基于非合作博弈的OFDMA无线多跳中继网络上行链路资源分配算法

研究了基于OFDMA多址技术的无线多跳中继网络上行链路资源分配问题。首先,在最大发射功率等约束条件下,建立了多小区OFDMA无线多跳中继网络上行链路的资源分配优化模型。将非合作博弈论和定价机制引入后,该优化问题可转化为在每个子信道上独立地进行功率分配。基于非合作博弈的功率分配模型中的纳什均衡点的存在性和唯一性得到了证明,并给出了具体的分布式求解算法。仿真结果表明,所提算法能在大幅减少系统总发射功率的情况下,有效地提升系统吞吐量,达到较高的能效比。     

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。     

多小区OFDMA解码转发中继通信系统的分布式资源分配算法

现有的多小区OFDMA中继通信系统资源分配的研究主要集中在单个小区的场景下,而不考虑由相邻小区引起的共信道干扰的影响。然而,实际系统中更高的频率复用因子和较小的小区半径,会导致严重的小区间干扰。该文考虑了多小区OFDMA解码转发中继通信系统的资源分配,它是一个混合离散型优化问题,即使在单小区场景下也是NP-hard难解的。由于全局最优求解的复杂性,该文提出一种分布式的次优的资源分配算法。算法分成两步:首先基于较低的信道反馈系统开销,分配子载波以满足用户的QoS要求;然后,将功率控制问题进一步简化并分解为多个凸优化的子问题,由椭球算法不断收敛的对偶变量迭代调整各个子问题的最优求解。仿真结果表明,与参考算法相比,所提算法的系统容量和边缘用户的吞吐量性能都有很大的提升。     

基于广义纳什议价解的CR网络子载波分配算法

在基于OFDMA的感知无线电网络中,提出了基于广义纳什议价解的动态子载波分配方案,通过在用户数目和业务需求不对等的两个从网络小区之间,实现了公平的子载波分配,仿真结果表明,方案明显优于基于最大最小公平算法的资源分配方案。     

多小区OFDMA系统基于改进PSA的资源分配算法

针对载波、功率资源分配问题,考虑本小区对其它小区的干扰情况,提出了一种应用于多小区正交频分多址复用(OFDMA)系统中的改进罚函数模拟退火(PSA)算法.该问题模型是在传输速率和性能一定的条件下,最小化传输功率.该算法是一种随机寻优算法,是一种能将局部搜索扩展为全局搜索的启发式算法.仿真结果表明,改进算法简化了问题模型...     

基于无线中继网络的OFDMA-MIMO资源分配算法研究

无线中继技术与OFDMA-MIMO技术相结合,能够大大提高系统的性能,是一种极具潜力的技术。本文结合无线中继网络中OFDMA的帧结构,以轮询调度算法和贪婪算法为参考,提出了多天线下的OFDMA无线中继网络的比例公平资源分配算法;并和无中继的OFDMA系统及单天线下的OFDMA无线中继网络资源分配算法相比较。通过仿真表明本文提出的资源分配算法在考虑每个用户数据速率的同时能够大大提高系统的传输速率,满足用户的多种业务需求。     

多小区放大转发系统基于用户公平性的分布式资源分配

全频率复用的OFDMA系统中,小区间干扰严重降低了整个系统,特别是小区边缘的频谱效率。文中考虑了多小区放大转发系统下行链路的资源管理,须联合优化用户调度、载波和功率的分配,并设计了一个分布式的资源分配算法,算法基于本小区局部信息分步完成用户调度和功率控制,小区之间只需要较少的交互信息。仿真结果表明,文中算法较传统算法具有更好的系统性能。     

基于博弈论的OFDMA系统多小区功率协调分配算法

提出了一种用于正交频分多址(OFDMA)通信系统下行链路的多小区功率协调分配算法,协调相邻小区在同频子信道上的发送功率,抑制小区间干扰,提高信道容量.将博弈论引入到多小区功率分配中,给出基于定价的多小区非合作功率分配博弈模型,并分析了该博弈的纳什均衡解的存在性和惟一性,给出了该模型的分布式求解算法.通过仿真分别给出了单小区场景与多小区场景下算法性能,讨论了定价因子对系统性能的影响.仿真结果表明,提出的多小区功率协调分配算法可以充分利用无线资源,提高系统吞吐量,降低平均发送功率.     

