Zobrazeno 1 - 10
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pro vyhledávání: '"Thornton, Charles A"'
Autor:
Brown, Samuel B., Young, Stephen, Wagenknecht, Adam, Jakubisin, Daniel, Thornton, Charles E., Orndorff, Aaron, Headley, William C.
Denoising autoencoders for signal processing applications have been shown to experience significant difficulty in learning to reconstruct radio frequency communication signals, particularly in the large sample regime. In communication systems, this c
Externí odkaz:
http://arxiv.org/abs/2410.03423
We study jamming of an OFDM-modulated signal which employs forward error correction coding. We extend this to leverage reinforcement learning with a contextual bandit to jam a 5G-based system implementing some aspects of the 5G protocol. This model i
Externí odkaz:
http://arxiv.org/abs/2409.11191
Autor:
Thornton, Charles E., Allen, Evan, Jones, Evar, Jakubisin, Daniel, Templin, Fred, Liu, Lingjia
This work investigates the potential of 5G and beyond sidelink (SL) communication to support multi-hop tactical networks. We first provide a technical and historical overview of 3GPP SL standardization activities, and then consider applications to cu
Externí odkaz:
http://arxiv.org/abs/2309.16628
This paper attempts to characterize the kinds of physical scenarios in which an online learning-based cognitive radar is expected to reliably outperform a fixed rule-based waveform selection strategy, as well as the converse. We seek general insights
Externí odkaz:
http://arxiv.org/abs/2304.11233
When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy? We seek insight regarding this question by examining a dynamic spectrum access scenario, i
Externí odkaz:
http://arxiv.org/abs/2212.00597
This paper describes important considerations and challenges associated with online reinforcement-learning based waveform selection for target identification in frequency modulated continuous wave (FMCW) automotive radar systems. We present a novel l
Externí odkaz:
http://arxiv.org/abs/2212.00615
We consider a scenario where a fusion center must decide which updates to receive during each update period in a communication-limited cognitive radar network. When each radar node in the network only is able to obtain noisy state measurements for a
Externí odkaz:
http://arxiv.org/abs/2211.11587
A key component of cognitive radar is the ability to generalize, or achieve consistent performance across a range of sensing environments, since aspects of the physical scene may vary over time. This presents a challenge for learning-based waveform s
Externí odkaz:
http://arxiv.org/abs/2207.06917
It has been shown (Amuru et al. 2015) that online learning algorithms can be effectively used to select optimal physical layer parameters for jamming against digital modulation schemes without a priori knowledge of the victim's transmission strategy.
Externí odkaz:
http://arxiv.org/abs/2207.02365
Online selection of optimal waveforms for target tracking with active sensors has long been a problem of interest. Many conventional solutions utilize an estimation-theoretic interpretation, in which a waveform-specific Cram\'{e}r-Rao lower bound on
Externí odkaz:
http://arxiv.org/abs/2202.05294