Search Results for author:
Found 17 papers, 5 papers with code
Radio Maps for Beam Alignment in mmWave Communications with Location Uncertainty
Next generation communication systems require accurate beam alignment to counteract the impairments that characterize propagation in high-frequency bands.
Spoofing Detection in the Physical Layer with Graph Neural Networks
In a spoofing attack, a malicious actor impersonates a legitimate user to access or manipulate data without authorization.
Radio Map Estimation: Empirical Validation and Analysis
The main features of the RME problem are analyzed and the capabilities of existing estimators are compared using large measurement datasets collected in this work.
Spoofing Attack Detection in the Physical Layer with Robustness to User Movement
In a spoofing attack, an attacker impersonates a legitimate user to access or modify data belonging to the latter.
Spoofing Attack Detection in the Physical Layer with Commutative Neural Networks
In a spoofing attack, an attacker impersonates a legitimate user to access or tamper with data intended for or produced by the legitimate user.
Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography
Radio maps characterize quantities of interest in radio communication environments, such as the received signal strength and channel attenuation, at every point of a geographical region.
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.
Deep Completion Autoencoders for Radio Map Estimation
In contrast, this paper proposes a novel approach in which the spatial structure of propagation phenomena such as shadowing is learned beforehand from a data set with measurements in other environments.
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.
Online Topology Identification from Vector Autoregressive Time Series
Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multivariate data sets in a format amenable for human interpretation, forecasting, and anomaly detection.
Location-free Spectrum Cartography
Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements.
Kernel-based Inference of Functions over Graphs
The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced.
Inference of Spatio-Temporal Functions over Graphs via Multi-Kernel Kriged Kalman Filtering
Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications.
Randomized Block Frank-Wolfe for Convergent Large-Scale Learning
To circumvent the limitations of existing methods, the present work develops step sizes for RB-FW that enable a flexible selection of the number of blocks to update per iteration while ensuring convergence and feasibility of the iterates.
Kernel-based Reconstruction of Space-time Functions on Dynamic Graphs
Graph-based methods pervade the inference toolkits of numerous disciplines including sociology, biology, neuroscience, physics, chemistry, and engineering.
Learning Power Spectrum Maps from Quantized Power Measurements
Power spectral density (PSD) maps providing the distribution of RF power across space and frequency are constructed using power measurements collected by a network of low-cost sensors.
Kernel-based Reconstruction of Graph Signals
A number of SPoG notions such as bandlimitedness, graph filters, and the graph Fourier transform are naturally accommodated in the kernel framework.
