Estimation method and information processing apparatus

Abstract

To accurately estimate a traffic distribution.SOLUTION: An estimation program according to an embodiment causes a computer to execute a plurality of solving processes, a calculating process, a selecting process, and an outputting process. The plurality of solving processes solve a plurality of optimization problems of a traffic distribution within a predetermined range by using a predetermined term based on a used bandwidth and a throughput of a base station in operation within the predetermined range. Calculating a degree of similarity between candidates for a traffic distribution within a predetermined range, the candidates being a plurality of solutions to an optimization problem; In the selection processing, one traffic distribution is selected from among the candidates for the traffic distribution within the predetermined range based on the calculated similarity. Outputting the selected candidate of the traffic distribution as the traffic distribution within the predetermined range.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an estimation program, an estimation method, and an information processing device. [Background technology]

[0002] One method for reducing power consumption in a Radio Access Network (RAN) is to switch some base stations (BSs) into sleep mode. One method of switching off the RAN is to reduce power consumption by switching as many BSs as possible into sleep mode while maintaining the communication quality between the BSs and the terminals (User Equipment, UE) based on the traffic situation based on Performance Management data (PM data) obtained during BS operation. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] US Patent Application Publication No. 2002 / 0013152 Summary of the Invention [Problem to be solved by the invention]

[0004] However, because PM data cannot be obtained from a sleeping BS, the above-mentioned conventional technology must perform broadcast shutdown control without knowing the traffic situation within the coverage area of ​​the sleeping BS, which can result in a deterioration in the performance of the broadcast shutdown control.In addition, if the sleeping BS is periodically activated to check the traffic situation within its coverage area, it is difficult to achieve a reduction in power consumption.

[0005] In one aspect, an object of the present invention is to provide an estimation program, an estimation method, and an information processing device that can accurately estimate traffic distribution. [Means for solving the problem]

[0006] In one proposal, the estimation program causes a computer to execute a multiple solving process, a calculation process, a selection process, and an output process. The multiple solving process solves multiple optimization problems for traffic distribution within a predetermined range using predetermined terms based on the bandwidth used and throughput of base stations operating within the predetermined range. The calculation process calculates similarities between multiple candidates for traffic distribution within the predetermined range, which are solutions to the optimization problems. The selection process selects one traffic distribution from the candidates for traffic distribution within the predetermined range based on the calculated similarities. The output process outputs the selected traffic distribution candidate as the traffic distribution within the predetermined range. [Effects of the Invention]

[0007] According to one embodiment, traffic distribution can be estimated with high accuracy. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is an explanatory diagram for explaining network outage control. [Figure 2] FIG. 2 is an explanatory diagram illustrating the balance between throughput and bandwidth usage. [Figure 3] FIG. 3 is an explanatory diagram illustrating the superposition of two-dimensional normal distributions. [Figure 4] FIG. 4 is an explanatory diagram for explaining the expression of traffic distribution. [Figure 5] FIG. 5 is a block diagram illustrating an example of a functional configuration of the information processing device according to the embodiment. [Figure 6] FIG. 6 is a flowchart illustrating an example of the operation of the information processing device according to the embodiment. [Figure 7] FIG. 7 is a flowchart illustrating an example of the traffic distribution generation process. [Figure 8]FIG. 8 is an explanatory diagram for explaining a calculation example. [Figure 9] FIG. 9 is an explanatory diagram for explaining a calculation example. [Figure 10] FIG. 10 is an explanatory diagram for explaining a calculation example. [Figure 11] FIG. 11 is an explanatory diagram illustrating an example of a computer configuration. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an estimation program, an estimation method, and an information processing device according to embodiments will be described with reference to the drawings. Components having the same functions in the embodiments will be assigned the same reference numerals, and duplicated descriptions will be omitted. Note that the estimation program, the estimation method, and the information processing device described in the following embodiments are merely examples and do not limit the embodiments. Furthermore, the following embodiments may be combined as appropriate within a range that does not cause contradictions.

[0010] 1 is an explanatory diagram illustrating network outage control. As shown in FIG. 1, an information processing device 1 according to an embodiment is a device that performs data conversion (S1) based on the positions of each BS operating in a RAN 2 and PM data D1, and estimates a traffic distribution D2 in a target area. For example, a PC (personal computer) or the like can be used as the information processing device 1. Here, the target area is set in advance as a predetermined range for determining the traffic distribution, and is set to, for example, an area of ​​several square kilometers.

[0011] The PM data D1 is data acquired at predetermined time intervals (every 5, 15, 30, or 60 minutes) during BS operation. The PM data D1 includes information such as the resource block usage rate of the BS, the average throughput of the UE, and delay. The traffic distribution D2 is data showing the traffic at each predetermined position (point) within the target area.

