共查询到20条相似文献,搜索用时 31 毫秒
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In this paper, we propose an efficient power control algorithm for the downlink wireless CDMA systems. The goal of our paper
is to achieve the optimum and fair resource utilization by maximizing a weighted sum utility with the power constraint. In
fact, the objective function in the power optimization problem is always nonconcave, which makes the problem difficult to
solve. We make progress in solving this type of optimization problem using PSO (particle swarm optimization). PSO is a new
evolution algorithm based on the movement and intelligence of swarms looking for the most fertile feeding location, which
can solve discontinuous, nonconvex and nonlinear problems efficiently. It’s proved that the proposed algorithm converges to
the global optimal solutions in this paper. Numerical examples show that our algorithm can guarantee the fast convergence
and fairness within a few iterations. It also demonstrates that our algorithm can efficiently solve the nonconvex optimization
problems when we study the different utility functions in more realistic settings. 相似文献
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The design and optimization of multicarrier communications systems often involve a maximization of the total throughput subject to system resource constraints. The optimization problem is numerically difficult to solve when the problem does not have a convexity structure. This paper makes progress toward solving optimization problems of this type by showing that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function. Further, we show that the time-sharing condition is satisfied for practical multiuser spectrum optimization problems in multicarrier systems in the limit as the number of carriers goes to infinity. This result leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain. We show that the recently proposed optimal spectrum balancing algorithm for digital subscriber lines can be interpreted as a dual algorithm. This new interpretation gives rise to more efficient dual update methods. It also suggests ways in which the dual objective may be evaluated approximately, further improving the numerical efficiency of the algorithm. We propose a low-complexity iterative spectrum balancing algorithm based on these ideas, and show that the new algorithm achieves near-optimal performance in many practical situations. 相似文献
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针对射频频谱环境愈发拥挤问题,深入研究了通过波形设计的手段实现频谱共享的问题。为紧密贴近工程实践,提出了一种新的方法设计恒定幅度信号的问题。该算法首先针对雷达发射端,提出雷达波形满足特定的时域与频谱要求,施加约束,然后考虑雷达接收滤波器接收杂波,以优化最大信干噪比建立优化问题模型,得到了一个非凸的分式规划问题模型。最后,利用分步优化方法分解为两个优化问题,并且将非凸问题松弛为可解的凸问题再利用高斯随机化方法得到优化信号,多次循环优化。仿真结果验证了该算法的有效性,该方法设计得到的探测信号能够实现频谱共存,而且信干噪比性能能够得到保证。 相似文献
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双有源桥(DAB)变换器三重移相(TPS)调制方法需要预先求取系统的调制参数,难以建立并求解精确的全局效率最优目标函数。本文提出了一种基于粒子群优化(PSO)算法实现DAB变换器全局效率最优的寻优方法以解决上述问题,使变换器能以高效率在整个工作范围内运行,并保持较低的无功环流和电流峰值。仿真与实验结果都验证了所提方法的正确性与可行性。 相似文献
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基于人工蜂群算法的支持向量机参数优化及应用 总被引:2,自引:1,他引:1
为了解决常用的支持向量机(SVM)参数优化方法在寻优过程不同程度的陷入局部最优解的问题,提出一种基于人工蜂群(ABC)算法的SVM参数优化方法。将SVM的惩罚因子和核函数参数作为食物源位置,分类正确率作为适应度,利用ABC算法寻找适应度最高的食物源位置。利用4个标准数据集,将其与遗传(GA)算法、蚁群(ACO)算法、标准粒子群(PSO)算法优化的SVM进行性能比较,结果表明,本文方法能克服局部最优解,获得更高的分类正确率,并在小数目分类问题上有效降低运行时间。将本文方法运用到计算机笔迹鉴别,对提取的笔迹特征进行分类,与GA算法、ACO算法、PSO算法优化的SVM相比,得到了更高的分类正确率。 相似文献
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针对双连接可行的异构无线网络中关于用户关联和回传带宽配置的联合优化问题,构建了一个新的网络吞吐量效用和最大化框架。将该联合优化问题建模为一个非凸的混合整数分式优化问题。为了便于求解,首先将原建模问题进行去分式化转换,然后针对转换后依旧非凸的混合整数非线性优化问题,将其分解为两个优化子问题分别求解。通过固定用户关联变量,得到了最优的回传带宽配置机制;通过固定回传带宽配置因子变量,提出一个有效的迭代算法求解双连接可行的用户关联子问题。相比固定的回传带宽配置机制,所提算法可以获得最优的回传单位带宽配置因子值,同时拥有最优的系统吞吐量和系统吞吐量效用和性能。 相似文献
