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。     

基于改进图着色的D2D资源分配和功率控制

针对蜂窝网络中D2D（Device-to-Device）用户复用蜂窝信道带来的同频干扰问题,提出了一种基于改进图着色的资源分配和功率控制算法。首先通过构建干扰图和候选集进行用户之间干扰关系建模,并定义指数型累积因子改进图着色算法,为D2D用户分配蜂窝信道;再采用基于信干噪比的闭环功率控制算法动态调整D2D用户发射功率,减小由于信道复用产生的干扰。仿真结果表明,与现有算法相比,所提算法能够有效提升系统吞吐量和D2D用户接入率,实现信道资源的合理分配。     

采用最优放大因子的全双工中继选择方案

在多中继协作通信系统中,提出一种基于放大转发协议的全双工中继选择算法并进行了相应的理论分析。由于全双工中继中自干扰的存在,为保证通信的质量,首先利用传统的最大化边界信干噪比算法选择一个中继,然后利用中继的最优功率放大因子来减小自干扰对系统的不利影响。基于最优放大因子的全双工中继选择方案在系统中断概率、中继节点功率消耗、目的节点接收信干噪比方面都优于采用最大化边界信干噪比的系统。最后,我们利用蒙特卡洛仿真结果证明了该选择算法的优势。      

认知无线电系统的顽健资源分配算法

摘 要：针对多用户下垫式认知无线电网络中参数不确定性问题,提出了一种顽健分布式功率控制算法。在干扰温度门限和次用户信干噪比（SINR）的约束下,考虑信道不确定性,实现认知系统功率消耗最小化。基于欧几里得球形不确定性描述,利用拉格朗日对偶分解理论给出了顽健功率控制问题的解。仿真结果表明,该顽健功率分配算法能同时满足主用户和次用户的QoS需求,与非顽健算法和传统SOCP算法对比可提升系统性能。     

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。     

基于合作博弈的多信道认知无线网络中的频谱共享算法简

采用合作博弈对多信道认知无线网络中的频谱共享问题进行了建模分析,提出了次用户在各信道上的信干噪比乘积作为合作博弈的效用函数。次用户在各信道上保证对主用户的干扰小于一定门限的要求下,通过最大化各自效用函数的乘积来进行功率分配。由于最大化次用户效用函数的乘积问题是非凸的,通过变量替换将其转化为了一个等价的凸优化问题,利用该凸优化问题的对偶分解,提出了一种次用户间的频谱共享算法。仿真结果表明,所提算法在次用户和速率与公平性之间进行了有效折中。     

分布式高空气球通信网络功率分配算法

针对高空气球组网通信中干扰严重和电量供应受限的问题,提出了一种基于非合作博弈的分布式功率分配模型.通过在效用函数中增加干扰限制,保证了网络的整体信干噪比,提升了网络的通信质量;通过在效用函数中关注节点的剩余能量,使高空气球的工作时间得到延长.利用数学分析验证了模型中纳什均衡点的存在性和唯一性,并给出了纳什均衡点的求解方法.仿真结果表明,该模型可以延长高空气球的工作时间,增加网络传输的数据总量.     

优先级保护的分布式动态频谱分配算法

对认知无线网络中动态频谱分配算法进行了研究,在分析原有的ADP（Asynchronou s Distributed Pricing）算法的前提下,运用合作博弈的理论提出了具有干扰价格因子的A DP算法,分配过程中所有用户发布自己的干扰价格,用户在选择信道和功率的时候需要考虑 到自身的效用和对其它用户的干扰,可以在有效保证高优先级用户性能的前提下最大化网络 效用。仿真结果表明,该算法可以达到纳什均衡并且具有极快的收敛速度,可以提高高优先 级用户的信干噪比。     

CDMA系统中基于代价函数的联合功率控制算法

在CDMA系统中，利用多用户检测中的干扰抑制效果可以降低对功率控制的要求。本文提出了一种新的基于代价函数的联合功率控制算法。在该算法中，除了需要对功率向量进行迭代更新以外，还要对接收机滤波器的抽头系数不断迭代更新。实验结果表明，采用本文提出的功率控制算法。在系统内用户发射总功率一定的条件下。可以使各用户获得更高的信干比服务质量，系统容量也具有进一步提高的潜力。     

