ID 原文 译文
58078 首先，将用户和基站进行单频段分簇，将同一小小区的多个边缘用户放在不同的用户簇中，将位于相邻小小区并且距离较近的边缘用户放在同一个用户簇中，根据中心用户到已有基站簇的干扰强度将其归簇; Firstly，users and small base stations are divided into several clusters according to the uni-subband clustering. The multiple edge users in the same small cell are assigned to different user clusters. The users atthe close edge of the adjacent small cell are assigned to the same cluster. The cell-center users are clustered according to the interference intensity of them to the existing base station clusters.
58079 然后，对用户簇进行多重子频段分配，为包含同一个小小区用户的多个簇分配不同的子频段，并且尽量为每个用户簇多分配子频段; Then，multi-subband allocation is performed for user clusters. The user clusters containing the same small cell users areallocated different sub-bands，and the number of allocated sub-bands for each user clusters is as much aspossible.
58080 最后，以最大化系统总传输速率为目标，采用注水算法为每个用户分配功率. Finally，the water-filling algorithm is used to allocate appropriate power to each user with thegoal of maximizing the total transmission rate of the system.
58081 仿真结果显示，所提方案能显著提高频谱效率. Simulation results show that spectral efficiency can be significantly improved by the proposed scheme.
58082 针对一般稀疏矩阵－矩阵乘法( SpGEMM) 的性能问题，提出了一种基于任务分类和低延迟散列表的图形处理器上的加速 SpGEMM 算法 ＲBSparse. Aiming at the performance problem of general sparse matrix-matrix multiplication( SpGEMM) ，a graphics processing unit ( GPU) -accelerate SpGEMM algorithm based on task classification and low-latency Hashing table，ＲBSparse，was presented.
58083 该算法由一种低成本子任务复杂度预分析方法和一种低延迟共享内存上的散列表的方法组成，可达到最大效率. ＲBSparse consists of a low-cost pre-analysis method to identify the complexity of sub-tasks，and a Hashing table-based algorithm which could utilize low-latency shared memory to achieve max efficiency.
58084 通过解决负载均衡和内存延迟问题，ＲBSparse 算法可以显著减少计算的总时间. By taking the load balancing issue and thememory latency issue into consideration，ＲBSparse could significantly reduce the overall time in computation.
58085 比较了 ＲBSparse 和 BHSparse 算法，ＲBSparse 算法是最快的 SpGEMM 算法，ＲBSparse 算法的性能平均是BHSparse 算法的 3. ＲBSparse and BHSparse are compared. BHSparse is the previous state-of-the-art algorithm forSpGEMM. The result shows that our algorithm is 3.
58086 1 倍，在最佳情况下可达到 14. 1 times faster than BHSparse on average，and couldachieve a maximum 14.
58087 49 倍. 49 times faster speed in the best scenario.