic Design Training Program, 2005-2007

Undergraduate course, Undergraduate Innovation Center, University of Electronic Science and Technology of China, 2007

This was a 2-year extra-curricular training program for 100+ undergraduate students selected from university wide, directly funded by the university administration. From 2005 to 2007, the program was ran by about 20 professors, 3 stuff members, and 8 student teaching assistants (all former trainees). 6 and 10 teams (3 students per team) won the national 1st and 2nd prizes, respectively, in 2007 National Undergraduate Electronic Design Contest.

Multi-Agent System, Fall 2017

Graduate/undergraduate course, University of Nebraska-Lincoln, Department of Computer Science and Engineering, 2017

As the only graduate teaching assistant, I helped Prof. Leen-Kiat Soh in the interactive learning sessions (Game Days), in which over 20 students work in teams to learn strategies in different settings by interacting with other teams. My job was to setup an online website developed by previous TAs, and helped in preparing Game Day reports afterwards.

Data Structure and Algorithms, Fall 2017

Undergraduate course, University of Nebraska-Lincoln, Department of Computer Science and Engineering, 2017

I work as graduate teaching assistant with about 10 undergraduate TAs to assist Prof. Charles Riedesel in grading and tutoring over 40 students from Jeffery S. Raikes School of Computer Science and Management. Meanwhile, I assisted in organizing 4 hackathon events for ACM 2017 North Central North American Regional Contest as the practical session of this course,