activity
20072026
most citedJoint Design and Separation Principle for Opportunistic Spectrum Access in the Presence of Sensing Errors

433 citations · 1.3k across the 96 of their papers we have counts for

collaborators
Showing eess.SPShow all

14 papers · 1 filter

eess.SP2025

Topology Identification and Inference over Graphs

Gonzalo Mateos, Yanning Shen, Georgios B. Giannakis +1

Topology identification and inference of processes evolving over graphs arise in timely applications involving brain, transportation, financial, power, as well as social and inform…

eess.SP2024★ 1 cited

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…

eess.SP2023

Joint channel estimation and data detection in massive MIMO systems based on diffusion models

Nicolas Zilberstein, Ananthram Swami, Santiago Segarra

We propose a joint channel estimation and data detection algorithm for massive multilple-input multiple-output systems based on diffusion models. Our proposed method solves the bli…

eess.SP2023

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…

eess.SP2022

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…

eess.SP2022

Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks

Zhongyuan Zhao, Ananthram Swami, Santiago Segarra

Distributed scheduling algorithms for throughput or utility maximization in dense wireless multi-hop networks can have overwhelmingly high overhead, causing increased congestion, e…

eess.SP2021

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…

eess.SP2021★ 52 cited

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…

eess.SP2020

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…

eess.SP2020

Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networks

Gojko Cutura, Boning Li, Ananthram Swami +1

We study the temporal reconstruction of epidemics evolving over networks. Given partial or aggregated temporal information of the epidemic, our goal is to estimate the complete evo…

eess.SP2020

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…

eess.SP2020★ 6 cited

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…

eess.SP2020

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…

eess.SP2020

Topology Inference with Multivariate Cumulants: The Möbius Inference Algorithm

Kevin D. Smith, Saber Jafarpour, Ananthram Swami +1

Many tasks regarding the monitoring, management, and design of communication networks rely on knowledge of the routing topology. However, the standard approach to topology mapping-…