WO2016188498A1 - 一种无线网络吞吐量的评估方法及装置 - Google Patents
一种无线网络吞吐量的评估方法及装置 Download PDFInfo
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- H04W16/22—Traffic simulation tools or models
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- This document relates to, but is not limited to, data mining technology, and in particular to a method and device for evaluating wireless network throughput.
- Embodiments of the present invention provide a method and an apparatus for evaluating wireless network throughput, which can satisfy current network throughput behavior analysis.
- a method for evaluating a wireless network throughput including:
- N and M are both positive integers and N is greater than M.
- the historical data of acquiring the throughput of the N base stations includes:
- the value of the sequence of the preset percentage thresholds in which the sequence value is prior to each other in the original throughput sequence of each base station is replaced by the calculated average of the throughput sequence values. Value, get the new throughput sequence for each base station;
- the normalized throughput sequence of each base station is obtained by normalizing the time series of the new throughput of each base station, and the obtained normalized throughput sequence is used as the historical data of the throughput.
- the constructing the base station relationship network of the N base stations according to the historical data of the acquired throughput of the N base stations includes:
- the M base stations that use the constructed base station relationship network and the acquired historical data of the throughput to find an important effect on the base station relationship network throughput evaluation performance include:
- the best throughput evaluation effect is selected, and the m base stations corresponding to the selected best throughput evaluation effects are used as the base stations.
- M ⁇ m, M ⁇ N, and m ⁇ N.
- the number of undirected edges of each base station is proportional to the size of the base station.
- the estimating the remaining N-M base station throughputs by using the determined historical data of the throughput of the M important base stations includes:
- the estimated throughput of the remaining N-M base stations is obtained by using the constructed throughput relationship model and the throughput history data of the M important base stations.
- an apparatus for evaluating wireless network throughput including:
- An acquisition module configured to collect historical data of throughput of N base stations
- a building module configured to construct a base station relationship network of the N base stations according to the acquired historical data of the throughput of the N base stations;
- a searching module configured to use the constructed base station relationship network and the acquired historical data of the throughput to find M base stations that play an important role in evaluating the throughput of the base station relationship network, and use the M base stations as important base stations ;
- An evaluation module configured to use the historical data of the determined throughput of the M important base stations to evaluate the remaining N-M base station throughputs
- N and M are both positive integers and N is greater than M.
- the collecting module includes:
- Calculating a throughput average unit configured to collect a raw throughput sequence of each base station, and calculate an average value of the throughput sequence values in the collected original throughput sequence
- the obtaining unit is configured to replace the calculated throughput with a sequence of a preset percentage threshold of the sequence value before each sequence of the original throughput sequence of each of the acquired base stations is replaced by the calculated The average of the sequence values, the new throughput sequence for each base station, and the normalization of the time series of the new throughput for each base station, resulting in each The normalized throughput sequence of the base station, the resulting normalized throughput sequence is taken as historical data of the throughput.
- the building module includes:
- Calculating the correlation coefficient unit is configured to calculate correlation coefficients between two of the N base stations according to the normalized throughput sequence of each of the obtained base stations;
- the building unit is configured to construct the base station relationship network of the N base stations by using an undirected edge generated between two base stations of the N base stations.
- the searching module includes:
- the obtaining unit is configured to obtain the degree of each base station by counting the number of undirected edges of each base station in the base station relationship network, and sequentially select the m base stations with the medium most middle of the N base stations, according to the support vector machine Assessing the throughput of the Nm base stations, and obtaining the throughput evaluation effect of the m base station relationship networks;
- the searching unit is configured to select the best throughput evaluation effect in the throughput evaluation effect of the obtained m base station relationship networks, and use the m base stations corresponding to the selected best throughput evaluation effect as the base station M base stations that play an important role in the network throughput evaluation effect;
- M ⁇ m, M ⁇ N, and m ⁇ N.
- the technical solution provided by the embodiment of the present invention includes: acquiring historical data of throughput of N base stations; and constructing a base station relationship network of N base stations according to the acquired historical data of throughput of the N base stations According to the constructed base station relationship network and the historical data of the acquired throughput, find M base stations that play an important role in the base station relationship network throughput evaluation effect, and use the M base stations as important base stations; use the determined M important Historical data of the throughput of the base station, and the remaining NM base station throughput is evaluated.
- the embodiment of the present invention uses the historical data of the base station throughput of a selected number of important base stations to evaluate the characteristics of other base stations, thereby reducing the complexity of data analysis;
- the throughput of the base part of the base station is known, the throughput of other unknown base stations in the spatial range is evaluated, thereby providing a reference for optimizing the wireless network resources.
- FIG. 1 is a flowchart of a method for evaluating wireless network throughput according to an embodiment of the present invention
- FIG. 2 is a schematic diagram of an apparatus for evaluating wireless network throughput according to an embodiment of the present invention
- FIG. 3 is a flowchart of a method for evaluating a wireless network throughput according to an embodiment of the present invention
- FIG. 4 is a flowchart of an algorithm of a support vector machine according to an embodiment of the present invention.
- FIG. 5 is a network diagram for constructing a base station relationship according to a first embodiment of the present invention
- FIG. 6 is a diagram showing the variation of the average value of SMAPE (Symmetric mean absolute percentage error) with m according to the first embodiment of the present invention
- FIG. 7 is a diagram showing evaluation results of two base stations according to a first embodiment of the present invention.
- FIG. 8 is a network diagram for constructing a base station relationship according to a second embodiment of the present invention.
- Figure 9 is a diagram showing the variation of the average value of SMAPE with m according to the second embodiment of the present invention.
- FIG. 10 is a diagram showing evaluation results of two base stations according to a second embodiment of the present invention.
- FIG. 1 is a flowchart of a method for evaluating throughput of a wireless network according to an embodiment of the present invention. As shown in FIG. 1 , the method includes the following steps:
- Step 101 Acquire historical data of throughput of N base stations
- N may include the number of base stations in the area where the wireless network throughput is evaluated.
- Step 102 Construct a base station relationship network of N base stations according to the acquired historical data of the throughput of the N base stations.
- Step 103 Using the constructed base station relationship network and the historical data of the acquired throughput, find M base stations that play an important role in evaluating the throughput of the base station relationship network, and use the M base stations as important base stations;
- Step 104 Evaluate the remaining N-M base station throughputs by using historical data of the determined throughput of the M important base stations;
- N and M are both positive integers and N is greater than M.
