Control device, control method, and program

Abstract

This improves the downlink throughput of user terminals for cells where PUCCH's SINR is declining and the DTX rate is increasing. [Solution] A control device comprising: an acquisition unit that acquires performance data for each of multiple cells; a target cell identification unit that identifies a target cell from the multiple cells based on the performance data of the multiple cells; and a change control unit that changes the parameter settings of a wireless base station managing the target cell in order to increase the transmission power of the user terminal's PUCCH in the target cell identified by the target cell identification unit. The control device takes the performance data or feature quantities of the performance data of multiple cells as input and outputs the cell for which the parameter setting change to increase the transmission power of the PUCCH will be performed as input to a learning model, and identifies the cell output from the learning model as the target cell.

Description

Technical Field

[0001] The present invention relates to a control device, a control method, and a program.

Background Art

[0002] Patent Document 1 describes P0nominalPUCCH (Physical Uplink Control Channel), which is a parameter for adjusting the transmission power of a UE (User Equipment). [Prior Art Document] [Patent Document] [Patent Document 1] Japanese Unexamined Patent Application Publication No. 2020-137068

Summary of the Invention

Means for Solving the Problems

[0003] According to an embodiment of the present invention, a control device is provided. The control device may include a data acquisition unit that acquires performance data of each of a plurality of cells. The control device may include a target cell identification unit that identifies a target cell from the plurality of cells based on the performance data of the plurality of cells. The control device may include a change control unit that changes parameter settings of a radio base station that manages the target cell so as to increase the transmission power of a PUCCH of a user terminal in the target cell identified by the target cell identification unit.

[0004] The control device may include a storage unit that stores a learning model that takes performance data or feature quantities of the performance data of a plurality of cells as input and outputs a cell that is the target of parameter setting changes to increase the transmission power of the PUCCH, and the target cell identification unit may input the performance data or feature quantities of the performance data of the plurality of cells into the learning model and identify the cell output from the learning model as the target cell. The control device may include a learning data generation unit that generates learning data for each of the plurality of cells after the parameter settings of a plurality of wireless base stations that manage the plurality of cells have been changed to increase the transmission power of the PUCCH of the user terminal in the identified plurality of cells, and the learning model generation unit that generates the learning model by machine learning using the plurality of learning data, and the storage unit may store the learning model generated by the learning model generation unit. The learning data generation unit may, after identifying a plurality of cells from the plurality of cells in which the SINR and DTX rate of PUCCH for a predetermined period satisfy certain conditions, and after changing the parameter settings of the plurality of wireless base stations that manage the plurality of cells in order to increase the transmission power of the user terminal's PUCCH in the identified plurality of cells, generate learning data for each of the plurality of cells, which includes performance data or features of said performance data between the day before the day on which the parameter settings were changed and a predetermined number of days prior to that day, and improvement data related to the improvement of the communication environment due to the setting change.In any of the control devices described above, the learning data generation unit may generate learning data that includes performance data or features of such performance data, which include at least one of the following: KPIs related to the number of connected users, KPIs related to connectivity, KPIs related to disconnection rate, KPIs related to throughput, KPIs related to wireless resources, KPIs related to radio wave strength and quality of the wireless environment, and KPIs related to wireless transmission and reception characteristics, and the improvement data. The features of the performance data may include calculated values ​​for at least one of the performance data from a specific period, which is the period between the day before the day on which the parameter settings were changed and a predetermined number of days prior to that day. At least one of the performance data from the specific period may include at least one of the performance data for a specific day of the week within the specific period, and the performance data for a specific time of day within the specific period. The calculated value of at least one of the performance data for the specified period may include the mean, sum, maximum, minimum, median, standard deviation, skewness, and slope when linearly regressed for each of the multiple types of KPIs included in the performance data, as well as at least one of the sum, difference, product, and division of the multiple types of KPIs included in the performance data.

[0005] In any of the control devices described above, the target cell identification unit may identify as the target cell any of the plurality of cells in which the SINR and DTX rate of PUCCH for a predetermined period satisfy the conditions.

[0006] In any of the control devices described above, the change control unit may change the parameter settings of the wireless base station at different times for a plurality of target cells among the plurality of target cells identified by the target cell identification unit, where the distance between the cells is shorter than a predetermined distance.

[0007] In any of the control devices described above, the target cell identification unit may identify the target cells by excluding cells whose set parameters are higher than a predetermined level.

[0008] In any of the control devices described above, the target cell identification unit may identify the target cells by excluding cells whose Uplink RSSI is higher than a predetermined threshold.

[0009] According to one embodiment of the present invention, a control method performed by a computer is provided. The control method may include an acquisition step of acquiring performance data for each of a plurality of cells. The control method may include a target cell identification step of identifying a target cell from the plurality of cells based on the performance data of the plurality of cells. The control method may include a change control step of changing the parameter settings of a radio base station managing the target cell in order to increase the transmission power of the user terminal PUCCH in the target cell identified in the target cell identification step.

[0010] According to one embodiment of the present invention, a program is provided for a computer to perform the following steps: an acquisition step of acquiring performance data for each of a plurality of cells; a target cell identification step of identifying a target cell from the plurality of cells based on the performance data of the plurality of cells; and a change control step of changing the parameter settings of a wireless base station managing the target cell so as to increase the transmission power of the user terminal PUCCH in the target cell identified in the target cell identification step.

