基于PSO-GA算法的多用户OFDM系统资源分配

为了最小化多用户OFDM系统的总发射功率,提出利用改进的粒子群算法与遗传算法相结合的联合算法(PSO-GA)来搜索最优的子载波和比特分配。该算法首先利用改进粒子群算法对系统的子载波和比特分配进行优化。算法运行过程中,当更新后的粒子速度大于最大粒子速度或小于最小粒子速度时,取最大粒子速度与最小粒子速度区间中的一个随机值作为更新的粒子速度。待PSO-GA算法的改进粒子群算法收敛后,将收敛后的种群作为遗传算法的初始种群,再利用遗传算法进行系统的子载波和比特优化分配,进而得出最优解。仿真结果表明,利用该算法比利用遗传算法、粒子群算法与Zhang算法的分配方案使系统需要的总发射功率降低2~10 dB。     

一种用于离散比特分配的改进注水算法

该文针对OFDM系统的离散比特分配问题,提出一种改进的注水算法。该算法的实现借助于文中定义的比特水线分配给某个子载波的功率直接满足整数比特约束的注水线。先用最大信道增益子载波的比特水线进行离散比特分配,再调整分配结果以满足总发射功率约束。理论证明和分析显示,该算法能实现最优比特分配,运算复杂度仅与子载波数量有关。     

基于多维离散粒子群优化的协同 OFDMA 系统跨层资源分配

摘 要：针对多服务情况下协同OFDMA（orthogonal frequency division multiple access）系统的资源分配问题,在基站和中继单独功率约束条件下,以最大化用户的效用(utility)总和为目标,提出了一种基于多维离散粒子群（MDPSO）的渐进最优资源分配算法。该算法采用多值离散变量来编码粒子位置,并针对多维离散空间构建了新的基于概率信息的粒子速度和位置更新算法,且引入变异操作来克服粒子群算法的早熟问题。此外,还采用了迭代注水法进行最优功率分配。仿真结果表明,所提算法在总效用、吞吐量和公平性上均明显优于已有资源分配算法。     

基于鲶鱼粒子群优化算法的认知无线电频谱分配

目前亟待解决如何获得认知无线电系统效益最大化问题,而求解最优频谱分配方法是一项关键技术,针对传统粒子群(PSO)算法收敛速度慢、易陷入局部最优解等缺陷,提出一种基于鲶鱼粒子群算法(CE-PSO)的认知无线电频谱分配方法。首先建立认知无线电频谱分配优化的数学模型,然后以用户取得的效益最大化为优化目标,引入"鲶鱼效应",保持粒子群的多样性,通过粒子间信息交流找到空闲频谱最优分配方案,最后采用仿真实验测试CE-PSO算法的有效性。结果表明,CE-PSO算法克服了PSO算法的缺陷,可以快速、准确地寻找到最优频谱分配方案,更好地实现系统效益的最大化,可以满足认知无线电系统的应用需求。     

三目标柯西粒子群算法的电力系统无功优化

通过建立有功网损最小、电压偏差最小和静态稳定电压裕度最大的三目标无功优化模型。提出柯西粒子群算法,并针对IEEE14节点系统进行三目标电力系统无功优化。当种群多样性较差时,通过对交叉的粒子进行柯西变异从而扩大搜索空间,提高种群多样性,防止出现过早的收敛,进而避免了算法陷入局部最优解的问题,同时也提高了收敛速度。通过数据测试和比较柯西粒子群算法在收敛速度、精度、全局搜索能力上均优于常规差分进化算法和常规粒子群算法。其结果验证了该模型和算法的有效性,为电力系统安全经济运行提供了参考。     

Power allocation in multibeam satellites based on particle swarm optimization

In this paper, a power allocation in multibeam satellite (MBS) communication based on heuristic particle swarm optimization (PSO) is proposed. The PSO algorithm is evocated to solve the problem of power allocation in the multiple narrow spotbeams aiming to provide the minimum signal-to-noise plus interference ratio (SNIR) required by Earth station users to establish reliable communication. In the developed model it is considered the multibeam interference and the different channel conditions of each beam by rain attenuation. The numerical results have been generated taking into account different sky situations, including clear and rainy scenarios; such results have revealed the viability and accuracy of the PSO algorithm deployment in solving the power allocation problem. In addition, with the proposed scheme it is observed the decrement of transmitted power for non-rainy beams with guarantee of the minimum SNIR at the Earth receivers input, while increase the power availability to the rainy beams; as a consequence, the overall energy efficiency of the MBS system has been improved substantially. Moreover, the convergence of the proposed heuristic PSO-based algorithm is discussed while such heuristic approach comes with computational complexity reduction when compared with others efficient power allocation schemes.     

