ID 原文 译文
21685 该文针对这一问题，以数据流的源目的地址特征为例，对不同特征数据流的控制资源消耗进行了分析，提出在控制资源分配中应对数据流的特征分布加以考虑。 To address this issue, the control resource consumption of flow requests processing with different characteristics is analyzed taking the source and destination of flow as an example, from which a conclusion is drawn that the characteristics distribution of flow should be taken into account when allocating control resource.
21686 然后，设计了一种流特征感知的控制器关联决策机制，并针对网络流的动态变化特性设计了一种快速求解算法。 Then, a flow characteristics-aware controller assignment model is designed, and a fast algorithm coping with the fluctuation of flow request is proposed.
21687 仿真结果表明，与基于负载均衡的机制对比，所提机制在使用模拟退火算法求解时能节省10%～20%的控制资源消耗； Simulation results show that when solving with the simulated annealing algorithm, the model can save 10%～20% of control resource compared with the load balancing model;
21688 所提快速求解算法可节省10%的资源消耗，且相比模拟退火算法具有较大的速度优势和良好的可扩展性。 with 10% of resource saving, the proposed algorithm outperforms the simulated annealing algorithm in execution speed and scalability.
21689 该文针对平坦衰落信道下存在信道参数差异的多天线接收信号联合参数估计和符号检测问题，提出一种基于变分贝叶斯的联合处理算法。 For the issue of joint parameter estimation and symbol detection for multi-antenna signals with channel parameters difference over flat-fading channels, a new joint processing scheme is proposed based on the Variational Bayes (VB) method.
21690 算法直接利用多个接收数据流进行信息符号的估计，抑制传统信号合成与解调解耦处理带来的性能损失。 The proposed scheme uses directly multiple received signals for the estimation of information symbols, restraining the information loss in conventional decoupled scheme of signals combination and demodulation.
21691 将问题建模为已知多组观测数据条件下发送符号、信道传输时延、信道增益和噪声功率的联合最大后验估计问题。 The problem is modeled as the joint Maximum A Posteriori (MAP) estimation of information symbols, time-delays, complex channel gains, and noise powers, given multiple observations, and approximately solved by means of VB approach.
21692 基于变分贝叶斯理论对该最大后验进行近似求解，在相对熵最小化的准则下，推导得到了各个待估参数解析形式的近似后验分布——变分分布。 Based on the criterion of minimum relative entropy, analytical-form of the approximate distributions, i.e., variational distributions, for all unknown parameters are derived.
21693 所提算法无需计算各参数精确的点估计值，而是采用信道参数和信息符号变分分布迭代处理的方式进行联合求解。 There is no need to determine accurate point estimates of the parameters. Instead, the proposed scheme proceeds iteratively by alternating between the variational distributions of channel parameters and the information symbols.
21694 仿真结果表明，所提算法通过多信号、多参数的联合处理能够获得优于经典解耦处理和部分联合处理技术的系统误码率性能，且在接收天线数目较多和观测数据长度较短时性能优势体现更加明显。 Simulation results show that the proposed joint processing scheme has significant performance improvements in comparison with conventional decoupled or partly joint processing schemes especially with large array sizes and short signal lengths.