[0012] The outage controller 3 performs outage control to put each BS in RAN 2 into active / sleep mode based on the traffic distribution D2 estimated by the information processing device 1 (S2). For example, the outage controller 3 puts a BS covering an area with low traffic in traffic distribution D2 into sleep mode, and a BS covering an area with high traffic into active mode. In this way, the outage controller 3 puts as many BSs as possible into sleep mode to reduce power consumption while maintaining communication quality between the BSs and the UE.

[0013] The information processing device 1 sets, for example, a grid for the target area, and assumes that a terminal (UE) that communicates with the BS exists at each of multiple grid points of the grid. Hereinafter, the multiple grid points in the target area will be referred to as a UEgrid. Note that the grid point (UEgrid) is an example of a predetermined position within the target area.

[0014] The UEgrid may be set arbitrarily within the target area. For example, the density of grid points may be increased in densely populated areas and decreased in sparsely populated areas.

[0015] The information processing device 1 according to the embodiment assumes that a UE is present at each grid point and calculates the traffic between each UE grid and the BS, thereby calculating the spatial traffic distribution.

[0016] In this embodiment, the relationship (balance) between the traffic of each UEgrid and the throughput and bandwidth used by the BS is assumed as follows. FIG. 2 is an explanatory diagram for explaining the balance between the throughput and bandwidth used. As shown in FIG. 2, at a certain point (UEgrid k ) traffic k Let's say.

[0017] One assumption is that traffic k UEgrid kThe traffic is distributed to each BS according to the intensity between each BS, etc. Specifically, as shown in the following formula (1), the throughput (unit: bps) of a certain BS (e.g., BS1) is the sum of the traffic distributed from all UEgrids.

[0018]

number

[0019] In addition, one of the assumptions is that UEgrid k traffic k To satisfy this requirement, bandwidth is consumed according to the SINR (Signal-to-Interference-plus-Noise Ratio) between the connected BS. Specifically, as shown in the following equation (2), the bandwidth (unit: Hz) used by a certain BS (e.g., BS1) is the sum of the bandwidths used by all UEgrids at that BS.

[0020]

number

[0021] Here, the information on the left side of equations (1) and (2) (BS throughput and bandwidth used) can be obtained from PM data D1.

[0022] The information processing device according to the embodiment calculates intensity for a combination of all UE grids and BSs. The intensity is a value obtained by subtracting the path loss from the transmission power of the BS. The path loss can be calculated based on the location, frequency, height, etc. of the BS, as specified in 3GPP (registered trademark) (3rd Generation Partnership Project) and the like.

[0023] Next, the information processing device according to the embodiment calculates the allocation ratio to each BS for each UEgrid based on the intensity of each combination of all UEgrids and BSs, and sets the calculated ratio as a matrix P.

[0024] For example, the calculation of the allocation ratio may be based on a rule that the UE connects to the BS with the highest intensity, with the BS with the highest intensity for each UE being set to 1 and the other BSs being set to 0. The calculation of the allocation ratio is not limited to the above, and may be determined appropriately based on the connection rules used during network operation. For example, a rule may be used in which the UE connects to multiple BSs in descending order of intensity. The index used to calculate the intensity may also be changed as needed.

[0025] Next, the information processing device according to the embodiment calculates, for example, SINR as an index of communication quality for each combination of all UE grids and BSs.

[0026] Here, the information processing device according to the embodiment sets the matrix R as the product of the inverse of the rate calculated based on the SINR and the elements of the matrix P. The rate is calculated as log2(1+SINR), for example. Note that the rate may also be determined based on the MCS.

[0027] Furthermore, unless the BS is overwhelmed with traffic and the bandwidth used exceeds the total bandwidth of the BS, the balance is assumed to be as shown in the following equation (3).

[0028]

number

[0029] Here, x is a vector (length is the number of UEgrids) that represents the traffic [bps] for each UE. y_throughput is a vector (length is the number of BSs) that represents the PM data D1 (throughput [bps]). y_band is a vector (length is the number of BSs) that represents the PM data D1 (used bandwidth [Hz]). Matrix P is a matrix (size is the number of BSs, number of UEgrids) that determines the allocation ratio to each BS, i.e., the connection destination from the UE to the BS. Matrix R is a matrix (size is the number of BSs, number of UEgrids) that represents the used bandwidth per speed [Hz / bps].

[0030] The information processing device 1 according to the embodiment solves the optimization problem in consideration of the balance between the throughput and the bandwidth used, and obtains the vector x (traffic [bps] of each UEgrid), that is, the traffic distribution.

[0031] Here, the size of matrices P and R is the number of BSs × the number of UEgrids. If the number of UEgrids to be calculated is greater than the number of BSs, or if the rank of matrices P and R is low, the number of unknowns will be greater than the number of equations, and a single solution will not be found.

