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。     

认知无线网络基于信号博弈的分布式功率控制算法

针对认知无线网络分布式环境下信道信息不对称导致资源分配冲突的问题,该文提出一种基于信号博弈的分布式功率分配算法。考虑具有竞争关系的次用户在不使用控制信道的情况下,通过信号博弈策略依次选择功率分配策略,达到信道信息共享的目的,能有效避免对主用户正常传输的干扰以及竞争次用户之间功率分配冲突。该文对均衡结果进行分析,仿真结果表明该算法可以有效估计信道增益,次用户的吞吐量相对于已有研究得到明显提升。     

认知小蜂窝网络中基于干扰温度限制的下行能效资源分配算法

为最大化认知小蜂窝基站的能量效率,本文基于博弈论模型分析了下行联合频谱资源块和功率分配行为.在干扰受限环境下,多个基站采用分布式结构共享空闲频谱资源.为避免累加干扰损害主用户的通信,算法中引入了功率和干扰温度限制.由于具有耦合限制的分数形式的能量效用函数是非凸最优的,通过将其转化为等价的减数形式进行迭代求解.给定频谱资源块分配策略后,主博弈模型可被重新建模为便于求解发射功率的等价子博弈模型,并通过代价的形势解除耦合限制.仿真结果表明,本文所提算法能够收敛到纳什均衡,并有效提高了系统资源利用率和能量效率.     

基于能量效率的双层非正交多址系统资源优化算法

该文针对双层非正交多址系统(NOMA)中基于能量效率的资源优化问题,该文提出基于双边匹配的子信道匹配方法和基于斯坦科尔伯格(Stackelberg)博弈的功率分配算法。首先将资源优化问题分解成子信道匹配与功率分配两个子问题,在功率分配问题中,将宏基站与小型基站层视作斯坦科尔伯格博弈中的领导者与追随者。然后将非凸优化问题转换成易于求解的方式,分别得到宏基站和小型基站层的功率分配。最后通过斯坦科尔伯格博弈,得到系统的全局功率分配方案。仿真结果表明,该资源优化算法能有效地提升双层NOMA系统的能量效率。     

全负载蜂窝网络下多复用D2D通信功率分配算法研究

针对全负载蜂窝网络中D2D通信的功率分配问题,该文提出了一种基于非合作完全信息博弈纳什均衡解的多复用D2D通信功率分配算法。以优先保证蜂窝用户通信质量与D2D用户接入率为前提,设置D2D通信系统上行链路帧结构,之后建立非合作完全信息博弈系统模型,引入定价机制到功率分配博弈模型中并分析纳什均衡解的存在性与唯一性,最后给出该模型的分布式迭代求解算法。仿真结果表明,随着D2D用户复用数量的增加,该算法在提升系统吞吐量的同时,能有效地控制系统内部干扰,大幅度降低系统总能耗。     

OFDMA认知无线电网络中面向需求的频谱共享

从满足次网络通信需求的角度,设计一个两阶段模型,求解OFDMA 认知无线电网络中频谱租赁与分配问题。模型第1阶段,次基站收集次网络通信需求,向多个主基站租用频谱资源。运用Bertrand博弈对主、次基站的交易行为进行建模,并将纳什均衡作为最终定价方案。第2阶段,基于纳什议价方案,将次基站子载波和功率分配问题定义成非线性规划问题,并通过拉格朗日乘数法进行求解。仿真实验表明,相对于其他频谱共享方案,所提方案高效地满足每个次用户的通信需求。     

基于博弈论的二次博弈波束成形算法

针对波束成形算法中,用户的信号方向估计值和用户之间的功率分配存在着相互矛盾,本文提出了一种基于博弈论的二次博弈波束成形算法,构建了波束成形博弈算法数学模型,首先在第一次博弈的时候,将波束成形算法中的信号方向和功率分配映射为博弈论数学模型中的局中人,将其建模为函数的极大极小值求解问题,先求解出信号方向;然后在第二次博弈的时候,将不同用户的功率分配过程描述为一个多用户的博弈过程,设计了功率分配更新算法,通过数学推导论证了纳什平衡点的存在性和唯一性。最后在仿真中,与传统最大信噪比算法进行比较。结果表明该文算法的性能要优于最大信噪比算法,并且讨论了不同参数对该文算法的影响。     