[0011] It should be noted that the above summary of the invention does not enumerate all the necessary features of the present invention. Furthermore, subcombinations of these features may also constitute an invention. [Brief explanation of the drawing]

[0012] [Figure 1] An example of System 10 is shown in a schematic manner. [Figure 2]A schematic example of the situation in cell 310 in a high RSSI environment is shown below. [Figure 3] An example of processing performed by the control device 100 is shown in a schematic manner. [Figure 4] An example of the processing flow by the control device 100 is schematically shown. [Figure 5] An example of the processing flow by System 10 is shown in general terms. [Figure 6] An example of KPI data 510 is shown in general terms. [Figure 7] A schematic example of label data 520 is shown below. [Figure 8] An example of training data 530 is shown in general terms. [Figure 9] An example of the functional configuration of the control device 100 is schematically shown. [Figure 10] An example of the functional configuration of the learning device 200 is shown in a schematic manner. [Figure 11] A schematic example of the hardware configuration of a computer 1200 that functions as a control device 100 or a learning device 200 is shown below. [Modes for carrying out the invention]

[0013] The present invention will be described below through embodiments, but these embodiments are not intended to limit the scope of the claims. Furthermore, not all combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0014] In conventional mobile communication systems, various vendors provide various radio base station equipment. Radio base station equipment has various functions, one example being Uplink Power Control. Uplink Power Control is a function that adjusts the transmission power of signals transmitted by user terminals, aiming to optimize system performance and user quality, reduce radio interference, and suppress battery power consumption. Uplink Power Control has two types: open-loop control and closed-loop control. In open-loop control, the user terminal calculates the transmission power. This calculation uses parameters transmitted from the radio base station, as well as information such as received power and path loss measured by the user terminal. Parameters transmitted from the radio base station include p0NominalPucch, etc. In closed-loop control, the radio base station transmits feedback information to the user terminal based on the signal quality of the user terminal measured by the radio base station. An example of the signal quality of the user terminal measured by the radio base station is Uplink SINR (Signal-to-Interference-plus-Noise Ratio). The user terminal determines the transmission power based on this feedback information. The functions of wireless base station equipment vary depending on the device. For example, some devices have the function to perform closed-loop transmit power control that takes uplink SINR into consideration, while others do not. If the installed equipment does not have this function, the quality of the uplink SINR may not be maintained, and the communication quality may deteriorate. For example, in environments with a high uplink RSSI (Received Signal Strength Indicator), the uplink SINR tends to deteriorate, and when the uplink SINR deteriorates, the wireless base station cannot receive a normal reception confirmation (Ack / Nack). As a result, the wireless base station will detect a DTX (Discontinuous Transmission) (DTX means detecting that there is no Ack / Nack response).When a wireless base station detects a DTX (Disrupted Transmission Time), it determines that the corresponding data has not been properly received by the user terminal and retransmits the same data. As a result, downlink throughput decreases. In the system 10 according to this embodiment, for example, a rule-based system identifies cells where the PUCCH SINR is trending downwards and the DTX rate is increasing, and modifies the parameter settings of the wireless base station managing the identified cells to increase the PUCCH transmission power of the user terminal in those cells. Specifically, system 10 modifies the parameter settings of the wireless base station to increase the value of p0NominalPUCCH. Furthermore, system 10 generates training data that includes performance data or features of performance data for a predetermined period before the parameter setting change, and improvement labels indicating whether the communication environment has improved due to the parameter setting change. Using machine learning with multiple training data sets, it generates a learning model that predicts cells whose communication environment will improve due to the parameter setting change from the performance data or features of performance data for a predetermined period, and modifies the parameter settings of the wireless base station managing the identified cells using this learning model. These measures contribute to improving the communication environment.

[0015] Figure 1 schematically shows an example of system 10. System 10 comprises a control device 100 and a learning device 200.

[0016] The control device 100 acquires performance data from each of the multiple cells 310 managed by the multiple radio base stations 300 and controls the multiple radio base stations 300 based on the performance data. For example, the control device 100 changes the parameter settings of the multiple radio base stations 300 based on the performance data. By changing the parameter settings of the radio base stations 300, the control device 100 may change the content of the instructions given by the radio base stations 300 to the user terminals 400 located in the cells 310 of the radio base stations 300.

[0017] The control device 100 communicates with the radio base station 300 via the network 20. The network 20 includes a RAN (Radio Access Network). The network 20 may include a core network. The control device 100 may be arranged in the RAN. The control device 100 may also be arranged in the core network. The control device 100 may be arranged in MEC (Multi-access Edge Computing). Note that the system 10 may include a plurality of control devices 100 each corresponding to one or more radio base stations 300 among the plurality of radio base stations 300. In this case, the control device 100 may be installed near the radio base station 300.

[0018] The control device 100 may be applied to a system that realizes RAN control and AI processing through a management infrastructure and multiple distributed infrastructures managed by the management infrastructure. The RAN referred to here may be a virtualized vRAN (Virtual RAN) or a physical RAN. The AI ​​processing referred to here may include RAN-controlled AI processing and non-RAN-controlled AI processing. An example of RAN-controlled AI processing is a RIC (RAN Intelligent Controller). Note that RAN-controlled AI processing is not limited to RIC. Non-RAN-controlled AI processing may correspond to so-called MEC applications. The distributed infrastructure may be installed in corresponding regions. The distributed infrastructure may be located in regions with a range roughly equivalent to each prefecture of Japan. The distributed infrastructure may be located in regions with a range finer than each prefecture of Japan. The distributed infrastructure may be located in regions with a range broader than each prefecture of Japan. The control device 100 may be located on the distributed infrastructure. The control device 100 may be located on the management infrastructure. The distributed infrastructure may be data centers located in various locations. The distributed infrastructure may consist of multiple devices. The distributed infrastructure may be implemented on a virtualization infrastructure consisting of multiple devices. The distributed infrastructure may also be implemented on a single device; that is, the distributed infrastructure may be a distributed device. The management infrastructure may be a data center that manages multiple distributed infrastructures. The management infrastructure may be composed of multiple devices. The management infrastructure may be implemented on a virtualization infrastructure consisting of multiple devices. The management infrastructure may also be implemented on a single device; that is, the management infrastructure may be a management device. The management infrastructure may be called a Core Brain, and the distributed infrastructure may be called a Regional Brain. Multiple layers of distributed infrastructure may be arranged below the management infrastructure. For example, if two layers of distributed infrastructure are arranged below the management infrastructure, the management infrastructure may be called a Core Brain, the distributed infrastructure in the layer below it may be called a Regional Brain, and the distributed infrastructure in the layer below that may be called a Sub-Regional Brain. One or more CPUs (Central Processing Units) may be arranged in the distributed infrastructure. One or more GPUs may be arranged in the distributed infrastructure.On the distributed substrate, a plurality of superchips in which a CPU and a GPU (Graphics Processing Unit) are connected by an interconnect may be arranged. The interconnect may be an interconnect with memory coherence, capable of realizing high bandwidth and low latency. Thus, the distributed substrate may have CPU resources and GPU resources as computing resources.

