基于信干比的认知无线电自适应功率控制算法

该文以空时编码(STBC) MC-CDMA网络系统为认知无线电通信平台,将一种基于SIR的非合作功率控制博弈算法应用于该认知无线电系统,并根据用户的SIR需求差异,对算法改进设计出新的自适应功率控制博弈算法(CR-NCPCG)。仿真结果表明CR-NCPCG算法以不同用户SIR需求为前提,通过不同用户功率的有效控制,实现了用户公平共享频谱资源的需求。     

基于归一化效用函数的功率控制算法研究

功率控制技术是认知无线电的关键技术之一,分布式功率控制问题可以转化为非合作博弈问题来解决,结合认知无线电特点,提出一个新的S型效用函数,应用归一化的形式表示,并证明了其纳什均衡解的存在和唯一性.仿真结果表明,该算法不受系统调制方式的限制,且具有较快的收敛速度,在一定程度上兼顾了用户之间的公平性,使用户能公平地共享信道资源.     

认知无线电系统中一种新的自适应功率控制算法

本文针对认知无线电系统中的远近不公平性,提出将信道增益因子引入到基于信干比（SIR）的分布式非合作博弈功率控制算法中,并证明了其纳什均衡的存在性及唯一性。同时在此算法的基础上,针对部分用户的SIR不能满足下限阀值的情况,设计了一种新的自适应加权系数以控制发射功率,满足不同用户的输出SIR需求。仿真结果表明,与其它的分布式功率控制算法相比,该算法不仅有效地解决了远近不公平现象,满足了认知用户公平共享频谱资源的需求,同时使认知用户的SIR能同时满足上下限阀值的要求,且具有快速收敛的特性。      

认知无线电系统中一种改进的功率控制博弈算法

分布式功率控制是认知无线电(CR)系统中的关键技术之一,它直接影响到无线系统的性能。本文采用了博弈论的方法来实现对CR用户的分布式功率控制,在David Goodman的非协作博弈算法的基础上,给出了一种改进的效用函数,它使各用户在满足要求信干比条件下发送功率最小,同时使整个系统内由各种干扰引起的失真最小。本文通过理论推导证明了新的效用函数存在纳什均衡,并且均衡点唯一,同时仿真验证了该算法的收敛性,仿真结果表明这种兼顾用户自身利益以及用户间公平性的效用函数能降低发送功率,并且有效提高CR系统的性能。     

基于感知无线电非合作博弈功率控制算法的研究

本文基于Goodman提出的非合作博弈功率控制模型改进了代价函数。针对感知无线电系统（CR）中各用户的通信需求,采用多载波码分多址（MC-CDMA）感知无线电系统,解决感知用户对主用户干扰和通信中断等问题,为实现感知频谱资源的有效分配,提出了一种新的感知无线电系统功率控制博弈算法。通过仿真表明,该算法同几种经典算法相比,既满足不同种类用户SIR要求,又提高了系统吞吐量,实现了对不同用户发射功率的有效控制,且系统性能明显提高。     

一种新的认知无线电非合作功率控制博弈算法

当认知无线电网络以"衬底式"(Underlay)的方式与主用户网络共享频谱时,需要对认知用户进行功率控制,以确保认知用户在不干扰主用户的前提下,公平地共享认知频谱资源。利用博弈分析的方法,设计了一个基于链路增益因子的代价函数,并据此提出了一种新的非合作功率控制博弈算法。仿真结果表明,该算法的均衡结果既改善了用户的帕累托(Pareto)性能,又提高了链路增益较差的用户的吞吐量,实现了网络资源的平等共享。     

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。     

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。     

认知无线电网络中基于随机学习博弈的信道分配与功率控制

传统的认知无线电频谱分配算法往往忽略节点的传输功率对网络干扰的影响,且存在节点间交互成本高的问题.为此,通过量化传输功率等级,以最大化弹性用户收益为目标,构建联合频谱分配与功率控制非合作博弈模型,证明了该博弈为严格潜在博弈且收敛到纳什均衡点.进一步,将随机学习理论引入博弈模型,提出了基于随机学习的策略选择算法,并给出了该算法收敛到纯策略纳什均衡点的充分条件及严格证明.仿真结果表明,所提算法在少量信息交互前提下能获得较高的传输速率,并提升用户满意度.     