一种分布式的OFDMA系统资源分配算法

本文研究OFDMA系统的资源分配问题,把该问题建模为一个在基站的总发射功率一定的条件下,使系统中各个用户的权重速率之和最大化的数学模型。并提出一种基于对偶分解的分布式资源分配算法,将该问题分解为一个关于基站的主问题以及若干个关于用户的子问题。各个用户可以通过对子问题的求解获得各自的子载波以及功率的分配方案;而基站通过对主问题的求解使得满足子载波与功率的分配能够满足约束条件的要求,实现各用户权重速率和最大化的优化目标。所提算法能够把一个复杂的优化问题分解为若干个独立的子问题进行并行求解,因此可以有效地降低计算的复杂度以及基站的运算量。仿真结果表明,该算法能够在较少的迭代步数内得到一个近似最优解。      

基于非均衡求解的D2D多复用通信资源块分配算法研究

针对小区内D2D多复用的通信资源块分配问题,该文以一个D2D用户分别复用2个和3个蜂窝为基础,提出基于非均衡求解的D2D多复用模式下的资源块分配方案。利用博弈论将资源块划分问题转化为求解被复用蜂窝用户收益联合最大问题。当纳什均衡解不存在时,分析目标函数特性,在可行域内求解“最优解”,保证对不均衡解处理的最优性;对于均衡解存在的情况,将其取整后作为资源分配方案依据,保持其最优性。通过理论分析及仿真实验表明该算法可以提升系统吞吐率,提高小区通信性能。     

基于非合作博弈论的多小区OFDMA系统动态资源分配算法研究

该文采用非合作博弈论的方法研究了多小区OFDMA系统中的动态资源分配问题,首先将各基站的发射功率平均分配给各子载波,然后由所有小区在每个子载波上独立地进行资源分配博弈,给出了用户调度与功率分配联合博弈框架。为了进一步简化,将用户调度和资源分配分开完成,通过将信道增益引入到定价函数中,提出了一种新的定价机制,建立了用户确定时的非合作功率分配博弈模型,分析了其纳什均衡的存在性和唯一性,并设计了具体的博弈算法。仿真结果表明,所提算法在保证吞吐量性能的同时,进一步提升了系统的公平性。     

认知无线多跳网中保证信干噪比的频谱分配算法

为解决无线多跳网络在固定频谱分配方式下所固有的信道冲突等问题,利用认知无线电的动态频谱分配技术,提出了一种适用于次用户组成的无线多跳网络的、underlay方式下的全分布式频谱分配算法。该算法将频谱分配问题建模成静态非合作博弈,证明了纳什均衡点的存在,并给出了一种求解纳什均衡点的迭代算法。大量仿真实验证明,该算法能实现信道与功率的联合分配,在满足主用户干扰功率限制的同时,保证次用户接收信干噪比要求。     

Competitive Pricing for Spectrum Sharing in Cognitive Radio Networks: Dynamic Game, Inefficiency of Nash Equilibrium, and Collusion

We address the problem of spectrum pricing in a cognitive radio network where multiple primary service providers compete with each other to offer spectrum access opportunities to the secondary users. By using an equilibrium pricing scheme, each of the primary service providers aims to maximize its profit under quality of service (QoS) constraint for primary users. We formulate this situation as an oligopoly market consisting of a few firms and a consumer. The QoS degradation of the primary services is considered as the cost in offering spectrum access to the secondary users. For the secondary users, we adopt a utility function to obtain the demand function. With a Bertrand game model, we analyze the impacts of several system parameters such as spectrum substitutability and channel quality on the Nash equilibrium (i.e., equilibrium pricing adopted by the primary services). We present distributed algorithms to obtain the solution for this dynamic game. The stability of the proposed dynamic game algorithms in terms of convergence to the Nash equilibrium is studied. However, the Nash equilibrium is not efficient in the sense that the total profit of the primary service providers is not maximized. An optimal solution to gain the highest total profit can be obtained. A collusion can be established among the primary services so that they gain higher profit than that for the Nash equilibrium. However, since one or more of the primary service providers may deviate from the optimal solution, a punishment mechanism may be applied to the deviating primary service provider. A repeated game among primary service providers is formulated to show that the collusion can be maintained if all of the primary service providers are aware of this punishment mechanism, and therefore, properly weight their profits to be obtained in the future.     

Dynamics of Multiple-Seller and Multiple-Buyer Spectrum Trading in Cognitive Radio Networks: A Game-Theoretic Modeling Approach

We consider the problem of spectrum trading with multiple licensed users (i.e., primary users) selling spectrum opportunities to multiple unlicensed users (i.e., secondary users). The secondary users can adapt the spectrum buying behavior (i.e., evolve) by observing the variations in price and quality of spectrum offered by the different primary users or primary service providers. The primary users or primary service providers can adjust their behavior in selling the spectrum opportunities to secondary users to achieve the highest utility. In this paper, we model the evolution and the dynamic behavior of secondary users using the theory of evolutionary game. An algorithm for the implementation of the evolution process of a secondary user is also presented. To model the competition among the primary users, a noncooperative game is formulated where the Nash equilibrium is considered as the solution (in terms of size of offered spectrum to the secondary users and spectrum price). For a primary user, an iterative algorithm for strategy adaptation to achieve the solution is presented. The proposed game-theoretic framework for modeling the interactions among multiple primary users (or service providers) and multiple secondary users is used to investigate network dynamics under different system parameter settings and under system perturbation.     

