scholarly journals An Anti-Jamming Hierarchical Optimization Approach in Relay Communication System via Stackelberg Game

2019 ◽  
Vol 9 (16) ◽  
pp. 3348 ◽  
Author(s):  
Zhibin Feng ◽  
Guochun Ren ◽  
Jin Chen ◽  
Chaohui Chen ◽  
Xiaoqin Yang ◽  
...  
Keyword(s):  
Communication System ◽  
Stackelberg Game ◽  
Learning Algorithm ◽  
Stackelberg Equilibrium ◽  
Hierarchical Optimization ◽  
Optimization Approach ◽  
Selection Probability ◽  
Control Optimization ◽  
Relay Communication ◽  
Feedback Error

In this paper, we study joint relay selection and the power control optimization problem in an anti-jamming relay communication system. Considering the hierarchical competitive relationship between a user and jammer, we formulate the anti-jamming problem as a Stackelberg game. From the perspective of game, the user selects relay and power strategy firstly which acts as the leader, while the jammer chooses power strategy then that acts as follower. Moreover, we prove the existence of Stackelberg equilibrium. Based on the Q-learning algorithm and multi-armed bandit method, a hierarchical joint optimization algorithm is proposed. Simulation results show the user’s strategy selection probability and the jammer’s regret. We compare the user’s and jammer’s utility under the proposed algorithm with a random selection algorithm to verify the algorithm’s superiority. Moreover, the influence of feedback error and eavesdropping error on utility is analyzed.

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

2018 ◽  
Author(s):  
Tingting Yang ◽  
Kailing Yao ◽  
Youming Sun ◽  
Fei Song ◽  
Yang Yang ◽  
...  
Keyword(s):  
Bandwidth Allocation ◽  
Stackelberg Game ◽  
Learning Algorithm ◽  
Utilization Efficiency ◽  
Stackelberg Equilibrium ◽  
Potential Game ◽  
Joint Power ◽  
Delay Performance ◽  
Hierarchical Learning ◽  
Backhaul Networks

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.

scholarly journals Optimally Deceiving a Learning Leader in Stackelberg Games

2021 ◽  
Vol 72 ◽  
pp. 507-531
Author(s):  
Georgios Birmpas ◽  
Jiarui Gan ◽  
Alexandros Hollender ◽  
Francisco J. Marmolejo-Cossío ◽  
Ninad Rajgopal ◽  
...  
Keyword(s):  
Strategic Behavior ◽  
Stackelberg Game ◽  
Learning Algorithm ◽  
Stackelberg Equilibrium ◽  
Stackelberg Games ◽  
Main Challenge ◽  
Interesting Connection ◽  
Finite Set ◽  
Payoff Matrices ◽  
Best Responses

Recent results have shown that algorithms for learning the optimal commitment in a Stackelberg game are susceptible to manipulation by the follower. These learning algorithms operate by querying the best responses of the follower, who consequently can deceive the algorithm by using fake best responses, typically by responding according to fake payoffs that are different from the actual ones. For this strategic behavior to be successful, the main challenge faced by the follower is to pinpoint the fake payoffs that would make the learning algorithm output a commitment that benefits them the most. While this problem has been considered before, the related literature has only focused on a simple setting where the follower can only choose from a finite set of payoff matrices, thus leaving the general version of the problem unanswered. In this paper, we fill this gap by showing that it is always possible for the follower to efficiently compute (near-)optimal fake payoffs, for various scenarios of learning interaction between the leader and the follower. Our results also establish an interesting connection between the follower’s deception and the leader’s maximin utility: through deception, the follower can induce almost any (fake) Stackelberg equilibrium if and only if the leader obtains at least their maximin utility in this equilibrium.

Approximating Stackelberg Equilibrium in Anti-UAV Jamming Markov Game with Hierarchical Multi-Agent Deep Reinforcement Learning Algorithm

2021 ◽  
Author(s):  
Zikai Feng ◽  
Yuanyuan Wu ◽  
Mengxing Huang ◽  
Di Wu
Keyword(s):  
Reinforcement Learning ◽  
Stackelberg Game ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Stackelberg Equilibrium ◽  
Large State Space ◽  
Comparable Performance ◽  
Aerial Vehicle ◽  
Multi Agent ◽  
Policy Optimization