一种多小区OFDMA系统中有效公平的资源分配方法

在多小区OFDMA系统中,一个主要的研究方向就是如何通过控制来自邻小区的同道干扰来优化系统性能。采用了联盟形成以及合作博弈中纳什讨价还价解(NBS)的方法,使小区内用户随机地结合成合作型双人联盟并对子载波的使用进权行讨价还价,从而换取有利于自己的子载波。所提出的算法不仅复杂度低而且能产生一个公平的解,另外还证明了这种机制下的帕累托最优性。仿真结果充分表明了所提算法的有效性与公平性。     

一种基于多小区OFDMA系统用户公平性约束的分布式资源分配算法

针对多小区OFDMA系统下行链路,研究了用户公平性约束下的资源分配问题,提出了一种多基站协作的迭代优化的分布式资源分配算法。每个小区根据干扰状况及用户公平性,迭代地进行子载波和功率的资源优化;而每次迭代中,根据用户公平性准则分配子载波,并将非凸的小区功率优化问题转化为其下界的凸问题,通过一个分布式算法来求解。通过仿真验证了算法的有效性;仿真结果表明,与传统网络的固定功率分配的情形相比,所提算法保证了用户之间的公平性并显著提高了系统吞吐量。     

Distributed interference suppression scheme for multi-cell uplink OFDMA system based on non-cooperative game

In this paper,a distributed interference suppression scheme is proposed for multi-cell uplink orthogonal frequency division multiple access(OFDMA) system.Firstly,we model resource allocation process as a non-cooperative game.Then we show the concept of Nash equilibrium(NE) and investigate its existence and uniqueness in detail.To enhance the performance of multi-cell OFDMA system,the further improvement process based on NE is given.Several adjustable parameters are set to make the system achieve different tradeoffs between the total capacity and complexity.Simulation results show that the proposed scheme can greatly improve the system performance comparing with conventional scheme.     

Distributed Resource Allocation Based on Game Theory in Multi-cell OFDMA Systems

A game theoretic solution for distributed uplink resource allocation in multi-cell OFDMA systems is presented in this paper. Convergence rule and steady state characterization are analyzed with potential game. Simulation results show that the proposed non-cooperative potential game algorithm for dynamic subcarrier and power allocation can converge to Nash Equilibrium (NE) quickly and achieve higher energy efficiency compared with pure iterative water-filling algorithm.     

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.     

Modeling radio resource management in cognitive radio system based on OFDMA using colored Petri Net

Cognitive Radio (CR) system based on Orthogonal Frequency Division Multiple Access (OFDMA), such as Wireless Regional Area Networks (WRAN) and Worldwide Interoperability for Microwave Access (WiMAX), often attempt to improve performance via dynamic radio resource management, which is characterized as concurrent processing of different traffic and nondeterministic system capacity. It is essential to design and evaluate such complex system using proper modeling and analysis tools. In the previous work, most of the communication systems were modeled as Markov Chain (MC) and Stochastic Petri Nets (SPN), which have the explicit limitation in evaluating adaptive OFDMA CR system with wide area traffic. In this paper, we develop an executable top-down hierarchical Colored Petri Net (CPN) model for adaptive OFDMA CR system, and analyze its performance using CPN tools. The results demonstrate that the CPN can model different radio resource management algorithms in CR Systems, and the CPN tools require less computational effort than Markov model using Matlab, with its flexibility and adaptability to the traffics which arrival interval and processing time are not exponentially distributed.     

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。