- obtaining historical data of the throughput of the N base stations includes: collecting an original throughput sequence of each base station, and calculating an average value of the throughput sequence values in the collected original throughput sequence; The value of the sequence of the preset percentage thresholds in which the sequence value is prior to each sequence in the original throughput sequence of each base station is replaced by the average of the calculated throughput sequence values, and the new throughput of each base station is obtained.
- the sequence of normalization throughput is obtained by normalizing the sequence of the new throughput of each base station, and the normalized throughput sequence obtained is used as the historical data of the throughput.
- the preset percentage threshold may be 3%, and 3% is only an optional value.
- the preset percentage threshold is a value obtained by a person skilled in the art according to empirical analysis, and may be 2% to 5%.
- the original throughput data may be collected by using the original throughput sequence of the base station for all the durations, or the original throughput sequence of the preset duration.
- the preset duration is generally greater than or equal to 14 days when the original throughput sequence of the preset duration is used.
- the base station relationship network for constructing the N base stations according to the historical data of the acquired throughput of the N base stations includes: calculating, according to the normalized throughput sequence of each base station obtained, two base stations of the N base stations respectively. Correlation coefficient; when the correlation coefficient is greater than the correlation coefficient threshold, an undirected edge is generated between the two base stations; the base station relationship of the N base stations is constructed by the undirected edges generated between the two base stations in the N base stations The internet.
- the correlation coefficient threshold may be 0.6.
- the M base stations that play an important role in evaluating the throughput of the base station relationship network include: performing statistics on the undirected side of each base station in the base station relationship network. The number of the nodes is obtained, and the degree of each base station is obtained; the m base stations with the most moderate base stations are sequentially selected, and the throughput of the Nm base stations is evaluated according to the support vector machine, and the throughput evaluation effect of the m base station relationship networks is obtained; Among the throughput evaluation effects of the m base station relationship networks, the best throughput evaluation effect is selected, and the m base stations corresponding to the selected best throughput evaluation effects are regarded as important for the base station relationship network throughput evaluation effect.
- the number of undirected edges is proportional to the size of the base station, and such a proportional relationship is common knowledge of those skilled in the art.
- the utilization of the throughput history data of the M important base stations to evaluate the remaining NM base station throughput includes: constructing a throughput relationship model between the remaining NM base stations and the M important base stations by using a support vector machine algorithm; The relationship model and the throughput history data of the M important base stations obtain the estimated throughput of the remaining NM base stations.
- the construction of the throughput relationship model is a common technical means by those skilled in the art.
- other algorithms may be used to determine the throughput relationship model. After constructing the throughput relationship model, the throughput history data of the M base stations is taken as an input, and the evaluation throughput of the N-M base stations can be output.
- the embodiment of the invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are used to perform the above-mentioned wireless network throughput evaluation method.
- the method includes: an acquisition module 201, a construction module 202, a lookup module 203, and an evaluation module 204.
- the collecting module 201 is configured to acquire historical data of the throughput of the N base stations;
- the constructing module 202 is configured to construct a base station relationship network of the N base stations according to the acquired historical data of the throughput of the N base stations;
- the searching module 203 is configured In order to utilize the constructed base station relationship network and the historical data of the acquired throughput, find M base stations that play an important role in evaluating the throughput of the base station relationship network, and use the M base stations as important base stations;
- the evaluation module 204 is configured to utilize The historical data of the throughput of the M important base stations is determined, and the remaining NM base station throughputs are evaluated; wherein N and M are both positive integers, and N is greater than M.
- the collecting module 201 includes: a calculating throughput average unit, configured to collect an original throughput sequence of each base station, and calculate an average value of the throughput sequence values in the collected original throughput sequence;
- the obtaining unit is configured to replace the calculated throughput sequence with the value of the sequence of the preset percentage threshold of the sequence value before each sequence of the original throughput sequence of each base station collected
- the average of the values, the new throughput sequence of each base station is obtained, and the normalized throughput sequence of each base station is obtained by normalizing the time series of the new throughput of each base station, and the obtained normalized throughput sequence is obtained.
- the throughput sequence is used as historical data for throughput.
- the constructing module 202 includes: calculating a correlation coefficient unit configured to calculate a correlation coefficient between two base stations of the N base stations according to the normalized throughput sequence of each obtained base station;
- the generating undirected edge unit is set to generate an undirected edge between the two base stations when the calculated correlation coefficient is greater than the correlation coefficient threshold;
- the building unit is configured to construct a base station relationship network of N base stations by using undirected edges generated between two base stations of the N base stations.
- the lookup module 203 includes:
- the obtaining unit is configured to obtain the degree of each base station by counting the number of undirected edges of each base station in the base station relationship network, and sequentially select m base stations with the most moderate among the N base stations, and evaluate Nm base stations according to the support vector machine. Throughput, the throughput evaluation effect of m base station relationship networks is obtained;
- the searching unit is configured to select the best throughput evaluation effect in the throughput evaluation effect of the obtained m base station relationship networks, and use the m base stations corresponding to the best throughput evaluation effect as the base station relationship.
- M ⁇ m, M ⁇ N, and m ⁇ N.
- the embodiment of the invention mainly comprises the following four modules: a data preprocessing module (equivalent to an acquisition module), a base station relationship network construction module (equivalent to a construction module), an important base station selection module (equivalent to a search module), and a space throughput evaluation module. (Equivalent to the evaluation module).
- the data pre-processing module is configured to select the N base stations to be studied and eliminate the abnormal data points;
- the base station relationship network construction module is configured to construct the base station relationship between the base stations according to the historical data of the throughput of the collected N base stations.
- the network the important base station selection module is configured to select M important base stations from the N base stations according to the constructed historical data of the base station relationship network and the throughput; the space throughput evaluation module is set to be used for the time to be evaluated, according to the known The determined throughput of the M base stations evaluates the throughput of the other NM base stations.
- the data preprocessing module can be configured to include:
- Excluding the abnormal point in the historical data of the throughput of each base station includes the point where the sequence value is extremely large, that is, the value of the sequence of the preset percentage threshold after the sequence value is sorted, and the sequence value is before, Excluding the anomaly point includes replacing the value of the sequence of the outliers with the average of the calculated throughput sequence values.