[0019] The control device 100 may receive performance data of the cell 310 of the radio base station 300 from the radio base station 300. The control device 100 may receive performance data of the cells 310 of a plurality of radio base stations 300 from a management device 50 that manages the performance data of the cells 310 of the plurality of radio base stations 300.

[0020] The learning device 200 generates a learning model and provides it to the control device 100. The control device 100 and the learning device 200 may communicate via the network 20. The control device 100 and the learning device 200 may communicate directly. The control device 100 and the learning device 200 may be integrated. That is, the control device 100 may have the functions of the learning device 200.

[0021] FIG. 2 schematically shows an example of the situation of the cell 310 in an environment with high Uplink RSSI. As described above, in an environment with high Uplink RSSI, the Uplink SINR tends to deteriorate. When the Uplink SINR deteriorates, as shown in FIG. 2, Acks or Nacks transmitted by the user terminal 400 with respect to the downlink transmission signal from the radio base station 300 do not reach the radio base station 300, and the DTX rate increases. As a result, the downlink throughput of the user terminal 400 decreases.

[0022] Figure 3 schematically shows an example of processing by the control device 100. As shown in Figure 2, the control device 100 sends a change instruction to the radio base station 300 managing cell 310, where the SINR of PUCCH is decreasing and the DTX rate is increasing, to increase the p0NominalPUCCH. The radio base station 300 then sends a change instruction to the user terminal 400 located in the area to increase the PUCCH transmission power. As a result, the PUCCH transmission power from the user terminal 400 increases, allowing Ack and Nack signals to reach the radio base station 300, thereby improving the downlink throughput of the user terminal 400.

[0023] Figure 4 schematically shows an example of the processing flow by the control device 100. Here, we will explain the processing flow in which the control device 100 identifies target cells from among the multiple cells 310 that are to be targeted for increasing the PUCCH transmission power, based on the performance data of the multiple cells 310, and changes the parameter settings of the radio base station 300 that manages the identified target cells.

[0024] In step 102 (sometimes abbreviated as S), the control device 100 acquires performance data for multiple cells 310. For each of the multiple cells 310, the control device 100 acquires performance data for a predetermined past period. In the example shown in Figure 4, the control device 100 acquires performance data for the past week. Also in the example shown in Figure 4, the control device 100 acquires the DTX rate and PUCCH SINR for each of the multiple cells 310.

[0025] In S104, the control device 100 identifies a target cell from among multiple cells 310 based on the performance data of the multiple cells 310. In the example shown in Figure 4, the control device 100 identifies a cell 310 as a target cell that satisfies condition A (SINR ≤ -6dB rate is 10% or more in one week), condition B (SINR ≤ -6dB rate is 10% or more in the BusyHour band, and the DTX rate is 10% or more), and condition C (there are 3 or more days in one week that satisfy condition B). The SINR ≤ -6dB rate refers to the percentage of user terminals 400 located in the area whose PUCCH SINR is -6dB or less. The DTX rate is calculated, for example, by dividing the number of detected DTXs by the sum of the number of received Ack / Nacks and the number of detected DTXs. The BusyHour band refers to the busiest hour of the day. The busiest hour may be the hour with the most connected users. The busiest hour may be the hour with the highest amount of data traffic (data volume). The busiest hour may also be the hour with the highest number of connections (attempts). Condition A makes it possible to extract only cell 310 where the PUCCH SINR is consistently degraded. Condition B makes it possible to extract cell 310 where the PUCCH SINR is trending downwards and the DTX rate is increasing during peak hours.

[0026] The control device 100 may exclude cells that have no further configuration changes. For example, the control device 100 may exclude cells where the set p0NominalPUCCH has reached a predetermined level. This predetermined level may be an upper limit determined considering the interference caused by the PUCCH transmission power of the user terminal 400 to other user terminals 400. This prevents situations where the transmission power of the user terminal 400 becomes too high, leading to increased interference and a deterioration in the communication quality of other user terminals 400.

[0027] The control device 100 may exclude cells whose Uplink RSSI is an abnormal value. For example, the control device 100 may exclude cells whose Uplink RSSI is higher than a predetermined threshold. This predetermined threshold may be a value that, if the RSSI is higher than this, can be considered abnormal. This prevents making changes to cells for which there is no point in changing p0NominalPUCCH.

[0028] In S106, the control device 100 changes the parameter settings of the radio base station 300 that manages the target cell identified in S104. The control device 100 may change the parameter settings to increase p0NominalPUCCH. The control device 100 may change the parameter settings to slightly increase the set p0NominalPUCCH (sometimes referred to as the prior value). For example, if the prior value is -120dB ≤ p0NominalPUCCH ≤ -106dB, the value may be changed according to Table 1 below. As shown in Table 1, by controlling the p0NominalPUCCH to increase gradually, it is possible to suppress the p0NominalPUCCH from increasing excessively, which would increase interference and degrade the communication environment. Note that Table 1 is just an example, and the conditions for the prior value and the changed value may differ from those in Table 1. Any change that increases the set p0NominalPUCCH is acceptable.

[0029] [Table 1]

[0030] The control device 100 may prohibit simultaneous parameter changes for multiple neighboring cells. For example, the control device 100 may control multiple target cells whose distance from each other is shorter than a predetermined distance to change the p0NominalPUCCH parameter at different times. If neighboring radio base stations 300 are configured simultaneously, and one of the radio base stations 300 fails, users under its control will be unable to receive service. Therefore, by adjusting the configuration so that neighboring radio base stations 300 do not configure simultaneously, even if one radio base station 300 fails, users can receive service from another nearby radio base station 300, thus preventing any impact on users.

