ID 原文 译文
47716 通过仿真验证算法的性能。 The performance of the algorithm isverified by simulation.
47717 针对非频率选择性瑞利衰落信道，在三维（3D，three-dimensional）空间域信道模型中分析和研究紧凑型双环阵列（DUCA，double uniform circular array）多天线 MIMO 系统。 A MIMO multi-antenna system of compact double uniform circular array (DUCA) in three dimensional direc-tional frequency non-selective Rayleigh fading channel was analyzed and investigated.
47718 采用互耦效应的等效电路模型，首次导出 DUCA 天线阵元间的信号衰落相关性的通用表达式，阐明互耦效应的影响机理。 Equivalent network model of MIMO multi-antenna array considering MC effect was established, general expressions of correlations were derived andthe relationship between correlations with and without MC was classfied.
47719 分析结果与传统 MIMO 线性阵列（ULA，uniform linear array）和单环阵列（UCA，uniform circular array）相比较， Then, the results were compared with generaluniform linear array (ULA) and uniform circular array (UCA).
47720 阵元空间配置及其互耦效应将对信号衰落相关性起到决定性作用。 It was concluded that the deployment of antennas plays adecisive role in correlations between antennas.
47721 研究结果对未来 massive MIMO 多天线的设计和性能优化具有很好的指导意义。 The research has a good sense on designation of spatial massive MIMOmulti-antenna array and system optimization.
47722 针对反向粒子群优化算法存在的易陷入局部最优、计算开销大等问题，提出了一种带自适应精英粒子变异及非线性惯性权重的反向粒子群优化算法（OPSO-AEM&NIW），来克服该算法的不足。 An opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight(OPSO-AEM&NIW) was proposed to overcome the drawbacks, such as falling into local optimization, slow convergence speed of opposition-based particle swarm optimization.
47723 OPSO-AEM&NIW算法在一般性反向学习方法的基础上，利用粒子适应度比重等信息，引入了非线性的自适应惯性权重（NIW）调整各个粒子的活跃程度，继而加速算法的收敛过程。 The first one was nonlinear adaptive inertia weight(NIW), which aim to accelerate the process of convergence of the algorithm by adjusting the active degree of each par-ticle using relative information such as particle fitness proportion.
47724 为避免粒子陷入局部最优解而导致搜索停滞现象的发生，提出了自适应精英变异策略（AEM）来增大搜索范围，结合精英粒子的反向搜索能力，达到跳出局部最优解的目的。 The second one was adaptive elite mutation strategy(AEM), which aim to avoid algorithm trap into local optimum by trigging particle’s activity.
47725 上述 2 种机制的结合，可以有效克服反向粒子群算法的探索与开发的矛盾。 Two strategies were introduced to balance the contradiction be-tween exploration and exploitation during its iterations process.