- the base station relationship network building module can be configured to include:
- the important base station selection module can be set to include:
- the space throughput assessment module includes:
- FIG. 3 is a flowchart of a method for evaluating a wireless network throughput according to an embodiment of the present invention. As shown in FIG. 3, the method includes:
- Step 1 data preprocessing
- Data preprocessing mainly consists of the following steps:
- each base station throughput sequence to obtain a normalized throughput sequence S i of the i-th base station, and use the obtained normalized throughput sequence as historical data of throughput.
- max(p i ),min(p i ) represent the maximum and minimum values of the original throughput sequence, respectively, and L is the total length of the sequence of the throughput sequence.
- Step 2 construct a base station relationship network
- c For a given correlation coefficient threshold c, if ⁇ ij is greater than c, it is considered that there is a significant correlation between base station i and base station j, and an undirected edge is added between them, so that the base station relationship of N base stations can be constructed.
- the internet if ⁇ ij is greater than c, it is considered that there is a significant correlation between base station i and base station j, and an undirected edge is added between them, so that the base station relationship of N base stations can be constructed.
- Step 3 Select an important base station
- a support vector machine (SVM) is used to evaluate base station throughput.
- SVM support vector machine
- the algorithm flow of the SVM is shown in Figure 4 and includes the following steps:
- step 7 When it is judged that it is smaller than the given error e, the process proceeds to step 7, and when it is judged that it is not smaller than the given error e, the parameter is adjusted, and the process returns to step 3.
- the final throughput evaluation effect is measured by the symmetric mean relative error (SMAPE), and the SMAPE reflects the relative error between the evaluation value and the real value, and solves the problem that the real value is too small.
- SMAPE symmetric mean relative error
- the vector machine evaluates the throughput of other Nm base stations. Calculate the SMAPE of each base station that is evaluated, and select the M base stations with the lowest average SMAPE as the important base stations.
- Step 4 Use other important base stations to evaluate other base station throughput.
- the SVM algorithm is used, and the throughput relationship model of other N-M base stations and M important base stations is trained using the historical data of the throughput.
- the throughput of the M base stations in the time period to be evaluated is input into the relational model, and the throughput of the corresponding N-M base stations can be output.
- the data in this example is derived from the statistics of all base stations in a large city.
- the time granularity is 60 minutes and the total length of time is 21 consecutive days.
- the wireless network space throughput evaluation method in the embodiment of the present invention includes the following steps:
- Step 1 Data preprocessing
- Step 2 Construct a base station relationship network for 95 base stations to be studied
- Step 4 Select an important base station
- the first 15 days of data is selected as the training sample set, and the last 3 days of data is used as the test sample set; the first 15 days of data of all base stations is used as the input of the support vector machine algorithm (SVM), and the output training is obtained.
- SVM support vector machine algorithm
- Step 5 Use the support vector machine algorithm to estimate the space throughput.
- the support vector machine algorithm uses the historical data of the throughput to train the throughput relationship model of the other 87 base stations and the 8 important base stations.
- the throughput of the eight base stations in the last three days of the original 21-day data can be output.
- the data in this example is derived from the statistical data of a typical area in a large city with a time granularity of 60 minutes and a total length of time of 18 consecutive days.
- the wireless network space throughput evaluation method in the embodiment of the present invention includes the following steps:
- Step 1 Data preprocessing
- Step 2 117 base stations to be studied, and construct a base station relationship network
- Step 4 Select an important base station
- the first 12 days of data are selected as the training set, and the last three days of data are used as the test set; the first 12 days of data from all base stations are used as input to the support vector machine algorithm (SVM), and the output training is obtained.
- SVM support vector machine algorithm
- Step 5 Evaluate space throughput using the SVM algorithm.
- the support vector machine algorithm uses the historical data of the throughput to train the throughput relationship model of the other 106 base stations and 11 important base stations.
- the throughput of 11 base stations can be output.
- FIG. 10 it is an example of the evaluation result, where 3 is an evaluation value and 4 is a true value.
- the embodiment of the invention obtains the relationship between the throughput changes of the base stations according to the historical data of the base station, and constructs The base station relationship network selects a few important base stations from the network to evaluate the throughput of other large base stations. It has high practical value. For example, in the data acquisition of the base station, there are many base stations whose data is missing. With the embodiment of the present invention, the missing data can be evaluated for further network analysis. At the same time, it can be flexibly selected according to the historical data of the throughput of different regions or time periods to evaluate, with universal applicability and better prediction accuracy.
- each module/unit in the foregoing embodiment may be implemented in the form of hardware, for example, by implementing an integrated circuit to implement its corresponding function, or may be implemented in the form of a software function module, for example, being executed by a processor and stored in a memory. Programs/instructions to implement their respective functions.
- the invention is not limited to any specific form of combination of hardware and software.
