141 citations · 438 across the 53 of their papers we have counts for
4 papers · 1 filter
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…
Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learning
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1
In this paper, we address task-oriented (or goal-oriented) communications where an encoder at the transmitter learns compressed latent representations of data, which are then trans…
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…
Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning
Xueyuan Wang, M. Cenk Gursoy, Tugba Erpek +1
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks. In this paper, we aim to find collision-free paths for multiple cellular-connected UAVs, w…