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cs.LG2025
How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning
Yalin E. Sagduyu, Tugba Erpek, Kemal Davaslioglu +1
This paper studies the problem of mitigating reactive jamming, where a jammer adopts a dynamic policy of selecting channels and sensing thresholds to detect and jam ongoing transmi…
cs.LG2024
Augmenting Training Data with Vector-Quantized Variational Autoencoder for Classifying RF Signals
Srihari Kamesh Kompella, Kemal Davaslioglu, Yalin E. Sagduyu +1
Radio frequency (RF) communication has been an important part of civil and military communication for decades. With the increasing complexity of wireless environments and the growi…
cs.LG2024
Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation
Kemal Davaslioglu, Sastry Kompella, Tugba Erpek +1
Deep Reinforcement Learning (DRL) has been highly effective in learning from and adapting to RF environments and thus detecting and mitigating jamming effects to facilitate reliabl…