UAV Relay in VANETs Against Smart Jamming With Reinforcement Learning

Xiao, Liang; Lu, Xiaozhen; Xu, Ding; Tang, Yuliang; Wang, Lei; Zhuang, Weihua

doi:10.1109/tvt.2018.2789466

Cited by 247 publications

(31 citation statements)

References 30 publications

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“…Next, the issue of smart attacks on UAV-enhanced vehicular ad hoc networks (VANETs) has been studied in Reference [52]. There, a UAV acted as a relay for forwarding the messages of an on-board unit (OBU) to a roadside unit (RSU) when the latter experiences severe interference or jamming.…”

Section: Security and Safety Issuesmentioning

confidence: 99%

“…In addition, the anti-jamming PHC-based algorithm of Reference [50] can be further developed to provide an anti-jamming mechanism for VANETs at the initial phase of the learning process, while achieving low-complexity RL solutions are necessary to improve PLS performance against smart jammers. Next, in GPS-spoofing scenarios [52], online learning can further improve the security provided by the NN, while unsupervised ML, capable of handling unlabeled data, can perform classification prior to processing, reducing the delay and increasing the accuracy. To further improve the performance of current ML-inspired detection methods, future work could be devoted to combining these methods with other conventional detection techniques, such as camera images and videos, radar echoes, and acoustic recordings, and to exploiting the advantages of each method.…”

Section: Open Issuesmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Machine-Learning Techniques for UAV-Based Communications

Bithas

Michailidis

Νομικός

et al. 2019

Sensors

243

144

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Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

show abstract

Section: Security and Safety Issuesmentioning

confidence: 99%

Section: Open Issuesmentioning

confidence: 99%

A Survey on Machine-Learning Techniques for UAV-Based Communications

Bithas

Michailidis

Νομικός

et al. 2019

Sensors

243

144

View full text Add to dashboard Cite

show abstract

“…Considering the impact of the channel estimation error, a Q-learning based power control algorithm using non-cooperative game theory was proposed to suppress the joint smart jamming attack [9]. In [10], the authors used UAVs to relay the messages and improve the communication performance of VANETs with a Q-learning based scheme. The application areas of Q-learning algorithms continue to expand.…”

Section: Related Workmentioning

confidence: 99%

Smart Anti-jamming Mobile Communication for Cloud and Edge-Aided UAV Network

2020

KSII TIIS

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“…This method could generate the optimal policy to avoid jamming. A UAV was used in [15] to send onboard units information, which improved the communication performance of vehicular ad-hoc networks in the presence of intelligent jammer. As for swarm UAV, Li Haitao et al proposed a countermeasure model based on airborne cognitive radio and used an improved energy detection method to detect jamming [16].…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems

Wang

et al. 2019

Applied Sciences

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The networked unmanned aerial vehicle (UAV) radar system may exploit inter-UAV cooperation for enhancing information acquisition capabilities, meanwhile its inter-UAV communications may be interfered with by external jammers. This paper is devoted to quantifying and optimizing the anti-jamming performance of networked UAV radar systems in adversarial electromagnetic environments. Firstly, instead of using the conventional metric of signal-to-interference ratio (SIR), this paper explores use of the theory of radar information representation as the basis of evaluating the information acquisition capabilities of the networked UAV radar systems. Secondly, this paper proposes a modified Q-Learning method based on double greedy algorithm to optimize the anti-jamming performance of the networked UAV radar systems, through joint programming in the frequency-motion-antenna domain. Simulation results prove the effectiveness of the algorithm in two different networking scenarios.

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UAV Relay in VANETs Against Smart Jamming With Reinforcement Learning

Cited by 247 publications

References 30 publications

A Survey on Machine-Learning Techniques for UAV-Based Communications

A Survey on Machine-Learning Techniques for UAV-Based Communications

Smart Anti-jamming Mobile Communication for Cloud and Edge-Aided UAV Network

Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems

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