[0031] Figure 5 schematically shows an example of the processing flow by System 10. Here, we will explain the processing flow for generating training data to create a learning model that predicts which cells 310 will have improved communication environments by changing their parameters.

[0032] In S202, the control device 100 acquires performance data for multiple cells 310. For each of the multiple cells 310, the control device 100 acquires performance data for a predetermined period in the past. In the example shown in Figure 5, the control device 100 acquires performance data for the past week. In addition, in the example shown in Figure 5, the control device 100 acquires performance data for each of the multiple cells 310, including KPIs related to the number of users, connectivity, disconnection rate, throughput, wireless resources, wireless signal strength and quality, and wireless transmission / reception characteristics.

[0033] In S204, the control device 100 identifies the target cell from among the multiple cells 310 based on the performance data of the multiple cells 310. The control device 100 identifies the target cell in the same manner as in S104.

[0034] In S206, the control device 100 changes the parameter settings of the radio base station 300 that manages the target cell identified in S204. The control device 100 changes the parameter settings in the same way as in S106.

[0035] In S208, the learning device 200 makes a determination regarding the improvement of the communication environment due to the parameter setting change for each of the multiple target cells whose parameter settings have been changed. In the example shown in Figure 5, the learning device 200 determines whether the communication environment has improved due to the parameter setting change. In the example shown in Figure 5, the learning device 200 determines that there has been an improvement if the ratio of DL_User_Throughput_AF, which is the average throughput for the post-parameter change period (7 days from the day after the parameter change) to DL_User_Throughput_BF, which is the average throughput for the pre-parameter period (7 days up to the day before the parameter change), is greater than or equal to a predetermined threshold, and determines that there has been no improvement if it is lower than the threshold. Any value greater than or equal to 1 may be set as the threshold. A value aimed at balancing improvement and non-improvement may be set as the threshold. In addition, a value aimed at identifying cells 310 with a greater degree of improvement may be set as the threshold. An example of such a value is 1.336, but it is not limited to this. The number of days between the pre-parameter and post-parameter is not limited to 7 days, but may be other numbers. The learning device 200 may also determine whether or not the communication environment has improved by other means.

[0036] In S210, the learning device 200 generates learning data for each of the multiple target cells, including performance data from the day before the parameter setting change was made and a predetermined number of days prior to that day, as well as an improvement label indicating whether the communication environment improved due to the parameter setting change. Seven days is one example of the predetermined number of days, but it is not limited to this and may be any other number of days. In the example shown in Figure 5, the performance data includes KPIs related to the number of users, connectivity, disconnection rate, throughput, wireless resources, radio wave strength and quality of the wireless environment, and wireless transmission and reception characteristics. The learning device 200 generates a learning model using machine learning with multiple learning data, taking the performance data of multiple cells 310 as input and outputting the cells 310 that are the target cells for parameter setting changes to increase the transmission power of PUCCH. The learning device 200 transmits the generated learning model to the control device 100. After receiving the learning model, the control device 100 may use the learning model to identify the target cells from the multiple cells 310.

[0037] The learning device 200 may generate training data for each of a plurality of target cells, including performance data features and improvement labels for the period between the day before the parameter setting change and a predetermined number of days prior to that day. The performance data features may include at least one of the following: the mean, sum, maximum, minimum, median, standard deviation, skewness, and the slope when linearly regressed, all related to the number of users over a predetermined number of days. The performance data features may include at least one of the following: the mean, sum, maximum, minimum, median, standard deviation, skewness, and the slope when linearly regressed, all related to connectivity over a predetermined number of days. The performance data features may include at least one of the following: the mean, sum, maximum, minimum, median, standard deviation, skewness, and the slope when linearly regressed, all related to the disconnection rate over a predetermined number of days. The performance data features may include at least one of the following: the mean, sum, maximum, minimum, median, standard deviation, skewness, and the slope when linearly regressed, all related to throughput over a predetermined number of days. The features of the performance data may include at least one of the mean, sum, maximum, minimum, median, standard deviation, skewness, and the slope when linearly regressed, for a predetermined number of days of KPIs related to wireless resources. The features of the performance data may include at least one of the mean, sum, maximum, minimum, median, standard deviation, skewness, and the slope when linearly regressed, for a predetermined number of days of KPIs related to radio wave strength and quality of the wireless environment. The features of the performance data may include at least one of the mean, sum, maximum, minimum, median, standard deviation, skewness, and the slope when linearly regressed, for a predetermined number of days of KPIs related to wireless transmission and reception characteristics. The learning device 200 uses machine learning with multiple training data to generate a learning model that takes the features of the performance data of multiple cells 310 as input and outputs the cells 310 for which parameter settings to be changed to increase the transmission power of PUCCH. The learning device 200 transmits the generated learning model to the control device 100. After receiving the learning model, the control device 100 may use the learning model to identify the target cell from among the multiple cells 310.

[0038] The learning device 200 may generate training data for each of the multiple target cells, including performance data from the day before the day on which the parameter settings were changed and a predetermined number of days prior to that day, the features of the performance data, and improvement labels.

[0039] Figure 6 schematically shows an example of KPI data 510. KPI data 510 contains the hourly values ​​of multiple types of KPIs for each of the multiple cells 310.

[0040] Figure 7 schematically shows an example of label data 520. Label data 520 contains improvement labels for each of the multiple cells 310.

[0041] Figure 8 schematically shows an example of training data 530. The training data 530 includes, for each of the multiple cells 310, the mean, sum, max, min, median, std (standard deviation), skew, and trend (slope in linear regression) for each of the multiple types of KPIs, along with improvement labels.

[0042] Figure 9 schematically shows an example of the functional configuration of the control device 100. The control device 100 includes a storage unit 102, an acquisition unit 104, a target cell identification unit 106, a change control unit 108, and a data transmission unit 110.