[0032] Therefore, the information processing device 1 according to the embodiment restricts the solution to the optimization problem by assuming spatial continuity as a property of traffic distribution, whereby traffic values ​​of UEgrids that are close to each other have similar values.

[0033] Specifically, the information processing device 1 according to the embodiment represents a vector x representing traffic [bps] for each UEgrid as a superposition of two-dimensional normal distributions (hereinafter sometimes simply referred to as normal distributions) centered at each UEgrid, and uses the scaling coefficients of each normal distribution as the design variables for optimization.

[0034] FIG. 3 is an explanatory diagram illustrating the superposition of two-dimensional normal distributions. As shown in FIG. 3, UE1 ,y UE1 The traffic d1 of UEgrid1 in (x, y) is the sum of two-dimensional normal distributions h1(x, y), h2(x, y) ... centered at each position. The calculation formula for this traffic d1 is as follows:

[0035]

number

[0036] Here, m is the number of UEgrids (number of positions). k (x,y) is a two-dimensional normal distribution. For this two-dimensional normal distribution, the mean is kThe position of the diagonal term σ of the variance-covariance matrix 2 is the distance between UEgrids, and other elements of the variance-covariance matrix are 0. The scaling coefficient of the two-dimensional normal distribution is the design variable for optimization.

[0037] 4 is an explanatory diagram illustrating the representation of traffic distribution. As shown in FIG. 4, the information processing device 1 according to the embodiment represents the traffic distribution by superimposing two-dimensional normal distributions. Specifically, the information processing device 1 according to the embodiment creates a matrix H that stores values ​​at each UEgrid position of a two-dimensional normal distribution centered at each UEgrid position.

[0038] In this embodiment, the scaling coefficient of each two-dimensional normal distribution is defined as a vector u. Also, in this embodiment, the matrix A is defined as the following equation (5), and the vector y is defined as the following equation (6).

[0039]

number

[0040]

number

[0041] Next, the information processing device 1 according to the embodiment solves the optimization problem of the following equation (7) for the vectors u, y, and matrix A described above.

[0042]

number

[0043] Next, the information processing device 1 according to the embodiment obtains a vector x, which is the traffic of each UEgrid, as shown in the following equation (8).

[0044]

number

[0045] Here, when creating the matrix H, the diagonal terms of the variance-covariance matrix of each two-dimensional normal distribution, σ 2 The diagonal terms of this variance-covariance matrix are an example of predetermined terms. If the true traffic distribution can be obtained separately from the PM data D1 during the operation of RAN2, the accuracy of the generated traffic distribution D2 relative to the true traffic distribution will be increased. 2 However, it is usually difficult to obtain true traffic distribution due to the cost of measuring and storing data.

[0046] Therefore, the information processing device 1 according to the embodiment repeatedly solves the optimization problem by changing the optimization method (interior point method, active constraint method, sequential quadratic programming, etc.) or by changing the random numbers using the same optimization method, thereby obtaining multiple solutions (traffic distribution candidates).

[0047] Next, the information processing device 1 according to the embodiment calculates the similarity between the multiple solutions, i.e., the similarity between the traffic distribution candidates, and outputs one traffic distribution candidate from the multiple traffic distribution candidates based on the calculated similarity as the traffic distribution estimated for the target area. As a result, the information processing device 1 according to the embodiment can obtain a traffic distribution D2 that has a high calculated similarity and is estimated to be close to the true traffic distribution, even when it is difficult to obtain the true traffic distribution due to the presence of a sleeping BS or the like. Note that the two traffic distributions, which are solved using different optimization methods for solving the optimization problem, are sufficiently similar and both are close to the true distribution, so it is considered that either traffic distribution can be used as the final output. In other words, it is considered that there is no difference in the performance of the outage control between the final output traffic distributions. The final output traffic distribution may be the average of the two traffic distributions.

[0048] Specifically, the information processing device 1 according to the embodiment uses σ 2 Select a value of σ2 A candidate traffic distribution using the value of is output.

[0049] For example, the information processing device 1 according to the embodiment may select candidate optimization methods (interior point method, active constraint method, sequential quadratic programming, etc.) and σ 2 Candidate values ​​of (σ 2 Prepare values ​​(1, 2, 4, etc.).

[0050] Next, the information processing device 1 according to the embodiment selects the optimization method candidates and σ 2 An optimization problem is solved for combinations of candidate values ​​of , and candidate traffic distributions are generated.

[0051] Next, the information processing device 1 according to the embodiment calculates the same value of σ 2 For each, the same value of σ 2 The similarity between the traffic distribution candidates calculated in (1) is calculated. This similarity is calculated, for example, according to the following equation (9).