DOA and Power Estimation Using Genetic Algorithm and Fuzzy Discrete Particle Swarm Optimization

Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival (DOA). In this method, genetic algorithm (GA) and fuzzy discrete particle swarm optimization (FDPSO) are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, chaotic factor and crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as Newton-like algorithm, Akaike information critical (AIC), particle swarm optimization (PSO), and genetic algorithm with particle swarm optimization (GA-PSO).     

Biological Swarm Intelligence Based Opportunistic Resource Allocation for Wireless Ad Hoc Networks

Particle swarm optimization (PSO) is one of the most important biological swarm intelligence paradigms. However, the standard PSO algorithm can easily get trapped in the local optima when solving complex multimodal problems. In this paper, an improved adaptive particle swarm optimization (IAPSO) is presented. Based on IAPSO, a joint opportunistic power and rate allocation (JOPRA) algorithm is proposed to maximize the sum of source utilities while minimize power allocation for all links in wireless ad hoc networks. It is shown that the proposed JOPRA algorithm can converge fast to the optimum and reach larger total data rate and utility while less total power is consumed by comparison with the original APSO. This thanks to the dynamic change of the maximum movement velocity of the particles, the use of the modified replacement procedure in constraint handling, and the consideration of the state of the optimization run and the population diversity in stopping criteria. Numerical simulations further verify that our algorithm with the IAPSO outperforms that with the original APSO.     

Investigation on the evolutionary algorithms with their applications in MIMO detecting systems

In this paper, with the purpose of integrating the advantages of both the genetic algorithm and the particle swarm optimization, a new genetic particle swarm optimization (GPSO) algorithm is proposed. Furthermore, these three evolutionary algorithms are successfully applied to address the MIMO detection problem. Simulation results reveal that the GPSO‐based detection algorithm takes much less population size and iteration number when compared with the particle swarm optimization‐based detection method and the genetic algorithm‐based detection method. Besides, when compared with the optimal maximum likelihood detection method, the GPSO‐based detection algorithm can strike a much better balance between the BER performance and the computational complexity. Copyright © 2012 John Wiley & Sons, Ltd.     

OFDM系统频率选择性信道功率分配方案

自适应分配技术根据子信道的瞬时估计值动态地分配传输比特数和发送功率,可以优化正交频分复用（OFDM,Orthogonal Frequency Division Multiplex）系统的整体性能。这里讨论了基于容量优化的自适应比特分配算法,基于误比特率优化的最佳功率分配算法和次佳功率分配算法。仿真结果表明,对不同信道环境下三种算法的特点和性能进行了分析和比较。仿真结果表明,自适应分配技术可以优化系统的容量和误比特率。     

改进粒子群算法在频谱功率分配中的应用

在认知无线电中,由于次用户干扰门限要求的存在,传统频谱功率分配方式获得的次用户有效信道容量较低。针对这一问题,提出了一种基于粒子群算法的频谱功率分配算法。首先建立基于干扰距离的认知网络干扰模型,将频谱功率分配问题转化为函数优化问题,并借助混合随机变异思想的粒子群算法进行求解;针对寻优过程中的约束问题,提出了一种基于投入产出比的外点法,保证粒子群在可行域中寻优,最终获得频谱功率分配。仿真结果表明,与传统算法相比,所提算法能够获得较高的次用户有效信道容量。     

频率选择性衰落信道中多用户OFDM系统的自适应分配算法

本文提出一种适用于多径频率选择性衰落信道中多用户OFDM系统的自适应分配算法.算法根据信道瞬时估计值,自适应地为多用户分配子信道和传输比特数,在给定误比特率的条件下,使系统总的发送功率达到最小.作者根据时分复用的基本思想,提出多用户最佳子信道和比特分配算法,导出系统最小发送功率的下限,并在此基础上,进一步提出次佳自适应分配算法.数值模拟表明:次佳算法所需的发送功率比下限值高约1dB;与等比特分配方案相比自适应分配算法可节省功率约3-4dB;与静态信道分配方案相比,自适应分配算法可节省功率6-8dB.     

求解多输入多输出检测新算法——遗传粒子群优化

设计两种基于粒子群优化算法(PSO)和基于遗传算法(GA)的多输入多输出(MIMO)系统检测算法.提出一种新的融合GA和PSO进化机制的遗传粒子群进化(GPSO)算法,并将其应用于MIMO系统检测问题求解.新算法改善了初始化种群,并将每一代粒子划为精英粒子、次优粒子和糟糕粒子三部分,对这三种粒子分别采用极值扰动、PSO...     

