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Deep Reinforcement Learning-Based Anti-Jamming Algorithm Using Dual Action Network
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DOI：10.1109/TWC.2022.3227575.png)
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Abstract
En 中文
Due to the open nature of wireless communication, malicious electromagnetic jamming has long been a severe threat to the establishment and stability of communication links. To address this anti-jamming problem, a Markov decision process (MDP) with a two-dimensional action space consisting of transmit frequency and power is proposed in this paper, modeling the interaction between a normal communication link and the presence of malicious jammers in a frequency hopping (FH) communication system. Furthermore, we also prove the existence of the deterministic optimal policy of the proposed model theoretically. To obtain a policy for the communication link to avoid being jammed, the Dual Action Network-Based Deep Reinforcement Learning Algorithm, and Action Feedback Mechanism are proposed. The energy consumption and frequency switching overhead are considered and evaluated in both the proposed model and the algorithm. Finally, the proposed model and algorithm are verified not only in a virtual simulation environment but also in the field testing environment. The result suggests that the proposed algorithm is of great practical value for solving anti-jamming problems.
Keywords:
Jamming
Wireless communication
Games
Receivers
Electromagnetics
Testing
Switches
Anti-jamming
Markov decision process
Index Terms
deep reinforcement learning
dual action network
action feedback mechanism
power efficiency
frequency switching overhead
field testing
Journal
IF:
10.7
Papers:
1.3W
Citations:
5.3W
Organization
No organization information available
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