蜂窝网络中基于双层博弈的吞吐量最大化研究

如何提升系统的吞吐量是蜂窝网络中研究的热点。利用设备到设备通信(D2D)技术为蜂窝边缘用户设备提供中继支持，进而提升系统的吞吐量。描述一种中继节点选择和频谱分配的联合问题，帮助蜂窝边缘用户设备寻找合适的中继节点，并为D2D链路分配频谱，在满足D2D和传统蜂窝用户设备干扰约束的条件下使系统吞吐量最大化。为此，提出一种基于双层博弈模型的分布式算法，对上述问题进行求解。该博弈模型分为内层和外层；内层通过Stackelberg博弈理论为蜂窝边缘用户设备选择中继节点，并将其作为主节点，蜂窝边缘用户设备作为从节点；外层采用联合博弈理论为蜂窝边缘用户设备及其中继节点间的链路分配合适的频谱。仿真结果表明本文算法在能耗、吞吐量等方面的性能要优于其他典型算法。     

认知网络中基于博弈论的联合功率控制与速率分配算法

针对认知无线网络上行链路中的资源分配问题,提出了一种适应于多小区认知无线网络的基于功率控制与速率分配的博弈算法。为了更加合理地控制用户的功率和速率,减小各次用户间的干扰,首先,在效用函数中分别给功率和速率设置了不同的代价因子,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。其次,从理论上证明了该算法纳什均衡的存在性、唯一性以及算法的收敛性。最后,为了解决发射功率和传输速率的最优化问题,给出了联合功率控制和速率分配的迭代更新算法流程图。理论分析及仿真结果表明,与同类博弈算法相比,在保证通信质量的前提下,所提算法可以使得用户以较小的发射功率获得较大的传输速率和较高的信干噪比（SINR）,并且减小了用户间的干扰,提高了次用户系统容量。     

Channel allocation and power control schemes for cross-tier 3GPP LTE networks to support multimedia applications

In this paper, we present the framework of a channel allocation (CA) and power control (PC) schemes for the minimization of interference in cross-tier 3GPP LTE networks that aim to support internet of multimedia things. Channel allocation scheme based on an interference graph preserving the minimum number of interfered MUEs by femtocells (IG-MIM) is proposed to mitigate interference amongst femtocells, and a game theory based power control algorithm is also proposed to reduce interference to surrounding macrocell users (MUEs). The proposed IG-MIM scheme constructs the interference graph based on a predefined threshold and allocates the subchannels to the femtocells that maintain the smallest number of interfered MUEs. For the power control, we design a payoff function based on the rewards from the achieved data rates and the penalties from the interference in regards to its adjacent femtocells. The simulation results show that the IG-MIM channel allocation significantly improves the SINR performance for the femtocell users (FUEs) being served; the game theory based power control decreases the power requirements of a femtocell and alleviates the interference to the MUEs.     

A cross-layer resource allocation scheme for ICIC in LTE-Advanced

As a new technology, inter-eNB coordination has been included in LTE-Advanced study items. Moreover, the network architecture in LTE-Advanced system is modified to take into account coordinated transmission. In our study, we explore the problem of jointly optimizing the power level and scheduling of resource blocks for LTE-Advanced network based on orthogonal frequency division multiplexing (OFDM). We propose a distributed optimization scheme based on evolutionary potential games, and in the process of objective function modeling we employ the Lagrangian multiplier method to solve the constraint objective optimization problem. Then particle swarm optimization (PSO) method is adopted to find the optimal power allocation and scheduling for each resource block in the multi-cell framework. Numerical results prove that proposed algorithm notably improves the overall throughput, while user fairness is guaranteed. Importantly, additional computation and communication cost introduced by cross-layer optimization is also evaluated.     