基于改进遗传算法的C-RAN网络动态无线资源分配

针对云无线网络(Cloud Radio Access Network,C-RAN)中传统静态资源分配效率低下以及动态无线资源分配中资源种类单一的问题,提出了一种基于用户服务质量(Qulity of Service,QoS)约束的动态无线资源分配方案,对无线资源从无线射频单元(Remote Radio Head,RRH)选择、子载波分配和RRH功率分配三个维度进行研究.首先,根据传统的C-RAN系统传输模型和QoS约束在时变业务环境下建立了以发射功率为变量,以吞吐量最大为优化目标的优化问题;然后,基于改进的遗传算法,将原优化方案转变为通过优化RRH选择、子载波分配和RRH功率分配来达到提高系统吞吐量的目的;最后,将改进的遗传算法与其他智能算法在种群规模变化下进行了时间复杂度对比.实验结果表明,所提算法具有较低时间复杂度,所提资源分配方案下的平均吞吐量增益为17％.     

采用PSO算法的S波段EDFA的优化设计

成功地使用粒子群优化(PSO)算法优化设计了多级S波段EDFA,仿真结果表明,输入信号功率为-20 dBm时在1486～1520 nm可实现平坦增益,两级泵浦总功率为380 mW,平均增益可达10 dB以上,增益平坦度小于0.1 dB,噪声系数小于5 dB,满足WDM/DWDM系统的需求.另外,还重点对插入长波长ASE...     

基于改进粒子群算法的后向泵浦RFA设计北大核心CSCD

为了满足第六代移动通信(6G)系统对光通信网络的高速率及大容量的要求,进一步提高光传输网络中光纤放大器的带宽、响应速率及放大倍数等成为目前的研究重点。在使用碲酸盐光纤作为光纤增益介质的同时,提出一种改进粒子群优化算法,通过在迭代过程中动态的调整速度、位置及惯性权重值,获得更高收敛速度,增强全局搜索的能力。应用该算法对拉曼光纤放大器的各个泵浦光参数配置进行优化、分析及仿真验证,最终设计出平均开关增益为23.738 8 dB,增益平坦度为0.209 8 dB的后向泵浦拉曼光纤放大器。结果表明,改进的粒子群优化算法对拉曼放大器泵浦光的参数配置有很强的适应性,能够获得较低的增益平坦度,对未来拉曼光纤放大器的设计具有一定的参考意义。     

数字调制识别SVM分类器参数优化及仿真

对于基于SVM数字信号调制识别分类器,参数选取过程中如何优化惩罚因子和径向基核函数参数问题,提出了一种改进算法。该算法将自适应惯性权重粒子群算法和k折交叉验证法结合,利用交叉验证法计算粒子适应度值,通过粒子群算法实现最优参数值搜索,最终得到分类器惩罚因子和径向基核函数参数最优值。仿真结果表明,该算法性能明显优于网格搜索法和遗传算法。     

空天地一体化网络中联合地面基站选择及功率分配

针对由卫星通信网络、地面移动网络以及空中飞行平台所组成的空天地一体化网络(Hybrid Satellite-Aerial-Terrestrial Network,HSAT)下行链路的系统吞吐量问题,提出了基于多目标遗传算法的地面基站选择及功率分配算法。在这一网络中,空中飞行平台需要地面基站为其进行中继通信。因此,地面中继基站的选择和功率分配决定了系统的吞吐量。所提算法为了取得对系统吞吐量更好的优化效果,将优化系统吞吐量和满足约束条件建模成两个优化目标,通过设计特有的迭代选择过程使地面基站选择和功率分配不断优化。仿真分析表明,所提算法在保证用户最低通信需求的前提下,有效提升了系统的吞吐量。     

Multi-point path planning based on the algorithm of colony-particle swarm optimization

The problem of multi-point path planning is a NP-hard problem,which is equivalent to finding the shortest path of a starting point and some specific node.Aiming at the problem of multi-point path planning,a retrospective ant colony-particle swarm optimization algorithm was proposed.This algorithm used Floyd-Warshall to transform the graph and combined ant colony algorithm and particle swarm algorithm to find the shortest path.The experimental results show that this algorithm can find the precise solution under small data,at the same time,under a large amount of data,can be better than the maximum minimum ant colony algorithm and genetic algorithm.     

Particle swarm optimization–based power allocation for Alamouti amplify and forward relaying protocol

The performance of wireless communication systems is improved over flat fading channel by using Alamouti coding scheme, which provides the quality of diversity gain. In this paper, performance analysis of symbol error rate (SER) and particle swarm optimization (PSO)–based power allocation (PA) for Alamouti amplify and forward (AF) relaying protocol using maximum ratio combining (MRC) technique is presented. Analytical expression of SER upper bound and SER approximation is derived for Alamouti AF relaying protocol with quadrature phase shift keying (QPSK) modulation over Rayleigh fading channel and Rician fading channel. In addition, PSO‐based optimum PA factor is calculated on the basis of the minimum SER of proposed method. PSO‐based optimum PA gives 0.5 dB of improved signal‐to‐noise ratio (SNR) compared with the equal power allocation (EPA). The theoretical approximate SER result is compared with the simulated SER. The proposed protocol provides full diversity gain and reduces SER compared with the existing AF and decode and forward (DF) relaying protocols over Rayleigh fading channel and Rician fading channel.