Abstract In order to avoid the malicious jamming of the intelligent unmanned aerial vehicle (UAV) to ground users in the downlink communications, a new anti-UAV jamming strategy based on multi-agent deep reinforcement learning is studied in this paper. In this method, ground users aim to learn the best mobile strategies to avoid the jamming of UAV. The problem is modeled as a Stackelberg game to describe the competitive interaction between the UAV jammer (leader) and ground users (followers). To reduce the computational cost of equilibrium solution for the complex game with large state space, a hierarchical multi-agent proximal policy optimization (HMAPPO) algorithm is proposed to decouple the hybrid game into several sub-Markov games, which updates the actor and critic network of the UAV jammer and ground users at different time scales. Simulation results suggest that the hierarchical multi-agent proximal policy optimization -based anti-jamming strategy achieves comparable performance with lower time complexity than the benchmark strategies. The well-trained HMAPPO has the ability to obtain the optimal jamming strategy and the optimal anti-jamming strategies, which can approximate the Stackelberg equilibrium (SE).

scholarly journals Towards an Efficient CNN Inference Architecture Enabling In-Sensor Processing

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1955
Author(s):  
Md Jubaer Hossain Pantho ◽  
Pankaj Bhowmik ◽  
Christophe Bobda
Keyword(s):  
Power Consumption ◽  
High Speed ◽  
Image Sensor ◽  
Machine Learning Algorithms ◽  
Hierarchical Optimization ◽  
Computation Method ◽  
Optimization Approach ◽  
Efficient Computation ◽  
Feature Maps ◽  
Dynamic Power

The astounding development of optical sensing imaging technology, coupled with the impressive improvements in machine learning algorithms, has increased our ability to understand and extract information from scenic events. In most cases, Convolution neural networks (CNNs) are largely adopted to infer knowledge due to their surprising success in automation, surveillance, and many other application domains. However, the convolution operations’ overwhelming computation demand has somewhat limited their use in remote sensing edge devices. In these platforms, real-time processing remains a challenging task due to the tight constraints on resources and power. Here, the transfer and processing of non-relevant image pixels act as a bottleneck on the entire system. It is possible to overcome this bottleneck by exploiting the high bandwidth available at the sensor interface by designing a CNN inference architecture near the sensor. This paper presents an attention-based pixel processing architecture to facilitate the CNN inference near the image sensor. We propose an efficient computation method to reduce the dynamic power by decreasing the overall computation of the convolution operations. The proposed method reduces redundancies by using a hierarchical optimization approach. The approach minimizes power consumption for convolution operations by exploiting the Spatio-temporal redundancies found in the incoming feature maps and performs computations only on selected regions based on their relevance score. The proposed design addresses problems related to the mapping of computations onto an array of processing elements (PEs) and introduces a suitable network structure for communication. The PEs are highly optimized to provide low latency and power for CNN applications. While designing the model, we exploit the concepts of biological vision systems to reduce computation and energy. We prototype the model in a Virtex UltraScale+ FPGA and implement it in Application Specific Integrated Circuit (ASIC) using the TSMC 90nm technology library. The results suggest that the proposed architecture significantly reduces dynamic power consumption and achieves high-speed up surpassing existing embedded processors’ computational capabilities.

scholarly journals Optimal Decision-Making to Charge Electric Vehicles in Heterogeneous Networks: Stackelberg Game Approach

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 325 ◽  
Author(s):  
Shijun Chen ◽  
Huwei Chen ◽  
Shanhe Jiang
Keyword(s):  
Electric Vehicles ◽  
Heterogeneous Networks ◽  
Demand Uncertainty ◽  
Stackelberg Game ◽  
Optimal Solution ◽  
Stackelberg Equilibrium ◽  
Optimal Decision ◽  
Charging Station ◽  
Simulation Results ◽  
Grid Operators

Electric vehicles (EVs) are designed to improve the efficiency of energy and prevent the environment from being polluted, when they are widely and reasonably used in the transport system. However, due to the feature of EV’s batteries, the charging problem plays an important role in the application of EVs. Fortunately, with the help of advanced technologies, charging stations powered by smart grid operators (SGOs) can easily and conveniently solve the problems and supply charging service to EV users. In this paper, we consider that EVs will be charged by charging station operators (CSOs) in heterogeneous networks (Hetnet), through which they can exchange the information with each other. Considering the trading relationship among EV users, CSOs, and SGOs, we design their own utility functions in Hetnet, where the demand uncertainty is taken into account. In order to maximize the profits, we formulate this charging problem as a four-stage Stackelberg game, through which the optimal strategy is studied and analyzed. In the Stackelberg game model, we theoretically prove and discuss the existence and uniqueness of the Stackelberg equilibrium (SE). Using the proposed iterative algorithm, the optimal solution can be obtained in the optimization problem. The performance of the strategy is shown in the simulation results. It is shown that the simulation results confirm the efficiency of the model in Hetnet.