[0043] The acquisition unit 104 acquires various types of data. The acquisition unit 104 stores the acquired data in the storage unit 102. For example, the acquisition unit 104 acquires performance data for each of the multiple cells 310. The acquisition unit 104 may receive performance data from each of the multiple cells 310. The acquisition unit 104 may receive performance data for each of the multiple cells 310 from the management device 50.

[0044] The target cell identification unit 106 identifies the target cell from among multiple cells 310 based on the performance data of multiple cells 310 acquired by the acquisition unit 104.

[0045] For example, the target cell identification unit 106 identifies cells 310 whose PUCCH SINR is trending downwards as target cells. The target cell identification unit 106 may identify cells 310 whose PUCCH SINR meets the conditions for a predetermined period as target cells. The target cell identification unit 106 may identify cells 310 as target cells in which the percentage of users whose PUCCH SINR is lower than a predetermined threshold out of the total number of connected users during a predetermined period is higher than a predetermined threshold. As a specific example, the target cell identification unit 106 identifies cells 310 with an SINR ≤ -6dB rate of 10% or more over a one-week period as target cells.

[0046] For example, the target cell identification unit 106 identifies cells 310 whose DTX rate is rising as target cells. The target cell identification unit 106 may identify cells 310 whose DTX rate for a predetermined period meets the conditions as target cells. The target cell identification unit 106 identifies cells 310 whose DTX rate for a predetermined period is higher than a predetermined threshold as target cells. As a specific example, the target cell identification unit 106 identifies cells 310 whose DTX rate for a predetermined period is 10% or higher as target cells.

[0047] For example, the target cell identification unit 106 identifies a cell 310 as a target cell if its PUCCH SINR is trending downwards and its DTX rate is increasing. The target cell identification unit 106 may identify a cell 310 as a target cell if its PUCCH SINR and DTX rate for a predetermined period meet the conditions. For example, the target cell identification unit 106 identifies a cell 310 as a target cell if, for a predetermined period, the proportion of users whose PUCCH SINR is below a predetermined threshold out of the total number of connected users is higher than a predetermined threshold, and its DTX rate for the predetermined period is higher than a predetermined threshold. For example, the target cell identification unit 106 identifies cell 310 as a target cell that satisfies condition A (SINR ≤ -6dB rate is 10% or more in one week), condition B (SINR ≤ -6dB rate is 10% or more in the BusyHour band, and the DTX rate is 10% or more), and condition C (there are 3 or more days in one week that satisfy condition B).

[0048] The target cell identification unit 106 may identify the target cell by excluding cells 310 from among a plurality of cells 310 whose set PUCCH transmission power is higher than a predetermined level. This predetermined level may be an upper limit value determined considering the interference that the PUCCH transmission power of the user terminal 400 causes to other user terminals 400.

[0049] The target cell identification unit 106 may identify target cells by excluding cells 310 whose Uplink RSSI is higher than a predetermined threshold. The predetermined threshold may be a value that, if the RSSI is higher than this value, can be considered abnormal.

[0050] The change control unit 108 changes the parameter settings of the radio base station 300 that manages the target cell in order to increase the transmission power of the PUCCH of the user terminal 400 in the target cell identified by the target cell identification unit 106. The change control unit 108 may change the parameter settings of the radio base station 300 by transmitting a change instruction for p0NominalPUCCH to the radio base station 300. The radio base station 300 instructs the user terminal 400 to set the value of the PUCCH transmission power according to the changed parameter settings. The radio base station 300 may, for example, transmit the changed value of p0NominalPUCCH to the user terminal 400. The radio base station 300 may transmit the instruction to the user terminal 400 using an SIB (System Information Block). Specifically, the radio base station 300 instructs the user terminal 400 to set a higher value than before using an SIB.

[0051] The change control unit 108 may control multiple target cells identified by the target cell identification unit 106, where the distance between cells is shorter than a predetermined distance, to increase the PUCCH transmission power at staggered intervals. For example, the change control unit 108 may control these multiple target cells to increase the PUCCH transmission power at staggered intervals of a predetermined period. Examples of predetermined periods include, but are not limited to, several hours, one day, several days, one week, or several weeks.

[0052] When the change control unit 108 changes the parameter settings of a wireless base station 300, it stores in the storage unit 102 which wireless base station 300's parameter settings were changed and the details of the parameter setting changes.

[0053] The data transmission unit 110 transmits the data stored in the storage unit 102 to the learning device 200. The data transmission unit 110 may also transmit to the learning device 200 the wireless base station 300 whose parameter settings have been changed by the change control unit 108, the performance data of the cell 310 of the wireless base station 300, and the details of the parameter setting changes.

[0054] Figure 10 schematically shows an example of the functional configuration of the learning device 200. The learning device 200 comprises a storage unit 202, an acquisition unit 204, a learning data generation unit 206, a learning model generation unit 208, and a learning model transmission unit 210.

[0055] The acquisition unit 204 acquires various types of data. The acquisition unit 204 stores the acquired data in the storage unit 202. For example, the acquisition unit 204 receives data transmitted by the data transmission unit 110. The acquisition unit 204 receives data transmitted by the data transmission unit 110, including the wireless base station 300 whose parameter settings have been changed by the change control unit 108, the performance data of the cell 310 of the wireless base station 300, and the details of the parameter setting changes.

[0056] The acquisition unit 204 acquires performance data from each of the multiple cells 310. The acquisition unit 204 may receive performance data from each of the multiple cells 310. The acquisition unit 204 may also receive performance data from each of the multiple cells 310 from the management device 50.

[0057] The learning data generation unit 206 generates learning data based on the data acquired by the acquisition unit 204. For example, after a plurality of wireless base stations 300 that manage multiple cells 310, which are identified by the target cell identification unit 106 and controlled by the change control unit 108, have changed their parameter settings to increase the transmission power of PUCCH in the identified plurality of cells 310, the learning data generation unit 206 generates learning data for each of the plurality of cells 310, which includes performance data or features of the performance data between the day before the parameter setting change and a predetermined number of days prior to that day, and improvement data related to the improvement of the communication environment due to the parameter setting change.

