认知网络中基于最大和速率的协同通信方案

为了提高认知无线网络主用户通信与次用户通信的和速率,提出了一种新的协同通信方案.该方案中主用户通过机会选择次用户是否协同自身数据传输、允许通信的次用户通过匹配最佳可用信道来最大化认知无线网络的和速率.通过建模将该和速率最大化问题表示为一个组合优化问题,进一步将问题转化为了一个等价的标准指派问题,从而提出了基于匈牙利算法的协同通信方案.仿真结果表明,所提方案显著提高了主次用户的和速率.     

多速率认知网络中协作资源共享机制研究

针对多速率认知网络分析了不同体制网络间相互协作的动机及策略.基于排队论模型,提出了不同协作策略的时延分析模型,得到了不同类型用户的业务到达率、分组长度、信道传输速率与业务平均服务时间之间的解析关系,进而揭示了不同协作资源共享机制的时延性能以及适用的场景.数值仿真验证了分析模型的准确性和有效性,同时证明:通过合理利用用户间的协作,不仅可以更好地保障主用户业务的QoS性能,还可以有效提升次级用户的接入机会和QoS保障能力.     

基于控制帧改进的速率自适应算法

针对目前经典的IEEE 802.11速率自适应算法进行分析研究,提出了在基于接收端的自适应速率算法(RBAR)和机遇式自适应速率算法(OAR)基础上根据信道质量对控制帧采用速率自适应从而减少控制帧开销的改进机制,以提高网络资源的利用率和系统的吞吐量。采用NS2网络仿真工具仿真验证其吞吐量,并与经典算法吞吐量进行对比。仿真结果表明,根据对于控制帧采用速率自适应,可以有效提高系统吞吐量和网络资源的利用率,性能优于传统经典的速率自适应算法。     

基于局部拓扑控制的认知网络路由方法

针对认知网络研究一种融合主、次用户多因素优化的局部拓扑控制和路由方法。该方法综合考虑主用户频谱使用情况以及次用户对主用户干扰影响,预测认知链路的稳定性,结合链路功耗,定义一种联合链路代价,提出链路代价最小的局部认知拓扑控制路由 (LCTCR)算法,优化网络拓扑,并在优化后的拓扑上进行网络路由的选择。算法分析和仿真实验证明,在进行认知网络路径选择时,链路功耗和链路稳定性均为重要参数,需联合优化以保证选择更优的实际路由。     

基于网络控制的分层多点播送速率控制机制研究

本文在视频分层编码及分层传输协议的基础上，将基于网络控制与接收端控制机制相结合，提出一种新的分层多点播送速率控制机制。文中给出了该机制的拥塞检测、流行度权衡和数据层增加和丢弃算法。实验结果表明，该速率控制机制通史对拥塞做也快速响应，并在较好利用宽带的基础上保证高流行度的会话流具有较好的服务质量。     

认知无线网络中基于Stackelberg博弈的功率控制新算法

针对认知无线网络中存在的干扰问题和功率消耗过大问题,提出了一种新的基于斯坦克尔伯格(Stackelberg)博弈的功率控制算法。建立了主次用户双层网络模型,将主用户设置为领导者,次用户设置为追随者,并对次用户产生的总干扰进行定量分析。在次用户多次博弈的过程中,动态地调整主用户单位干扰价格,在保证主用户自身正常通信前提下,尽量使得收益最大化,提高主用户的参与积极性。仿真实验表明,该算法在保证主次用户的服务质量（QoS）前提下,有效地减小了次用户的发射功率,而且能获得更高的系统容量。     

基于速率的流量控制机理的研究

本文研究了两种基于速率的流量控制机制EPRCA和ERICA,并比较了它们的性能,仿真结果表明在瓶颈交换机缓冲区队列长度和源端的允许信元速率（ACR）的稳态、瞬态响应性能和性能的鲁棒性方面,ERICA优于EPRCA。     

