Zhongyuan Zhao

Zhongyuan Zhao

Postdoctoral Research Associate

Posts by Tags

AI

ChatGPT

Cloud Radio Access Networks

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.

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.

Cog-TV

MWIS

OFDM

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.

Policy gradient

V2B

amateur radios

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.

backpressure routing

career

channel estimation

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.

combinatorial optimization

complex-valued neural networks

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.

computational offloading

constrained reinforcement learning

cosecran

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

course

cpn

custom gradient

deep learning

distributed AI

distributed scheduling

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.

distributed systems

gradient descent

graph convolutional networks

graph neural networks

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.

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.

high frequency

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.

large-scale wireless testbed

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.

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.

latency

link duty cycle

link scheduling

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.

massive access

military communications

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.

network science

neural twin

news

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.

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 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.

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.

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

nextt

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

non-differentiable policy network

orthogonal access

postdoc

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.

reinforcement learning

shortest path bias

shortest path routing

squared loss

wireless ad-hoc 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.

wireless communications

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.

wireless multi-hop networks

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.

wireless multihop networks

wirless ad-hoc networks

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.