[0052]

number

[0053] In equation (9), the same value of σ 2 The similarity between the solution of the first optimization method (method1) and the solution of the second optimization method (method2) is calculated, where k corresponds to each element of the vector x. This similarity has the meaning of distance between traffic distributions, and for example, the more similar the traffic distributions are (the higher the similarity), the smaller the value becomes.

[0054] The information processing device 1 according to the embodiment performs the above calculation for each combination of the optimization method candidates (three combinations if the number of candidates is three), and obtains the same value of σ 2 Calculate the average for each.

[0055] Note that the information processing device 1 according to the embodiment has a small value of σ 2 σ where the similarity is below the predetermined threshold (the similarity is sufficiently high).2 , or σ where the change in similarity is below the threshold. 2 In this case, the information processing device 1 may select σ 2 After selecting, the trial (process of solving multiple problems) is terminated.

[0056] If the solution is restricted, σ 2 The smaller the value of σ, the easier it is to obtain a solution without introducing unnecessary assumptions. 2 By performing trial and selection from the above, the information processing device 1 according to the embodiment can obtain a solution (traffic distribution) with high accuracy. Furthermore, the information processing device 1 can eliminate unnecessary trials by rounding up trials (multiple solution processes).

[0057] Next, the information processing device 1 according to the embodiment will be described in detail. Fig. 5 is a block diagram showing an example of the functional configuration of the information processing device 1 according to the embodiment.

[0058] As shown in FIG. 5, the information processing device 1 includes a communication unit 10, an input unit 20, a display unit 30, a storage unit 40, and a control unit 50.

[0059] The communication unit 10 executes data communication with external devices etc. via a network. The input unit 20 accepts operations from a user. The display unit 30 displays the processing results of the control unit 50.

[0060] The storage unit 40 stores the above-described PM data D1 and traffic distribution D2, as well as BS information 41, UEgrid information 42, and setting information 43. For example, the storage unit 40 is realized by a memory or the like.

[0061] The BS information 41 is information about each base station (BS). Specifically, the BS information 41 includes the transmission power, frequency, position, height, etc. of each BS.

[0062] The UEgrid information 42 is information about each UEgrid. Specifically, the UEgrid information 42 includes the location of each UEgrid. If there is an observed value of traffic in the UEgrid at a specific time and location, the UEgrid information 42 also includes that information.

[0063] The setting information 43 includes various setting values ​​used when calculating the traffic distribution, such as values ​​(candidate values) for the diagonal terms (σ2) of the variance-covariance matrix of the normal distribution, and candidate optimization methods (interior point method, active constraint method, sequential quadratic programming, etc.).

[0064] The control unit 50 includes a setting unit 51, a traffic distribution generating unit 52, and an output unit 53. For example, the control unit 50 is realized by a processor.

[0065] The setting unit 51 is a processing unit that performs various settings related to traffic distribution based on data input via the communication unit 10, the input unit 20, etc. Specifically, the setting unit 51 accepts input from a user via the communication unit 10, the input unit 20, etc., and sets the positions of multiple grid points corresponding to the target area, the positions of base stations, and the communication volume of the base station for each predetermined time period (PM data D1). The setting unit 51 stores the set contents in the storage unit 40 as PM data D1, BS information 41, UEgrid information 42, and setting information 43.

[0066] The traffic distribution generation unit 52 is a processing unit that performs the above-mentioned calculation based on the setting by the setting unit 51 and estimates the communication volume of each UE grid at a predetermined time, i.e., traffic distribution D2. The traffic distribution generation unit 52 stores the estimated traffic distribution D2 in the storage unit 40.

[0067] The output unit 53 is a processing unit that reads out the traffic distribution D2 generated (estimated) by the traffic distribution generating unit 52 from the storage unit 40 and outputs it to the outage controller 3 via the communication unit 10.

[0068] 6 is a flowchart showing an example of operation of the information processing device 1 according to the embodiment. As shown in FIG. 6, the setting unit 51 receives BS information 41 related to the specifications of each BS (transmission power, frequency, position, height, etc.) through input from a user or the like. The setting unit 51 also receives PM data D1 related to the BSs in operation (traffic for each BS, bandwidth used for each BS, etc.). The setting unit 51 also receives setting information 43 such as the position of the UE grid, n candidates for σ, m candidates for optimization method, and a threshold value for traffic distribution similarity (S10). The setting unit 51 stores the received setting contents in the storage unit 40 as the PM data D1, BS information 41, UE grid information 42, and setting information 43.

[0069] Next, the traffic distribution generation unit 52 performs data conversion (traffic distribution generation) to a traffic distribution D2 based on the PM data D1, BS information 41, UEgrid information 42, and setting information 43 stored in the storage unit 40 (S11). Next, the output unit 53 outputs the traffic distribution D2 generated by the traffic distribution generation unit 52 to the outage controller 3 (S12).

