A hierarchical learning approach to anti-jamming channel selection strategies

Yao, Fuqiang; Jia, Luliang; Sun, Youming; Xu, Yuhua; Feng, Shuo; Zhu, Yimin

doi:10.1007/s11276-017-1551-9

Cited by 41 publications

(32 citation statements)

References 39 publications

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“…The objective of our study is to jointly optimize the delay durance and energy consumption. Inspired by the literature [16], the utility function can be defined as profit minus cost. In our system, we hope to make a tradeoff between the total delay and power or bandwidth consumption.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Proof. Referencing literature [16], the details are as follows. We construct a potential function as follows:…”

Section: Definition 2 (Nash Equilibrium) [16]mentioning

confidence: 99%

“…In this subsection, we show the theoretical proof of the algorithm convergence as follows. The specific proof process can refer to literature [16], because the hierarchical framework of the algorithm is similar.…”

Section: The Proof Of the Convergencementioning

confidence: 99%

“…The E-UAVs act as followers while the R-UAV acts as the leader. The backward induction method [16] is used to find the Stackelberg Equilibrium (SE) of the game. The lower level sub-game of followers are solved firstly is proved to be an exact potential game which has at least a Nash Equlibrium (NE).…”

Section: Introductionmentioning

confidence: 99%

“…At last, we default that the stop criterions is satisfying the maximum number of iterations. The proof of convergence for the proposed HLA can be referred from literature [16], which has also proved the HLA can always find a SE.…”

mentioning

confidence: 99%

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Joint Power and Bandwidth Allocation for UAV Backhaul Networks: A Hierarchical Learning Approach

Yang¹,

Yao²,

Sun³

et al. 2018

Preprint

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Unmanned Aerial Vehicles (UAVs) severing as the relay is an effective technology method to extend the coverage. It can also alleviate the congestion and increase the throughput, especially applied in UAV networks. However, since the energy of UAVs is limited and the resources in UAV networks are scarce, how to optimize the network delay performance under these constraints should be well investigated. Besides, the relationship among different resources, e.g. power and bandwidth, is coupled which makes the optimization more complex. This article investigates the problem of joint power and bandwidth allocation in UAV backhaul networks, which considers both the delay performance and the resource utilization efficiency. Considering the heterogeneous locations characteristics of different UAVs, we formulate the optimization problem as a Stackelberg game. The relay UAV acts as the leader and extended UAVs act as followers. Their utility functions take both the delay durance and the resource consumption into account. To capture the competitive relationship among followers, the sub-game is proved to be an exact potential game and exists Nash equilibriums (NE). The Stackelberg Equilibrium (SE) is proved afterwards. We utilize a hierarchical learning algorithm (HLA) to find out the best resource allocation strategies, which also reduces the computational complexity. Simulation results demonstrate the effectiveness of the proposed method.

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Section: Problem Formulationmentioning

confidence: 99%

“…Proof. Referencing literature [16], the details are as follows. We construct a potential function as follows:…”

Section: Definition 2 (Nash Equilibrium) [16]mentioning

confidence: 99%

Section: The Proof Of the Convergencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Joint Power and Bandwidth Allocation for UAV Backhaul Networks: A Hierarchical Learning Approach

Yang¹,

Yao²,

Sun³

et al. 2018

Preprint

Self Cite

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Joint relay and channel selection in relay‐aided anti‐jamming system: A reinforcement learning approach

Huang

Xu²,

Chen

et al. 2021

Trans Emerging Tel Tech

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In this article, a joint relay and channel selection problem is investigated in multi‐relay anti‐jamming communication system. Considering the jamming pattern and the relay node (RN) location distribution are unknown, the relay and channel selection problem is formulated as Markov decision processes (MDPs). Different from the existing research on anti‐jamming communication, in this article, the source node (SN) and all RNs are considered as agents who collaboratively learn the environment and make anti‐jamming decisions. A reinforcement‐learning‐based joint relay and channel selection method is proposed to achieve relay‐aided anti‐jamming communication. Specifically, the SN tries to make the optimal selection of relay which is under the least jamming threat, while the RN will access the jamming‐free channels. Various simulation results show that the algorithm can quickly learn the unknown changing pattern of the jamming environment, and make the effective joint relay and channel decisions to obtain high communication throughput which is close to the optimal.

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Towards faster big data analytics for anti‐jamming applications in vehicular ad‐hoc network

Bangui

Bühnová

et al. 2021

Trans Emerging Tel Tech

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Nowadays, Wireless Vehicular Ad-Hoc Network (VANET) has become a valuable asset for transportation systems. However, this advanced technology is characterized by highly distributed and networked environment, which makes VANET communications vulnerable to malicious jamming attacks. Although Big Data Analytics has been used to solve this critical security issue by supporting the development of anti-jamming applications, as the amount of vehicular data is growing exponentially, the anti-jamming applications face many challenges (i., reactions in real-time) due to the lack of specific solutions that can keep up with the fast advancement of VANET. In this paper, we propose a new vehicular data prioritization model based on coresets to accelerate the Big Data Analytics in VANET. Our experimental evaluation shows that our solution can significantly increase the efficiency for clustering in jamming detection while keeping and improving the clustering quality. Also, the proposed solution can enable the real-time detection and be integrated to anti-jamming applications.Hind Bangui, Mouzhi Ge, Barbora Buhnova, and TRANG Le Hong contributed equally to this study.

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A hierarchical learning approach to anti-jamming channel selection strategies

Cited by 41 publications

References 39 publications

Joint Power and Bandwidth Allocation for UAV Backhaul Networks: A Hierarchical Learning Approach

Joint Power and Bandwidth Allocation for UAV Backhaul Networks: A Hierarchical Learning Approach

Joint relay and channel selection in relay‐aided anti‐jamming system: A reinforcement learning approach

Towards faster big data analytics for anti‐jamming applications in vehicular ad‐hoc network

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