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Abstract
一种无线网络吞吐量的评估方法及装置,包括:获取N个基站的吞吐量的历史数据;根据所获取的N个基站的吞吐量的历史数据,构建N个基站的基站关系网络;根据构建的基站关系网络以及获取的吞吐量的历史数据,找到对基站关系网络吞吐量评估效果起重要作用的M个基站,并将该M个基站作为重要基站;利用确定出的M个重要基站的吞吐量的历史数据,对剩余的N-M个基站吞吐量进行评估;本发明实施例,采用选取出的重要基站的基站吞吐量的历史数据,对其他基站进行评估,降低了进行数据的复杂度。
Description
本文涉及但不限于数据挖掘技术,尤其涉及一种无线网络吞吐量的评估方法及装置。
随着无线网络的快速发展,移动互联网数据业务的种类和流量都有了很大的提高,流量***性增长、业务类型极其丰富,对网络流量行为分析也就愈加复杂。
为了有效实现网络规划设计、网络资源分配,精细化运营管理等,必须准确地分析网络吞吐量。由于数据业务的多样性、随机性和突发性等特点,相关技术中的数据分析方法过于复杂,已经不能够满足当前的网络吞吐量行为分析。
发明内容
以下是对本文详细描述的主题的概述。本概述并非是为了限制权利要求的保护范围。
本发明实施例提供一种无线网络吞吐量的评估方法及装置,能够满足当前的网络吞吐量行为分析。
根据本发明实施例的一个方面,提供了一种无线网络吞吐量的评估方法,包括:
获取N个基站的吞吐量的历史数据;
根据所获取的N个基站的吞吐量的历史数据,构建所述N个基站的基站关系网络;
根据构建的所述基站关系网络及获取的所述吞吐量的历史数据,找到对基站关系网络吞吐量评估效果起重要作用的M个基站,并将该M个基站作为重要基站;
利用所述确定出的M个重要基站的吞吐量的历史数据,对剩余的N-M
个基站吞吐量进行评估;
其中,N和M均为正整数,并且N大于M。
可选地,所述获取N个基站的吞吐量的历史数据包括:
采集每个基站的原始吞吐量序列,并计算出采集到的所述原始吞吐量序列中吞吐量序列数值的平均值;
通过将所采集到的每个基站的每一个原始吞吐量序列中按照序列数值大小排序后、序列数值在前的预设百分比阈值的序列的数值替换为计算出的所述吞吐量序列数值的平均值,得到每个基站的新吞吐量序列;
通过对每个基站的新吞吐量的时间序列进行归一化处理,得到每个基站的归一化吞吐量序列,将得到的归一化吞吐量序列作为所述吞吐量的历史数据。
可选地,所述根据所获取的N个基站的吞吐量的历史数据,构建所述N个基站的基站关系网络包括:
根据所得到每个基站的所述归一化吞吐量序列,分别计算所述N个基站中两两基站之间的相关系数;
当计算得到的所述相关系数大于相关系数阈值时,则在所述两两基站之间生成一条无向边;
通过所述N个基站中两两基站之间生成的无向边,构建所述N个基站的所述基站关系网络。
可选地,所述利用构建的所述基站关系网络以及获取的所述吞吐量的历史数据,找到对基站关系网络吞吐量评估效果起重要作用的M个基站包括:
通过统计所述基站关系网络中每个基站的无向边条数,得到每个基站的度;
依次选取所述N个基站中度最大的m个的基站,根据支持向量机评估N-m个基站的吞吐量,得到m种基站关系网络的吞吐量评估效果;
在所得到的m种基站关系网络的吞吐量评估效果中,选取最好的吞吐量评估效果,并将所选取的最好吞吐量评估效果相对应的m个基站作为对基站
关系网络吞吐量评估效果起重要作用的M个基站;
其中,m、M、N为正整数,M<=m,M<N,m<N。
可选地,所述每个基站的无向边条数与基站度的大小成正比。
可选地,所述利用确定出的所述M个重要基站的吞吐量的历史数据,对剩余的N-M个基站吞吐量进行评估包括:
通过支持向量机算法构造剩余的N-M个基站与M个重要基站的吞吐量关系模型;
利用构造的所述吞吐量关系模型和所述M个重要基站的吞吐量历史数据,得到剩余的N-M个基站的评估吞吐量。
根据本发明实施例的另一方面,提供了一种无线网络吞吐量的评估装置,包括:
采集模块,设置为采集N个基站的吞吐量的历史数据;
构建模块,设置为根据所获取的N个基站的吞吐量的历史数据,构建所述N个基站的基站关系网络;
查找模块,设置为利用构建的所述基站关系网络以及获取的所述吞吐量的历史数据,找到对基站关系网络吞吐量评估效果起重要作用的M个基站,并将该M个基站作为重要基站;
评估模块,设置为利用所述确定出的M个重要基站的吞吐量的历史数据,对剩余的N-M个基站吞吐量进行评估;
其中,N和M均为正整数,并且N大于M。
可选地,所述采集模块包括:
计算吞吐量平均值单元,设置为采集每个基站的原始吞吐量序列,并计算出采集到的所述原始吞吐量序列中吞吐量序列数值的平均值;
获取单元设置为,通过将所采集到的每个基站的每一个原始吞吐量序列中按照序列数值大小排序后、序列数值在前的预设百分比阈值的序列的数值替换为计算出的所述吞吐量序列数值的平均值,得到每个基站的新吞吐量序列,以及通过对每个基站的新吞吐量的时间序列进行归一化处理,得到每个
基站的归一化吞吐量序列,将得到的归一化吞吐量序列作为所述吞吐量的历史数据。
可选地,所述构建模块包括:
计算相关系数单元设置为,根据所得到每个基站的归一化吞吐量序列,分别计算所述N个基站中两两基站之间的相关系数;
生成无向边单元设置为,当计算得到的所述相关系数大于相关系数阈值时,则在所述两两基站之间生成一条无向边;
构建单元设置为,通过所述N个基站中两两基站之间生成的无向边,构建所述N个基站的所述基站关系网络。
可选地,所述查找模块包括:
获取单元设置为,通过统计所述基站关系网络中每个基站的无向边条数,得到每个基站的度,以及依次选取所述N个基站中度最大的m个基站,根据支持向量机评估N-m个基站的吞吐量,得到m种基站关系网络的吞吐量评估效果;
查找单元设置为,在所得到的m种基站关系网络的吞吐量评估效果中,选取最好的吞吐量评估效果,并将所选取的最好吞吐量评估效果相对应的m个基站作为对基站关系网络吞吐量评估效果起重要作用的M个基站;
其中,m、M、N为正整数,M<=m,M<N,m<N。
