| ID | 原文 | 译文 |
| 1523 | 在 LTE-A 网络的过载场景中，机器类通信(Machine Type Communication，MTC)设备的突发性接入会使得网络发生严重的拥塞，甚至死锁，造成网络的接入效率低下。 | In the overload scenario of the LTE-A network, the bursty access of the machine type communication(MTC)device may cause serious congestion or even deadlock in the network, resulting in low network access efficiency. |
| 1524 | 在可用前导资源有限的前提下，根据实时负载数控制发起接入的设备数可以有效降低前导的碰撞概率，但是控制方法尚不明确。 | Under the premise that available preambles are limited, controlling the number of devices that initiate the access according to the real-time load can effectively reduce the collision probability of the preamble, but the control method is not clear. |
| 1525 | 为此，本文提出了一种接入类别限制(AccessClass Barring，ACB)的动态接入机制来优化海量 MTC 的随机接入性能。 | To do this, this paper proposes a dynamic access class barring (ACB)mechanism to optimize the random access performance of the massive MTC. An estimation model based on back-off prediction is established. |
| 1526 | 建立了一种基于退避预测的估计模型，该模型根据重传的设备数和状态转移过程估计出了实时活跃的设备数。 | The model estimates the number of real-time active devices based on the number of retransmitted devices and the state transition process. |
| 1527 | 结合估计模型和 ACB 参数调整可以最优化实时成功接入的设备数，能够有效地提高设备的接入成功率。 | Moreover, combined with the adjustment of the ACB parameter, the number of successfully accessed devices in real time can be optimized, and the ac-cess success rate of the device is effectively improved. |
| 1528 | 本文在不同负载强度场景下，将提出的 ACB 动态接入机制和现有的动态 ACB 机制的接入性能进行了比较。 | The performance of the proposed dynamic ACB scheme is compared with that of the existing dynamic ACB schemes under different traffic degrees. |
| 1529 | 仿真结果证明，本文提出的 ACB 动态接入机制的接入成功率为 100% 。 | Simulation results prove that the access suc-cess probability of the proposed dynamic ACB scheme is 100% . |
| 1530 | 而且，与现有的 ACB 动态接入机制相比，所提的新方案的平均接入时延更低。 | The performance of the proposed dynamic ACB scheme is comparedwith that of the existing dynamic ACB schemes under different traffic degrees. Simultaneously, the proposed novel scheme can get loweraverage access delay comparing with the existing dynamic ACB schemes. |
| 1531 | 该文主要研究基于卷积神经网络(Convolutional Neural Networks，CNN)的海上目标探测背景分类方法。 | In this paper, the background classification method of marine target detection based on convolutional neuralnetwork (CNN)is mainly studied. |
| 1532 | 以 CNN 中的经典网络 LeNet 为例，基于 IPIX 雷达实测数据集，进行控制变量的模型训练，对分类准确率、训练速度、一维信号的二维特征图变化等进行分析，基于实测数据集验证了利用 CNN 在一维雷达回波信号中进行海杂波与噪声分类的可行性， | Taking LeNet as an example, based on the IPIX measured data set, the model training through controlling variables is carried out. The feasibility of using CNN in the classification of sea clutter and noise in onedimensional radar echo signal is studied, and the influence of factors such as data preprocessing, |