[0070] 7 is a flowchart showing an example of the traffic distribution generation process. As shown in FIG. 7, when the traffic distribution generation process starts, the traffic distribution generator 52 calculates the intensity for all combinations of UE grids and BSs (S20).

[0071] Next, the traffic distribution generation unit 52 calculates the bandwidth used per speed (S21) and creates the above-mentioned matrices P and R (S22). Next, the traffic distribution generation unit 52 initializes variables j and k to j=1 and k=1 (S23), and performs processing to solve multiple optimization problems by combining (n) candidates for σ and (m) candidates for optimization methods (S24 to S30).

[0072] Specifically, the traffic distribution generating unit 52 calculates the standard deviation of the normal distribution as σ kThen, the traffic distribution generating unit 52 generates the above-mentioned matrix H as follows (S24). j The traffic distribution generation unit 52 then calculates traffic for each UEgrid according to the above-mentioned equation (8) (S27).

[0073] Next, the traffic distribution generation unit 52 determines whether k=n (S28), and if k=n is not true (S28: No), it increments k to k+1 (S29) and returns the process to S24.

[0074] If k=n (S28: Yes), the traffic distribution generation unit 52 determines whether j=m (S30), and if j=m is not true (S30: No), it increments j=j+1 and sets k=1 (S31), and returns the process to S24.

[0075] If j=m (S30: Yes), the traffic distribution generation unit 52 calculates the similarity between the multiple calculated traffic distribution candidates for each of the same values ​​of σ using the above-mentioned formula (9). Next, the traffic distribution generation unit 52 selects the value of σ that maximizes the similarity of the traffic distributions (S33). Next, the traffic distribution generation unit 52 outputs the traffic distribution candidate that uses the selected value of σ as the estimated traffic distribution D2 (S34), and ends the process.

[0076] 8 to 10 are explanatory diagrams illustrating a calculation example. As shown in Fig. 8, in this calculation example, PM data D1 is obtained by simulation from traffic data (original data D0) in units of grids that indicate the true traffic distribution. In this simulation, the connection between the UE and the BS is set so that the UE is connected to the BS with the highest intensity.

[0077] In this calculation example, a traffic distribution D2 is calculated based on PM data D1 using the information processing device 1 according to the embodiment. Here, the rate [bps / Hz] between the UE and the BS is calculated as log2(1+SINR). In this calculation example, the accuracy of the estimated traffic distribution D2 is verified by comparing this traffic distribution D2 with original data D0 that indicates the true traffic distribution.

[0078] As shown in Figure 9, cases C1 and C2 are different areas (target areas) and σ 2 This is an example of calculation in which traffic distribution D2 is calculated in ascending order of σ = 1, 2, and 4. In case C1, σ 2 When σ = 1, it is equal to or less than the threshold value (here, 0.05) set in advance in the setting information 43. Therefore, in case C1, the information processing device 1 2 When calculating the traffic distribution D2 in ascending order of σ = 1, 2, and 4, the σ 2 = 1, the traffic distribution D2 is output to the outage controller 3.

[0079] Similarly, in case C2, σ 2 When σ = 2, it is equal to or less than the threshold value (here, 0.05) set in advance in the setting information 43. Therefore, in case C2, the information processing device 1 2 When calculating the traffic distribution D2 in ascending order of σ = 1, 2, and 4, the σ 2 The traffic distribution D2 when .times. ...

[0080] In both cases C1 and C2, the traffic distribution D2 output to the outage controller 3 has a shape similar to the traffic distribution of the original data D0, and highly accurate estimation results are obtained.

[0081] 10, in the original data D0a and D0b, a traffic peak occurs in the area within the frame where the BS is sleeping. Even in such a case, the traffic peak can be reproduced in the traffic distribution D2b estimated from the PM data D1 of the original data D0b.

[0082] As described above, the information processing device 1 solves multiple optimization problems for traffic distribution within a predetermined range (target area) using predetermined terms based on the bandwidths used and throughputs of base stations (BSs) operating within the predetermined range. The information processing device 1 calculates similarities between candidates for traffic distribution within the predetermined range, which are multiple solutions to the optimization problem. Based on the calculated similarities, the information processing device 1 selects one traffic distribution from the candidates for traffic distribution within the predetermined range and outputs the selected traffic distribution as the traffic distribution within the predetermined range.

[0083] As a result, even if there is a sleeping BS, the information processing device 1 can obtain a traffic distribution D2 that is estimated to be close to the true traffic distribution based on the similarity between a plurality of traffic distribution candidates.

[0084] Furthermore, the information processing device 1 solves a plurality of optimization problems by varying a method for solving the optimization problem and / or the value of a predetermined term, thereby enabling the information processing device 1 to obtain a plurality of candidates for traffic distribution.

