Avoiding Interference in Multi-Emitter Environments: A Reinforcement Learning Approach-Reference-Cited by-同舟云学术

Avoiding Interference in Multi-Emitter Environments: A Reinforcement Learning Approach

Published:2021-01-10 Issue: Volume: Page:
ISSN:
Container-title:2020 17th European Radar Conference (EuRAD)
language:
Short-container-title:

Author:

Ak Serkan,Bruggenwirth Stefan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9337288/9337294/09337445.pdf?arnumber=9337445

Reference14 articles.

1. Deep Q-Learning with Multiband Sensing for Dynamic Spectrum Access

2. Deep Reinforcement Learning Based Multi-User Anti-Jamming Strategy

3. Data reduction in spatially colored noise using a virtual uniform linear array

4. Removing the target network from deep Q-networks with the Mellowmax operator;kim;Proc of International Conference on Autonomous Agents and Multiagent Systems,2019

5. Adam: A method for stochastic optimization;kingma;Proc Int Conf Learn Represent,0

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Frequency Agile Anti-Interference Technology Based on Reinforcement Learning Using Long Short-Term Memory and Multi-Layer Historical Information Observation;Remote Sensing;2023-11-23

2. Optimal Jamming Frequency Selection for Cognitive Jammer based on Reinforcement Learning;2023 6th International Conference on Electronics Technology (ICET);2023-05-12

3. Multi-Domain Resource Scheduling for Surveillance Radar Anti-Jamming based on Q-Learning;2023 IEEE Radar Conference (RadarConf23);2023-05-01

4. Adaptation of Frequency Hopping Interval for Radar Anti-Jamming Based on Reinforcement Learning;IEEE Transactions on Vehicular Technology;2022-12