scholarly journals Dynamic SPICE-model of resource allocation in marketing networks

2020 ◽  
Vol 13 ◽  
pp. 8-23
Author(s):  
Movlatkhan T. Agieva ◽  
◽  
Olga I. Gorbaneva ◽  
Keyword(s):  
Resource Allocation ◽  
Stackelberg Game ◽  
Target Audience ◽  
Opinion Leaders ◽  
Stackelberg Equilibrium ◽  
Common Interest ◽  
Theoretic Model ◽  
Goods And Services ◽  
Spice Model ◽  
Private Interests

We consider a dynamic Stackelberg game theoretic model of the coordination of social and private interests (SPICE-model) of resource allocation in marketing networks. The dynamics of controlled system describes an interaction of the members of a target audience (basic agents) that leads to a change of their opinions (cost of buying the goods and services of firms competing on a market). An interaction of the firms (influence agents) is formalized as their differential game in strategic form. The payoff functional of each firm includes two terms: the summary opinion of the basic agents with consideration of their marketing costs (a common interest of all firms), and the income from investments in a private activity. The latter income is described by a linear function. The firms exert their influence not to all basic agents but only to the members of strong subgroups of the influence digraph (opinion leaders). The opinion leaders determine the stable final opinions of all members of the target audience. A coordinating principal determines the firms' marketing budgets and maximizes the summary opinion of the basic agents with consideration of the allocated resources. The Nash equilibrium in the game of influence agents and the Stackelberg equilibrium in a general hierarchical game of the principal with them are found. It is proved that the value of opinion of a basic agent is the same for all influence agents and the principal. It is also proved that the influence agents assign less resources for the marketing efforts than the principal would like.

scholarly journals Automatic UAV Relay Communication System Based on Microwave Directional Antenna

2020 ◽  
Vol 1650 ◽  
pp. 022019
Author(s):  
Jinchang Liu ◽  
Lili Zhang ◽  
Ping Gao ◽  
Jun Ma ◽  
Chuanfu Xia ◽  
...  
Keyword(s):  
Communication System ◽  
Directional Antenna ◽  
Relay Communication

scholarly journals Fundamentals of Force-Controlled Friction Riveting: Part II—Joint Global Mechanical Performance and Energy Efficiency

Materials ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 2489 ◽  
Author(s):  
Gonçalo Pina Cipriano ◽  
Lucian Blaga ◽  
Jorge dos Santos ◽  
Pedro Vilaça ◽  
Sergio Amancio-Filho
Keyword(s):  
Energy Efficiency ◽  
Mechanical Response ◽  
Mechanical Performance ◽  
Tensile Force ◽  
Optimization Approach ◽  
Additional Energy ◽  
Efficient Manner ◽  
Control Optimization ◽  
Friction Parameters ◽  
And Control

The present work investigates the correlation between energy efficiency and global mechanical performance of hybrid aluminum alloy AA2024 (polyetherimide joints), produced by force-controlled friction riveting. The combinations of parameters followed a central composite design of experiments. Joint formation was correlated with mechanical performance via a volumetric ratio (0.28–0.66 a.u.), with a proposed improvement yielding higher accuracy. Global mechanical performance and ultimate tensile force varied considerably across the range of parameters (1096–9668 N). An energy efficiency threshold was established at 90 J, until which, energy input displayed good linear correlations with volumetric ratio and mechanical performance (R-sq of 0.87 and 0.86, respectively). Additional energy did not significantly contribute toward increasing mechanical performance. Friction parameters (i.e., force and time) displayed the most significant contributions to mechanical performance (32.0% and 21.4%, respectively), given their effects on heat development. For the investigated ranges, forging parameters did not have a significant contribution. A correlation between friction parameters was established to maximize mechanical response while minimizing energy usage. The knowledge from Parts I and II of this investigation allows the production of friction riveted connections in an energy efficient manner and control optimization approach, introduced for the first time in friction riveting.

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