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In order to improve robustness and efficiency of the radio frequency identification (RFID) network, a random mating mayfly algorithm (RMMA) was proposed. Firstly, RMMA introduced the mechanism of random mating into the mayfly algorithm (MA), which improved the population diversity and enhanced the exploration ability of the algorithm in the early stage, and find a better solution to the RFID nework planning (RNP) problem. Secondly, in RNP, tags are usually placed near the boundaries of the working space, so the minimum boundary mutation strategy was proposed to make sure the mayflies which beyond the boundary can keep the original search direction, as to enhance the ability of searching near the boundary. Lastly, in order to measure the performance of RMMA, the algorithm is then benchmarked on three well -known classic test functions, and the results are verified by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), and MA. The results show that the RMMA algorithm is able to provide very competitive results compared to these well-known meta-heuristics, RMMA is also applied to solve RNP problems. The performance evaluation shows that RMMA achieves higher coverage than the other three algorithms. When the number of readers is the same, RMMA can obtain lower interference and get a better load balance in each instance compared with other algorithms. RMMA can also solve RNP problem stably and efficiently when the number and position of tags change over time. 相似文献
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Weiguo Ju Shanguo Huang ZhenZhen Xu Jie Zhang Wanyi Gu 《International Journal of Communication Systems》2015,28(2):358-373
Flexgrid optical networking is an attractive solution for efficiently matching allocated bandwidth with link demand but suffers from inevitable spectrum fragmentation. Spectrum fragmentation impairs network performance and results in high blocking rate and low spectrum utilization efficiency. Therefore, an optimization mechanism handling spectrum fragmentation is of vital importance in flexgrid optical networks. In this paper, we propose a genetic algorithm for solving the spectrum fragmentation problem with the objective of compacting occupation of the spectrum in flexgrid optical networks. A string of lightpaths are coded as the chromosome. The spectrum fusion degree and fragment fusion degree are introduced as the fitness functions to conduct the evolution in genetic algorithm, which can also be used to assess the degree of spectrum fragmentation in the network. As a result, the genetic algorithm provides a lightpath reconfiguration map, which identifies the candidate lightpaths to be reallocated, their reconfiguration sequence, and new locations. The proposed algorithm is compared with commonly used approaches under different network conditions. Simulation results demonstrate the ability of the proposed algorithm to efficiently solve the problem of spectrum defragmentation in flexgrid optical networks. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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在多输入多输出系统中,发射端和接收端的多天线配置提高了信道容量和传输可靠性,而天线选择技术能在保持系统优点的同时有效地降低运算复杂度以及硬件成本。为了能在时变的信道条件下快速地选择出一组最优的天线子集,提出了一种基于二进制粒子群算法的改进的天线选择算法。推导出了二进制粒子群联合收发端天线选择的信道容量公式,并将其作为粒子群算法的适应度函数,使天线选择问题转换成二进制编码串的组合优化问题。通过改进模糊函数提高粒子群算法的收敛性,让二进制粒子群尽可能地收敛于全局最优位置。仿真结果表明,改进的算法能在降低运算复杂度的同时提高收敛性,且系统信道容量趋近于最优算法。 相似文献
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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。 相似文献
