软件测试过程中的参与方关系分析

针对典型的第三方测试和内部测试情形进行建模,重点分析测试过程中参与方的合作关系问题.通过混合策略的贝叶斯纳什均衡给出第三方测试过程适用条件,采用沙普利值对参与方合作可能性进行探讨.根据内部测试与第三方测试过程的内在差别,利用重复博弈方法对其进行分析,提出了软件测试过程博弈分析的基本研究过程框架.     

基于Stackelberg博弈的动态频谱接入控制

为了解决多主用户和多次级用户共存网络的频谱资源分配问题,提出了一种基于斯塔科尔伯格（Stackelberg）博弈的动态频谱接入控制算法。该算法通过三阶段Stackelberg博弈模拟主用户频谱竞价,博弈过程中次级用户以最大化传输速率为目的接入主用户频谱,同时设计了一种迭代过程来求解纳什均衡。实验计算与结果分析证明了纳什均衡唯一存在性的充要条件,并说明了迭代过程的收敛性以及主用户最佳效用的影响因素。     

Defense decision-making method based on incomplete information stochastic game and Q-learning

Most of the existing stochastic games are based on the assumption of complete information,which are not consistent with the fact of network attack and defense.Aiming at this problem,the uncertainty of the attacker’s revenue was transformed to the uncertainty of the attacker type,and then a stochastic game model with incomplete information was constructed.The probability of network state transition is difficult to determine,which makes it impossible to determine the parameter needed to solve the equilibrium.Aiming at this problem,the Q-learning was introduced into stochastic game,which allowed defender to get the relevant parameter by learning in network attack and defense and to solve Bayesian Nash equilibrium.Based on the above,a defense decision algorithm that could learn online was designed.The simulation experiment proves the effectiveness of the proposed method.     

A hierarchical framework of dynamic relay selection for mobile users and profit maximization for service providers in wireless relay networks

Although extensive research has been carried out on the issue of how to optimally select relays in wireless relay networks, relay selection for mobile users is still a challenging problem because of the requirement that the dynamic selection should adapt to user mobility. Moreover, because the selected relays consume their energy on relaying data for the users, it is required that the users have to pay for this relay service. The price of selecting relays will affect the users' decisions. Assuming that different relays can belong to different service providers, we consider the situation that the service providers can strategically set the prices of their relays to maximize their profits. In this paper, we jointly study the dynamic relay selection for mobile users and profit maximization for service providers. Also, we design a Stackelberg‐game hierarchical framework to obtain the solution. At the lower level, we investigate the relay selection problem for the mobile users under given prices of selecting the relays. It is formulated as a Markov decision process problem with the objective to minimize the mobile user's long‐term average cost (which consists of the payment to the relay service and the cost due to packet loss), and solved by applying the linear programming technique. At the upper level, we study the game of setting relay prices for the service providers, with the knowledge that the mobile users will make relay selections based on their given prices. Nash equilibrium is obtained as the solution. Our results can help to provide a guidance for service providers to compete for providing relay services. Copyright © 2012 John Wiley & Sons, Ltd.     

博弈条件下雷达波形设计策略研究

为提高电子战中弹载雷达检测性能,该文提出基于纳什均衡的雷达波形设计方法。首先建立电子战条件下雷达与干扰信号博弈模型,基于最大化信干噪比(SINR)准则,分别设计了雷达和干扰的波形策略;然后通过数学推导论证了博弈纳什均衡解的存在性,设计了一种重复剔除严格劣势的多次迭代注水方法来实现纳什均衡;通过二步注水法推导了非均衡的maxmin优化方案;最后通过仿真实验测试不同策略下雷达检测性能。仿真结果证明,基于纳什均衡的雷达信号设计有助于提升博弈条件下雷达检测性能,对比未博弈时,雷达检测概率最高可提升12.02%,较maxmin策略最高可提升3.82%,证明所设计的纳什均衡策略更接近帕累托最优。     

