ID 原文 译文
733 一方面利用该相关运算的结果作频偏估计，另一方面利用取 α 为导频长度一半的相关运算结果的共轭形式与解调信号一同作最大似然相偏估计。 On the one hand, the frequency offset is estimated by the result of the correlation operation, and on the other hand the conjugate form of the correlation operation result which takes α as half of the pilot length α is used to implement maximum likelihood (ML)phase offset estimation.
734 理论分析和仿真结果均表明，考虑修正的数据帧结构，提出的 CPEDT-TCSP 可以实现传统载波同步模式中频偏估计和相偏估计的解耦合， Theoretical analysis and simulation results show that considering a modified data frame, the CPEDT-TCSP can implement the decoupling between both the frequency offset estimation and phase offset estima-tion in the TCSP,
735 同时还能够显著降低最大似然相偏估计的复乘运算量。 and can also reduce the complex multiplication operations for the ML phase offset estimation.
736 高性能深度包检测系统使用确定型有穷自动机 DFA(Deterministic Finite Automata)来执行数据包的检测过程。 Start-of-the-art deep packet inspection system uses deterministic finite automata (DFA)algorithms to per-form regular expression matching.
737 然而，DFA 所带来的存储消耗问题使其难以适用于片内资源稀缺的 FPGA。 Nevertheless, the storage consumption problem caused by DFA make it difficult to applyto FPGA with scarce on-chip resources.
738 目前已存在多种算法着眼于解决DFA 的空间爆炸问题，但是其在带来较好压缩率的同时，也在一定程度上影响到了系统的检测速度。 At present, there are many algorithms aiming at solving the space explosion problem of DFA, but it affects the detection speed of the system to some extent while bringing better compression ratio.
739 本文提出了一种无匹配时间损耗的 DFA 压缩算法，并在此基础上，基于 FPGA 硬件平台，设计实现了单个 DFA 匹配引擎。 In this paper, a DFA compression algorithm without matching time loss is proposed. Based on the hardware platform of FPGA, a single DFA matching engine is designed and implemented.
740 实验测试结果表明，本文所设计的算法，在未影响整个系统匹配性能的前提下，可以实现 10% ～ 30% 左右的压缩率。 Experimental results show that the algorithm can achieve a compression rate of 10% to 30% without affecting the matching performance of the whole system.
741 针对频分双工(Frequency Division Duplexing，FDD)大规模多入多出(Multiple-Input Multiple-Output，MI-MO)系统中现有信道状态信息(Channel State Information，CSI)反馈方法复杂度高、反馈精度低的问题，本文提出一种基于深度学习的 CSI 压缩反馈方法。 Existing channel state information (CSI)feedback methods for frequency division duplexing (FDD)mul-tiple-input multiple-output (MIMO)systems have high complexity and low feedback accuracy. In this paper, a deep learn-ing-based CSI compression feedback method is proposed.
742 该方法首先采用卷积神经网络(Convolutional Neural Network，CNN)提取信道特征矢量， The method first uses the convolutional neural network (CNN)toextract the channel feature vector,