JP2018032939A - Quality estimation device and quality estimation method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve estimation accuracy while securing areal comprehensiveness of quality estimation in mobile communication.SOLUTION: A quality estimation device for estimating quality in mobile communication includes: an area distribution estimation unit for estimating areal distribution of quality by radio frequency (RF) management method for calculating quality on the basis of radio wave propagation from a base station; a quality estimation unit for estimating quality in an area on the basis of the estimated areal distribution of quality and a measurement value of quality measured in advance at a measurement point in the area; and an output unit for outputting the estimated quality.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a quality estimation apparatus and a quality estimation method, and more particularly to a quality estimation apparatus and a quality estimation method for realizing area quality estimation of a user terminal (UE: User Equipment) in a mobile communication system.

  In the mobile communication field, studies are underway to focus on wireless access for the start of 5G services in 2020. New RAT (Radio Access Technologies), the main technology of 5G, is also investigating technologies that enable high-density cell deployment such as Massive MIMO / Beamforming, and is expected to contribute to alleviating traffic congestion in urban areas. Has been. For further development of 4G and introduction of 5G, it is assumed that finer quality control in area units is required for network control, management and operation.

  As an existing quality control technique, research is being conducted to simulate radio wave propagation in a communication environment in which buildings and trees exist around. In the radio wave propagation model such as the Sakai / Okumura model, it is possible to model the influence of radio waves in an urban area where buildings and trees are located or outside the premises, and obtain the electric field strength at each point in the area. Also in the ray tracing method, the electric field strength at each point in the area can be obtained in consideration of the influence of the building or the like (see Non-Patent Document 1).

Tetsuro Imai, "Movement propagation simulation by ray-tracing method", IEICE Transactions B, Vol. J92-B, No. 9, pp.1333-1347, 2009

  Existing RF (Radio Frequency) management methods that determine the electric field strength at each point in the area, such as the radio wave propagation model such as the Sakai / Okumura model and the ray tracing method, have a comprehensive coverage, but the estimation accuracy is constant. There is a limit. Also, the accuracy of the map and building numerical data necessary as input data is limited. In particular, the ray tracing method has a problem that it takes time to calculate.

  Also, in mobile communication, measurement data from each UE is collected to the base station at regular intervals in order to perform quality management / control and the like. The data can be extracted from the base station and used in the form of cell trace. Further, for example, it is possible to collect and use the field strength data measured at the same time as measurement results such as a speed test performed by each user in the cloud and performing statistical processing. In the UE, since the measurement is performed on-site, the reliability of the measurement value itself is relatively high. However, since data can be obtained only at the point where the UE is located, the area coverage is low.

  It is an object of the present invention to improve estimation accuracy while ensuring surface coverage of quality estimation in mobile communication.

The quality estimation apparatus according to an aspect of the present invention is
A quality estimation device for estimating quality in mobile communication,
An RF (Radio Frequency) management method that calculates quality based on radio wave propagation from the base station, an area distribution estimation unit that estimates the area distribution of quality,
A quality estimation unit that estimates the quality in the area based on the area distribution of the estimated quality and the measurement value of the quality measured in advance in the measurement point in the area;
An output unit for outputting the estimated quality;
It is characterized by having.

Moreover, the quality estimation method according to an aspect of the present invention includes:
A quality estimation method in a quality estimation apparatus for estimating quality in mobile communication,
Estimating the area distribution of quality by RF (Radio Frequency) management method that calculates the quality based on the radio wave propagation from the base station,
Estimating the quality in the area based on the area distribution of the estimated quality and the measurement value of the quality measured in advance at the measurement point in the area;
Outputting the estimated quality;
It is characterized by having.

  ADVANTAGE OF THE INVENTION According to this invention, it becomes possible to improve estimation accuracy, ensuring the surface coverage of the quality estimation in mobile communication.

