ID 原文 译文
21485 该文针对大规模MIMO-OFDM系统，研究当基站端仅配备低精度模数转换器且采用最大比合并(MRC)接收算法时系统中用户可达速率的性能。 The achievable rate performance for the massive MIMO-OFDM system is investigated, where each antenna is equipped with low-resolution Analog-to-Digital Converters (ADC) and the Maximum Ratio Combining (MRC) receiver is assumed to be employed.
21486 通过采用加性量化噪声模型(AQNM)将非线性量化函数转化为线性量化函数，首先推导得出用户上行可达速率的闭式表达式。 The closed-form expression for the uplink achievable rate is firstly derived by using the Additive Quantization Noise Model (AQNM) model, which reforms the nonlinear quantization function into a linear one.
21487 然后基于此表达式，将具备低精度模数转换器系统与传统具有无穷精度的模数转换器系统性能进行分析比较。 Then the performance between the low-resolution quantization system and the conventional system with infinite resolution ADCs is compared based on the derived closed-form expression.
21488 最后将该文所得到的结果进行仿真分析。 Simulation results are presented to verify the analytical results.
21489 同时，该文还指出通过增加基站端天线数目可以弥补由于低精度模数转换器所造成的系统性能的损失。 In addition, it is illustrated that the performance loss of using low-resolution ADCs can be compensated for by deploying more antennas at the base station.
21490 网络用户随时间变化的行为分析是近年来用户行为分析的热点，通常为了发现用户行为的特征需要对用户做聚类处理。 Behavioral analysis of Internet users over time is a hot spot in user behavior analysis in recent years, usually clustering users is a way to find the feature of user behavior.
21491 针对用户时序数据的聚类问题，现有研究方法存在计算性能差，距离度量不准确的缺点，无法处理大规模数据。 Problems like poor computing performance or inaccurate distance metric exist in present research about clustering user time series data, which is unable to deal with large scale data.
21492 为了解决上述问题，该文提出基于对称KL距离的用户行为时序聚类方法。 To solve this problem, a method for clustering time series in user behavior is proposed based on symmetric Kullback-Leibler (KL) distance.
21493 首先将时序数据转化为概率模型，从划分聚类的角度出发，在距离度量中引入KL距离，用以衡量不同用户间的时间分布差异。 First time series data is transformed into probability models, and then a distance metric named KL distance is introduced, using partition clustering method, the different time distribution between different users.
21494 针对实网数据中数据规模大的特点，该方法在聚类的各个环节针对KL距离的特点做了优化，并证明了一种高效率的聚类质心求解办法。 For the Large-scale feature of physical network data, each process of clustering is optimized based on the characteristics of KL distance. It also proves an efficient solution for finding the clustering centroids.