无线网络中基于图型演化博弈的动态频谱接入机制

无线网络中,为了实现用户高效的数据传输,针对用户的有限理性特性,提出了一种基于图型演化博弈的动态频谱接入机制,而图型博弈可以较好地反映用户之间真实的博弈关系。同时设计了一种动态频谱接入算法和与之对应的动态方程以降低博弈的复杂度,而且能以较快的速度收敛到Nash均衡并获得较高的系统吞吐量和效用。理论证明该动态方程具有全局逐步稳定性,当用户发生局部的理性偏移时依然能够保证较快收敛和较小性能偏离。仿真对比验证了该机制的上述优势。     

空间复用认知无线电系统信道选择优化

在异构认知网络中,认知用户相对于主用户空间位置的不同可能提供空间复用的频谱接入机会,且空间复用的机会受限于干扰容限。首先引入用户空间位置干扰图,度量干扰和评估空间复用机会,在此基础上讨论了基于空间复用的系统吞吐量优化问题,并借助博弈论求解一组最优信道选择集合,主要工作是证明了该博弈问题是至少具有一个纯策略纳什均衡的精确势能博弈,且纳什均衡点是上述优化问题的最优解。最后,数值仿真验证了理论分析的正确性,同时证明考虑认知用户位置带来的空间复用后,系统吞吐量显著增加,有效提高了频谱利用率。     

基于UCB算法的信道选择方案

物联网在设备中的应用导致了更多的网络交通堵塞,本地服务器无法满足大数据传输的需要。很难做到在大数据下的中央处理模式云计算。边缘计算的出现,将数据卸载到多个边缘服务器进行处理。卸载到服务器的数据需要通过信道,以前的信道选择方法是基站的统一分配。如果终端设备可以通过自己的学习选择信道,可以提高效率、减轻基站的负担。文章对此开展分析。     

认知无线网络中信道选择算法研究

研究认知无线网络中认知用户（secondary user,SU）的信道选择策略。在每个信道上,由于主用户（primary user,PU）返回的概率不相同,因此SU需要接入一个成功传输概率最大的信道,以尽量避免与PU发生冲突。提出了一个基于EWA学习的信道选择算法,仿真结果表明,SU通过学习历史信道选择的经验,能自适应地选择可用性最好的信道,从而最小化与PU发生冲突的概率,有效地降低了SU进行信道切换的可能性。     

超短波电台频谱动态博弈接入算法

在超短波电台通信中,用户移动性强、网络拓扑结构多变、网络环境复杂,目前的频谱分配方法无法满足其快速、准确地频谱接入的需要.博弈论作为一种重要的数学理论,在动态频谱接入中得到了越来越广泛的应用.以此为出发点,将博弈论与超短波电台频谱接入技术相结合,提出了时变信道的分布式信道选择算法和周期重启的分布式信道选择算法,使分布式...     

认知无线电网络中基于强化学习的智能信道选择算法

认知无线电系统不仅要具有自适应性,更应具备一定的智能性。该文将强化学习理论引入到认知无线电系统中,用于解决次用户在频谱感知过程中的信道选择问题,提出了一种基于强化学习的信道选择算法。该算法在未知主用户占用规律和动态特性的前提下,仅通过不断与环境进行交互学习,便能够引导次用户选择“较好”信道优先进行感知,使次用户吞吐量得到提高。仿真结果表明,相对于现有信道选择算法,所提算法可有效提高次用户的吞吐量,并且在主用户使用规律发生变化时,能够自动实现二次收敛,可作为认知无线电系统迈向智能化的一种尝试。      

异构无线网络中基于Stackelberg博弈的分布式定价和资源分配算法

针对异构无线网络资源管理问题,结合多主多从Stackelberg博弈模型,提出了一种同时满足网络运营商和用户效用最大的异构无线网络定价和资源分配方案。首先设计了一种基于收益和花费的移动用户效用函数,并证明在运营商的价格确定后,效用函数满足凹函数的条件,保证了移动用户间非合作博弈的纳什均衡点存在。为了获取移动用户的最优带宽策略和运营商的最优价格策略,提出了一种分布式迭代算法。最后通过仿真实验取得了参与者的最优策略和子博弈完美纳什均衡。     

基于优先级信道预留的快速动态信道分配算法

针对TD-SCDMA系统现有快速动态信道分配算法的不足,提出了一种基于优先级信道预留的快速动态信道分配算法.该算法根据接力切换用户的移动台属性设定不同的优先级,为接力切换呼叫预留信道,结合小分组借用算法,增加了可移动边界动态信道分配(MB DCA)策略的灵活性.仿真结果表明,此算法相对于混合数据速率、小分组借用(MRG,mixed-data rate grouping borrowed)MB DCA算法,实现了VIP和快速移动切换用户的优先接入,有效地降低了切换呼叫的阻塞率,提高了数据业务性能和系统的信道利用率.     

