共查询到18条相似文献,搜索用时 218 毫秒
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在多输入多输出(MIMO)系统中,天线选择技术平衡了系统的性能和硬件开销,但大规模MI-MO系统收发端天线选择复杂度问题一直没有得到很好的解决.基于信道容量最大化的准则,采用两个二进制编码字符串分别表示发射端和接收端天线被选择的状态,提出将二进制猫群算法(BCSO)应用于多天线选择中,以MIMO系统信道容量公式作为猫群的适应度函数,将收发端天线选择问题转化为猫群的位置寻优过程.建立了基于BCSO的天线选择模型,给出了算法的实现步骤.仿真结果表明所提算法较之于基于矩阵简化的方法、粒子优化算法具有更好的收敛性和较低的计算复杂度,选择后的系统信道容量接近于最优算法,非常适用于联合收发端天线选择的大规模MIMO系统中. 相似文献
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为解决麻雀搜索算法在多输入多输出(Multiple-Input Multiple-Output, MIMO)系统中进行天线选择时,存在过早收敛导致系统容量非最优的问题,提出了一种改进的麻雀搜索天线选择算法。该算法首先结合0-1规划推导出信道容量函数,将其作为适应度函数。在天线选择子集寻优过程中,通过引入自适应变异,有效增加了个体变化的多样性。最后将禁忌搜索算法融入到算法中,并加速算法收敛。仿真结果表明,改进的麻雀搜索天线选择算法能够有效降低MIMO系统进行天线选择时的运算复杂度,在保证收敛速度的同时,最终得到的系统信道容量趋近于最优算法。 相似文献
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传统的基于信道容量最大化准则的天线选择算法虽然使信道容量达到了最大化,但是计算复杂度很高。针对计算复杂度高的问题, 提出了一种基于Doolittle-QR 分解的低复杂度天线选择算法。该算法基于Doolittle-QR 分解,可以快速选择出使系统容量最大化的天线。与传统的天线选择算法相比,该算法的计算复杂度不仅有效地降低了, 而且容量性能相近。在60GHz 室内信道下,仿真实验结果表明, 该算法具有良好的容量性能,优于随机天线选择算法,接近最优天线选择算法。 相似文献
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为了在文本数据中选择有效的文本特征,本文提出一种新的基于改进二进制粒子群优化的特征选择算法,该算法利用翻转角度,局部翻转因子和全局翻转因子来决定粒子群的进化,通过求解目标函数的最优解,得到二进制特征选择系数,选择特征选择系数为1的特征为有效特征。实验证明,该方法不仅有效地降低了运算开销,而且提高了文本分类的准确度。 相似文献
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高峰均比(PAPR)是多载波码分多址(MC CDMA)技术应用中亟待解决的关键问题。对于采用Walsh Hadamard(WH)扩频码的系统来说,优化用户扩频码的分配方案可降低系统的PAPR,但最优扩频码分配方法运算复杂度太高。为此,采用具有优良迭代寻优能力的粒子群优化算法(PSO)来降低算法的复杂度。改进算法将最优分配方案的高维搜索问题转化为粒子群迭代寻优过程。分析比较和仿真结果表明,与最优算法相比,改进算法在降低PAPR性能方面有0.5~1.5 dB的性能损失,而复杂度远小于最优算法,是一种简单实用的峰均比降低方法。 相似文献
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Antenna selection is a low-cost low-complexity attractive approach in MIMO systems that capture many advantages of these systems. In this paper, our objective is to select the best antennas that maximize throughput with truncated selective repeat automatic repeat request at data link layer in zero-forcing MIMO receivers. We propose a novel binary particle swarm optimization method with throughput as its fitness function for joint transmit and receive antenna selection. The results of simulations demonstrate that the proposed throughput based antenna selection method has better performance compared to capacity based methods, and PSO algorithm can significantly reduce computational complexity. 相似文献
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This paper investigates the receive antenna selection problem to maximize capacity in wireless MIMO communication system, which can be formulated as an integer programming optimization problem and can not be directly solved because of its non-convex characteristics caused by the discrete binary antenna selection factor. To deal with this challenge, a computationally efficient approach, particle swarm optimization(PSO) algorithm is introduced, in which the particle is defined as the discrete binary antenna selection factor and the objective function is associated with the capacity corresponding to the specified antenna subsection represented by the particle. Furthermore, in order to meet the condition that the number of selected antennas should keep fixed, the particle elements are relaxed to change between [0 1] and the position of the higher elements are taken as the index of the antenna subsection to be activated. Then the best antenna subset can be found by seeking the global optimal particle in PSO. Numerical results reveal that PSO algorithm exhibits a promising performance when applied to both the classical benchmark function and our antenna selection scenario. 相似文献