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。      

A Non-cooperative Uplink Power Control for CDMA Wireless Communication System

In this project, the main focus is to enhance the existing power control algorithm that is applied in a single cell of CDMA network. Nash algorithm is selected and is further improved with modification of its cost function with a value as power of target SINR, which to reduce the SINR error at first iteration, so as to increase the rate of convergence effectively. Decision of a value is important to ensure the SINR error is reduced at first iteration. The uniqueness and algorithm convergence of enhanced cost function is proven with certain conditions requirements. Therefore, enhanced Nash algorithm (ENA) is proposed which only applicable in first iteration of power control method. The rest of iterations are applied by Nash algorithm due to its better convergence to target SINR. After simulations, with consideration of Rayleigh and Rician fading channels, a significant increase in rate of convergence while maintaining the SINR with error less than 0.01 is shown. The transmitted power is lower in some scenarios, or with very slight reduction less than 0.5%. The SINR error at first iteration is reduced about 20% more by using ENA. In overall, ENA has better performance than the existing Nash algorithm in terms of transmitted power and rate of convergence, without compromising SINR.

     

基于博弈论的分布式水声通信网络功率分配算法

针对水下多节点通信网络节点生存时间短、服务质量差等问题,提出了一种基于博弈论的分布式功率分配算法。首先,在效用函数中考虑网络的整体干扰水平,优化网络的SINR水平,改善网络的服务质量。其次,进一步地将节点剩余能量引入到效用函数中,使剩余能量较低时节点减小发射功率,从而提高节点生存时间。所构建的博弈模型纳什均衡点具有存在性与唯一性。最后,通过仿真验证该算法能够延长节点寿命,优化系统SINR,提高节点能量效率。      

Multi-cell uplink power allocation game for user minimum performance guarantee in OFDMA systems

The multi-cell uplink power allocation problem for orthogonal frequency division multiplexing access (OFDMA) cellular networks is investigated with the uplink transmission power allocation on each co-frequency subchannel being defined as a multi-cell non-cooperative power allocation game (MNPG). The principle of the design oftbe utility function is given and a novel utility function is proposed for MNPG. By using this utility function, the minimum signal to interference plus noise ratio (SINR) requirement of a user can be guaranteed. It can be shown that MNPG will converge to the Nash equilibrium and that this Nash equilibrium is unique. In considering the simulation results, the effect of the algorithm parameters on the system performance is discussed, and the convergence of the MNPG is verified. The performance of MNPG is compared with that of traditional power allocation schemes, the simulation results showing that the proposed algorithm increases the cell-edge user throughput greatly with only a small decrease in cell total throughput; this gives a good tradeoff between the throughput of cell-edge users and the system spectrum efficiency.     

Non-cooperative power control game for adaptive modulation and coding

Adaptive modulation and coding (AMC) provides the flexibility to match modulation and coding scheme (MCS) to signal to interference plus noise ratio (SINR) of users. To reduce the transmission power and maintain the transmission quality, power control is normally combined with AMC. While the target SINR of power control is fixed, therefore non-cooperative power control game for AMC (NPGA) algorithm was proposed to adapt to the dynamic target SINR changes according to MCS. In NPGA, we formulate system transmission efficiency via utility, where the utility function is constructed based on the modulation and coding efficiency with non-cooperative game theory. It is demonstrated theoretically that NPGA can satisfy the conditions of the supermodular games, and its solution is optimal. The simulation results show that NPGA can improve system transmission efficiency and signal quality with low transmission power, and the convergence performance of NPGA is more fast-effectiveness compared with geometric programming algorithms.     

