385 citations · 2k across the 117 of their papers we have counts for
43 papers · 1 filter
Mitigating Subpopulation Bias for Fair Network Topology Inference
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We consider fair network topology inference from nodal observations. Real-world networks often exhibit biased connections based on sensitive nodal attributes. Hence, different subp…
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…
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…
ML-Based Feedback-Free Adaptive MCS Selection for Massive Multi-User MIMO
Qing An, Mehdi Zafari, Chris Dick +3
As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulati…
Bayesian topology inference on partially known networks from input-output pairs
Martín Sevilla, Santiago Segarra
We propose a sampling algorithm to perform system identification from a set of input-output graph signal pairs. The dynamics of the systems we study are given by a partially known…
Joint Network Topology Inference in the Presence of Hidden Nodes
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations. While most joint inference methods assume that observations are avail…
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…
Signal Processing on Product Spaces
T. Mitchell Roddenberry, Vincent P. Grande, Florian Frantzen +2
We establish a framework for signal processing on product spaces of simplicial and cellular complexes. For simplicity, we focus on the product of two complexes representing time an…
Windowed Fourier Analysis for Signal Processing on Graph Bundles
T. Mitchell Roddenberry, Santiago Segarra
We consider the task of representing signals supported on graph bundles, which are generalizations of product graphs that allow for "twists" in the product structure. Leveraging th…
Unsupervised Learning of Sampling Distributions for Particle Filters
Fernando Gama, Nicolas Zilberstein, Martin Sevilla +2
Accurate estimation of the states of a nonlinear dynamical system is crucial for their design, synthesis, and analysis. Particle filters are estimators constructed by simulating tr…
Joint graph learning from Gaussian observations in the presence of hidden nodes
Samuel Rey, Madeline Navarro, Andrei Buciulea +2
Graph learning problems are typically approached by focusing on learning the topology of a single graph when signals from all nodes are available. However, many contemporary setups…
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 Filters for Signal Processing and Machine Learning on Graphs
Elvin Isufi, Fernando Gama, David I. Shuman +1
Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and mac…
Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the \emph{underdamped} Langevin (stochastic) dynamic. Our detector achieves state-of-the…
Enhanced graph-learning schemes driven by similar distributions of motifs
Samuel Rey, T. Mitchell Roddenberry, Santiago Segarra +1
This paper looks at the task of network topology inference, where the goal is to learn an unknown graph from nodal observations. One of the novelties of the approach put forth is t…
Annealed Langevin Dynamics for Massive MIMO Detection
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
Solving the optimal symbol detection problem in multiple-input multiple-output (MIMO) systems is known to be NP-hard. Hence, the objective of any detector of practical relevance is…
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…
Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Hence, the objective of any detector of practical relevance is to get r…
On Local Distributions in Graph Signal Processing
T. Mitchell Roddenberry, Fernando Gama, Richard G. Baraniuk +1
Graph filtering is the cornerstone operation in graph signal processing (GSP). Thus, understanding it is key in developing potent GSP methods. Graph filters are local and distribut…
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…
Robust MIMO Detection using Hypernetworks with Learned Regularizers
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Recently, there has been a growing interest to get reasonably close to…
A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood Filters
Victor M. Tenorio, Samuel Rey, Fernando Gama +2
Graph convolutional neural networks (GCNNs) are popular deep learning architectures that, upon replacing regular convolutions with graph filters (GFs), generalize CNNs to irregular…
Stability Analysis of Unfolded WMMSE for Power Allocation
Arindam Chowdhury, Fernando Gama, Santiago Segarra
Power allocation is one of the fundamental problems in wireless networks and a wide variety of algorithms address this problem from different perspectives. A common element among t…
Hodgelets: Localized Spectral Representations of Flows on Simplicial Complexes
T. Mitchell Roddenberry, Florian Frantzen, Michael T. Schaub +1
We develop wavelet representations for edge-flows on simplicial complexes, using ideas rooted in combinatorial Hodge theory and spectral graph wavelets. We first show that the Hodg…