基于分簇和Stackelberg博弈的D2D资源分配策略

当前,车辆密集通信场景下存在通信资源利用率低、DUE(D2D user)用户通信质量差等问题。针对上述问题,提出了一种基于分簇和Stackelberg博弈的D2D(device to device)资源分配策略,以解决DUE用户功率分配、信道匹配问题。首先,基于每个信道内DUE用户之间干扰最小原则,该模型对所有DUE用户进行分簇;然后,对于CUE(cellular user)用户与DUE用户簇,构建一对多的Stackelberg主从博弈模型,通过复用链路干扰参数和簇内干扰参数的迭代更新,优化每个DUE用户的发射功率;最后,利用匈牙利算法实现DUE用户簇与CUE用户的最佳信道匹配,最大化DUE用户的容量和。仿真结果表明,与基于价格迭代、等功率分配和高能效干扰约束的几种功率分配算法相比,所提算法能有效提升DUE用户的总容量。     

D2D通信网络中一种基于时间反演的无线资源优化机制

针对D2D(Device-to-Device)异构无线通信网络中D2D用户和蜂窝用户之间的干扰问题,提出一种基于时间反演的无线资源优化机制。该机制包括两个步骤:1)在上行传输系统中结合时间反演镜技术实现干扰消除,即对每个用户执行信道签名,提取有用信号,剔除干扰,获取系统用户信干噪比;2)根据用户信干噪比,采用功率控制算法并结合凸优化理论来调整用户的发射功率,以最大系统吞吐量为优化目标。仿真结果表明,该机制有效地抑制了D2D异构无线通信网络中蜂窝用户和D2D用户的相互干扰,提升了系统容量,同时满足了用户对通信可靠性的需求,保证了用户能够获得更高的QoS(Quality of Service)。     

异构网络中基于鸽群优化算法的D2D资源分配机制

针对异构蜂窝网络中D2D(device-to-device)通信用户复用蜂窝用户上行信道产生的频谱资源分配优化问题,提出一种基于改进离散鸽群算法(PIO)的D2D通信资源分配机制.通过设置信干噪比(SINR)门限值保证用户的通信服务质量(QoS),使用基于改进地图-指南针算子和认知因子的离散鸽群算法(IMCBPIO)为D2D用户进行资源分配,并采用基于接收SINR的闭环功率控制算法动态调整用户的发送功率,以减少用户与基站以及用户与用户之间存在的干扰.仿真结果表明,所提出方案能够有效抑制异构网络中由于引入D2D用户后导致的干扰,降低通信用户的中断概率,大大提高频谱利用率和系统吞吐量.     

异构蜂窝网络中功率和资源分配博弈算法研究

基于D2D和中继异构蜂窝网络进行资源复用可获得系统性能增益,但同时也使得网络中的干扰更加复杂。针对该问题,提出功率和资源分配博弈（PRAG）算法,通过功率控制和资源分配对D2D和中继异构蜂窝网络进行干扰协调。基于代价参数设定D2D和中继链路效用函数,确定最佳发射功率。在此基础上,将生成的效用值矩阵参与博弈,选择合适的蜂窝用户进行资源复用。仿真结果表明,与等功率分配随机（EPAR）算法相比,PRAG算法能够在消耗更少功率的基础上获得更大的系统吞吐量。     

Joint precoding selection diversity with limited feedback

Coordinated multiple-point transmission (CoMP) is one of important techniques for the reduction of inter-cluster interference. Three major schemes have been presented for CoMP-joint processing transmission (CoMP-JPT) under limited-feedback environments: global, local, and single-frequency network precoding methods. However, most previous studies have demonstrated effectiveness only in specific environments without presenting numerical analyses. In this paper, we verify the characteristics of each precoding scheme based on zero-forcing beamforming (ZFBF) with semi-orthogonal user selection (SUS) by exploiting numerical analysis. In addition, we propose a selection algorithm that achieves the diversity of the precoding schemes based on limited feedback according to the downlink channel status of cellular systems. In addition, for the 3rd Generation Partnership Project Long Term Evolution-Advanced (3GPP LTE-Advanced), a network protocol that is required to adapt the CoMP-JPT scheme is implemented. Through simulations, we demonstrate that the proposed system can effectively improve the quality of service for cell-edge users while guaranteeing fairness compared to the conventional precoding schemes.     