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基于IC-PSO和ISM的反馈控制算法设计 总被引:1,自引:1,他引:0
为了提高光纤偏振模色散(PMD)补偿系统的动态自适应补偿能力,提出了用改进粒子群优化(PSO)算法作为搜索算法,用改进单纯形法(ISM)作为跟踪算法的反馈控制算法设计方案,从而实现对PMD补偿单元的动态调整。在PSO算法中,引入免疫克隆(IC)原理提高了搜索算法的全局优化能力;对SM的反射操作和扩张操作进行改进,从而提高算法的收敛速度;用映射操作代替原有的顶点代换操作,从而使单纯形在迭代过程中不发生退化现象。实验结果证明了该算法用于PMD补偿系统的有效性和可行性。 相似文献
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针对相干信源波达方向估计的需要,结合粒子群优化算法,论文提出了一种基于混沌自适应变异粒子群优化的广义极大似然算法(CAMPSOGML),算法对阵列的几何结构没有任何约束,分辨的信源数可大于阵元数,算法把混沌初始化和自适应变异策略引进粒子群算法中,有效地提高了收敛速度,克服了粒子群算法容易陷入局部最优值的缺点。计算机仿真表明:与基于实数遗传算法和粒子群算法的广义极大似然估计方法相比,CAMPSOGML算法在收敛速度和估计精度上都有优势,是一种新颖的有效的解相干算法。 相似文献
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Da-Qing Guo Yong-Jin Zhao Hui Xiong Xiao Li 《中国电子科技》2007,5(2):149-152
A new class of hybrid particle swarm optimization (PSO) algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique (LDIW) and the mutative scale chaos optimization algorithm (MSCOA) are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence. 相似文献
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An optimal linear precoding scheme based on Particle Swarm Optimization (PSO), which aims to maximize the system capacity of the cooperative transmission in the downlink channel, is proposed for a multicell multiuser single input single output system. With such a scheme, the optimal precoding vector could be easily searched for each user according to a simplified objective function. Simulation results show that the proposed scheme can obtain larger average spectrum efficiency and a better Bit Error Rate (BER) performance than Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) algorithm. 相似文献
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Dynamic spectrum management (DSM) techniques mitigate crosstalk in digital subscriber line (DSL) networks by adapting the transmit spectra to the actual noise and channel conditions. Conventional DSM schemes are designed based on single-objective optimization, either belonging to the rate-adaptive or margin-adaptive category. In this paper, an efficient crosstalk-aware DSM (CA-DSM) algorithm which jointly considers both the data rate and power is proposed to search for the best rate-power tradeoff solution based on the network conditions. The crosstalk-aware power strategy prevents transmitters which contribute excessive crosstalk from being allocated high power, thereby reducing the aggregate crosstalk noise in the system. A convex cost function is used to formulate the DSM optimization problem wherein two coefficients are introduced to make the CA-DSM algorithm adaptive to different network conditions. An iterative power update strategy is proposed for the CA-DSM algorithm to minimize the cost function. Convergence properties of the CA-DSM algorithm along with existence and uniqueness of optimal power solutions are examined analytically and illustrated graphically. Simulation results show that the proposed CA-DSM algorithm can provide a significantly better rate-power tradeoff performance compared to existing spectrum management schemes. 相似文献
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当标准的CSO算法被应用于求解高维复杂优化问题时,存在易陷入局部最优解与较差的收敛精度等明显缺陷。本文提出了一种基于Cat混沌与柯西变异的改进鸡群优化算法(ICSO),然后使用6个标准函数对ICSO算法进行了仿真寻优,结果表明,相比PSO算法、BA算法和CSO算法,ICSO算法具有更强的跳出局部收敛的能力,且寻优精度也有显著提高。 相似文献
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粒子群优化算法(PSO)自提出以来,已经被广泛地应用于求解各类复杂的优化问题,过去对粒子群算法的研究主要集中在融入新的优化方法或对其相关参数进行调整,但这样只会使得PSO更加复杂.针对这一问题,文中提出一种改进的混沌粒子群优化算法(ICPSO),ICPSO从粒子群优化算法的时间与寻优实时角度出发(即在较短的时间内获得较好的解),对粒子速度更新算子进行了简化,每隔一定代数后,在最优解邻近区域引入混沌扰动以避免种群陷入局部最优解.数值实验结果表明:提出的算法相对于文献给出的PSO改进算法,不仅能够获得较好的最优解,而且还具有较快的收敛速度和较好的稳定性. 相似文献