[0058] The improvement data may include an improvement label indicating whether the communication environment has improved due to the change in parameter settings. The improvement data may also include an improvement score indicating the degree of improvement. The improvement score may be a numerical value between 0 and 1, for example, with a higher value indicating a greater degree of improvement. In this case, 0 may indicate no improvement. 0 may also indicate deterioration. The improvement score may take a negative value if deterioration has occurred.

[0059] For example, first, the learning data generation unit 206 identifies multiple cells 310 where parameter settings have been changed based on the data acquired by the acquisition unit 204, and reads performance data from the storage unit 202 between the day before the parameter setting change and a predetermined number of days prior to that day. The learning data generation unit 206 then reads performance data from the storage unit 202 between the day after the parameter setting change and a predetermined number of days after that day. Using the read performance data, the learning data generation unit 206 determines whether the communication environment has improved due to the parameter setting change. For example, the learning data generation unit 206 determines improvement if the ratio of DL_User_Throughput_AF, which is the average throughput for the post-parameter change period (7 days from the day after the parameter change) to DL_User_Throughput_BF, which is the average throughput for the prior period (7 days up to the day before the parameter change), is above a predetermined threshold, and determines no improvement if it is below the threshold. The learning data generation unit 206 then generates learning data for each of the multiple cells 310 whose parameter settings have been changed, including performance data or features of said performance data between the day before the day the parameter settings were changed and a predetermined number of days prior to that day, as well as improvement labels. The learning data generation unit 206 then stores the generated learning data in the storage unit 202.

[0060] For example, first, the learning data generation unit 206 identifies multiple cells 310 where parameter settings have been changed based on the data acquired by the acquisition unit 204, and reads performance data from the storage unit 202 between the day before the parameter setting change and a predetermined number of days prior to that day. The learning data generation unit 206 then reads performance data from the storage unit 202 between the day after the parameter setting change and a predetermined number of days after that day. Using the read performance data, the learning data generation unit 206 determines how much the communication environment has improved due to the parameter setting change. For example, the learning data generation unit 206 determines that the greater the ratio of DL_User_Throughput_AF, which is the average throughput for the post-change period (7 days from the day after the parameter change), to DL_User_Throughput_BF, which is the average throughput for the pre-change period (7 days up to the day before the parameter change), the greater the degree of improvement. The learning data generation unit 206 then generates learning data for each of the multiple cells 310 whose parameter settings have been changed, including performance data or features of said performance data between the day before the day the parameter settings were changed and a predetermined number of days prior to that day, as well as an improvement score. The learning data generation unit 206 then stores the generated learning data in the storage unit 202.

[0061] The performance data may include KPIs related to the number of users. The performance data may include KPIs related to connectivity. The performance data may include KPIs related to disconnection rates. The performance data may include KPIs related to throughput. The performance data may include KPIs related to wireless resources. The performance data may include KPIs related to radio wave strength and quality in the wireless environment. The performance data may include KPIs related to wireless transmission and reception characteristics. The performance data may include other KPIs.

[0062] The features of the performance data may include calculated values ​​for the performance data. The features of the performance data may include the mean over a predetermined period for each of the multiple types of KPIs. The features of the performance data may include the sum over a predetermined period for each of the multiple types of KPIs. The features of the performance data may include the maximum value over a predetermined period for each of the multiple types of KPIs. The features of the performance data may include the minimum value over a predetermined period for each of the multiple types of KPIs. The features of the performance data may include the median over a predetermined period for each of the multiple types of KPIs. The features of the performance data may include the standard deviation over a predetermined period for each of the multiple types of KPIs. The features of the performance data may include the skewness over a predetermined period for each of the multiple types of KPIs. The features of the performance data may include the slope of linear regression over a predetermined period for each of the multiple types of KPIs. The features of the performance data may include the sum of multiple types of KPIs. The features of the performance data may include the differences between multiple types of KPIs. The features of the performance data may include the products of multiple types of KPIs. The features of the performance data may include the divisions of multiple types of KPIs. The features of the performance data may include other calculated values ​​for each of the multiple types of KPIs.

[0063] The features of the performance data may include calculated values ​​for at least one of the performance data from a specific period, which is the period between the day before the parameter setting change and a predetermined number of days prior to that day. In other words, as described above, the features of the performance data may include calculated values ​​for performance data from a specific period, or it may include calculated values ​​for performance data from a portion of that specific period. For example, the features of the performance data may include calculated values ​​for performance data from a specific day of the week within a specific period. For example, the features of the performance data may include calculated values ​​for performance data from a specific time of day within a specific period.

[0064] The learning model generation unit 208 generates a learning model by machine learning using multiple learning data stored in the memory unit 202. The learning model generation unit 208 may generate a learning model that takes the feature quantities of performance data from multiple cells 310 as input and outputs the cells 310 for which parameter setting changes to increase the transmission power of PUCCH will be made. The learning model generation unit 208 may generate a learning model that takes the performance data from multiple cells 310 as input and outputs the cells 310 for which parameter setting changes to increase the transmission power of PUCCH will be made. The learning model generation unit 208 may generate a learning model that takes the performance data from multiple cells 310 and the feature quantities of said performance data as input and outputs the cells 310 for which parameter setting changes to increase the transmission power of PUCCH will be made. The learning model generation unit 208 stores the generated learning model in the memory unit 202.

[0065] The learning model transmission unit 210 transmits the learning model generated by the learning model generation unit 208 to the control device 100. The acquisition unit 104 of the control device 100 receives the learning model. The storage unit 102 stores the learning model received by the acquisition unit 104.

[0066] The target cell identification unit 106 may identify the target cell using the learning model stored in the memory unit 102. The target cell identification unit 106 may input the feature quantities of the performance data of multiple cells 310 acquired by the acquisition unit 104 into the learning model and identify the cell 310 output from the learning model as the target cell. The target cell identification unit 106 may input the performance data of multiple cells 310 acquired by the acquisition unit 104 into the learning model and identify the cell 310 output from the learning model as the target cell. The target cell identification unit 106 may input the performance data and the feature quantities of the performance data of multiple cells 310 acquired by the acquisition unit 104 into the learning model and identify the cell 310 output from the learning model as the target cell.

