ID 原文 译文
20975 在无线中继网络中，中继节点间的随机传输延迟将导致显著的性能下降。因此，针对慢衰落瑞利信道提出可容忍随机时延的分布式线性卷积空时码(DLC-STC)， In wireless relay networks, random transmission delays among relay nodes will lead to substantial performance degradation, for which delay-tolerant Distributed Linear Convolutive Space-Time Code (DLC-STC) is proposed.
20976 但该类空时码在快衰落信道下的分集性能尚未明确。 However, its diversity gain on fast fading Rayleigh channels is not clear.
20977 该文从理论上证明了DLC-STC在快衰落瑞利信道下的分集增益。 This paper analyzesthe diversity gain of the DLC-STC on fast fading Rayleigh channels.
20978 分析表明，DLC-STC虽然最初是在慢衰落信道下被提出的，但它在快衰落瑞利信道下通过利用最大似然(ML)接收机，仍可获得满异步协作分集增益， It is shown that the DLC-STC can achievefull asynchronous cooperative diversity order with Maximum Likelihood (ML) receivers on fast fading Rayleighchannels, although it is originally proposed for slow fading channels.
20979 仿真结果验证了该理论分析，仿真结果同时表明：在快衰落瑞利信道下，DLC-STC采用MMSE-DFE接收机能够获得与ML接收机相同的分集增益。 The numerical results verify the theoreticalanalysis and show that MMSE-DFE receivers, can collect the same diversity order as ML receivers on fastfading Rayleigh channels.
20980 针对网络功能虚拟化(NFV)环境下，现有服务功能链部署方法无法在优化映射代价的同时保证服务路径时延的问题，该文提出一种基于IQGA-Viterbi学习算法的服务功能链优化部署方法。 For Network Function Virtualization (NFV) environment, the existing placement methods can not guarantee the mapping cost while optimizing the network delay, a service function chaining optimal placement algorithm is proposed based on the IQGA-Viterbi learning algorithm.
20981 在隐马尔可夫模型参数训练过程中，针对传统Baum-Welch算法训练网络参数容易陷入局部最优的缺陷，改进量子遗传算法对模型参数进行训练优化， In the training process of Hidden MarkovModel (HMM) parameters, the traditional Baum-Welch algorithm is easy to fall into the local optimum, so thequantum genetic algorithm is proposed, which can better optimize the model parameters.
20982 在每一迭代周期内通过等比例复制适应度最佳种群的方式，保持可行解多样性和扩大空间搜索范围，进一步提高模型参数的精确度。 In each iteration, the improved algorithm maintains the diversity of feasible solutions and expands the scope of the spatial search by replicating the best fitness population with equal proportion, thus improving the accuracy of the model parameters.
20983 在隐马尔科夫链求解过程中，针对隐含序列无法直接观测这一难点，利用Viterbi算法能精确求解隐含序列的优势，解决有向图网络中服务路径的优化选择问题。 In the process of solving Hidden Markov chain, to overcome the problem that can not be directly observed for hidden sequences, Viterbi algorithm can solve the implicit sequences exactly and solve the problem of optimal service paths in the directed graph.
20984 仿真实验结果表明，与其它部署算法相比，所提IQGA-Viterbi学习算法能有效降低网络时延和映射代价的同时，提高了网络服务的请求接受率。 Experimental results show that the network delay and mappingcosts are lower compared with the existing algorithms. In addition, the acceptance ratio of requests is raised.