UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming

Lu, Xiaozhen; Xiao, Liang; Dai, Canhuang; Dai, Huaiyu

doi:10.1109/mwc.001.1900207

Cited by 97 publications

(47 citation statements)

References 12 publications

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“…In [61], a deep reinforcement learning and a weighted least squares algorithm [62] is incorporated to estimate the power of the jamming signal with a convolution neural network (CNN) [63]. In the first step of the proposed approach, a relay power factor is selected based on the bit error rate (BER) and the channel gain.…”

Section: Learning-based Intrusion Detectionmentioning

confidence: 99%

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

2021

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With the rapid growth in technology, the use of Unmanned Aerial Vehicles (UAVs) have increased in civil and military applications including rescue operations, disaster recovery, and military operations. Despite the utility and advantages of UAVs, they may lead to major security breaches in the context of hardware, software, and communication channel, due their ease of use and availability. UAVs are vulnerable to various types of attacks such as spoofing, false data injection, jamming, fuzzing, availability, confidentiality, and integrity attacks. To overcome these security threats, researchers have been investigating strong security protocols to keep UAVs safe from the attackers. Nevertheless, there are many flaws in the developed protocols which can be exploited by hackers. Therefore, it is becomes crucial to study and analyze the existing security protocols used in UAVs to discover and address their vulnerabilities and weaknesses. The purpose of this study is to explore the vulnerabilities in the security protocols and propose guidelines to improve the security and provide future research directions.

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Section: Learning-based Intrusion Detectionmentioning

confidence: 99%

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

2021

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“…Next, to minimize the cost function, the authors transformed the problem into a maximization case and used a reinforcement learning approach to find the optimal information exchange level between the agencies and the UAV. In another study, Lu, et al, [29] considered a scenario of a cellular network including a UAV, multiple base stations, mobiles users, and a single smart jammer. The authors assumed that the serving base station for the mobile user is under attack by the jammer.…”

Section: A Related Workmentioning

confidence: 99%

UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning

Shamsoshoara

Afghah

Blasch

et al. 2021

IEEE Open J. Commun. Soc.

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The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users. One solution to the problem is using unmanned aerial vehicles to augment the desired communication network. The paper demonstrates the design of a UAV-Assisted Imitation Learning (UnVAIL) communication system that relays the cellular users' information to a neighbor base station. Since the user equipment (UEs) are equipped with buffers with limited capacity to hold packets, UnVAIL alternates between different UEs to reduce the chance of buffer overflow, positions itself optimally close to the selected UE to reduce service time, and uncovers a network pathway by acting as a relay node. UnVAIL utilizes Imitation Learning (IL) as a data-driven behavioral cloning approach to accomplish an optimal scheduling solution. Results demonstrate that UnVAIL performs similar to a human expert knowledge-based planning in communication timeliness, position accuracy, and energy consumption with an accuracy of 97.52% when evaluated on a developed simulator to train the UAV.

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“…Further more, Xiao et al [10] combined the PHC frame and Q-learning algorithm to solve the problem of smart jamming in UAV-aided VANETs without knowing the VANET model and jamming model. The advantages of deep learning are also reflected in UAV-aided networks, on the basis of previous studies, in [19] the authors combined the reinforcement learning algorithm with deep learning techniques to address the issue of dimensional curses.…”

Section: Related Workmentioning

confidence: 99%

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

2020

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UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming

Cited by 97 publications

References 12 publications

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning

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

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