ID 原文 译文
46806 实验结果表明，用户数据的机密性得到了提升，并且相较于其他在云端加密的云存储方案，所提方案所带来的性能损耗降低了许多。 The result of experiment shows that the confidentiality of data improved and the performance loss is decreased considering to other cloud storage scheme that encrypt data in cloud.
46807 针对传统稀疏贝叶斯学习算法(SBL)在解决低信噪比条件下信号到达角(DOA)估计有效性的问题，提出基于酉变换的实数域稀疏贝叶斯学习(RV-OGSBL)的快速离格 DOA 估计方法。 A rapid off-grid DOA estimating method of RV-OGSBL was raised based on unitary transformation, against the problem of traditional sparse Bayesian learning (SBL) algorithm in solving effectiveness of signal’s DOA estimation under condition of lower signal noise ratio (SNR).
46808 该方法首先对均匀线阵的实际接收信号通过构造增广矩阵作为 DOA 估计的处理信号， Actual received signal of uniform linear array was generated through constructing augment matrix as the processing signal used by DOA estimation.
46809 然后利用酉变换将估计模型从复数域转化到实数域， Then, estimation model was transformed from complex value to real value by using unitary transformation.
46810 进一步在实数域下将离格模型与稀疏贝叶斯学习算法相结合迭代处理实现 DOA 估计， In the next step, off-grid model and sparse Bayesian learning algorithm were combined together to process the realization of DOA estimation iteratively.
46811 获得较高的估计精度。 The accuracy of estimation could made relatively high.
46812 仿真结果表明，RV-OGSBL 方法不仅能保持传统 SBL 算法的性能，而且显著降低了计算复杂度。 The simulation result demonstrates that the RV-OGSBL method not only maintainsthe performance of traditional SBL algorithm, but also reduces the computational complexity significantly.
46813 在低信噪比和低快拍数的情况下，算法运行时间降低约 50%， Under the sit-uation of lower signal noise ratio (SNR) and low number of snapshots, the running time of algorithm is reduced about50%.
46814 表明该方法是一种快速的 DOA 估计算法。 This shows the RV-OGSBL method is a rapid DOA estimation algorithm.
46815 编码协作通过将虚拟 MIMO 传输和信道编码相结合，在取得分集增益的同时实现了前向纠错，是 5G 通信中极具应用前景的技术。 By combining virtual MIMO transmission and channel coding, coded cooperation could do forward error cor-rection and obtain diversity gain simultaneously, making it a good candidate for the key technologies of 5G communica-tion.