[0067] The target cell identification unit 106 may exclude cells 310 from the target cells identified using the learning model if the set value of the PUCCH's transmission power is higher than a predetermined level. This predetermined level may be an upper limit determined considering the interference caused by the PUCCH's transmission power of the user terminal 400 to other user terminals 400.

[0068] The target cell identification unit 106 may exclude cells 310 from the target cells identified using the learning model if their UplinkRSSI is higher than a predetermined threshold. This predetermined threshold may be a value above which an RSSI is considered abnormal.

[0069] As described above, the control device 100 and the learning device 200 may be integrated. In this case, the control device 100 may include a storage unit 102, an acquisition unit 104, a target cell identification unit 106, a change control unit 108, a learning data generation unit 206, and a learning model generation unit 208. Furthermore, the storage unit 102 may also function as a storage unit 202, and the acquisition unit 104 may also function as an acquisition unit 204.

[0070] Figure 11 schematically shows an example of the hardware configuration of a computer 1200 that functions as a control device 100 or a learning device 200. A program installed on the computer 1200 can cause the computer 1200 to function as one or more "parts" of the apparatus according to this embodiment, or to cause the computer 1200 to execute operations associated with the apparatus according to this embodiment or such one or more "parts", and / or to cause the computer 1200 to execute a process or a stage of such process according to this embodiment. Such a program may be executed by the CPU 1212 to cause the computer 1200 to execute specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0071] The computer 1200 according to this embodiment includes a CPU 1212, RAM 1214, and a graphics controller 1216, which are interconnected by a host controller 1210. The computer 1200 also includes input / output units such as a communication interface 1222, a storage device 1224, a DVD drive, and an IC card drive, which are connected to the host controller 1210 via an input / output controller 1220. The DVD drive may be a DVD-ROM drive and a DVD-RAM drive, etc. The storage device 1224 may be a hard disk drive and a solid-state drive, etc. The computer 1200 also includes legacy input / output units such as a ROM 1230 and a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0072] The CPU 1212 operates according to the programs stored in the ROM 1230 and RAM 1214, thereby controlling each unit. The graphics controller 1216 acquires the image data generated by the CPU 1212 and stores it in the frame buffer provided in RAM 1214 or within itself, so that the image data is displayed on the display device 1218.

[0073] The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD drive reads programs or data from a DVD-ROM or the like and provides them to the storage device 1224. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0074] The ROM 1230 stores boot programs and / or hardware-dependent programs of the computer 1200, which are executed by the computer 1200 upon activation. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via USB ports, parallel ports, serial ports, keyboard ports, mouse ports, etc.

[0075] The program is provided on a computer-readable storage medium such as a DVD-ROM or IC card. The program is read from the computer-readable storage medium and installed on a storage device 1224, RAM 1214, or ROM 1230, which are examples of computer-readable storage media, and executed by the CPU 1212. The information processing described within these programs is read by the computer 1200, resulting in coordination between the program and the various types of hardware resources described above. The apparatus or method may be configured to realize the operation or processing of information in accordance with the use of the computer 1200.

[0076] For example, when communication is performed between a computer 1200 and an external device, the CPU 1212 may execute a communication program loaded into RAM 1214 and, based on the processing described in the communication program, instruct the communication interface 1222 to perform communication processing. Under the control of the CPU 1212, the communication interface 1222 reads transmission data stored in a transmission buffer area provided in a recording medium such as RAM 1214, storage device 1224, DVD-ROM, or IC card, transmits the read transmission data to the network, or writes received data received from the network to a reception buffer area provided on the recording medium.

[0077] Furthermore, the CPU 1212 may read all or necessary parts of a file or database stored on an external recording medium such as the storage device 1224, a DVD drive (DVD-ROM), or an IC card into the RAM 1214, and perform various types of processing on the data in the RAM 1214. The CPU 1212 may then write the processed data back to the external recording medium.

[0078] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and subjected to information processing. The CPU 1212 may perform various types of processing on the data read from RAM 1214, including various types of operations, information processing, conditional judgments, conditional branching, unconditional branching, information retrieval / replacement, etc., as described throughout this disclosure and specified by the program instruction sequence, and write the results back to RAM 1214. The CPU 1212 may also retrieve information in files, databases, etc., within the recording medium. For example, if multiple entries are stored in the recording medium, each having an attribute value of a first attribute associated with an attribute value of a second attribute, the CPU 1212 may search among the multiple entries for an entry that matches the specified condition for the attribute value of the first attribute, read the attribute value of the second attribute stored in that entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies the predetermined condition.

[0079] The program or software module described above may be stored on or near the computer 1200 in a computer-readable storage medium. Alternatively, a recording medium such as a hard disk or RAM provided within a server system connected to a dedicated communication network or the Internet can be used as a computer-readable storage medium, thereby providing the program to the computer 1200 via the network.

[0080] In this embodiment, blocks in the flowchart and block diagram may represent a stage in a process in which an operation is performed or a "part" of a device that has the role of performing an operation. A particular stage and "part" may be implemented by a dedicated circuit, a programmable circuit supplied with computer-readable instructions stored on a computer-readable storage medium, and / or a processor supplied with computer-readable instructions stored on a computer-readable storage medium. The dedicated circuit may include digital and / or analog hardware circuits, and may include integrated circuits (ICs) and / or discrete circuits. The programmable circuit may include reconfigurable hardware circuits, such as field-programmable gate arrays (FPGAs) and programmable logic arrays (PLAs), which include logical AND, logical OR, exclusive OR, negated AND, negated OR, and other logical operations, flip-flops, registers, and memory elements.