The figure which shows the outline | summary of the quality estimation in the Example of this invention. The figure which shows the function structure of the quality estimation apparatus which concerns on the Example of this invention. The figure which shows the outline of the quality estimation method in the quality estimation part The figure which shows the setting of the representative measurement point in the Example of this invention The figure which shows the outline | summary of the learning of the machine learning module in the Example of this invention. The figure which shows the example of the NN model used in the Example of this invention The figure which shows the outline | summary of the quality estimation using the machine learning module in the Example of this invention. The figure which shows the outline | summary of the learning of the machine learning module in the Example of this invention (when a land use division is input) The figure which shows the example of the land use classification used in the Example of this invention The figure which shows the outline | summary of the quality estimation using the machine learning module in the Example of this invention (when using a land use classification) The figure which shows the hardware structural example of the quality estimation apparatus which concerns on the Example of this invention.

  Embodiments of the present invention will be described below with reference to the drawings.

  In an embodiment of the present invention, a quality estimation apparatus for estimating quality in mobile communication will be described.

  FIG. 1 shows an outline of quality estimation in the embodiment of the present invention. In the embodiment of the present invention, an area estimation / interpolation algorithm that can take advantage of both the advantages of the above-mentioned RF management technique in terms of area coverage and the reliability of UE measurement reliability can be constructed, and a higher definition and interactive Aiming to realize effective area quality control.

  As a concrete policy of the algorithm, the concept of obtaining rough estimation by RF management method and using the data to improve the approximation accuracy is adopted for the vicinity of the point where UE measurement data is obtained.

  As shown in FIG. 1, a calculation plane representing an area distribution of quality (for example, RSRP (Reference Signal Received Power)) can be estimated by an RF management method. However, the quality by the RF management method is different from the quality measured by the actual UE (hereinafter referred to as UE measurement value). In this case, it is assumed that the UE measurement value is correct. For points where no UE exists, the quality is estimated by interpolating the quality estimated by the RF management technique with the UE measurement value. Although FIG. 1 is expressed in two dimensions for simplicity, the algorithm of the present invention can also be applied to three dimensions.

<Example 1>
FIG. 2 shows a functional configuration of the quality estimation apparatus 100 according to the embodiment of the present invention. The quality estimation apparatus 100 includes an area distribution estimation unit 101, a measurement value acquisition unit 103, a quality estimation unit 105, and a quality output unit 107.

  The area distribution estimation unit 101 estimates an area distribution of quality by an RF management method that calculates quality based on radio wave propagation from the base station. For example, the area distribution estimation unit 101 uses the radio wave propagation model such as the Sakai / Okumura model or the ray tracing method to receive the antenna specifications of the base station and obtain the area distribution of the electric field strength.

  The measurement value acquisition unit 103 acquires a quality measurement value (UE measurement value) by the UE. The UE measurement value is a value acquired in advance by the UE at a measurement point in the area and reported to the base station.

  The quality estimation unit 105 estimates the quality in the area based on the area distribution of the quality estimated by the area distribution estimation unit 101 and the UE measurement value acquired by the measurement value acquisition unit 103. Details of the quality estimation unit 105 will be described below.

  The quality output unit 107 outputs the quality estimated by the quality estimation unit 105 to a display or the like.

  FIG. 3 shows an outline of the quality estimation method in the quality estimation unit 105. First, UE measurement values are acquired from the base station in the form of cell trace or the like. In addition, the calculated value of quality is estimated by RF management method. Normally, there is a discrepancy between the UE measurement value and the calculated quality value, so the UE measurement value is respected at the point where the UE measurement value is obtained (that is, the point where the UE exists). Suppose that is correct. For the point where no UE exists, the quality is estimated by interpolating the calculated quality value estimated by the RF management technique with the UE measurement value.

  For example, a three-dimensional space

で表し、３次元ガウス関数gを

And the three-dimensional Gaussian function g

とする。ただし、Σは対称行列である分散共分散行列であり、μは平均ベクトルである。

And Here, Σ is a variance-covariance matrix that is a symmetric matrix, and μ is an average vector.

Let m _{i be the} difference between the UE measurement at coordinates (x, y, z) and the calculated quality E _RF (x, y, z, t) estimated by the RF management technique, and let e _i be Define.

