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 48 publications

(39 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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From Adaptive Communication Anti‐Jamming to Intelligent Communication Anti‐Jamming: 50 Years of Evolution

Zhou,

Niu

2024

Advanced Intelligent Systems

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Herein, a comprehensive review of the evolution of intelligent communication anti‐jamming techniques is provided. First, a clear definition of the concept and elaboration on the inherent connotations and capability characteristics of intelligent communication anti‐jamming is provided. Additionally, the initial construction of an intelligent communication anti‐jamming system architecture is outlined. Subsequently, the development of intelligent communication anti‐jamming is delved, tracing its progression from early‐stage adaptive anti‐jamming techniques to the more recent advancements in intelligent anti‐jamming, which are primarily based on game theory and machine learning. Moreover, the latest research findings in this domain are thoroughly examined and the existing challenges and bottlenecks that hinder current research progress are highlighted. Finally, several viable research directions for future studies in the field of intelligent anti‐jamming are proposed. The objective is to facilitate readers in gaining a comprehensive understanding of the concept, characteristics, and system architecture of intelligent communication anti‐jamming. Additionally, it is aimed to provide a comprehensive overview of the developmental process and current state of research, thereby furnishing conceptual and theoretical support for the design of more effective and practical intelligent communication anti‐jamming systems.

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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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A hierarchical learning approach to anti-jamming channel selection strategies

Cited by 48 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

From Adaptive Communication Anti‐Jamming to Intelligent Communication Anti‐Jamming: 50 Years of Evolution

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

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