基于博弈论的无线网络功率优化模型*

针对无线网络功率分配优化需求,将功率分配问题转换为信干噪比收益,利用非合作博弈原理,通过分析得到节点功率的迭代,与传统的功率分配方法进行对比,说明提出的方案要明显优于SINR平衡算法、Koskie-Gajic算法,从而为无线网络中的功率分配优化提供了理论支持。     

基于序贯博弈的Ad Hoc网络功率控制算法

针对分布式拓扑结构的Ad Hoc网络,将用户处于异步竞争方式下的功率控制问题抽象为动态博弈模型,通过多步迭代的逆向归纳法,逐步分析了两用户组及多用户组下的序贯博弈过程。并提出一种分布式的功率控制博弈算法,有效求解了用户发射功率的均衡策略,来优化用户之间的并发传输能力,提高频谱效率。仿真实验表明,建立基于序贯博弈的功率控制过程,能够有效地减轻用户之间的干扰影响,提升用户接收信干噪比质量,从而改善了系统吞吐率性能收益。     

基于博弈资源分配的认知异构网络干扰协调算法

基于underlay频谱共享模式的认知异构网络可有效缓解频谱资源短缺问题,但同时会加剧网络中的干扰。针对该问题,提出了一种基于非合作博弈模型的动态频谱分配和功率控制算法进行干扰协调。首先,考虑频谱共享造成的干扰问题,引入认知用户优先等级,将问题构建为联合动态频谱分配与功率控制的频谱定价博弈模型;其次,通过两阶段动态博弈得到纳什均衡解,实现认知网络层频谱资源合理分配和发射功率控制。仿真表明,所提算法能够实现不同优先级用户频谱资源的合理分配和认知基站发射功率控制,有效抑制认知异构网络的跨层干扰和层内干扰。     

LTE-A 异构网络的干扰抑制技术研究

针对传统的干扰协调算法存在着频谱效率不高,吞吐量低等问题,基于LTE-A,提出了一种改进的动态部分频率复用算法.在传统蜂窝网络中引入了低功率节点,与宏蜂窝网络组成异构网络.在改进的算法中,划分了干扰区域,以干扰区域比重为依据对用户进行分组,再为用户分配子信道.同时,建立干扰图,用点着色算法对图中顶点进行着色,再根据着色结果对节点进行分组,依据分组结果使用频谱资源分配算法进行资源分配.实验结果表明,该算法与传统算法相比具有更高的频谱效率,在保证系统的吞吐量的同时提高了用户的信道噪声比SINR.     

一种新型的LTE系统干扰协调算法

为了解决LTE多小区系统在重度边缘负载情况下所承受的严重的小区间干扰问题,提出了一种基于小区边缘用户数的干扰协调调度算法ICPF。该算法在软频率复用干扰协调场景下提出了改进型资源分配以及二次调度思想,在初步分配资源的基础上进行频率资源二次调整,并有效地与调度算法结合起来。仿真结果表明,ICPF算法能在保证频谱利用率的前提下,提高小区边缘用户性能以及多用户分集的效果。     

干扰受限系统的认知用户功率控制算法

根据认知无线电系统中认知用户的不同通信需求,结合干扰温度模型和非合作博弈理论,以单小区CDMA为系统平台,提出基于SINR的对数效用函数的干扰受限认知无线电系统功率控制算法。仿真实验结果表明,与经典SINR平衡算法及Koskie-Gajic算法相比,该算法在满足认知用户目标SINR和主用户干扰温度限制的前提下,通过适当增大认知用户发射功率,能够满足认知用户高速数据通信的需要。     

Hybrid heuristic-waterfilling game theory approach in MC-CDMA resource allocation