[0085] Furthermore, the information processing device 1 calculates the similarity between multiple candidates solved using a predetermined term with the same value for each value of the predetermined term. Based on the similarity calculated for each value of the predetermined term, the information processing device 1 selects one traffic distribution candidate that uses a predetermined value for the predetermined term from multiple traffic distribution candidates within a predetermined range. This allows the information processing device 1 to appropriately select the value of the predetermined term to be used in the optimization problem and select a traffic distribution that uses that value.

[0086] Furthermore, the information processing device 1 solves the optimization problem by varying the value of the predetermined term in ascending order. As a result, the information processing device 1 can obtain a solution in ascending order of the value of the predetermined term used in the optimization problem, that is, without introducing unnecessary assumptions.

[0087] Furthermore, when the similarity calculated by varying the value of a predetermined term in ascending order satisfies a predetermined condition, the information processing device 1 selects one traffic distribution candidate from among the multiple traffic distribution candidates obtained by varying the value of the predetermined term in ascending order, and stops the multiple solving process. This allows the information processing device 1 to omit unnecessary trials (multiple solving process).

[0088] Note that the components of each device shown in the figure do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0089] For example, in the above embodiment, a case where the output solution is restricted by assuming spatial continuity as a constraint on the solution of the optimization problem has been exemplified, but the output solution may also be restricted by assuming a correlation (temporal continuity) such that traffic at close times at each lattice point should have similar values. As an example, when restricting the output solution by assuming temporal continuity, the above two-dimensional normal distribution for traffic can be expanded to a three-dimensional normal distribution by adding a time axis.

[0090] Furthermore, when solving the optimization problem, constraints and conditions other than the spatial and temporal correlations described above may be added to solve the optimization problem. As an example, a range may be set for the traffic value of the UEgrid. Furthermore, if the observed traffic value of the UEgrid at a specific time and location is included in the UEgrid information 42, that value may be applied to the optimization problem. Furthermore, for a UEgrid with zero UEs, the traffic value may be set to zero.

[0091] Furthermore, since the traffic of the UEgrid is expressed by superimposing a distribution function (normal distribution), it is also possible to calculate traffic at locations other than the defined UEgrid and at times other than the time when the PM data was collected.

[0092] Furthermore, the normal distribution may be substituted with a radial basis function. The UEgrid may not be arranged in a grid pattern, but may be arranged in a way that covers the entire target area and obtains traffic distribution (for example, dots with varying densities corresponding to population density, etc.). This embodiment is also applicable to cases where the coverage of macro BSs and small BSs overlap, such as in a heterogeneous network.

[0093] Furthermore, the traffic distribution may be generated by considering only the balance of throughput or only the balance of bandwidth usage. When generating the traffic distribution by considering both the balance of throughput and the balance of bandwidth usage, the throughput and the bandwidth usage may be weighted by multiplying each by a predetermined coefficient.

[0094] Furthermore, when generating a traffic distribution, the balance of BS usage time may be taken into consideration instead of the balance of usage bandwidth. For example, when generating a traffic distribution taking into consideration the balance of BS usage time, an index that can reflect the load on the BS, such as SINR, which indicates communication quality, may be used.

[0095] Furthermore, the various processing functions of the setting unit 51, traffic distribution generation unit 52, and output unit 53 performed by the control unit 50 of the information processing device 1 may be executed in whole or in part on a CPU (or a microcomputer such as an MPU or MCU (Micro Controller Unit)). Needless to say, the various processing functions may be executed in whole or in part on a program analyzed and executed by the CPU (or a microcomputer such as an MPU or MCU), or on hardware using wired logic. The various processing functions performed by the information processing device 1 may be executed by multiple computers working together using cloud computing.

[0096] The various processes described in the above embodiments can be realized by executing a program prepared in advance on a computer. Therefore, an example of a computer configuration (hardware) that executes a program having the same functions as those of the above embodiments will be described below. Fig. 11 is an explanatory diagram illustrating an example of a computer configuration.

[0097] 11, computer 200 includes CPU 201 for executing various types of arithmetic processing, input device 202 for receiving data input, monitor 203, and speaker 204. Computer 200 also includes medium reading device 205 for reading programs and the like from a storage medium, interface device 206 for connecting with various devices, and communication device 207 for connecting with external devices via wired or wireless communication. Computer 200 also includes RAM 208 for temporarily storing various types of information, and hard disk drive 209. Each unit (201 to 209) within computer 200 is connected to bus 210.

[0098] The hard disk drive 209 stores a program 211 for executing various processes in the functional configuration (e.g., the setting unit 51, the traffic distribution generation unit 52, and the output unit 53) described in the above embodiment. The hard disk drive 209 also stores various data 212 referenced by the program 211. The input device 202, for example, accepts input of operation information from an operator. The monitor 203, for example, displays various screens operated by the operator. The interface device 206 is connected to, for example, a printing device. The communication device 207 is connected to a communication network such as a LAN (Local Area Network), and exchanges various information with external devices via the communication network.

