ID 原文 译文
19335 首先，上行传输是基于NOMA的MTC通信的瓶颈，考虑无线蜂窝网络中支持NOMA和高可靠低迟延性能要求，该文建立了上行无线资源优化的系统模型； Currently, uplink transmission is a bottleneck of MTC based on NOMA. Firstly, considering the performance requirements supporting NOMA and high reliability and low latency in wireless cellular networks, a system model for uplink wireless resource optimization is established.
19336 然后，分析上行传输迟延，导出基于距离的链路可靠性函数； Then, the uplink transmission delay is analyzed and the link reliability function based on distance is derived.
19337 进一步，以迟延、可靠性和带宽为约束下条件，提出一种最大化中心用户和速率的无线资源分配算法，并给出算法的收敛性证明和复杂度分析； Further, with the constraints of delay, reliability and bandwidth, a wireless resource allocation algorithm for maximizing the sum rates of central users is proposed, and also the convergence proof and complexity analysis of the algorithm are given.
19338 最后，实验仿真验证了所提算法的性能优势。 Finally, the simulation results show theperformance advantages of the proposed optimal scheme.
19339 针对卷积神经网络(CNN)计算量大、计算时间长的问题，该文提出一种基于现场可编程逻辑门阵列(FPGA)的卷积神经网络硬件加速器。 Considering the large computational complexity and the long-time calculation of ConvolutionalNeural Networks (CNN), an Field-Programmable Gate Array(FPGA)-based CNN hardware accelerator is proposed.
19340 首先通过深入分析卷积层的前向运算原理和探索卷积层运算的并行性，设计了一种输入通道并行、输出通道并行以及卷积窗口深度流水的硬件架构。 Firstly, by deeply analyzing the forward computing principle and exploring the parallelism of convolutional layer, a hardware architecture in which parallel for the input channel and output channel, deep pipeline for the convolution window is presented.
19341 然后在上述架构中设计了全并行乘法-加法树模块来加速卷积运算和高效的窗口缓存模块来实现卷积窗口的流水线操作。 Then, a full parallel multi-addition tree is designed to accelerate convolution and efficient window buffer to implement deep pipelining operation of convolution window.
19342 最后实验结果表明，该文提出的加速器能效比达到32.73 GOPS/W，比现有的解决方案高了34%，同时性能达到了317.86 GOPS。 The experimental results show that the energy efficiency ratio of proposed accelerator reaches 32.73 GOPS/W, which is 34% higher than the existing solutions, as the performance reaches 317.86 GOPS.
19343 该文研究macro-femto异构蜂窝网络中移动用户的功率控制问题，首先建立了以最小接收信号信干噪比为约束条件，最大化毫微微小区的总能效为目标的优化模型； The power control problem of mobile users in macro-femto heterogeneous cellular networks is studied. Firstly, an optimization model that maximizes the total energy efficiency of femtocells with the minimum received signal-to-noise ratio as the constraint is established.
19344 然后提出了基于Q-Learning算法的毫微微小区集中式功率控制(PCQL)算法，该算法基于强化学习，能在没有准确信道状态信息的情况下，实现对小区内所有用户终端的发射功率统一调整。 Then, a femtocell centralized Power Controlalgorithm based on Q-Learning (PCQL) is proposed. Based on reinforcement learning, the algorithm can adjustthe transmit power of the user terminal without accurate channel state information simultaneously.