无线传感器网络中支持并行传输的信道与功率联合优化博弈算法

针对无线传感器网络中干扰日益增大引起网络容量下降、能耗增加的问题,该文建立了信道分配与功率控制联合优化博弈模型。在该模型中链路将既能保持自身成功传输又不影响其它链路传输的信道作为可选信道,以实现链路的并行传输。继而基于该模型设计了一种支持并行传输的信道分配与功率控制联合优化博弈算法(JCPGC)。该算法利用最佳响应策略对模型求解,并通过超模博弈等理论证明了JCPGC能够收敛到纳什均衡。此外,该算法充分考虑信道分配和功率控制之间独立又相互影响的关系提高了网络容量。仿真实验结果表明,JCPGC具有大容量、低干扰和低能耗的特性。     

基于模糊动态博弈的多无人机空战策略研究

不确定环境下多无人机空战决策问题已成为无人机作战系统的一个重要研究课题。首先通过分析无人机空战态势信息的不确定性, 建立模糊信息下的多无人机动态博弈的作战优势函数; 将动态扩展式博弈转化成静态策略式博弈, 构建基于模糊信息的双方博弈的支付矩阵。将模糊结构元方法和粒子群算法相结合, 给出模糊信息下动态博弈的混合战略的纳什均衡求解方法。最后通过仿真实验验证了该方法的可行性和有效性。     

基于攻防信号博弈模型的防御策略选取方法

当前基于博弈理论的防御策略选取方法大多采用完全信息或静态博弈模型,为更加符合网络攻防实际,从动态对抗和有限信息的视角对攻防行为进行研究。构建攻防信号博弈模型,对策略量化计算方法进行改进,并提出精炼贝叶斯均衡求解算法。在博弈均衡分析的基础上,设计了最优防御策略选取算法。通过实验验证了模型和算法的有效性,并在分析实验数据的基础上总结了攻防信号博弈的一般性规律,能够指导不同类型防御者的决策。     

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。     

Chain store game based channel allocation in cognitive radio system

In adaptive channel allocation for secondary user(SU) of cognitive radio(CR) system,it is necessary to consider allocation process from the temporal perspective.In this article,a chain store game is modeled to achieve SU's equilibrium state.Due to the computational complexity of solving equilibrium states,the authors explore the correlated equilibrium(CE) by importing signal mechanisms based on time and sequence number.Also,correlated equilibrium based game algorithms are presented.Simulations show that the...     

Dynamic channel selection with reinforcement learning for cognitive WLAN over fiber

The Internet of Things (IoT) is the next big possibility and challenge for the future information networks. It makes the interaction between people and things more active and provides the connection among different existing networks. Ubiquitous short‐range wireless access and cognitive radio are key technologies for the IoT's realization. This paper deals with some problems in an integrated system of wireless local area network (WLAN) and cognitive radio — cognitive WLAN over fiber (CWLANoF). CWLANoF is a cost‐effective and efficient architecture that combines radio over fiber and cognitive radio technologies to provide centralized radio resource management and equal spectrum access in infrastructure‐based IEEE 802.11 WLANs. In this paper, a reinforcement learning approach is applied to implement dynamic channel selection in CWLANoF. The cognitive access points select the best channels among the industrial, scientific, and medical band for data packet transmission, given that the objective is to minimize external interference and acquire better network‐wide performance. The reinforcement learning method avoids solving complex optimization problems while being able to explore the states of a CWLANoF system during normal operations. Simulation results reveal that the proposed strategy is effective in avoiding aggregated interference, reducing outage probability, and improving network throughput. Copyright © 2012 John Wiley & Sons, Ltd.     

Chain store game based channel allocation iu cognitive radio system

In adaptive channel allocation for secondary user (SU) of cognitive radio (CR) system, it is necessary to consider allocation process from the temporal perspective. In this article, a chain store game is modeled to achieve SU's equilibrium state. Due to the computational complexity of solving equilibrium states, the authors explore the correlated equilibrium (CE) by importing signal mechanisms based on time and sequence number. Also, correlated equilibrium based game algorithms are presented. Simulations show that these algorithms are superior to other allocation algorithms both in channel utilization and communication time.     

Optimal strategy selection method for moving target defense based on signaling game

To solve the problem of the optimal strategy selection for moving target defense,the defense strategy was defined formally,the defense principle from the perspective of attack surface shifting and exploration surface enlarging was taken into account.Then,network attack-defense behaviors were analyzed from the sight of dynamic confrontation and bounded information.According to the analysis of attack-defense game types and confrontation process,the moving target defense model based on signaling game was constructed.Meanwhile,the method to quantify strategies was improved and the solution of perfect Bayesian equilibrium was proposed.Furthermore,the optimal defense strategy selection algorithm was designed by the equilibrium analysis.Finally,the simulation demonstrates the effectiveness and feasibility of the proposed optimal strategy and selection method.     

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.     

基于博弈论的P2P网络研究

在P2P网络中，节点可以根据它们的效用函数来决定是否接受服务。效用函数方法可以作为激励机制，促进节点之间的资源共享和相互提供服务。某个节点从其他节点获得服务的可能性直接同它的效用函数值相联系，并且节点要提高它的效用函数值的唯一方法就是为其他节点提供服务，因为可以最大限度地降低空载现象的发生。