与相关技术相比,本发明实施例提供的技术方案,包括:获取N个基站的吞吐量的历史数据;根据所获取的N个基站的吞吐量的历史数据,构建N个基站的基站关系网络;根据构建的基站关系网络以及获取的吞吐量的历史数据,找到对基站关系网络吞吐量评估效果起重要作用的M个基站,并将该M个基站作为重要基站;利用确定出的M个重要基站的吞吐量的历史数据,对剩余的N-M个基站吞吐量进行评估。本发明实施例的有益效果在于:本发明实施例采用选取出的少量重要基站的基站吞吐量的历史数据,对其他基站的特性进行评估,减小了数据分析的复杂度;同时,使得能够在已知空间部分基站吞吐量的情况下,评估出空间范围内其他未知基站的吞吐量,从而对无线网络资源的优化提供参考。
在阅读并理解了附图和详细描述后,可以明白其他方面。
附图概述
图1是本发明实施例提供的一种无线网络吞吐量的评估方法流程图;
图2是本发明实施例提供的一种无线网络吞吐量的评估装置示意图;
图3是本发明实施例提供的无线网络吞吐量评估方法的流程图;
图4是本发明实施例提供的支持向量机的算法流程图;
图5是本发明第一实施例提供的构建基站关系网络图;
图6是本发明第一实施例提供的SMAPE(Symmetric mean absolute percentage error,对称平均相对误差)平均值随m的变化情况图;
图7是本发明第一实施例提供的两个基站的评估结果图;
图8是本发明第二实施例提供的构建基站关系网络图;
图9是本发明第二实施例提供的SMAPE平均值随m的变化情况图;
图10是本发明第二实施例提供的两个基站的评估结果图。
下文中将结合附图对本申请的实施例进行详细说明。需要说明的是,在不冲突的情况下,本申请中的实施例及实施例中的特征可以相互任意组合。
图1是本发明实施例提供的一种无线网络吞吐量的评估方法流程图,如图1所示,包括以下步骤:
步骤101:获取N个基站的吞吐量的历史数据;
需要说明的是,本发明实施例,N可以包括无线网络吞吐量的评估所在区域范围内的基站的数目。
步骤102:根据所获取的N个基站的吞吐量的历史数据,构建N个基站的基站关系网络;
步骤103:利用构建的基站关系网络以及获取的吞吐量的历史数据,找到对基站关系网络吞吐量评估效果起重要作用的M个基站,并将该M个基站作为重要基站;
步骤104:利用确定出的M个重要基站的吞吐量的历史数据,对剩余的N-M个基站吞吐量进行评估;
其中,N和M均为正整数,并且N大于M。
可选的,获取N个基站的吞吐量的历史数据包括:采集每个基站的原始吞吐量序列,并计算采集到的原始吞吐量序列中吞吐量序列数值的平均值;通过将所采集的每个基站的每一个原始吞吐量序列中按照序列数值大小排序后、序列数值在前的预设百分比阈值的序列的数值替换为计算出的吞吐量序列数值的平均值,得到每个基站的新吞吐量序列;通过对每个基站的新吞吐量的序列进行归一化处理,得到每个基站的归一化吞吐量序列,将得到的归一化吞吐量序列作为吞吐量的历史数据。
需要说明的是,本发明实施例,预设百分比阈值可以为3%,3%只是一个可选数值,预设百分比阈值为本领域技术人员根据经验分析获得的数值,可以是2%~5%中的一个值。采集原始吞吐量数据可以采用基站的所有时长的原始吞吐量序列,也可以是预设时长的原始吞吐量序列,预设时长的原始吞吐量序列时,预设时长一般大于或等于14天。
其中,根据所获取的N个基站的吞吐量的历史数据,构建N个基站的基站关系网络包括:根据所得到每个基站的归一化吞吐量序列,分别计算N个基站中两两基站之间的相关系数;当相关系数大于相关系数阈值时,则在两两基站之间生成一条无向边;通过N个基站中两两基站之间生成的无向边,构建N个基站的基站关系网络。
需要说明的是,计算两两基站之间的相关系数为本领域技术人员的惯用技术手段,相关系数阈值可以取0.6。
可选的,利用构建的基站关系网络以及获取的吞吐量的历史数据,找到对基站关系网络吞吐量评估效果起重要作用的M个基站包括:通过统计基站关系网络中每个基站的无向边条数,得到每个基站的度;依次选取N个基站中度最大的m个基站,根据支持向量机评估N-m个基站的吞吐量,得到m种基站关系网络的吞吐量评估效果;在所得到的m种基站关系网络的吞吐量评估效果中,选取最好的吞吐量评估效果,并将所选取的最好吞吐量评估效果相对应的m个基站作为对基站关系网络吞吐量评估效果起重要作用的M个
基站;其中,m、M、N为正整数,M<=m,M<N,m<N。本发明实施例,无向边条数与基站度的大小成正比,这种正比关系为本领域技术人员的公知常识。
其中,利用M个重要基站的吞吐量历史数据,对剩余的N-M个基站吞吐量进行评估包括:通过支持向量机算法构造剩余的N-M个基站与M个重要基站的吞吐量关系模型;利用吞吐量关系模型和M个重要基站的吞吐量历史数据,得到剩余的N-M个基站的评估吞吐量。
需要说明的是,构建吞吐量关系模型为本领域技术人员的惯用技术手段,除了本发明实施例的通过支持向量机算法进行构造外,还可以采用其他算法进行吞吐量关系模型的判断。在构建吞吐量关系模型后,将M个基站的吞吐量历史数据作为输入,可以输出N-M个基站的评估吞吐量。
本发明实施例还提供一种计算机存储介质,计算机存储介质中存储有计算机可执行指令,计算机可执行指令用于执行上述的无线网络吞吐量的评估方法。
图2是本发明实施例提供的一种无线网络吞吐量的评估装置示意图,如图2所示,包括:采集模块201、构建模块202、查找模块203以及评估模块204。采集模块201,设置为获取N个基站的吞吐量的历史数据;构建模块202,设置为根据所获取的N个基站的吞吐量的历史数据,构建N个基站的基站关系网络;查找模块203设置为利用构建的基站关系网络以及获取的吞吐量的历史数据,找到对基站关系网络吞吐量评估效果起重要作用的M个基站,并将该M个基站作为重要基站;评估模块204,设置为利用确定出的M个重要基站的吞吐量的历史数据,对剩余的N-M个基站吞吐量进行评估;其中,N和M均为正整数,并且N大于M。
可选的,采集模块201包括:计算吞吐量平均值单元,设置为采集每个基站的原始吞吐量序列,并计算出采集到的原始吞吐量序列中吞吐量序列数值的平均值;
