A new anti-jamming strategy based on deep reinforcement learning for MANET

Yingying Xu, Ming Lei, Min Li, Minjian Zhao, Bing Hu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

5 Citations (Scopus)


Mobile Ad-hoc Network (MANET) is a self-configuring network that is widely used but vulnerable to the malicious jammers in practice. In this paper, we consider a jamming channel problem in MANET where a jammer intermittently interrupts the communication channels and the transmitter needs to determine which time slot to send data in order to avoid the interruption. Learning from the historical experience, a Deep Q-Network (DQN) based approach is proposed to generate transmission decisions at the transmitter. In addition, a variant of DQN, termed adaptive DQN, is introduced to cope with the change of jamming conditions. The simulation results demonstrate that the proposed scheme can learn an optimal policy to guide the transmitter to avoid jamming more quickly and efficiently than a Q-learning baseline. Moreover, the effectiveness and robustness of the adaptive DQN is also numerically verified.

Original languageEnglish
Title of host publication2019 IEEE 89th Vehicular Technology Conference (VTC Spring)
Subtitle of host publicationproceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781728112176, 9781728112169
ISBN (Print)9781728112183
Publication statusPublished - 2019
Event89th IEEE Vehicular Technology Conference, VTC Spring 2019 - Kuala Lumpur, Malaysia
Duration: 28 Apr 20191 May 2019

Publication series

ISSN (Print)1090-3038
ISSN (Electronic)2577-2465


Conference89th IEEE Vehicular Technology Conference, VTC Spring 2019
CityKuala Lumpur


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