UAV Anti-Jamming Video Transmissions With QoE Guarantee: A Reinforcement Learning-Based Approach

Xiao, Liang; Ding, Yong; Huang, Jinhao; Tang, Yuliang; Dai, Huaiyu

doi:10.1109/tcomm.2021.3087787

Cited by 54 publications

(8 citation statements)

References 57 publications

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“…[45] put forward a Bayesian game-theoretic mitigation strategy. [46] introduces a reinforcement learning-based technique for addressing jamming attacks. Similarly, [47] proposes a 2-D mobile communication scheme using a deep Q-network approach with deep convolutional neural networks and macro-action techniques to expedite learning in dynamic scenarios.…”

Section: E Reactive Jamming Attackmentioning

confidence: 99%

BBAD: Blockchain-Backed Assault Detection for Cyber Physical Systems

Anwar,

Tariq,

Ashraf

et al. 2024

IEEE Access

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Cybersecurity challenges pose a significant threat to Healthcare Cyber Physical Systems (CPS) because they heavily rely on wireless communication. Particularly, jamming attacks can severely disrupt the integrity of these CPS networks. This research introduces a decentralized system to address this issue. Therefore, this paper suggested a system that leverages trust and blockchain technology to detect jamming attacks in healthcare CPS effectively. It proposes a layered model to improve CPS networks' lifetime and performance. In smart healthcare environments, it ensures secure and reliable communication between sensor nodes, wearable sensors, medical devices, and monitoring systems. Results show that the suggested approach outperforms the baseline model in identifying and minimizing jamming assaults, with an average percentage difference of 15.71% more detection rate, 20.21% less packet loss rates, 16.65% less node-level energy consumption, reduced network latency of 8.29%, and 9.63% more network throughput.

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Section: E Reactive Jamming Attackmentioning

confidence: 99%

BBAD: Blockchain-Backed Assault Detection for Cyber Physical Systems

Anwar,

Tariq,

Ashraf

et al. 2024

IEEE Access

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“…In the same time, mobile edge computing (MEC) has become one of the most advanced technologies for reducing communication latency and energy consumption [12], [13]. For example, MEC could be used for video transmission to suppress jamming [14], where the compression parameter and power control were optimized by reinforcement learning. Besides, similar concept was used to decide offloading against jamming attacks and interference in [15], which could achieve a significant reduction in latency and energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Relay-Assisted Federated Edge Learning: Performance Analysis and System Optimization

Chen

Fan

Lei

et al. 2023

IEEE Trans. Commun.

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In this paper, we study a relay-assisted federated edge learning (FEEL) network under latency and bandwidth constraints. In this network, N users collaboratively train a global model assisted by M intermediate relays and one edge server. We firstly propose partial aggregation and spectrum resource multiplexing at the relays in order to improve the communication of the relay-assisted FEEL system. Furthermore, we derive analytical and asymptotic expressions of the system outage probability and convergence rate. For the purpose of improving the system performance, we further optimize the relayassisted FEEL network by maximizing the number of users who participate in each round of federated learning, through allocation of the wireless bandwidth among users and relays. Specifically, two bandwidth allocation (BA) schemes have been proposed, assuming either instantaneous or statistical channel state information (CSI). Simulations show the advantages of the proposed BA schemes over other benchmarks, regarding the accuracy and convergence rate of the considered relay-assisted FEEL network.

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“…An online dualtime scale power distribution algorithm is proposed, using multiagent Q-learning [10]. In addition, UAVs can use RL-based antijamming transmission schemes to adaptively counter external interference attacks [21]. The environment of VLC-based UDN is composed of APs, user equipment (UE), and time-varying channel states and ICI.…”

Section: Introductionmentioning

confidence: 99%

Power and Interference Control for VLC-Based UDN: A Reinforcement Learning Approach

Tang¹

2023

Preprint

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Visible light communication (VLC) has been widely applied as a promising solution for modern short range communication. When it comes to the deployment of LED arrays in VLC networks, the emerging ultra-dense network (UDN) technology can be adopted to expand the VLC network's capacity. However, the problem of inter-cell interference (ICI) mitigation and efficient power control in the VLC-based UDN is still a critical challenge. To this end, a reinforcement learning (RL) based VLC UDN architecture is devised in this paper. The deployment of the cells is optimized via spatial reuse to mitigate ICI. An RL-based algorithm is proposed to dynamically optimize the policy of power and interference control, maximizing the system utility in the complicated and dynamic environment. Simulation results demonstrate the superiority of the proposed scheme, it increase the system utility and achievable data rate while reducing the energy consumption and ICI, which outperforms the benchmark scheme.

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UAV Anti-Jamming Video Transmissions With QoE Guarantee: A Reinforcement Learning-Based Approach

Cited by 54 publications

References 57 publications

BBAD: Blockchain-Backed Assault Detection for Cyber Physical Systems

BBAD: Blockchain-Backed Assault Detection for Cyber Physical Systems

Relay-Assisted Federated Edge Learning: Performance Analysis and System Optimization

Power and Interference Control for VLC-Based UDN: A Reinforcement Learning Approach

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