ID 原文 译文
17505 然而，这显著地增加了用户设备(UE)的能耗，特别是在信道条件较差或者相邻D2D链路干扰较严重的情况下。 However, this increases significantly the power consumption of the User Equipment (UE),especially in the case of poor channel conditions or severe interference.
17506 该文基于中继和能耗模型构造了一个传输成功率和能耗折中的优化问题，进而提出一个基于二分法的通信时隙最优配置算法。 An optimization problem based on therelay and energy consumption models is constructed to seek a compromise between transmission successprobability and energy consumption, and a communication slot optimal configuration algorithm based ondichotomy is proposed in this paper.
17507 数值结果表明较多的预留时隙数量极大地增加了UE的能耗，但是不会显著提高传输成功率， The numerical results show that the larger number of reserved time slotscan lead to an excessive increase in energy consumption without significantly increasing the transmissionsuccess probability.
17508 与多中继传输、随机中继传输和100%成功传输等其他算法相比，该文提出的预留时隙最优配置算法获得了最小的能耗和几乎最大的传输成功率(只比100%成功传输方案低)。 Compared with other algorithms such as multi-relay transmission, random relaytransmission and 100% successful transmission, the proposed reserved time slot optimal configuration algorithmcan obtain the least energy consumption and almost the highest transmission success probability (it is onlylower than that under 100% successful transmission scheme).
17509 该文利用色集合事先分配法、构造染色法、反证法讨论了完全三部图K2,4,p的点可区别IE-全染色问题，确定了K2,4,p的点可区别IE-全色数。 Vertex-distinguishing IE-total colorings of complete tripartite graphs K2,4,p are discussed by using themethods of distributing the color sets in advance, constructing the colorings and contradiction. The vertex-distinguishing IE-total chromatic numbers of K2,4,p are determined.
17510 该文利用色集事先分配法、构造染色法、反证法探讨了完全三部图K4,4,p (p≥1008)的点可区别IE-全染色问题，确定了K4,4,p (p≥1008)的点可区别IE-全染色数。 The vertex-distinguishing IE-total coloring of complete tripartite graphs K4,4,p (p≥1008) is discussed,by using of the methods of distributing the color sets in advance, constructing the colorings and contradiction.The vertex-distinguishing IE-total chromatic number of K4,4,p (p≥1008) is determined.
17511 在密集异构蜂窝网络和无线局域网络构成的超密集异构无线网络中, 变速移动的车辆终端会面临更加频繁的切换，导致用户服务质量(QoS)变差。 In the ultra-dense heterogeneous wireless network composed of heterogeneous cellular networks and wireless local area networks, vehicle terminals with variable speeds will face more frequent handovers, resulting in the deterioration of user’s Quality of Service (QoS).
17512 该文针对上述问题，首先，利用高斯马尔可夫移动模型，预测车辆下一时刻的位置，筛选出满足终端服务质量的候选网络集，与当前的候选网络集做交运算， For the above problems, firstly, the Gauss Markovmobility model is used to predict the position of the vehicle terminal at the next moment, and the candidatenetwork set that meets the terminal service quality is selected to make the intersection with the currentcandidate network set.
17513 其次，当前接入网络不在交集中，则使用变步长的萤火虫算法寻找最佳网络； Secondly, if the current access network is not in the intersection, the variable-step fireflyalgorithm is used to find the best network.
17514 再次，对因预测误差导致的切换失效，则把终端用户迁移到宏蜂窝，以保证通信的持续性。 Thirdly, the terminal that fails to switch due to the prediction erroris migrated to the macro cellular to ensure the continuity of communication.