This paper discusses the power allocation with fixed rate constraint problem in multi-carrier code division multiple access (MC-CDMA) networks, that has been solved through game theoretic perspective by the use of an iterative water-filling algorithm (IWFA). The problem is analyzed under various interference density configurations, and its reliability is studied in terms of solution existence and uniqueness. Moreover, numerical results reveal the approach shortcoming, thus a new method combining swarm intelligence and IWFA is proposed to make practicable the use of game theoretic approaches in realistic MC-CDMA systems scenarios. The contribution of this paper is twofold: (i) provide a complete analysis for the existence and uniqueness of the game solution, from simple to more realist and complex interference scenarios; (ii) propose a hybrid power allocation optimization method combining swarm intelligence, game theory and IWFA. To corroborate the effectiveness of the proposed method, an outage probability analysis in realistic interference scenarios, and a complexity comparison with the classical IWFA are presented.     

Game theoretic distributed uplink power control for CDMA networks with real-time services

In this paper, the issue of efficient power allocation in the uplink of CDMA wireless networks supporting real-time services with various QoS constraints, is addressed. Within the proposed framework, utility functions are adopted to reflect a user’s degree of satisfaction with respect to its actual throughput requirements satisfaction and respective power consumption. The corresponding problem is formulated as a non-cooperative game where users aim selfishly at maximizing their utility-based performance under the imposed limitations. The existence and uniqueness of a Nash equilibrium point of the proposed Uplink Power Control (UPC) game is proven, at which all users have attained a targeted SINR threshold value or transmit with their maximum power, leading essentially to an SINR-balanced system. Moreover, a distributed iterative algorithm for reaching UPC game’s equilibrium is provided. The properties of equilibrium in a pure optimization theoretical framework are studied, and the tradeoffs between users’ overall throughput performance and real-time services’ QoS requirements satisfaction, in channel aware resource allocation processes, are revealed and quantified. Through modeling and simulation the efficacy of the introduced framework and proposed UPC algorithm are demonstrated and evaluated.     

一种基于价格竞争的D2D通信资源分配算法

针对蜂窝用户与D2D用户所构成的混合网络系统中同频干扰问题,提出一种基于价格竞争的D2D通信资源分配算法.该算法不仅考虑利用基站定价来减小对蜂窝系统的干扰,而且联合基站端的干扰容限对干扰功率进行约束.首先将问题公式化制定非合作博弈过程,利用效用函数模型分析D2D用户的利润和对蜂窝基站的干扰,从而调整发射功率最大化系统整体收益;然后通过通信的D2D用户来更新干扰继而更新复用价格;最后采用注水算法利用拉格朗日条件来优化价格.仿真表明,该算法不仅可以简单地控制干扰功率,而且还有效地提高了系统吞吐量.     

一种联合D2D资源分配的中继选择算法的研究

为提高蜂窝网络中边缘用户的QoS,可在蜂窝网络中引入了D2D辅助中继技术,然而由于D2D链路复用蜂窝资源会与蜂窝链路之间产生同频干扰,为了降低这种干扰,提出了一种联合资源分配的中继选择算法,该算法首先使用一种资源分配策略,使得D2D链路对蜂窝链路产生的干扰最小,然后结合资源分配结果提出了一个中继选择方案,该方案不仅考虑了D2D链路和回程链路的信道质量,而且还同时考虑到了对蜂窝链路的干扰问题。通过仿真验证,本文所提出的算法不仅能有效提升边缘用户吞吐量,同时保证了中心用户的QoS。     

认知无线网络的Rubinstein博弈频谱共享方法研究

针对传统的功率控制算法限制认知用户的发射功率而影响其服务质量(Quality of Service, QoS)的问题,提出了一种基于功率控制的多人Rubinstein博弈频谱分配算法。该算法通过牛顿迭代公式降低认知用户的发射功率并依据各个用户间的干扰得到相应的链路质量;将经济学中的贴现因子与用户的链路质量建立映射关系;通过链路质量调整认知用户子博弈顺序使得网络总传输速率达到一个相对稳定的状态。仿真结果表明:多个认知用户在同一信道下共享频谱时,采用多人Rubinstein博弈算法对系统的总传输速率有明显的提升,使系统处于稳定且高速的传输状态并节省了一半以上的频谱分配时间。