52 citations · 100 across the 20 of their papers we have counts for
24 papers
Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version)
Zhongyuan Zhao, Gunjan Verma, Ananthram Swami +1
In wireless networks characterized by dense connectivity, the significant signaling overhead generated by distributed link scheduling algorithms can exacerbate issues like congesti…
Joint Task Offloading and Routing in Wireless Multi-hop Networks Using Biased Backpressure Algorithm
Zhongyuan Zhao, Jake Perazzone, Gunjan Verma +3
A significant challenge for computation offloading in wireless multi-hop networks is the complex interaction among traffic flows in the presence of interference. Existing approache…
Fully Distributed Online Training of Graph Neural Networks in Networked Systems
Rostyslav Olshevskyi, Zhongyuan Zhao, Kevin Chan +3
Graph neural networks (GNNs) are powerful tools for developing scalable, decentralized artificial intelligence in large-scale networked systems, such as wireless networks, power gr…
Ant Backpressure Routing for Wireless Multi-hop Networks with Mixed Traffic Patterns
Negar Erfaniantaghvayi, Zhongyuan Zhao, Kevin Chan +3
A mixture of streaming and short-lived traffic presents a common yet challenging scenario for Backpressure routing in wireless multi-hop networks. Although state-of-the-art shortes…
GLANCE: Graph-based Learnable Digital Twin for Communication Networks
Boning Li, Gunjan Verma, Timofey Efimov +2
As digital twins (DTs) to physical communication systems, network simulators can aid the design and deployment of communication networks. However, time-consuming simulations must b…
Biased Backpressure Routing Using Link Features and Graph Neural Networks
Zhongyuan Zhao, Bojan Radojičić, Gunjan Verma +2
To reduce the latency of Backpressure (BP) routing in wireless multi-hop networks, we propose to enhance the existing shortest path-biased BP (SP-BP) and sojourn time-based backlog…
Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks
Zhongyuan Zhao, Jake Perazzone, Gunjan Verma +1
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,…
Learning Non-myopic Power Allocation in Constrained Scenarios
Arindam Chowdhury, Santiago Paternain, Gunjan Verma +2
We propose a learning-based framework for efficient power allocation in ad hoc interference networks under episodic constraints. The problem of optimal power allocation -- for maxi…
Enhanced Backpressure Routing Using Wireless Link Features
Zhongyuan Zhao, Gunjan Verma, Ananthram Swami +1
Backpressure (BP) routing is a well-established framework for distributed routing and scheduling in wireless multi-hop networks. However, the basic BP scheme suffers from poor end-…
Learnable Digital Twin for Efficient Wireless Network Evaluation
Boning Li, Timofey Efimov, Abhishek Kumar +4
Network digital twins (NDTs) facilitate the estimation of key performance indicators (KPIs) before physically implementing a network, thereby enabling efficient optimization of the…
Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks
Arindam Chowdhury, Gunjan Verma, Ananthram Swami +1
We develop an efficient and near-optimal solution for beamforming in multi-user multiple-input-multiple-output single-hop wireless ad-hoc interference networks. Inspired by the wei…
Delay-aware Backpressure Routing Using Graph Neural Networks
Zhongyuan Zhao, Bojan Radojicic, Gunjan Verma +2
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. Cl…
Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks
Boning Li, Gunjan Verma, Santiago Segarra
We develop a novel graph-based trainable framework to maximize the weighted sum energy efficiency (WSEE) for power allocation in wireless communication networks. To address the non…
Delay-Oriented Distributed Scheduling Using Graph Neural Networks
Zhongyuan Zhao, Gunjan Verma, Ananthram Swami +1
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…
ML-aided power allocation for Tactical MIMO
Arindam Chowdhury, Gunjan Verma, Chirag Rao +2
We study the problem of optimal power allocation in single-hop multi-antenna ad-hoc wireless networks. A standard technique to solve this problem involves optimizing a tri-convex f…
Link Scheduling using Graph Neural Networks
Zhongyuan Zhao, Gunjan Verma, Chirag Rao +2
Efficient scheduling of transmissions is a key problem in wireless networks. The main challenge stems from the fact that optimal link scheduling involves solving a maximum weighted…
Efficient power allocation using graph neural networks and deep algorithm unfolding
Arindam Chowdhury, Gunjan Verma, Chirag Rao +2
We study the problem of optimal power allocation in a single-hop ad hoc wireless network. In solving this problem, we propose a hybrid neural architecture inspired by the algorithm…
Adaptive Contention Window Design using Deep Q-learning
Abhishek Kumar, Gunjan Verma, Chirag Rao +2
We study the problem of adaptive contention window (CW) design for random-access wireless networks. More precisely, our goal is to design an intelligent node that can dynamically a…
Distributed Scheduling using Graph Neural Networks
Zhongyuan Zhao, Gunjan Verma, Chirag Rao +2
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 s…
Adversarial Examples in Constrained Domains
Ryan Sheatsley, Nicolas Papernot, Michael Weisman +2
Machine learning algorithms have been shown to be vulnerable to adversarial manipulation through systematic modification of inputs (e.g., adversarial examples) in domains such as i…
Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation
Arindam Chowdhury, Gunjan Verma, Chirag Rao +2
We study the problem of optimal power allocation in a single-hop ad hoc wireless network. In solving this problem, we depart from classical purely model-based approaches and propos…
Rapid Top-Down Synthesis of Large-Scale IoT Networks
Pradipta Ghosh, Jonathan Bunton, Dimitrios Pylorof +6
Advances in optimization and constraint satisfaction techniques, together with the availability of elastic computing resources, have spurred interest in large-scale network verific…
Information Flow Optimization in Inference Networks
Aditya Deshmukh, Jing Liu, Venugopal V. Veeravalli +1
The problem of maximizing the information flow through a sensor network tasked with an inference objective at the fusion center is considered. The sensor nodes take observations, c…
Attribution-driven Causal Analysis for Detection of Adversarial Examples
Susmit Jha, Sunny Raj, Steven Lawrence Fernandes +5
Attribution methods have been developed to explain the decision of a machine learning model on a given input. We use the Integrated Gradient method for finding attributions to defi…