この場合、時間tにおける座標(x,y,z)の品質は以下のように推定できる。

In this case, the quality of the coordinates (x, y, z) at time t can be estimated as follows.

次に、機械学習モジュールを用いて品質の推定精度を高める手法について説明する。まず、UE測定値の中から代表測定点を定める。図４に、本発明の実施例における代表測定点の設定を示す。あらかじめ、或るエリア内の複数の測定点において測定をし、面的に凋密なUE測定値を得ておく。それらの中から、一部の点集合を選び出し、点集合の中から代表測定点x1を定める。代表測定点x1をUEに見立てて、そのUEからの距離に対して、各点x2, x3, x4,...のUE測定値とRF管理手法による値の差を入力とし、例えば、ニューラルネットワーク（NN：Neural Network）モデルのような機械学習モジュール等を用いて学習する。

Next, a method for improving the quality estimation accuracy using the machine learning module will be described. First, representative measurement points are determined from UE measurement values. FIG. 4 shows the setting of representative measurement points in the embodiment of the present invention. In advance, measurement is performed at a plurality of measurement points in a certain area, and UE measurement values that are dense in terms of area are obtained. Some point sets are selected from them, and representative measurement points x1 are determined from the point sets. Assume the representative measurement point x1 as a UE, and input the difference between the UE measurement value of each point x2, x3, x4, ... and the value by RF management method with respect to the distance from the UE, for example, a neural network Learning using a machine learning module such as (NN: Neural Network) model.

  FIG. 5 shows an outline of learning by the machine learning module in the embodiment of the present invention. RSRP measurement values (UE measurement values) at measurement points x2, x3, x4, ... other than the representative measurement points shown in Fig. 4 and RF management methods at measurement points x2, x3, x4, ... The difference from the RSRP calculation value (quality calculation value) is input to the NN model. Further, the distance r from the representative measurement point x1 to the measurement points x2, x3, x4,... Is input to the NN model. Note that the UE height, the distance to a structure such as a building, an area code, and various other parameters such as indoor / outdoor may also be input to the NN model.

In the NN model, as shown in FIG. 6, a model that outputs _ouput with respect to the input x _{j as} follows can be used.

ただし、f(t)はシグモイド関数であり、重みw_i,j ¹及びw_j,k ²は、バックプロパゲーションにより繰り返し更新される。y_outoputがRSRP補正値であり、このRSRP補正値が収束した場合、学習が完了する。なお、NNモデルの学習においては、測定点x2, x3, x4,...の全てを入力して学習させる必要はなく、或る収束条件を満たした場合に学習を完了させる。

However, f (t) is a sigmoid function, and the weights w _{i, j} ¹ and w _{j, k} ² are repeatedly updated by backpropagation. If _youtoput is an RSRP correction value and this RSRP correction value has converged, learning is completed. In learning of the NN model, it is not necessary to input all of the measurement points x2, x3, x4,..., And learning is completed when a certain convergence condition is satisfied.

  FIG. 7 shows an outline of quality estimation using the machine learning module in the embodiment of the present invention. After the learning is completed, the distance r from the representative measurement point at an arbitrary point (a point where no UE exists) and other various parameters are input to the NN model, and an RSRP correction value is obtained. The quality is estimated by adding this RSRP correction value to the RSRP calculation value obtained by the RF management method at an arbitrary point.

<Example 2>
Next, a method for further improving the quality estimation accuracy by learning the land use classification in the area as an input of the machine learning model will be described.

  FIG. 8 shows an outline of learning by the machine learning module in the embodiment of the present invention when the land use classification is input. In this embodiment, in addition to the input parameters shown in FIG. 5, the land use classification is further used as an input parameter. FIG. 9 shows an example of land use classification used in the embodiment of the present invention. The land use classification may be classified into buildings, agricultural land, bridges, public land, etc., and may be further classified according to the height of the building, the height of trees, and the like. Appropriate values are assigned to the land use categories, and those values are input to the NN model. The learning of the NN model is performed as described with reference to FIG.