[0099] The CPU 201 reads out the program 211 stored in the hard disk drive 209, expands it in the RAM 208, and executes it to perform various processes related to the above-described functional configuration (for example, the setting unit 51, the traffic distribution generation unit 52, and the output unit 53). The program 211 does not have to be stored in the hard disk drive 209. For example, the program 211 stored in a storage medium readable by the computer 200 may be read out and executed. Examples of the storage medium readable by the computer 200 include portable storage media such as CD-ROMs, DVD discs, USB (Universal Serial Bus) memory, semiconductor memories such as flash memory, and hard disk drives. The program 211 may also be stored in a device connected to a public line, the Internet, a LAN, or the like, and the computer 200 may read out and execute the program 211 from the device.

[0100] The following additional notes are provided regarding the above-described embodiments.

[0101] (Supplementary Note 1) Based on the bandwidths and throughputs of base stations operating within a predetermined range, a plurality of optimization problems of traffic distribution within the predetermined range are solved using predetermined terms; calculating a similarity between a plurality of candidates for traffic distribution within the predetermined range, the candidates being solutions to the optimization problem; selecting one traffic distribution from among the candidate traffic distributions within the predetermined range based on the calculated similarity; outputting the selected traffic distribution as a traffic distribution within the predetermined range; An estimation program that causes a computer to execute processing.

[0102] (Appendix 2) The multiple solving process may be a method for solving the optimization problem, and / or may involve varying the value of the predetermined term to solve the optimization problem multiple times. 2. The estimation program according to claim 1,

[0103] (Supplementary Note 3) The calculating process calculates the similarity between the plurality of candidates solved using the predetermined term of the same value for each value of the predetermined term; the selecting process selects one traffic distribution candidate using a predetermined value for the predetermined term from among a plurality of traffic distribution candidates within the predetermined range based on the similarity calculated for each value of the predetermined term; 2. The estimation program according to claim 1,

[0104] (Note 4) The multiple solving process solves the optimization problem by varying the value of the predetermined term in ascending order. 3. The estimation program according to claim 2,

[0105] (Appendix 5) In the selecting process, when the similarity calculated by varying the value of the predetermined term in ascending order satisfies a predetermined condition, one traffic distribution candidate is selected from the plurality of traffic distribution candidates obtained by varying the value of the predetermined term in ascending order, and the plurality of solving processes is terminated. 5. The estimation program according to claim 4,

[0106] (Supplementary Note 6) The predetermined term is a term related to the variance of traffic distribution within the predetermined range. 2. The estimation program according to claim 1,

[0107] (Supplementary Note 7) Based on the bandwidths and throughputs of base stations operating within a predetermined range, a plurality of optimization problems of traffic distribution within the predetermined range are solved using predetermined terms; calculating a similarity between a plurality of candidates for traffic distribution within the predetermined range, the candidates being solutions to the optimization problem; selecting one traffic distribution from among the candidate traffic distributions within the predetermined range based on the calculated similarity; outputting the selected traffic distribution as a traffic distribution within the predetermined range; An estimation method characterized in that the processing is executed by a computer.

[0108] (Appendix 8) The multiple solving process may be a method for solving the optimization problem, and / or may involve varying the value of the predetermined term to solve the optimization problem multiple times. 8. The estimation method according to claim 7,

[0109] (Supplementary Note 9) The calculating process calculates the similarity between the plurality of candidates solved using the predetermined term of the same value for each value of the predetermined term; the selecting process selects one traffic distribution candidate using a predetermined value for the predetermined term from among a plurality of traffic distribution candidates within the predetermined range based on the similarity calculated for each value of the predetermined term; 8. The estimation method according to claim 7,

[0110] (Supplementary Note 10) The multiple solving process solves the optimization problem by varying the value of the predetermined term in ascending order. 9. The estimation method according to claim 8,

[0111] (Supplementary Note 11) When the similarity calculated by varying the value of the predetermined term in ascending order satisfies a predetermined condition, the selecting process selects one traffic distribution candidate from among the plurality of traffic distribution candidates obtained by varying the value of the predetermined term in ascending order, and terminates the plurality solving process. 11. The estimation method according to claim 10,

[0112] (Supplementary Note 12) The predetermined term is a term related to the variance of traffic distribution within the predetermined range. 8. The estimation method according to claim 7,

[0113] (Supplementary Note 13) Based on the bandwidth and throughput of base stations operating within a predetermined range, a plurality of optimization problems of traffic distribution within the predetermined range are solved using predetermined terms; calculating a similarity between a plurality of candidates for traffic distribution within the predetermined range, the candidates being solutions to the optimization problem; selecting one traffic distribution from among the candidate traffic distributions within the predetermined range based on the calculated similarity; outputting the selected traffic distribution as a traffic distribution within the predetermined range; An information processing device comprising: a control unit that executes processing.

