Game theory and reinforcement learning for anti-jamming defense in wireless communications: Current research, challenges, and solutions

L Jia, N Qi, Z Su, F Chu, S Fang… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Due to the inherently open and shared nature of the wireless channels, wireless
communication networks are vulnerable to jamming attacks, and effective anti-jamming …

Design and implementation of reinforcement learning‐based intelligent jamming system

S Zhang, H Tian, X Chen, Z Du, L Huang… - IET …, 2020 - Wiley Online Library
Here the intelligent jammer issue is studied. With the rapid development of cognitive radio
technology, current cognitive terminals can adaptively or intelligently switch channel by …

Considerations of reinforcement learning within real-time wireless communication systems

AM Jones, WC Headley - MILCOM 2022-2022 IEEE Military …, 2022 - ieeexplore.ieee.org
This paper investigates the practical limitations and considerations of implementing a
reinforcement learning framework within real-time wireless communication systems. Trade …

Intelligent Anti-Jamming Decision With Continuous Action and State in Bivariate Frequency Agility Communication System

Y Zhang, Z Zhao, S Zheng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The conventional frequency hopping (FH) system is susceptible to malicious jamming due to
the prearranged hopping frequency table. In this paper, we develop a bivariate frequency …

基于多智能体深度强化学习的多域协同抗干扰方法研究

张彪, 汪西明, 徐逸凡, 李文, 韩昊, 刘松仪… - 物联网学报, 2022 - infocomm-journal.com
动态的传输需求和有限的缓存空间给恶意干扰环境下的无线数据传输带来巨大挑战.
针对上述问题, 从频域和时域的角度出发, 研究了面向分布式物联网的协同抗干扰信道选择和 …

Bandwidth-efficient frequency hopping based anti-jamming game for cognitive radio assisted wireless sensor networks

K Ibrahim, IM Qureshi, AN Malik… - 2021 IEEE 93rd …, 2021 - ieeexplore.ieee.org
Sensors can be interconnected to form a wireless sensor network (WSN) for monitoring the
environment. However, there is an increasing demand for innovative automation systems …

A hidden anti-jamming method based on deep reinforcement learning

Y Wang, X Liu, M Wang, Y Yu - arXiv preprint arXiv:2012.12448, 2020 - arxiv.org
Most of the current anti-jamming algorithms for wireless communications only consider how
to avoid jamming attacks, but ignore that the communication waveform or frequency action …

[PDF][PDF] 基于快速强化学习的无线通信干扰规避策略

李芳, 熊俊, 赵肖迪, 赵海涛, 魏急波, 苏曼 - 电子与信息学报, 2022 - jeit.ac.cn
针对无线通信环境中存在未知且动态变化的干扰, 该文联合考虑通信信道接入和发射功率控制
提出了基于快速强化学习的未知干扰规避策略, 以确保通信收发端的可靠通信 …

Intelligent Frequency Decision Communication with Two-Agent Deep Reinforcement Learning

X Liu, M Shi, M Wang - Electronics, 2023 - mdpi.com
Traditional intelligent frequency-hopping anti-jamming technologies typically assume the
presence of an ideal control channel. However, achieving this ideal condition in real-world …

基于强化学习的无人机电磁干扰感知与抗干扰传输方法.

李博扬, 刘洋, 万诺天, 许魁, 夏晓晨… - Telecommunication …, 2023 - search.ebscohost.com
无人机对于无线信道的依赖性和无线传播环境的开放性, 导致其通信易受到恶意的电磁干扰.
针对其中恶意的信道跟随干扰, 在感知干扰信道信息的基础上, 将无人机的发射功率和信道选择 …