  FIG. 10 shows an outline of quality estimation using the machine learning module in the embodiment of the present invention when the land use classification is input. After learning is completed, the distance r from the representative measurement point at any point (point where no UE exists), the land use classification at that point, and other various parameters are input to the NN model, and the RSRP correction value Get. The quality is estimated by adding this RSRP correction value to the RSRP calculation value obtained by the RF management method at an arbitrary point.

  Thus, by considering land use classification, the learning accuracy of machine learning is improved and the area estimation system is further improved.

  As described above, according to the embodiment of the present invention, it is possible to improve the estimation accuracy while ensuring the surface coverage of quality estimation in mobile communication. Further, the area distribution of the electric field strength obtained by the embodiment of the present invention can be combined with indexes of other layers such as interference and traffic volume to estimate a higher index.

<Hardware configuration example>
FIG. 11 shows a hardware configuration example of the quality estimation apparatus 100 according to the embodiment of the present invention. The quality estimation apparatus 100 may be a computer including a processor such as a CPU (Central Processing Unit) 151, a memory device 152 such as a RAM (Random Access Memory) and a ROM (Read Only Memory), a storage device 153 such as a hard disk, and the like. . For example, the functions and processing of the quality estimation apparatus 100 are realized by the CPU 151 executing data and programs stored in the storage device 153 or the memory device 152. Information necessary for the quality estimation apparatus 100 may be input from the input / output interface apparatus 154, and a result obtained by the quality estimation apparatus 100 may be output from the input / output interface apparatus 154.

<Supplement>
For convenience of explanation, the quality estimation apparatus according to the embodiment of the present invention is described using a functional block diagram. However, the quality estimation apparatus according to the embodiment of the present invention may be hardware, software, or a combination thereof. It may be realized. For example, an embodiment of the present invention includes a program that causes a computer to realize the function of the quality estimation apparatus according to the embodiment of the present invention, a program that causes a computer to execute each procedure of the method according to the embodiment of the present invention, and the like. May be realized. In addition, the functional units may be used in combination as necessary. In addition, the method according to the embodiment of the present invention may be performed in an order different from the order shown in the embodiment.

  As described above, the method for improving the estimation accuracy while ensuring the surface coverage of the quality estimation in the mobile communication has been described, but the present invention is not limited to the above-described embodiments, and Various modifications and applications can be made within.

DESCRIPTION OF SYMBOLS 100 Quality estimation apparatus 101 Area distribution estimation part 103 Measurement value acquisition part 105 Quality estimation part 107 Quality output part 151 CPU
152 Memory device 153 Storage device 154 Input / output interface device

Claims (5)

A quality estimation device for estimating quality in mobile communication,
An RF (Radio Frequency) management method that calculates quality based on radio wave propagation from the base station, an area distribution estimation unit that estimates the area distribution of quality,
A quality estimation unit that estimates the quality in the area based on the area distribution of the estimated quality and the measurement value of the quality measured in advance in the measurement point in the area;
An output unit for outputting the estimated quality;
A quality estimation device.   The quality estimation apparatus according to claim 1, wherein the quality estimation unit estimates a quality in the area using a neural network model.   The quality estimation unit sets a representative measurement point from a plurality of measurement points in the area, and estimates the quality measured in advance at a certain measurement point in the area and the quality at the certain measurement point. The quality in the area is estimated by learning the difference from the calculated value of the quality calculated by the area distribution and the distance from the representative measurement point to the certain measurement point as an input of the neural network model. The quality estimation apparatus according to claim 2.   The quality estimation apparatus according to claim 3, wherein the quality estimation unit further estimates a quality in the area by learning a land use classification in the area as an input of a neural network model. A quality estimation method for estimating quality in a quality estimation device in mobile communication,
Estimating the area distribution of quality by RF (Radio Frequency) management method that calculates the quality based on the radio wave propagation from the base station,
Estimating the quality in the area based on the area distribution of the estimated quality and the measurement value of the quality measured in advance at the measurement point in the area;
Outputting the estimated quality;
A quality estimation method comprising:

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