基于贝叶斯网络的认知网络推理方法

文章阐述了认知网络的控制架构,并在此架构上建立了基于贝叶斯网络的认知网络推理模型,通过此推理模型预测出网络的性能参数,为认知网络的自主决策提供依据,使得认知网络能在网络环境变化时自适应地做出反应,从而真正实现端到端的QoS（服务质量）目标。     

无线自组网中最大化网络寿命的速率调整问题

该文针对发送速率可以调整的无线自组网,研究了最大化网络寿命的速率调整问题。将该问题模型化为混合整数非线性规划问题,通过分支限界法可以求出最优解。该文还提出了一个基于贪心策略的分布式最大化网络寿命速率调整算法MNLRAA,其基本思想是根据网络中节点的流量和剩余能量,尽可能为每条链路选择较低传输速率发送分组来节能。模拟实验结果表明,同所有节点使用最高传输速率的经典技术相比,MNLRAA可延长网络寿命20%以上。     

基于合作微分博弈的无线传感器网络速率分配

文章主要是在一段频谱上建立无线传感器网络数据传输合作微分博弈模型,使得新模型能适应无线传感器网络计算能力有限、存储能力有限及电池供电的特点,之后对此合作微分博弈模型进行求解。求出的解计算简洁,提高了无线传感器网络数据传输速率、频谱利用率及能量利用率。     

Pricing and power control for energy‐efficient radio resource management in cognitive femtocell networks

Cognitive femtocell has been considered as a promising technique that can improve the capacity and the utilization of spectrum efficiency in wireless networks because of the short transmission distance and low transmit power. In this paper, we study the win–win solution of energy‐efficient radio resource management in cognitive femtocell networks, where the macrocell tries to maximize its revenue by adjusting spectrum utilization price while the femtocells try to maximize their revenues by dynamically adjusting the transmit power. When the spectrum utilization price is given by macrocell, we formulate the power control problem of standalone femtocells as an optimization problem and introduce a low‐complexity iteration algorithm based on gradient‐assisted binary search algorithm to solve it. Besides, non‐cooperative game is used to formulate the power control problem between collocated femtocells in a collocated femtocell set, and then low complexity and widely used gradient‐based iteration algorithm is applied to obtain the Nash‐equilibrium solution. Specially, asymptotic analysis is applied to find the approximate spectrum utilization price in macrocell, which can greatly reduce the computational complexity of the proposed energy‐efficient radio resource management scheme. Finally, extensive simulation results are presented to verify our theoretical analysis and demonstrate the performance of the proposed scheme. Copyright © 2013 John Wiley & Sons, Ltd.     

认知无线网络中一种非合作博弈功率控制算法

针对现有的功率控制算法中存在的干扰问题和功率消耗过大问题,设计出一种新的效用函数,并根据此效用函数提出了一个基于非合作博弈的新的功率控制算法。首先,在效用函数中分别为信干噪比和功率设定了不同的代价因子,并将信道状态概念引入到代价因子里面,使其能够更加合理地控制用户,避免用户过度增加发射功率,同时减小了用户间的干扰;其次,证明了该算法纳什均衡的存在性和唯一性;最后,给出了所提算法的流程图。仿真结果表明,与Nash算法相比,在保证非授权用户服务质量( QoS)前提下,该算法功率消耗明显降低,并且具有较好的抗背景噪声性能;与K-G( Koskie-Gajic)算法相比,该算法保证了所有的用户的信干噪比满足上下限阈值要求,并且提高了系统容量。     

