| ID | 原文 | 译文 |
| 18945 | 然后利用Chernoff边界特性得到该概率模型的最小误差，并以此确定该时刻分类器的权重，通过对各时刻分类器的加权，实现同步脑机接口的信号分类。 | and then the probability model is established to compute the Chernoff error bound, which is adopted as the weight of common classifier to take the discriminant process. |
| 18946 | 以脑机接口竞赛数据作为测试，并与线性判决分析、支持向量机和极限学习方法分别结合构成新的集成方法。 | The test experiments are based on the datasets from BCIcompetitions, and the proposed frame method is employed to compose with LDA, SVM, ELM respectively. |
| 18947 | 由实验结果可知，加权集成框架方法的分类性能比原独立分类方法有显著提高。 | The experimental results demonstrate that the proposed frame method shows competitive performance compared with other methods. |
| 18948 | 超密集网络(UDNs)拉近了终端与节点间的距离，使得网络频谱效率大幅度提高，扩展了系统容量，但是小区边缘用户的性能严重下降。 | Ultra-Dense Networks (UDNs) shorten the distance between terminals and nodes, which improve greatly the spectral efficiency and expand the system capacity. But the performance of cell edge users is seriously degraded. |
| 18949 | 合理规划的虚拟小区(VC)只能降低中等规模UDNs的干扰，而重叠基站下的用户的干扰需要协作用户簇的方法来解决。 | Reasonable planning of Virtual Cell (VC) can only reduce the interference of moderate scaleUDNs, while the interference of users under overlapped base stations in a virtual cell needs to be solved bycooperative user clusters. |
| 18950 | 该文提出了一种干扰增量降低(IIR)的用户分簇算法，通过在簇间不断交换带来最大干扰的用户，最小化簇内的干扰和，最终最大化系统和速率。 | A user clustering algorithm with Interference Increment Reduction (IIR) is proposed,which minimizes the sum of intra-cluster interference and ultimately maximizes system sum rate bycontinuously switching users with maximum interference between clusters. |
| 18951 | 该算法在不提高K均值算法的复杂度的同时，不需要指定簇首，避免陷入局部最优。 | Compared with K-means algorithm, this algorithm, no need of specifying cluster heads, avoids local optimum without increasement of the computation complexity. |
| 18952 | 仿真结果表明，网络密集部署时，有效提高系统和速率，尤其是边缘用户的吞吐量。 | The simulation results show that the system sum rate, especially the throughput ofedge users, can be effectively improved when the network is densely deployed. |
| 18953 | 针对大规模多入多出(MIMO)系统上行链路非平稳空间相关信道的估计问题，该文利用信道的时间-空间2维稀疏结构信息，应用狄利克雷过程(DP)和变分贝叶斯推理(VBI)，设计了一种低导频开销和计算复杂度的信道估计迭代算法，提高了信道估计精度。 | To deal with the estimation problem of non-stationary channel in massive Multiple-Input Multiple-Output (MIMO) up-link, the 2D channels’ sparse structure information in temporal-spatial domain is used, to design an iterative channel estimation algorithm based on Dirichlet Process (DP) and Variational BayesianInference (VBI), which can improve the accuracy under a lower pilot overhead and computation complexity. |
| 18954 | 由于平稳空间相关信道难以适用于大规模MIMO系统，该文借助于狄利克雷过程构建了非平稳空间相关信道先验模型，可将具有空间关联的多个物理信道映射为具有相同时延结构的概率信道，并应用变分贝叶斯推理设计了低导频开销和计算复杂度的信道估计迭代算法。 | On account of that the stationary channel models is not suitable for massive MIMO systems anymore, a non-stationary channel prior model utilizing Dirichlet Process is constructed, which can map the physical spatial correlation channels to a probabilistic channel with the same sparse temporal vector. By applying VBItechnology, a channel estimation iteration algorithm with low pilot overhead and complexity is designed. |