获取单元设置为,通过将所采集到的每个基站的每一个原始吞吐量序列中按照序列数值大小排序后、序列数值在前的预设百分比阈值的序列的数值替换为计算出的吞吐量序列数值的平均值,得到每个基站的新吞吐量序列,以及通过对每个基站的新吞吐量的时间序列进行归一化处理,得到每个基站的归一化吞吐量序列,将得到的归一化吞吐量序列作为吞吐量的历史数据。
构建模块202包括:计算相关系数单元设置为,根据所得到每个基站的归一化吞吐量序列,分别计算N个基站中两两基站之间的相关系数;
生成无向边单元设置为,当计算得到的相关系数大于相关系数阈值时,则在两两基站之间生成一条无向边;
构建单元设置为,通过N个基站中两两基站之间生成的无向边,构建N个基站的基站关系网络。
查找模块203包括:
获取单元设置为,通过统计基站关系网络中每个基站的无向边条数,得到每个基站的度,以及依次选取N个基站中度最大的m个基站,根据支持向量机评估N-m个基站的吞吐量,得到m种基站关系网络的吞吐量评估效果;
查找单元设置为,在所得到的m种基站关系网络的吞吐量评估效果中,选取最好的吞吐量评估效果,并将取的最好吞吐量评估效果相对应的m个基站作为对基站关系网络吞吐量评估效果起重要作用的M个基站;
其中,m、M、N为正整数,M<=m,M<N,m<N。
本发明实施例主要包含以下四个模块:数据预处理模块(相当于采集模块),基站关系网络构建模块(相当于构建模块),重要基站选取模块(相当于查找模块),空间吞吐量评估模块(相当于评估模块)。数据预处理模块,设置为选取待研究的N个基站,剔除其中的异常数据点;基站关系网络构建模块,设置为根据已采集的N个基站的吞吐量的历史数据构建基站之间的基站关系网络;重要基站选取模块,设置为根据构建的基站关系网络和吞吐量的历史数据,从N个基站中选取出M个重要基站;空间吞吐量评估模块,设置为对于待评估时间,根据已知的确定出的M个基站的吞吐量评估出其他N-M个基站的吞吐量。
数据预处理模块可以设置为包括:
A1.选取空间位置上处于同一区域的N个基站;
A2.剔除每个基站吞吐量的历史数据中的异常点;这里,异常点包括序列数值极大的点,即按照序列数值大小排序后、序列数值在前的预设百分比阈值的序列的数值,剔除异常点包括将异常点的序列的数值替换为计算出的吞吐量序列数值的平均值。
A3.对数据进行一次归一化。
基站关系网络构建模块可以设置为包括:
B1.计算N个基站两两之间的相关系数;
B2.根据相关系数,构建一个给定的相关系数阈值的基站关系网络。
重要基站选取模块可以设置为包括:
C1.统计基站关系网络中每一个基站度的大小;
C2.依次选取度前M(M=1,2……N)大的基站作为重要基站,根据支持向量机评估其他N-M个基站的吞吐量;
C3.选取在吞吐量的历史数据上评估效果最好时(起重要作用)的M个基站作为重要基站。
空间吞吐量评估模块包括:
D1.根据选出的M个重要基站,评估其他N-M个基站的吞吐量。
图3是本发明实施例提供的无线网络吞吐量评估方法的流程图,如图3所示,包括:
步骤1、数据预处理;
为了未来根据部分基站的吞吐量评估其他大量基站的吞吐量,需要先获取到所有基站的吞吐量的历史数据,然后对获取到的历史数据进行预处理。数据预处理主要包含以下几个步骤:
a、根据需求选取空间位置上处于同一区域的N个基站;
b、整理N个基站原始吞吐量序列,将每一个吞吐量序列中按照序列数值大小排序后、序列数值在前的预设百分比阈值的序列的数值替换为计算出
的吞吐量序列数值的平均值;例如、将吞吐量序列中前3%大的吞吐量替换为该吞吐量序列的序列数值的平均值,得到第i个基站的吞吐量序列pi(i=1,2……N),作为新吞吐量序列;
c、对每一个基站吞吐量序列进行归一化处理,得到第i个基站的归一化吞吐量序列Si,将得到的归一化吞吐量序列作为吞吐量的历史数据。
步骤2、构建基站关系网络;
对待研究的N个基站,L为所采集数据(这里的数据为吞吐量序列)的总时长,取L中前T(一般左右)个时间数据计算第i(i=1,2,3……N)个基站与第j(j=1,2,3……N)个基站之间的相关系数ρij,计算公式为
Si为第i个基站的吞吐量序列,为第i个基站在总时长内的平均吞吐量,为第i个基站在时刻t时的吞吐量大小(t=1,2,3……T);Sj为第j个基站的吞吐量序列,为第j个基站在总时长内的平均吞吐量,为第j个基站在时刻t时的吞吐量大小(t=1,2,3……T)。对于一个给定的相关系数阈值c,若ρij大于c,则认为基站i与基站j存在明显的相关关系,在他们之间添加一条无向边,这样就可以构建出N个基站的基站关系网络。
步骤3、选取重要基站;
在本发明中,采用支持向量机(SVM,Support Vector Machine)来评估基站吞吐量。SVM的算法流程如图4所示,包括以下步骤:
1、根据评估样本建立训练样本集和测试样本集;
2、根据训练样本集建立目标函数;
3、求解目标函数,得到最优参数;
4、将最优参数代入目标函数,得到决策回归方程;
5、使用测试数据验证决策回归方程;
6、是否小于给定误差e;
当判断小于给定误差e时,进入步骤7,当判断不小于给定误差e时,调整参数,并返回到步骤3。
7、将评估样本输入决策回归方程计算其他基站吞吐量。
在本发明中,最终吞吐量评估效果好坏使用对称平均相对误差(SMAPE)来衡量,SMAPE反映了评估值与真实值之间相对误差的大小,同时解决了由于真实值过小可能带来的相对误差太大的问题,其公式为:
其中,Ft为评估值,At为实际值。
为了筛选出部分基站作为重要基站,依次选取度最大的前m(m=1,2……N)基站作为重要基站,即对基站关系网络吞吐量评估效果起重要作用的M个基站,根据支持向量机评估其他N-m个基站的吞吐量。计算评估出来的每一个基站的SMAPE,选取平均SMAPE最小时的M个基站作为重要基站。
步骤4、使用重要基站评估其他基站吞吐量。
在本发明实施例中,根据选出的M个重要基站,采用SVM算法,使用吞吐量的历史数据训练出其他N-M个基站与M个重要基站的吞吐量关系模型。将待评估时间段内的M个基站的吞吐量输入到关系模型中,即可输出对应的N-M个基站的吞吐量。
