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Our paper “Biased Backpressure Routing using Link Features and Graph Neural Networks” has been accepted for publication in IEEE Transactions on Machine Learning in Communications and Networking.
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Our paper “Joint Task Offloading and Routing in Wireless Multi-hop Networks Using Biased Backpressure Algorithm” is submitted to IEEE ICASSP 2025. In this paper, we propose a new formulation for computation offloading that can simultaneously achieve task offloading, load balancing, routing, and scheduling under a unified biased Backpressure routing and scheduling framework.
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The preprint and source code of our paper “Biased Backpressure Routing using Link Features and Graph Neural Networks”, submitted to IEEE Transactions on Machine Learning in Communications and Networking, are now available.
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Our paper “Exploring the Opportunities and Challenges of Graph Neural Networks in Electrical Engineering” is accepted for publication in Nature Reviews Electrical Engineering.
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Our paper “Biased Backpressure Routing using Link Features and Graph Neural Networks” is submitted to IEEE Transactions on Wireless Communications.
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Our paper “Distributed Link Sparsification for Scalable Scheduling using Graph Neural Networks” is submitted to IEEE Transactions on Wireless Communications.
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Our paper “Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks” is accepted to IEEE ICASSP 2024.
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Our paper “Enhanced Backpressure Routing with Wireless Link Features” has been accepted to IEEE CAMSAP 2023.
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Paper “Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks” is submitted to IEEE ICASSP 2024.
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Our paper “Delay-aware Backpressure Routing Using Graph Neural Networks” has been accepted to IEEE ICASSP 2023.
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This work arXiv has been accepted to IEEE Transactions on Wireless Communications (Early Access). The preprint arXiv will be updated in the next few days.
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Our papers “Delay-Oriented Distributed Scheduling Using Graph Neural Networks” and “Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks” are both accepted by ICASSP 2022.
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Preprint: https://arxiv.org/abs/2203.14339
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Preprint: https://arxiv.org/pdf/2111.07017
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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.
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Our paper “Distributed Scheduling using Graph Neural Networks” is presented at at 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
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“Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks” is now accepted to IEEE Journal on Selected Areas in Communications, and will appear in Volumne: 39, Issue: 8, Aug. 2021.
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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.
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“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.
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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.
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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.
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I moved to Houston, Texas–a city with no winter– with everything fitted in a sedan.
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Pushing the boundary is not easy
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Smart Barriers Show Potential To Reduce ‘Run-Off-Road’ Vehicle Crashes
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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
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US Ignite, Inc. Announces Lincoln, Nebraska will Join Rapidly Growing Network of Smart Gigabit Communities, November 29, 2017