MIMO-CDMA系统中一种基于博弈方式的分布式功率控制

针对MIMO-CDMA系统中的无线数据业务,本文研究了分布式非合作功率控制博弈。对MIMO-CDMA系统中的无线数据业务建立了收益函数,该收益函数对功率效率和频谱效率都进行了考虑,并能够反映系统中无线数据用户对服务质量(QoS)的满意程度。以收益函数为基础,建立了两种非合作功率控制博弈模型,并对模型纳什均衡的存在性和唯一性进行了推导。另外,还研究了两种代价函数机制。最后,给出了一种获得纳什均衡的算法,数值仿真结果表明该算法具有良好的性能,有效地控制了各用户的发射功率。     

Differential Game Based Cooperative Power Control in Cognitive Radio Networks

This paper presents a cooperative power control algorithm in Cognitive radio (CR) system. The algorithm is based on the economic concepts of non-cooperative differential game, with interference constraint at the primary user. Based on the model, optimal power allocated to each secondary user for data transmission can be derived to maximize the secondary users’ utilization. The algorithm can solve the interference problem between secondary users and primary user and achieve high power efficiency. It is shown by way of simulation that by introducing game theory in the power control algorithm, performance improvements can be obtained in terms of game theory in CR system.     

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.     

基于认知无线电的新型非合作功率控制博弈算法

从认知无线电技术特性出发,在干扰温度门限的限制条件下,提出了基于新的代价函数功率控制博弈论算法,以有效地提高认知用户的信干噪比。根据信道状况、吞吐量最大化和功率限制的需求,将功率控制问题转化为一个干扰受限的多约束非线性规划问题。结合博弈论和最优化理论,证明了其纳什均衡的存在性和唯一性,给出了该模型的分布式求解算法。仿真实验表明,该算法可以有效提高认知用户的QoS和系统性能,充分利用无线资源。     

Pareto-Efficient and Goal-Driven Power Control in Wireless Networks: A Game-Theoretic Approach With a Novel Pricing Scheme

A Pareto-efficient, goal-driven, and distributed power control scheme for wireless networks is presented. We use a noncooperative game-theoretic approach to propose a novel pricing scheme that is linearly proportional to the signal-to-interference ratio (SIR) and analytically show that with a proper choice of prices (proportionality constants), the outcome of the noncooperative power control game is a unique and Pareto-efficient Nash equilibrium (NE). This can be utilized for constrained-power control to satisfy specific goals (such as fairness, aggregate throughput optimization, or trading off between these two goals). For each one of the above goals, the dynamic price for each user is also analytically obtained. In a centralized (base station) price setting, users should inform the base station of their path gains and their maximum transmit-powers. In a distributed price setting, for each goal, an algorithm for users to update their transmit-powers is also presented that converges to a unique fixed-point in which the corresponding goal is satisfied. Simulation results confirm our analytical developments.     

Joint random access and power control game in ad hoc networks with noncooperative users

We consider a distributed joint random access and power control scheme for interference management in wireless ad hoc networks. To derive decentralized solutions that do not require any cooperation among the users, we formulate this problem as noncooperative joint random access and power control game, in which each user minimizes its average transmission cost with a given rate constraint. Using supermodular game theory, the existence and uniqueness of Nash equilibrium are established. Furthermore, we present an asynchronous distributed algorithm to compute the solution of the game based on myopic best response updates, which converges to Nash equilibrium globally. Finally, a link admission algorithm is carried out to guarantee the reliability of the active users. Performance evaluations via simulations show that the game-theoretical based cross-layer design achieves high performance in terms of energy consumption and network stability.     

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.     

多用户MIMO系统中的一种基于博弈论的功率控制

多输入多输出(MIMO)系统是未来移动通信热点技术之一。单小区多用户MIMO多接入系统性能受限于同信道干扰。该文提出了采用博弈论的分析方法,对系统进行分布式功率控制,优化系统资源配置。仿真结果表明,该文建立的非合作博弈模型是合理的,而分布式功率控制算法也是有效可行的。     

Efficient power control via pricing in wireless data networks

A major challenge in the operation of wireless communications systems is the efficient use of radio resources. One important component of radio resource management is power control, which has been studied extensively in the context of voice communications. With the increasing demand for wireless data services, it is necessary to establish power control algorithms for information sources other than voice. We present a power control solution for wireless data in the analytical setting of a game theoretic framework. In this context, the quality of service (QoS) a wireless terminal receives is referred to as the utility and distributed power control is a noncooperative power control game where users maximize their utility. The outcome of the game results in a Nash (1951) equilibrium that is inefficient. We introduce pricing of transmit powers in order to obtain Pareto improvement of the noncooperative power control game, i.e., to obtain improvements in user utilities relative to the case with no pricing. Specifically, we consider a pricing function that is a linear function of the transmit power. The simplicity of the pricing function allows a distributed implementation where the price can be broadcast by the base station to all the terminals. We see that pricing is especially helpful in a heavily loaded system