DRL solves a time-division joint SAR and secure communication problem to maximize worst-case secrecy rate by tracking eavesdroppers with cognitive SAR ATI and adapting beamforming plus jamming, outperforming equal-aperture and random baselines in simulations.
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cs.IT 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Using stochastic geometry, the paper models sensing-assisted UAV networks over MPLP roads and derives detection probability, coverage probability, and non-monotonic rate coverage with altitude.
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Deep Reinforcement Learning for Cognitive Time-Division Joint SAR and Secure Communications
DRL solves a time-division joint SAR and secure communication problem to maximize worst-case secrecy rate by tracking eavesdroppers with cognitive SAR ATI and adapting beamforming plus jamming, outperforming equal-aperture and random baselines in simulations.
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Modeling and Analysis of Sensing Assisted UAV Networks for Urban Vehicular Communications
Using stochastic geometry, the paper models sensing-assisted UAV networks over MPLP roads and derives detection probability, coverage probability, and non-monotonic rate coverage with altitude.