[0114] (Appendix 14) The multiple solving process is a method for solving the optimization problem, and / or a method for solving the optimization problem by varying the value of the predetermined term. 14. The information processing device according to claim 13,

[0115] (Supplementary Note 15) The calculating process calculates the similarity between the plurality of candidates solved using the predetermined term of the same value for each value of the predetermined term; the selecting process selects one traffic distribution candidate using a predetermined value for the predetermined term from among a plurality of traffic distribution candidates within the predetermined range based on the similarity calculated for each value of the predetermined term; 14. The information processing device according to claim 13,

[0116] (Appendix 16) The multiple solving process solves the optimization problem by varying the value of the predetermined term in ascending order. 15. The information processing device according to claim 14,

[0117] (Supplementary Note 17) When the similarity calculated by varying the value of the predetermined term in ascending order satisfies a predetermined condition, the selecting process selects one traffic distribution candidate from the plurality of traffic distribution candidates obtained by varying the value of the predetermined term in ascending order, and terminates the plurality solving process. 17. The information processing device according to claim 16,

[0118] (Supplementary Note 18) The predetermined term is a term related to the variance of traffic distribution within the predetermined range. 14. The information processing device according to claim 13, [Explanation of symbols]

[0119] 1...Information processing device 2…RAN 3...Stoppage controller 10. Communications Department 20...Input section 30...Display section 40...Storage section 41…BS information 42...UEgrid information 43...Setting information 50...Control unit 51...Settings section 52...Traffic distribution generation unit 53...Output section 200...Computer 201...CPU 202...input device 203...Monitor 204...Speaker 205...Media reader 206...Interface device 207...Communication equipment 208...RAM 209...Hard disk drive 210...bus 211…Program 212...Various data C1, C2...case D0, D0a, D0b...Original data D1…PM data D2, D2a, D2b…traffic distribution

Claims

1. solving a plurality of optimization problems of traffic distribution within a predetermined range using predetermined terms based on the bandwidths used and the throughputs of base stations operating within the predetermined range; calculating a similarity between a plurality of candidates for traffic distribution within the predetermined range, the candidates being solutions to the optimization problem; selecting one traffic distribution from among the candidate traffic distributions within the predetermined range based on the calculated similarity; outputting the selected traffic distribution as a traffic distribution within the predetermined range; An estimation program that causes a computer to execute processing.

2. The multiple solving process may solve the optimization problem multiple times by varying a method for solving the optimization problem and / or a value of the predetermined term.

2. The estimation program according to claim 1, wherein:

3. the calculating step calculates the similarity between the plurality of candidates solved using the predetermined term of the same value for each value of the predetermined term; the selecting process selects one traffic distribution candidate using a predetermined value for the predetermined term from among a plurality of traffic distribution candidates within the predetermined range based on the similarity calculated for each value of the predetermined term; 2. The estimation program according to claim 1, wherein:

4. the multiple solving process solves the optimization problem by varying the value of the predetermined term in ascending order; 3. The estimation program according to claim 2.

5. the selecting process selects one traffic distribution candidate from the plurality of traffic distribution candidates obtained by varying the value of the predetermined term in ascending order when the similarity calculated by varying the value of the predetermined term in ascending order satisfies a predetermined condition, and terminates the plurality solving process; 5. The estimation program according to claim 4.

6. the predetermined term is a term related to the variance of traffic distribution within the predetermined range; 2. The estimation program according to claim 1, wherein:

7. solving a plurality of optimization problems of traffic distribution within a predetermined range using predetermined terms based on the bandwidths used and the throughputs of base stations operating within the predetermined range; calculating a similarity between a plurality of candidates for traffic distribution within the predetermined range, the candidates being solutions to the optimization problem; selecting one traffic distribution from among the candidate traffic distributions within the predetermined range based on the calculated similarity; outputting the selected traffic distribution as a traffic distribution within the predetermined range; An estimation method characterized in that the processing is executed by a computer.

8. solving a plurality of optimization problems of traffic distribution within a predetermined range using predetermined terms based on the bandwidths used and the throughputs of base stations operating within the predetermined range; calculating a similarity between a plurality of candidates for traffic distribution within the predetermined range, the candidates being solutions to the optimization problem; selecting one traffic distribution from among the candidate traffic distributions within the predetermined range based on the calculated similarity; outputting the selected traffic distribution as a traffic distribution within the predetermined range; An information processing device comprising: a control unit that executes processing.

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