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自适应阵列天线常需要采用宽零陷技术,以增强阵列天线抗干扰的稳健性。为此,提出了一种基于混沌粒子群算法(CPSO)的阵列天线宽零陷方向图综合方法。该算法首先采用混沌序列初始化粒子位置,以增强搜索多样性,并在对部分非优胜粒子的位置更新时引入混沌扰动项,在每次迭代中对全局最优位置进行变尺度混沌优化,提高了全局和局部搜索能力,加快了收敛速度。仿真结果验证了混沌粒子群算法在阵列天线宽零陷方向图综合时的收敛速度和精度方面均优于标准粒子群算法。 相似文献
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Orthogonal frequency division multiplexing (OFDM) which has been adopted in the long-term evolution (LTE) system can improve the system capacity obviously. However, it also brings about severe inter-cell interference (ICI) for cell-edge users (CEUs). To tackle this problem, multi-user selection and power control (MuS-PC) is proposed as an efficient scheme in uplink coordinated multi-point multi-user multi-input multi-output (CoMP-MU-MIMO) transmission/reception. This paper jointly considers user's signal to interference plus noise ratio (S1NR) and proportional fairness (PF) to maximize the total channel capacity in multi-user selection by formulating a penalty function. To simplify the penalty function's computation, particle swarm optimization (PSO) algorithm is introduced. In addition, power control is adopted to maximize overall energy efficiency. Simulation results demonstrate that the MuS-PC scheme can not only obtain the optimal total channel capacity while guarantee each user's quality of service (QoS) and PF, but also largely reduce computational complexity and improve energy efficiency. As a result, the poor communication quality of CEUs can be enhanced. 相似文献
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为了提高粒子群算法(PSO)的收敛性及多样性,提出一种基于区域分割的自适应变异粒子群算法(RSVPSO).算法采用区域分割的思想,利用粒子间信息交叉,使粒子搜索区间快速缩小;同时在迭代后期与自适应变异策略相结合,提高粒子跳出局部最优陷阱的能力和增强粒子多样性,达到寻优的目的.将所提出的算法应用于8个测试函数,并与精英免疫克隆选择的协同进化粒子群等算法进行比较,结果表明,新算法在收敛速度、搜索精度及寻优效率等方面有较大提高. 相似文献
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Chien‐Ching Chiu Yi‐Xiang Tong Yu‐Ting Cheng 《International Journal of Communication Systems》2019,32(9)
In this paper, ultrawide band (UWB) communication systems with eight transmitting and receiving ring antenna arrays are implemented to test the bit error rate and capacity performance. By using the ray‐tracing technique to compute any given indoor wireless environment, the impulse response of the system can be calculated. The synthesized beamforming problem can be reformulated into a multiobjective optimization problem. Self‐adaptive dynamic differential evolution (SADDE) and particle swarm optimization (PSO) are used to find the excitation current and the feed line length of each antenna to form the appropriate beam pattern. This pattern can then reduce the bit error rate and increase the channel capacity and receiving energy. Numerical results show that the fitness value and the convergence speed by the SADDE are better than those by the PSO. Moreover, the SADDE had better results for both line‐of‐sight and nonline‐of‐sight cases. In other words, compared with PSO, SADDE has improved more effectively the main beam radiation energy and reduced the multipath interference. 相似文献