A game theoretic approach for energy‐efficient communications in multi‐hop cognitive radio networks

In multi‐hop cognitive radio networks, it is a challenge to improve the energy efficiency of the radio nodes. To address this challenge, in this paper, we propose a two‐level Stackelberg game model, where the primary users and the secondary users act as the leaders and the followers, respectively. Based on the game model, our proposed scheme not only considers the power allocation problem for secondary users but also takes into account the price of spectrum. First, we give the cognitive radio network model, and show how to set up the game theoretic model in multi‐hop cognitive radio networks. We then analyze this problem and show the existence and uniqueness of the Nash equilibrium point for the game. We also study the impact of the spectrum price of the primary users in the cognitive radio network and study how to select the best price for the primary users to maximize their own profit. Finally, we implement simulations to show the performance of our schemes. Our work gives an insight on how to improve the energy efficiency and allocate spectrum resources in multi‐hop cognitive radio networks. Copyright © 2016 John Wiley & Sons, Ltd.     

Hybrid model of inter-stage spectrum trading in multistage game-theoretic framework

Dynamic spectrum access(DSA),consisting of spectrum sharing and spectrum trading stage,becomes a promising approach to increase the efficiency of spectrum usage and system performance.In this paper,from the perspective of individual interest optimization,we focus on strategy adaptation of network users and their interaction in spectrum trading process.Considering adverse effects on decision-making accuracy and the fairness among network users via local information acquirement,a hybrid game model based on global information of relevant spectrum is proposed to formulate intelligent behaviors of both primary and secondary users.Specifically,by using the evolutionary game theory,a spectrum-selection approach for the evolution process of secondary users is designed to converge to the evolutionary equilibrium gradually.Moreover,competition among primary users is modeled as a non-cooperative game and an iterative algorithm is employed to achieve the Nash equilibrium.The simulation results show that the proposed hybrid game model investigates network dynamics under different network parameter settings.     

Asynchronous Iterative Water-Filling for Gaussian Frequency-Selective Interference Channels

This paper considers the maximization of information rates for the Gaussian frequency-selective interference channel, subject to power and spectral mask constraints on each link. To derive decentralized solutions that do not require any cooperation among the users, the optimization problem is formulated as a static noncooperative game of complete information. To achieve the so-called Nash equilibria of the game, we propose a new distributed algorithm called asynchronous iterative water-filling algorithm. In this algorithm, the users update their power spectral density (PSD) in a completely distributed and asynchronous way: some users may update their power allocation more frequently than others and they may even use outdated measurements of the received interference. The proposed algorithm represents a unified framework that encompasses and generalizes all known iterative water-filling algorithms, e.g., sequential and simultaneous versions. The main result of the paper consists of a unified set of conditions that guarantee the global converge of the proposed algorithm to the (unique) Nash equilibrium of the game.     

A Stackelberg game for resource allocation in multiuser cooperative transmission networks

In this paper, we consider the problem of stimulating cooperation and resource allocation in cooperative transmission networks. We formulate this problem as a sellers' market competition where a relay is willing to share its resource with multiple users. We use a Stackelberg game to jointly consider the benefits of the relay and the users. Firstly, the relay determines the price of relaying according to the user demand. Secondly, the users purchase the optimal amount of resources to maximize their utilities. Although the Nash equilibrium, i.e., the solution of the game, can be obtained in a centralized manner, we develop a distributed algorithm to search the Nash equilibrium, which is more applicable in practical systems. Also, the convergence conditions of the algorithm are analyzed. Simulation results show, by using the distributed algorithm, the relay and the users could determine what price should ask for and how much bandwidth should buy, respectively. Copyright © 2010 John Wiley & Sons, Ltd.     

一种基于博弈论的认知CDMA频谱分配机制

针对认知无线电的频谱资源分配问题,提出了一种考虑认知用户间相互干扰行为的完全信息博弈模型。在最大化认知用户传输速率的条件下为其分配子载波以及功率,并且证明了纳什均衡的存在。仿真结果表明,该算法合理地对认知用户的效用函数进行了设计,算法能够很好地满足不同认知用户的速率需求,符合实际通信环境。提高了通信系统容量,并且能够较快地达到均衡状态以及具有较好的收敛性。