Power distribution algorithm based on game theory in the femtocell system

This paper studies the power control problem in femtocell system based on Nash non-cooperative game theory. It designs an utility function taking fem stations' transmit power as variable and relates it to the requirements of macro users' and fem users' signal to interference plus noise ratio (SINR). The utility also takes the impact of fem stations' location into account and improves the fairness of non-cooperative game. On this basis, this paper proposes a distributed power control algorithm and proves the existence and uniqueness of Pareto optimal point. The simulation results show that the algorithm improves the convergence speed and system performance through improving users' SINR.     

Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints

One of the most challenging problems in dynamic resource allocation for cognitive radio networks is to adjust transmission power of secondary users (SUs) while quality of service needs of both SUs and primary users (PUs) are guaranteed. Most power control algorithms only consider interference temperature constraint in single user scenario while ignoring the interference from PUs to SUs and minimum signal to interference plus noise ratio (SINR) requirement of SUs. In this paper, a distributed power control algorithm without user cooperation is proposed for multiuser underlay CNRs. Specifically, we focus on maximizing total throughput of SUs subject to both maximum allowable transmission power constraint and SINR constraint, as well as interference temperature constraint. To reduce the burden of information exchange and computational complexity, an average interference constraint is proposed. Parameter range and convergence analysis are given for feasible solutions. The resource allocation is transformed into a convex optimization problem, which is solved by using Lagrange dual method. In computer simulations, the effectiveness of our proposed scheme is shown by comparing with distributed constrained power control algorithm and Nash bargaining power control game algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

OFDMA毫微微小区网络中基于效用公平的功率控制策略

毫微微小区(Femtocell)网络能够增强室内覆盖,提高系统容量,但是在频谱共享正交频分多址(OFDMA) Femtocell网络中,同频干扰严重限制了网络的性能。针对频谱共享Femtocell网络中的上行链路,基于网络效率和毫微微小区用户间的公平性,该文提出合作纳什议价功率控制博弈模型,该博弈模型不仅考虑了对宏基站的干扰,而且考虑了毫微微小区用户最小信干噪比(SINR)需求。根据该博弈模型,进一步分析了具有帕累托(Pareto)最优的Kalai-Smorodingsky(KS)议价解。仿真结果表明,该策略既能保证用户公平性、最小SINR需求,又能够有效提高网络频谱利用率。     

分层异构网络中基于斯坦克尔伯格博弈的资源分配算法

分层异构网络中家庭基站与宏基站之间往往存在干扰,如何分配资源以获得高谱率和高容量、保证用户性能一直是研究的重点。为了解决这个问题,本文提出了一种异构蜂窝网络中基于斯坦克尔伯格博弈的家庭基站与宏基站联合资源分配算法,算法首先基于图论的分簇算法对家庭基站和宏用户进行分簇和信道分配,以减少家庭基站之间的同层干扰和家庭基站层与宏蜂窝网络的跨层干扰;然后建立了联合家庭基站发射功率以及宏用户接入选择的斯坦克尔伯格博弈,推导出达到纳什均衡时的家庭基站发射功率的表达式,并据此为宏用户选择合适的接入策略。仿真结果表明,该算法能够有效地提高宏用户的信干噪比（SINR）,家庭用户的性能也得到改善。      

认知无线电中基于多次博弈的功率控制算法

基于博弈论对认知无线电网络中的功率控制问题进行了建模分析,提出了一种基于多次博弈的功率控制算法,证明了该算法中纳什均衡的存在性和惟一性。仿真结果表明,基于该博弈模型的功率控制算法收敛性比传统算法好,经过7次左右迭代即可收敛,满足系统实时性要求,同时又能够以较低的功率水平满足不同用户对信干比的要求,实现了对不同用户发射功率的有效控制,系统性能明显提高。     

CDMA系统中一种新的分布式博弈功率控制算法

该文通过设计一种有效的代价函数,提出了一种用于蜂窝CDMA系统的博弈功率控制算法,并证明了该算法纳什均衡的存在及唯一性。通过仿真表明,该文提出的算法同几种经典的分布式算法相比,不但能够加快收敛速度,而且在系统中用户终端多于20的情况下,能够降低系统中用户发射功率。