Interference suppression using deep learning: Current approaches and open challenges
In light of the finite nature of the wireless spectrum and the increasing demand for spectrum
use arising from recent technological breakthroughs in wireless communication, the problem …
use arising from recent technological breakthroughs in wireless communication, the problem …
The Importance of Data in RF Machine Learning
WH Clark IV - 2022 - vtechworks.lib.vt.edu
While the toolset known as Machine Learning (ML) is not new, several of the tools available
within the toolset have seen revitalization with improved hardware, and have been applied …
within the toolset have seen revitalization with improved hardware, and have been applied …
Detecting Weak Wideband Signals in Interference-Dominated Environments
R Williamson, AAL Beex… - MILCOM 2024-2024 IEEE …, 2024 - ieeexplore.ieee.org
This paper addresses the problem of relatively wideband signal detection near the power
spectral density level of the background noise from baseband data records containing …
spectral density level of the background noise from baseband data records containing …
A Comprehensive Analysis of Deep Learning for Interference Suppression, Sample and Model Complexity in Wireless Systems
TR Oyedare - 2024 - vtechworks.lib.vt.edu
The wireless spectrum is limited and the demand for its use is increasing due to
technological advancements in wireless communication, resulting in persistent interference …
technological advancements in wireless communication, resulting in persistent interference …
On the Use of Uncalibrated Digital Phased Arrays for Blind Signal Separation for Interference Removal in Congested Spectral Bands
LO Lusk - 2023 - vtechworks.lib.vt.edu
With usable spectrum becoming increasingly more congested, the need for robust, adaptive
communications to take advantage of spatially-separated signal sources is apparent …
communications to take advantage of spatially-separated signal sources is apparent …