New coalition formation game for spectrum sharing in cognitive radio networks

In cognitive radio networks, Secondary Users (SUs) can access the spectrum simultaneously with the Primary Users (PUs) in underlay mode. In this case, interference caused to the licensed users has to be effectively controlled. The SUs have to make spectrum access decisions in order to enhance their quality of service, but without causing harmful interference to the coexisting PUs. In this paper, we propose a cooperative spectrum decision, which enables the SUs to share the spectrum with the PUs more efficiently. Our approach is based on a new coalitional game in which the coalition value is a function of the SUs' spectral efficiencies, the inter‐SUs interference, and the interference caused to the PUs. By applying new Enter and Leave rules, we obtain a stable coalition structure. Simulation results show that the SUs' spectral efficiencies are considerably increased and that the interference caused to the coexisting PU is reduced by about 7.5% as compared to an opportunistic spectrum access scheme. Moreover, the proposed coalitional game results in a more balanced spectrum sharing in the network. Copyright © 2014 John Wiley & Sons, Ltd.     

Non‐cooperative power control and spectrum allocation in cognitive radio networks: a game theoretic perspective

The invention of cognitive radio (CR) concept aims to overcome the spectral scarcity issues of emerging radio systems by exploiting under‐utilization of licensed spectrum. Determining how to allocate unused frequency bands among CR is one of the most important problems in CR networks. Because different CRs may have different quality‐of‐service requirements, they may have different objectives. In voice communication, high‐speed transmission is the most important factor; hence, voice radios always try to maximize their transmission rate. However, in data communication, the most important factor is the bit error rate. The data radios always try to maximize their signal‐to‐interference‐plus‐noise ratio (SINR). In this paper, two non‐cooperative games named interference minimization game and capacity maximization game, which reflect the target of data radios and voice radios, respectively, are proposed. From the simulations, after these games are applied, the average SINRs of all players at each channel are improved. The average SINR of players in each channel after applying the capacity maximization game is smaller than that after applying the interference minimization game. However, in comparison with that after applying the interference minimization game, the average capacity of players after applying capacity maximization approach is larger. Copyright © 2012 John Wiley & Sons, Ltd.     

Competitive flow control in general multi-node multi-link communication networks

In this paper, we consider the flow control in a general multi-node multi-link communication network with competing users. Each user has a source node, a destination node, and an existing route for its data flow over any set of links in the network from its source to its destination node. The flow rate for each user is a control variable that is determined by optimizing a user-specific utility function which combines maximizing the flow rate and minimizing the network congestion for that user. A preference parameter in the utility function allows each user to adjust the trade-off between these two objectives. Since all users share the same network resources and are only interested in optimizing their own utility functions, the Nash equilibrium of game theory represents a reasonable solution concept for this multi-user general network. The existence and uniqueness of such an equilibrium is therefore very important for the network to admit an enforceable flow configuration. In this paper, we derive an expression for the Nash equilibrium and prove its uniqueness. We illustrate the results with an example and discuss some properties and observations related to the network performance when in the Nash equilibrium. Copyright © 2007 John Wiley & Sons, Ltd.     

拥塞高速数据通信网优化控制模型

针对多信源——信宿高速数据通信网，引入N用户非零和微分对策优化控制路由模型。通信网中每个用户(决策者)，通过决策瞬时队列、路由及通信流量速率，使优化指标(损耗函数)达到最小；给出了反馈Nash平衡解的充要条件，并进行讨论。     

认知小蜂窝网络中基于能效的下行资源分配算法

在认知小蜂窝网络框架下,对基于OFDMA技术的下行联合频谱资源块和功率分配问题进行了研究。小蜂窝基站在分布式结构下采用开放式接入方式共享空闲频谱资源以最大化其能量效率,基站间的竞争关系使系统资源的动态分配过程可建模为非合作博弈模型。由于最大化具有多个限制条件的分数形势的能量效用函数属于非凸最优问题,可通过将其转化为等价的减数形势,并从串行和并行迭代的角度进行求解。在给定资源块分配策略后,原有博弈模型可被重新建模为便于独立求解发射功率的等价子博弈模型。仿真结果表明,所提算法在干扰受限的通信环境下能收敛到纳什均衡,并有效提高了系统资源利用率和能量效率。