下面结合附图5至附图10对本发明实施例进行说明。
实施例一
本实例中数据来源于某大型城市所有基站统计的数据,其时间颗粒度为60分钟,时间总长度为连续21天。本发明实施例中的无线网络空间吞吐量评估方法包含以下步骤:
步骤一:数据预处理;
A.根据需求选取空间位置上处于同一区域的95个基站;
B.剔除95个基站中的异常数据点,得到每一基站的吞吐量序列;
C.对每一个基站吞吐量序列进行归一化处理,得到第i个基站的归一化吞吐量序列Si。
步骤二:对待研究的95个基站,构建基站关系网络;
A.取这95个基站前18天的数据计算第i(i=1,2,3……95)个基站与第j(j=1,2,3……95)个基站之间的相关系数ρij,计算公式为
其中T=432,Si为第i个基站的吞吐量序列,为第i个基站在总时长内的平均吞吐量,为第i个基站在时刻t时的吞吐量大小(t=1,2,3……432);Sj为第j个基站的吞吐量序列,为第j个基站在总时长内的平均吞吐量,为第j个基站在时刻t时的吞吐量大小(t=1,2,3……432)。
B.在本发明实施例中,给定相关系数阈值c=0.6(一般认为相关系数大于0.6即为强相关),若ρij大于0.6,则在基站i与基站j之间添加一条无向边,这样就可以构建出95个基站的关系网络。如图5所示,其中点代表基站,无向边体现了基站之间的相关性,点越大代表该基站的度越大;其中,点越大,代表无向边越多,无向边越多,度越大。
步骤四:选取重要基站;
A.从18天的历史数据中,选取前15天数据作为训练样本集,后3天数据作为测试样本集;将所有基站前15天数据作为支持向量机算法(SVM)的输入,输出训练得到的其他95-m个基站与选取的m个基站吞吐量关系模型;
B.将度最大的m个基站的后3天数据作为吞吐量关系模型的输入,输出其他95-m个基站后3天的估计值;
C.计算95-m个基站每一个基站相应的SMAPE,做出SMAPE平均值随
m的变化情况,如图6所示,黑点为95-m个基站SMAPE的平均值,从图中可以看出,当m=8时,其他基站的平均SMAPE最小,也就是预测效果最佳,因此在本实施例中选取的重要基站个数为M=8。
步骤五:使用支持向量机算法评估空间吞吐量。
在本发明实施例中,根据选出的8个重要基站,采用支持向量机算法(SVM)使用吞吐量的历史数据训练出其他87个基站与8个重要基站的吞吐量关系模型。将原始21天数据中的最后3天的8个基站的吞吐量输入到关系模型中,即可输出对应的87个基站的吞吐量。
如图7所示,展示了87个基站中部分基站的评估结果,其中1为评估值,2为真实值。计算87个基站的评估误差,得到平均SMAPE=30.3%,可见本发明实施例方法具有较高的准确度
实施例二
本实例中数据来源于某大型城市中典型区域的统计数据,其时间颗粒度为60分钟,时间总长度为连续18天。本发明实施例中的无线网络空间吞吐量评估方法包含以下步骤:
步骤一:数据预处理;
A.根据需求选取空间位置上处于同一区域的117个基站;
B.剔除117个基站中的异常数据点,得到每一基站的吞吐量序列;
C.对每一个基站吞吐量序列进行归一化处理,得到第i个基站的归一化吞吐量序列Si。
步骤二:对待研究的117个基站,构建基站关系网络;
A.取这117个基站前15天的数据计算第i(i=1,2,3……117)个基站与第j(j=1,2,3……117)个基站之间的相关系数ρij,计算公式为
其中T=360,Si为第i个基站的吞吐量序列,为第i个基站在总时长内
的平均吞吐量,为第i个基站在时刻t时的吞吐量大小(t=1,2,3……360);Sj为第j个基站的吞吐量序列,为第j个基站在总时长内的平均吞吐量,为第j个基站在时刻t时的吞吐量大小(t=1,2,3……360)。
B.在本发明实施例中,给定相关系数阈值c=0.6(一般认为相关系数大于0.6即为强相关),若ρij大于0.6,则在基站i与基站j之间添加一条无向边,这样就可以构建出117个基站的关系网络。如图8所示,其中点代表基站,边体现了基站之间的相关性,点越大代表该基站的度越大;其中,点越大,代表无向边越多,无向边越多,度越大。
步骤四:选取重要基站;
A.从15天的历史数据中,选取前12天数据作为训练集,后三天数据作为测试集;将所有基站前12天数据作为支持向量机算法(SVM)的输入,输出训练得到的其他117-m个基站与选取的m个基站吞吐量关系模型;
B.将度最大的m个基站的后3天数据作为吞吐量关系模型的输入,输出其他117-m个基站后三天的估计值;
C.计算117-m的基站每一个基站相应的SMAPE,做出平均SMAPE随m的变化情况,如图9所示,黑点为117-m个基站SMAPE的平均值,从图中可以看出,当m=11时,其他基站的平均SMAPE最小,也就是预测效果最佳,因此在本实施例中我们选取的重要基站个数为M=11。
步骤五:使用SVM算法评估空间吞吐量。
在本发明实施例中,根据选出的11个重要基站,采用支持向量机算法(SVM)使用吞吐量的历史数据训练出其他106个基站与11个重要基站的吞吐量关系模型。将待评估时间段内的11个基站的吞吐量输入到关系模型中,即可输出对应的106个基站的吞吐量。
如图10所示,即为评估结果示例,其中3为评估值,4为真实值。计算106个基站的评估误差,得到平均SMAPE=36.4%,本发明实施例评估结果有较高的准确度。
综上所述,本发明实施例具有以下技术效果:
本发明实施例根据基站历史数据得到基站之间吞吐量变化关系,并构建
基站关系网络,从该网络中选取出少数重要基站,从而评估出其他大量基站的吞吐量。具有很高的实用价值,例如在基站数据采集中,有很多基站的数据会有缺失,采用本发明实施例,可以评估出缺失数据,从而做进一步的网络分析。同时,可以根据需求,灵活的选取不同地区或者时间段的吞吐量的历史数据来评估,具有普遍的适用性和更好的预测准确度。
尽管上文对本发明实施例进行了详细说明,但是本发明不限于此,本技术领域技术人员可以根据本发明的原理进行多种修改。因此,凡按照本发明原理所作的修改,都应当理解为落入本发明的保护范围。
