ID 原文 译文
16865 该算法可应用在其他各种新型网络通信系统中，如物联网(IoT)、射频识别(RFID)和未来的5G通信系统。 Its promising properties and excellentperformance enable its potential application to emerging networks, such as Internet of Things (IoT), RadioFrequency Intification (RFID) and the incoming 5G network.
16866 针对低信噪比 (SNR)下存在多径效应的传统单通道异步长码直接序列码分多址(DS-CDMA)信号伪码序列(PN)及信息序列难估计问题，该文提出一种基于平行因子的多通道盲估计方法。 For the problem of long code Direct Sequence-Code Division Multiple Access (DS-CDMA) signal in traditional asynchronous single-channel with multipath effect under low Signal-to-Noise Ratio (SNR), including blind estimation of the Pseudo-Noise (PN) sequence and information sequence, a method using multi-channel synchronous and asynchronous based on parallel factor is proposed.
16867 该方法先将接收到的多径信号建模为多通道模型， Firstly, the received signal in multipathenvironment is modeled as a multi-channel receiving model.
16868 然后将长码DS-CDMA信号建模成短码DS-CDMA信号的缺失数据模型，形成观测缺失数据矩阵，并将其等效为缺失平行因子模型， And then the long code DS-CDMA signal ismodeled as the short code DS-CDMA signal with missing data to form the observation missing-data matrix,which is equivalent to a parallel factor model with missing data.
16869 最后利用正则交替最小二乘法(ALS)对缺失平行因子进行低秩分解，实现多径环境下长码DS-CDMA信号各用户伪码序列及信息序列的盲估计。 Finally, the regularized Alternating LeastSquares (ALS) algorithm is applied to decompose the parallel factor, and the PN sequence and informationsequence of long code DS-CDMA signals in multipath environment can be further estimated.
16870 仿真结果表明，序列的估计性能与多径环境密切相关，且在莱斯因子为10，多径路数为3，通道数为4，用户数为6，信噪比大于–10 dB的条件下，伪码序列及信息序列的估计错误率均低于1%。 Simulation results show that the performance of sequences estimation closely relates with the multipath environment, and the estimation error rate of 6 user PN sequences and information sequence is less than 1% under the condition that the Rician factor is equal to 10 and the number of path and channel are 3 and 4 respectively when the SNR is higher than -10 dB.
16871 针对移动边缘计算(MEC)中用户的卸载任务及卸载频率可能使用户被攻击者锁定的问题，该文提出一种基于k-匿名的隐私保护计算卸载方法。 Users’ offloading tasks and offloading frequencies in Mobile Edge Computing(MEC) may cause usersto be locked out. A privacy-preserving computation offloading method based on k-anonymity is proposed in thispaper.
16872 首先，该方法基于用户间卸载任务及其卸载频率的差异性，提出隐私约束并建立基于卸载频率的隐私保护计算卸载模型； Firstly, based on the differences between offloading tasks and their frequencies, privacy constraint isproposed to establish a privacy-preserving computation offloading model based on offloading frequency;
16873 然后，提出基于模拟退火的隐私保护计算卸载算法(PCOSA)求得最优的k-匿名分组结果和组内各任务的隐私约束频率； Then, aPrivacy-preserving Computation Offloading algorithm based on Simulated Annealing (PCOSA) is utilized toobtain the optimal k-anonymous groups and the privacy constraint frequency of each task;
16874 最后，在卸载过程中改变用户原始卸载频率满足隐私约束，最小化终端能耗。 Finally, the user’soriginal offloading frequencies are changed to meet the privacy constraint while minimizing terminal energyconsumption.