Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Thornton, Charles E."'
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
Autor:
Thornton, Charles E. III
Society's newfound dependence on wireless transmission systems has driven demand for access to the electromagnetic (EM) spectrum to an all-time high. In particular, wireless applications related to the fifth generation (5G) of cellular technology alo
Externí odkaz:
http://hdl.handle.net/10919/98788