本领域普通技术人员可以理解上述方法中的全部或部分步骤可通过程序来指令相关硬件(例如处理器)完成,所述程序可以存储于计算机可读存储介质中,如只读存储器、磁盘或光盘等。可选地,上述实施例的全部或部分步骤也可以使用一个或多个集成电路来实现。相应地,上述实施例中的每个模块/单元可以采用硬件的形式实现,例如通过集成电路来实现其相应功能,也可以采用软件功能模块的形式实现,例如通过处理器执行存储于存储器中的程序/指令来实现其相应功能。本发明不限制于任何特定形式的硬件和软件的结合。
虽然本申请所揭露的实施方式如上,但所述的内容仅为便于理解本申请而采用的实施方式,并非用以限定本申请,如本发明实施方式中的具体的实现方法。任何本申请所属领域内的技术人员,在不脱离本申请所揭露的精神和范围的前提下,可以在实施的形式及细节上进行任何的修改与变化,但本申请的专利保护范围,仍须以所附的权利要求书所界定的范围为准。
上述技术方案降低了进行数据的复杂度。
Claims (10)
- 一种无线网络吞吐量的评估方法,所述评估方法包括:获取N个基站的吞吐量的历史数据;根据所获取的N个基站的吞吐量的历史数据,构建所述N个基站的基站关系网络;根据构建的所述基站关系网络及获取的所述吞吐量的历史数据,找到对基站关系网络吞吐量评估效果起重要作用的M个基站,并将该M个基站作为重要基站;利用所述确定出的M个重要基站的吞吐量的历史数据,对剩余的N-M个基站吞吐量进行评估;其中,N和M均为正整数,并且N大于M。
- 根据权利要求1所述的评估方法,其中,所述获取N个基站的吞吐量的历史数据包括:采集每个基站的原始吞吐量序列,并计算出采集到的所述原始吞吐量序列中吞吐量序列数值的平均值;通过将所采集到的每个基站的每一个原始吞吐量序列中按照序列数值大小排序后、序列数值在前的预设百分比阈值的序列的数值替换为计算出的所述吞吐量序列数值的平均值,得到每个基站的新吞吐量序列;通过对每个基站的新吞吐量的时间序列进行归一化处理,得到每个基站的归一化吞吐量序列,将得到的归一化吞吐量序列作为所述吞吐量的历史数据。
- 根据权利要求2所述的评估方法,其中,所述根据所获取的N个基站的吞吐量的历史数据,构建所述N个基站的基站关系网络包括:根据所得到每个基站的所述归一化吞吐量序列,分别计算所述N个基站中两两基站之间的相关系数;当计算得到的所述相关系数大于相关系数阈值时,则在所述两两基站之间生成一条无向边;通过所述N个基站中两两基站之间生成的无向边,构建所述N个基站的所述基站关系网络。
- 根据权利要求3所述的评估方法,其中,所述利用构建的所述基站关系网络以及获取的所述吞吐量的历史数据,找到对基站关系网络吞吐量评估效果起重要作用的M个基站包括:通过统计所述基站关系网络中每个基站的无向边条数,得到每个基站的度;依次选取所述N个基站中度最大的m个的基站,根据支持向量机评估N-m个基站的吞吐量,得到m种基站关系网络的吞吐量评估效果;在所得到的m种基站关系网络的吞吐量评估效果中,选取最好的吞吐量评估效果,并将所选取的最好吞吐量评估效果相对应的m个基站作为对基站关系网络吞吐量评估效果起重要作用的M个基站;其中,m、M、N为正整数,M<=m,M<N,m<N。
- 根据权利要求4所述的评估方法,其中,所述每个基站的无向边条数与基站度的大小成正比。
- 根据权利要求5所述的评估方法,其中,所述利用确定出的所述M个重要基站的吞吐量的历史数据,对剩余的N-M个基站吞吐量进行评估包括:通过支持向量机算法构造剩余的N-M个基站与M个重要基站的吞吐量关系模型;利用构造的所述吞吐量关系模型和所述M个重要基站的吞吐量历史数据,得到剩余的N-M个基站的评估吞吐量。
- 一种无线网络吞吐量的评估装置,所述评估装置包括:采集模块,设置为获取N个基站的吞吐量的历史数据;构建模块,设置为根据所获取的N个基站的吞吐量的历史数据,构建所述N个基站的基站关系网络;查找模块,设置为利用构建的所述基站关系网络以及获取的所述吞吐量的历史数据,找到对基站关系网络吞吐量评估效果起重要作用的M个基站,并将该M个基站作为重要基站;评估模块,设置为利用所述确定出的M个重要基站的吞吐量的历史数据,对剩余的N-M个基站吞吐量进行评估;其中,N和M均为正整数,并且N大于M。
- 根据权利要求7所述的评估装置,其中,所述采集模块包括:计算吞吐量平均值单元,设置为采集每个基站的原始吞吐量序列,并计算出采集到的所述原始吞吐量序列中吞吐量序列数值的平均值;获取单元设置为,通过将所采集到的每个基站的每一个原始吞吐量序列中按照序列数值大小排序后、序列数值在前的预设百分比阈值的序列的数值替换为计算出的所述吞吐量序列数值的平均值,得到每个基站的新吞吐量序列,以及通过对每个基站的新吞吐量的时间序列进行归一化处理,得到每个基站的归一化吞吐量序列,将得到的归一化吞吐量序列作为所述吞吐量的历史数据。
- 根据权利要求8所述的评估装置,其中,所述构建模块包括:计算相关系数单元设置为,根据所得到每个基站的归一化吞吐量序列,分别计算所述N个基站中两两基站之间的相关系数;生成无向边单元设置为,当计算得到的所述相关系数大于相关系数阈值时,则在所述两两基站之间生成一条无向边;构建单元设置为,通过所述N个基站中两两基站之间生成的无向边,构建所述N个基站的所述基站关系网络。
- 根据权利要求9所述的评估装置,其中,所述查找模块包括:获取单元设置为,通过统计所述基站关系网络中每个基站的无向边条数,得到每个基站的度,以及依次选取所述N个基站中度最大的m个基站,根据支持向量机评估N-m个基站的吞吐量,得到m种基站关系网络的吞吐量评估效果;查找单元设置为,在所得到的m种基站关系网络的吞吐量评估效果中,选取最好的吞吐量评估效果,并将所选取的最好吞吐量评估效果相对应的m个基站作为对基站关系网络吞吐量评估效果起重要作用的M个基站;其中,m、M、N为正整数,M<=m,M<N,m<N。
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