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Zhongyuan Zhao

7 accepted papers

2025

Joint Task Offloading and Routing in Wireless Multi-hop Networks Using Biased Backpressure Algorithm

ICASSP 2025accepted

A significant challenge for computation offloading in wireless multi-hop networks is the complex interactions among traffic flows in the presence of interference. Existing approaches often ignore these key effects and/or rely on outdated queueing and channel state information. To fill these gaps, we…

Cited by 0SourceScholar
2024

Congestion-Aware Distributed Task Offloading in Wireless Multi-Hop Networks Using Graph Neural Networks

ICASSP 2024accepted

Computational offloading has become an enabling component for edge intelligence in mobile and smart devices. Existing offloading schemes mainly focus on mobile devices and servers, while ignoring the potential network congestion caused by tasks from multiple mobile devices, especially in wireless mu…

Cited by 0SourceScholar
2023

Delay-Aware Backpressure Routing Using Graph Neural Networks

ICASSP 2023accepted

We propose a throughput-optimal biased backpressure (BP) algorithm for routing, where the bias is learned through a graph neural network that seeks to minimize end-to-end delay. Classical BP routing provides a simple yet powerful distributed solution for resource allocation in wireless multi-hop net…

Cited by 0SourceScholar
2023

Graph-based Deterministic Policy Gradient for Repetitive Combinatorial Optimization Problems

ICLR 2023poster

We propose an actor-critic framework for graph-based machine learning pipelines with non-differentiable blocks, and apply it to repetitive combinatorial optimization problems (COPs) under hard constraints. Repetitive COP refers to problems to be solved repeatedly on graphs of the same or slowly chan…

2022

Delay-Oriented Distributed Scheduling Using Graph Neural Networks

ICASSP 2022accepted

In wireless multi-hop networks, delay is an important metric for many applications. However, the max-weight scheduling algorithms in the literature typically focus on instantaneous optimality, in which the schedule is selected by solving a maximum weighted independent set (MWIS) problem on the inter…

Cited by 0SourceScholar
2022

Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks

ICASSP 2022accepted

Distributed scheduling algorithms for throughput or utility maximization in dense wireless multi-hop networks can have overwhelmingly high overhead, causing increased congestion, energy consumption, radio footprint, and security vulnerability. For wireless networks with dense connectivity, we propos…

Cited by 0SourceScholar
2021

Distributed Scheduling Using Graph Neural Networks

ICASSP 2021accepted

A fundamental problem in the design of wireless networks is to efficiently schedule transmission in a distributed manner. The main challenge stems from the fact that optimal link scheduling involves solving a maximum weighted independent set (MWIS) problem, which is NP-hard. For practical link sched…

Cited by 0SourceScholar