ID 原文 译文
16845 该文设计了一种基于深度学习网络(DQN)的博弈算法，帮助车辆用户进行信道选择，并通过神经网络多次迭代学习，为用户提供最优的功率分配策略。 In this paper, considering the multipleaspects of task offloading impact factor, a mixed unloading strategy based on NOMA-MEC is proposed. A game algorithm based on Deep Q-learning Network (DQN) is designed to make channel selection for vehicle users and provide an optimal power allocation strategy through multiple iterative learning of neural networks.
16846 仿真结果表明，该文所提出的混合NOMA-MEC卸载策略能够有效地优化多用户卸载的时延以及能耗，最大限度保证用户效益。 The simulation results show that the proposed hybrid NOMA-MEC offloading strategy can effectively optimize the multi-user offloading delay and energy consumption and ensure maximize the benefits of users.
16847 为解决大位宽变长数据包情况下包尾数据的循环冗余校验(CRC)32算法处理存在的臃肿低效问题，将循环冗余校验算法变换为矩阵线性运算，利用逆矩阵反向回滚运算，得到正确的CRC运算结果； In order to overcome the complicated implementation to process tail data in high bit-width CyclicRedundancy Check(CRC) calculation for variable length packet, linear matrix computation is used to investigate CRC inverse calculation. And a rollback algorithm is introduced to simplify the regular algorithm.
16848 并在FPGA上进行了实验验证。 Then the experiment is conducted to implement the rollback algorithm in Altera FPGA device.
16849 结果表明：回滚运算的算法可行，并且实现简单，资源占用少。 The results show that rollback algorithm utilizes fewer resource and is more easily to implement.
16850 在512 bit位宽的情况下，回滚算法使得资源占用降低到了传统算法的15%； In 512 bit data widthvariable length CRC calculation implement in FPGA, the resource utilization is decreased to 15% of regularalgorithm by applying rollback algorithm.
16851 综合耗时降低到了传统算法的30%，布局/布线的耗时降低到了传统算法的40%。 Synthesis time is decreased to 30%, and Place&Route time is deceased to 40%. It is concluded that the new rollback algorithm has great advantage.
16852 随着物联网(IoT)规模的不断发展，其业务需求呈现出多样化、全球化的趋势。 With the continuous development of the Internet of Things(IoT), its business demands show a trend of diversification and globalization.
16853 针对地面物联网无法覆盖全球的缺点，卫星物联网尤其是低轨卫星星座(LEOSC)物联网可以有效地为地面物联网提供覆盖性能上的补充和延伸。 As the ground Internet of Things can not cover the whole world, the satellite IoT, especially the Low Earth Orbit Satellite Constellation (LEOSC) IoT, can supplement and extend the ground network.
16854 由于低轨卫星星座物联网系统广覆盖、高动态的特点，其业务量统计特性需要考虑到环境因素造成的影响，这导致其业务量分布与地面物联网存在显著差异。 Due to the wide coverage and high dynamic characteristics of the LEOSC IoT system,there are significant differences between it and the ground IoT in terms of traffic statistics.