认知雷达抗干扰中的博弈论分析综述

雷达对抗的核心研究内容主要是干扰策略与抗干扰策略之间的对抗博弈,其作为电子战研究领域的热点一直备受学者们关注.该文综述了学者们利用合作与非合作博弈方法来分析雷达在进行目标探测和干扰抑制时所使用的策略,主要通过不同体制的雷达利用认知技术感知和学习外界复杂的电磁环境,合理地分配发射功率、控制编码序列、设计波形、研究检测和跟踪方法以及分配雷达通信资源等.这样雷达既节约发射所消耗的功率,又可以自适应地搜索和跟踪目标而不被敌方所发现,从而使雷达在复杂多变的现代战场环境中达到自身最优的性能.最后,对认知雷达抗干扰中的博弈论分析研究进行总结和展望,并指出了一些博弈论在认知雷达抗干扰策略应用中所面临的潜在问题和挑战.     

基于纳什均衡的需求响应多用户策略研究

随着能源互联网的出现和智能电网的发展,使得电能供给与电能需求能够更紧密的联系,也促进了电力交易市场条件下需求响应的信息化和智能化,用户根据自身情况在需求响应市场上进行出价与竞争,也更好的反映了电能的市场价值。本文结合智能电网下的需求响应市场,描述了多用户对电能竞争的一般场景与模型,从理论和算法上分析用户出价与市场调节的最优化问题,在给定的一般情况下,证明了存在一个纳什均衡,使得市场竞价能得到一个最终解的集合,实现用户竞争后的稳定解,同时给出了优化问题解的一个高效算法。     

Disturbance Attenuation in Hyperbolic 2D-systems

We consider disturbed linear 2D-systems of Fornasini–Marchesini type in the continuous time case. These systems are also named Goursat-type systems. Conditions for unique solvability of the disturbed optimal control problem with a quadratic cost functional are obtained. The disturbed or worst case optimal control guarantees to minimize the cost functional for any unknown disturbance input. In a second part then we consider a disturbance attenuation by boundary controls, i.e., by using the Goursat-data as control functions. In the 2D-case this results in two control functions acting independently on the system. We study a cooperative and a non-cooperative situation. In the cooperative situation we again obtain a disturbed optimal control problem and get conditions for existence and uniqueness of such a disturbed optimal control problem. If the action of the agents is non-cooperative we assume that it is agreed that they act on an open-loop Nash/worst-case equilibrium strategy. Sufficient conditions for the existence of such an equilibrium are obtained. Published online: April 2006     

无线数据网络中基于斯塔克尔博格博弈的功率控制

功率控制是无线数据网络中资源管理的关键技术。为使无线数据网络中非合作博弈功率控制算法得到帕累托改进,将斯塔克尔博格博弈引入到无线数据网络功率控制算法中,使所有系统终端都工作在最佳的等信干比下,提出一个基于斯塔克尔博格博弈的分布式功率控制算法,并进行了数值仿真。仿真结果表明,该算法明显提高了系统的性能,使系统终端具有相对较高的效用和较低的发射功率,并使得无线网络资源的使用更加合理和公平,同时算法拥有较好的收敛性。     

Game-theoretic solutions through intelligent optimization for efficient resource management in wireless visual sensor networks

We propose a quality-driven cross-layer optimization scheme for wireless direct sequence code division multiple access (DS-CDMA) visual sensor networks (VSNs). The scheme takes into account the fact that different nodes image videos with varying amounts of motion and determines the source coding rate, channel coding rate, and power level for each node under constraints on the available bit rate and power. The objective is to maximize the quality of the video received by the centralized control unit (CCU) from each node. However, since increasing the power level of one node will lead to increased interference with the rest of the nodes, simultaneous maximization of the video qualities of all nodes is not possible. In fact, there are an infinite number of Pareto-optimal solutions. Thus, we propose the use of the Nash bargaining solution (NBS), which pinpoints one of the infinite Pareto-optimal solutions, based on the stipulation that the solution should satisfy four fairness axioms. The NBS results in a mixed-integer optimization problem, which is solved using the particle swarm optimization (PSO) algorithm. The presented experimental results demonstrate the advantages of the NBS compared with alternative optimization criteria.