Search Results for author:
Found 30 papers, 7 papers with code
Sampling and Recovery of Signals on a Simplicial Complex using Neighbourhood Aggregation
In this work, we focus on sampling and recovery of signals over simplicial complexes.
Coded Aperture Radar Imaging Using Reconfigurable Intelligent Surfaces
In this paper, we focus on radar imaging using active sensing with a single transceiver and reconfigurable intelligent surface (RIS).
Clustering with Simplicial Complexes
Then, leveraging the Cheeger inequality, we propose the simplicial spectral clustering algorithm.
Learning to Precode for Integrated Sensing and Communications Systems
In this paper, we present an unsupervised learning neural model to design transmit precoders for integrated sensing and communication (ISAC) systems to maximize the worst-case target illumination power while ensuring a minimum signal-to-interference-plus-noise ratio (SINR) for all the users.
Optimal Placement of Active and Passive Elements in Hybrid RIS-assisted Communication Systems
Hybrid reconfigurable intelligent surfaces (HRIS) are RIS architectures having both active and passive elements.
Quantized Precoding and RIS-Assisted Modulation for Integrated Sensing and Communications Systems
In this paper, we present a novel reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system with 1-bit quantization at the ISAC base station.
Integrated Sensing and Communications with Reconfigurable Intelligent Surfaces
Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless networks, especially when operating at the millimeter-wave (mmWave) and terahertz (THz) frequency bands.
RIS-assisted Integrated Sensing and Communications: A Subspace Rotation Approach
We first show that the sensing and communication (S&C) performance can be jointly improved due to the capability of the RISs to control the ISAC channel.
Generative Models and Learning Algorithms for Core-Periphery Structured Graphs
In this paper, we focus on learning the core scores of a graph from its node attributes and connectivity structure.
Beamforming in Integrated Sensing and Communication Systems with Reconfigurable Intelligent Surfaces
On the other hand, the dual-RIS assisted ISAC system improves both minimum user SINR as well as worst-case target illumination power at the targets, especially when the users and targets are not directly visible.
Beamforming in Hybrid RIS assisted Integrated Sensing and Communication Systems
In this paper, we consider a hybrid reconfigurable intelligent surface (RIS) comprising of active and passive elements to aid an integrated sensing and communication (ISAC) system serving multiple users and targets.
Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs
To overcome the combinatorial complexity of this problem, a dynamic programming approach is proposed to obtain lists of waypoints through areas of large uncertainty in linear time.
Graph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification
We focus on graph classification using a graph neural network (GNN) model that precomputes the node features using a bank of neighborhood aggregation graph operators arranged in parallel.
Faster Rates for the Frank-Wolfe Algorithm Using Jacobi Polynomials
A classic approach to obtain faster rates is to combine previous iterates to obtain the next iterate.
Learning Sparse Graphs with a Core-periphery Structure
In this paper, we focus on learning sparse graphs with a core-periphery structure.
Channel Estimation in MIMO Systems with One-bit Spatial Sigma-delta ADCs
Using the developed noise model, we then present a two-step channel estimation algorithm to estimate a multipath channel parameterized by the gains, angles of arrival (AoAs), and angles of departure (AoDs).
Joint Communication and Radar Sensing with Reconfigurable Intelligent Surfaces
In this paper, we use a reconfigurable intelligent surface (RIS) to enhance the radar sensing and communication capabilities of a mmWave dual function radar communication system.
Self Calibration of Scalar and Vector Sensor Arrays
This joint estimation problem is referred to as self calibration.
Product Graph Learning from Multi-domain Data with Sparsity and Rank Constraints
We assume that the product graph is formed by the Cartesian product of two smaller graphs, which we refer to as graph factors.
Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing
In this work, we explore computational data-driven methods for drug repurposing and propose a dedicated graph neural network (GNN) based drug repurposing model, called Dr-COVID.
Channel Estimation for RIS-assisted Millimeter-wave MIMO Systems
Channel estimation in reconfigurable intelligent surface (RIS) assisted multiple input multiple output (MIMO) communication systems is challenging as pilots cannot be decoded at or transmitted from a RIS with only passive elements.
Multiview Variational Graph Autoencoders for Canonical Correlation Analysis
We present a novel multiview canonical correlation analysis model based on a variational approach.
Millimeter Wave MIMO Channel Estimation with 1-bit Spatial Sigma-delta Analog-to-Digital Converters
This paper focuses on channel estimation for mmWave MIMO systems with 1-bit spatial sigma-delta analog-to-digital converters (ADCs).
Federated Deep Unfolding for Sparse Recovery
This paper proposes a federated learning technique for deep algorithm unfolding with applications to sparse signal recovery and compressed sensing.
Signal Processing
Learning Multi-layer Graphs and a Common Representation for Clustering
In this paper, we focus on graph learning from multi-view data of shared entities for spectral clustering.
Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs
In contrast, this paper proposes a scheme in which a UAV collects measurements along a trajectory.
Hybrid Beamforming for Active Sensing using Sparse Arrays
Simulations demonstrate that a hybrid sparse array with very few elements, and even fewer front ends, can achieve the resolution of a fully digital uniform array at the expense of a longer image acquisition time.
Sampling and Reconstruction of Signals on Product Graphs
In this paper, we consider the problem of subsampling and reconstruction of signals that reside on the vertices of a product graph, such as sensor network time series, genomic signals, or product ratings in a social network.
Sparse Sampling for Inverse Problems with Tensors
We consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors that admit a known multilinear decomposition.
Information Theory Signal Processing Information Theory
Learning Sparse Graphs Under Smoothness Prior
Given the noisy data, we show that the joint sparse graph learning and denoising problem can be simplified to designing only the sparse edge selection vector, which can be solved using convex optimization.
