no code implementations • 2 Apr 2024 • Aditya Deshmukh, Venugopal V. Veeravalli, Gunjan Verma
Our goal is to design a feature compression {scheme} that can adapt to the varying communication constraints, while maximizing the inference performance at the fusion center.
1 code implementation • 18 Jan 2024 • Arindam Chowdhury, Santiago Paternain, Gunjan Verma, Ananthram Swami, Santiago Segarra
The problem of optimal power allocation -- for maximizing a given network utility metric -- under instantaneous constraints has recently gained significant popularity.
1 code implementation • 5 Dec 2023 • Zhongyuan Zhao, Jake Perazzone, Gunjan Verma, Santiago Segarra
Computational offloading has become an enabling component for edge intelligence in mobile and smart devices.
no code implementations • 11 Jun 2023 • Boning Li, Timofey Efimov, Abhishek Kumar, Jose Cortes, Gunjan Verma, Ananthram Swami, Santiago Segarra
Network digital twins (NDTs) facilitate the estimation of key performance indicators (KPIs) before physically implementing a network, thereby enabling efficient optimization of the network configuration.
1 code implementation • 2 Apr 2023 • Arindam Chowdhury, Gunjan Verma, Ananthram Swami, Santiago Segarra
We develop an efficient and near-optimal solution for beamforming in multi-user multiple-input-multiple-output single-hop wireless ad-hoc interference networks.
no code implementations • 19 Nov 2022 • Zhongyuan Zhao, Bojan Radojicic, Gunjan Verma, Ananthram Swami, Santiago Segarra
In this work, we improve upon the widely-used metric of hop distance (and its variants) for the shortest path bias by introducing a bias based on the link duty cycle, which we predict using a graph convolutional neural network.
no code implementations • 27 Jan 2022 • 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.
1 code implementation • 13 Nov 2021 • Zhongyuan Zhao, Gunjan Verma, Ananthram Swami, Santiago Segarra
In wireless multi-hop networks, delay is an important metric for many applications.
1 code implementation • 12 Sep 2021 • Zhongyuan Zhao, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra
Test results on medium-sized wireless networks show that our centralized heuristic can reach a near-optimal solution quickly, and our distributed heuristic based on a shallow GCN can reduce by nearly half the suboptimality gap of the distributed greedy solver with minimal increase in complexity.
1 code implementation • 18 Nov 2020 • Zhongyuan Zhao, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra
In small- to middle-sized wireless networks with tens of links, even a shallow GCN-based MWIS scheduler can leverage the topological information of the graph to reduce in half the suboptimality gap of the distributed greedy solver with good generalizability across graphs and minimal increase in complexity.
1 code implementation • 18 Nov 2020 • Arindam Chowdhury, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
1 code implementation • 18 Nov 2020 • Abhishek Kumar, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra
We study the problem of adaptive contention window (CW) design for random-access wireless networks.
no code implementations • 2 Nov 2020 • Ryan Sheatsley, Nicolas Papernot, Michael Weisman, Gunjan Verma, Patrick McDaniel
To assess how these algorithms perform, we evaluate them in constrained (e. g., network intrusion detection) and unconstrained (e. g., image recognition) domains.
1 code implementation • 22 Sep 2020 • Arindam Chowdhury, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
2 code implementations • NeurIPS 2019 • Gunjan Verma, Ananthram Swami
Modern machine learning systems are susceptible to adversarial examples; inputs which clearly preserve the characteristic semantics of a given class, but whose classification is (usually confidently) incorrect.
1 code implementation • NeurIPS 2019 • Susmit Jha, Sunny Raj, Steven Fernandes, Sumit K. Jha, Somesh Jha, Brian Jalaian, Gunjan Verma, Ananthram Swami
These experiments demonstrate the effectiveness of the ABC metric to make DNNs more trustworthy and resilient.
no code implementations • 14 Mar 2019 • Susmit Jha, Sunny Raj, Steven Lawrence Fernandes, Sumit Kumar Jha, Somesh Jha, Gunjan Verma, Brian Jalaian, Ananthram Swami
We study the robustness of machine learning models on benign and adversarial inputs in this neighborhood.