ID	原文	译文
3203	实验结果表明，复杂环境下所提方法具有较高跟踪精度及较好稳健性。	The experiment results show that the proposed method has higher tracking accuracyand better robustness in complex environments.
3204	为解决毫米波多天线系统大量移相器（PS）引起的高功耗问题，设计了一种新型动态连接结构，以最大化频谱效率为目标提出了连续干扰消除（SIC）和连续干扰消除−交替方向乘子（SIC-ADMM）2 种混合预编码方案。	To solve the problem of large power consumption caused by a large number of phase shifter (PS) in millimeterwave multi-antenna systems, a new type of dynamic connection structure was designed. With the goal of maximizingspectrum efficiency, two hybrid precoding schemes, successive interference cancellation (SIC) and successive interfer-ence cancellation-alternating direction multiplier (SIC-ADMM), were proposed.
3205	SIC 将总速率问题分解为不同射频链路的子速率问题，先对模拟部分进行逐次迭代，转化为低维度等效矩阵后再对数字部分进行求解。	In SIC scheme, the sum-rate problemwas decomposed into multiple sub-rate problems of different radio frequency links, the analog part was solved by itera-tion, and the digital part was solved by a low-dimensional equivalent matrix.
3206	SIC-ADMM 是对 SIC 的改进，通过研究多变量子速率问题实现算法的快速收敛，并能有效地降低计算复杂度。	Based on SIC scheme, an improvedSIC-ADMM scheme was further proposed by studying the multi-variable sub-rate problem to achieve rapid convergence and reduce computational complexity effectively.
3207	仿真结果表明，所提方案在频谱效率和能量效率方面优于现有典型预编码方案，且更适用于大规模天线系统。	The simulation results show that the proposed schemes have good spectrum and energy efficiencies by comparison with the existing schemes, and are more suitable for large-scale antennasystems.
3208	此外，当关闭一定比例 PS 后，所提方案能够通过牺牲少量频谱效率换取能量效率的大幅提升。	In addition, when a certain percentage of PS is turned off, the proposed scheme can greatly improve energy effi-ciency by sacrificing a small amount of spectrum efficiency.
3209	针对大数据环境下基于 Can 树的增量关联规则算法存在树结构空间占用过大、支持度阈值无法动态设置以及 Map 与 Reduce 阶段数据传输耗时等问题，提出了一种基于信息熵和遗传算法的并行关联规则增量挖掘算法MR-PARIMIEG。	Aiming at the problems that in the big data environment, the Can-tree based incremental association rule algo-rithm had problems such as too much space occupation of the tree structure, inability to dynamically set the supportthreshold, and too much time consumption during the data transfer process between the Map and Reduce stages, the MapReduce-based parallel association rules incremental mining algorithm using information entropy and genetic algorithm(MR-PARIMIEG)was proposed.
3210	首先，该算法设计基于信息熵的相似项合并策略（SIM-IE）来合并相似数据项，并根据合并后的数据集进行 Can 树构造，从而减少树结构的空间占用；	Firstly, a similar items merging based on information entropy (SIM-IE) was designed tomerge similar data items, and a Can tree based on the merged data set was constructed, thereby reducing the space occu-pation of the tree structure.
3211	其次，提出基于遗传算法的 DST-GA 策略获取大数据环境下相对最优的动态支持度阈值，根据此阈值进行频繁项集挖掘，避免了冗余的频繁模式挖掘导致的时间消耗；	Secondly, the dynamic support threshold obtaining using genetic algorithm (DST-GA) wasproposed to obtain the relatively optimal dynamic support threshold in the big data environment, and frequent itemsetmining was performed according to this threshold to avoid the unnecessary time consumption caused by mining redun-dant frequent patterns.
3212	最后，在 MapReduce 并行化运算过程中使用并行 LZO 数据压缩算法对 Map 端输出数据进行压缩，从而减少传输的数据规模，最终提升算法的运行速度。	Finally, in the process of MapReduce parallel operation, the parallel LZO data compression algorithmwas used to compress the output data of the Map stage, thereby reducing the size of the transmitted data, and finally improv-ing the running speed of the algorithm.