[0081] A computer-readable storage medium may include any tangible device capable of storing instructions that can be executed by a suitable device, and as a result, a computer-readable storage medium having instructions stored therein will comprise a product that includes instructions that can be executed to create means for performing operations specified in a flowchart or block diagram. Examples of computer-readable storage media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable storage media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disk read-only memory (CD-ROM), digital multipurpose disc (DVD), Blu-ray® disc, memory stick, integrated circuit card, etc.

[0082] Computer-readable instructions may include assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, Java®, C++, and traditional procedural programming languages ​​such as the C programming language or similar programming languages.

[0083] Computer-readable instructions may be provided locally or via a wide area network (WAN) such as a local area network (LAN) or the internet to a processor or programmable circuit of a general-purpose computer, special-purpose computer, or other programmable data processing device, so that the processor or programmable circuit of the programmable data processing device, such as a computer, can execute the instructions to generate means for performing operations specified in a flowchart or block diagram. Here, the computer may be a PC (personal computer), tablet computer, smartphone, workstation, server computer, general-purpose computer, or special-purpose computer, and may also be a computer system in which multiple computers are connected. Such a computer system in which multiple computers are connected is also called a distributed computing system and is a computer in a broad sense. In a distributed computing system, multiple computers execute a program collectively by each computer executing a part of the program and passing data during program execution between computers as needed.

[0084] Examples of processors include computer processors, central processing units, processing units, microprocessors, digital signal processors, controllers, and microcontrollers. A computer may have one or more processors. In a multiprocessor system with multiple processors, each processor executes a portion of the program, and the processors collectively execute the program by passing program execution data between them as needed. For example, in the execution of multitasks, each of the multiple processors may execute a portion of each task in small chunks by switching tasks at each time slice. In this case, which part of a program each processor executes changes dynamically. Which part of a program each of the multiple processors executes may also be statically determined by multiprocessor-aware programming.

[0085] By using the invention according to this embodiment, it is possible to contribute to improving the wireless communication environment and to achieving at least one of the Sustainable Development Goals (SDGs): Goal 7 "Affordable and Clean Energy", Goal 9 "Industry, Innovation and Infrastructure", and Goal 11 "Sustainable Cities and Communities".

[0086] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications or improvements can be made to the above embodiments. It will be clear from the claims that such modified or improved forms may also be included in the technical scope of the present invention.

[0087] It should be noted that the execution order of operations, procedures, steps, and stages in the apparatus, systems, programs, and methods shown in the claims, specifications, and drawings is not explicitly stated as "before" or "prior to," and that these can be implemented in any order unless the output of a previous process is used in a later process. Even if the operation flow in the claims, specifications, and drawings is described using phrases such as "first," and "next," for convenience, this does not mean that it is essential to perform the operations in that order. [Explanation of symbols]

[0088] 10 System, 20 Network, 50 Management device, 100 Control device, 102 Storage unit, 104 Acquisition unit, 106 Target cell identification unit, 108 Change control unit, 110 Data transmission unit, 200 Learning device, 202 Storage unit, 204 Acquisition unit, 206 Learning data generation unit, 208 Learning model generation unit, 210 Learning model transmission unit, 300 Wireless base station, 310 Cell, 400 User terminal, 510 KPI data, 520 Label data, 530 Learning data, 1200 Computer, 1210 Host controller, 1212 CPU, 1214 RAM, 1216 Graphics controller, 1218 Display device, 1220 Input / Output controller, 1222 Communication interface, 1224 Storage device, 1230 ROM, 1240 Input / Output chip

Claims

1. An acquisition unit that acquires performance data for each of multiple cells, A target cell identification unit identifies a target cell from the plurality of cells based on the performance data of the plurality of cells, A change control unit that changes the parameter settings of the radio base station managing the target cell in order to increase the transmission power of the user terminal's PUCCH in the target cell identified by the target cell identification unit. A control device equipped with the following features.

2. A storage unit that stores a learning model in which performance data of multiple cells or the features of said performance data are input, and the cells to which parameter settings are changed to increase the transmission power of the PUCCH are output. Equipped with, The control device according to claim 1, wherein the target cell identification unit inputs the performance data or feature quantities of the performance data of the plurality of cells into the learning model, and identifies the cells output from the learning model as the target cells.

3. After the parameter settings of multiple wireless base stations that manage the multiple cells are changed to increase the transmission power of the PUCCH of the user terminal in the specified multiple cells, a learning data generation unit generates learning data for each of the multiple cells, including performance data or feature quantities of said performance data between the day before the parameter setting change and a predetermined number of days prior to that day, and improvement data related to the improvement of the communication environment due to the parameter setting change. A learning model generation unit generates the learning model by machine learning using multiple sets of the aforementioned learning data. Equipped with, The control device according to claim 2, wherein the storage unit stores the learning model generated by the learning model generation unit.

4. The control device according to claim 3, wherein the learning data generation unit identifies a plurality of cells from among the plurality of cells in which the SINR and DTX rates of the PUCCH for a predetermined period satisfy certain conditions, and after the parameter settings of a plurality of wireless base stations that manage the plurality of cells are changed to increase the transmission power of the PUCCH of the user terminal in the identified plurality of cells, the control device generates learning data for each of the plurality of cells, which includes performance data or feature quantities of the performance data between the day before the day on which the parameter settings were changed and a predetermined number of days prior to that day, and improvement data related to the improvement of the communication environment due to the setting change.

5. The control device according to claim 3, wherein the learning data generation unit generates learning data which includes performance data or feature quantities of the performance data, which includes at least one of the following: KPIs related to the number of connected users, KPIs related to connectivity, KPIs related to disconnection rate, KPIs related to throughput, KPIs related to wireless resources, KPIs related to radio wave intensity and quality of the wireless environment, and KPIs related to wireless transmission and reception characteristics, and the improvement data.

6. The control device according to claim 5, wherein the feature quantities of the performance data include calculated values ​​for at least one of the performance data for a specific period, which is the period between the day before the day on which the parameter settings were changed and a predetermined number of days prior to that day.

7. The control device according to claim 6, wherein at least one of the performance data for the specified period includes at least one of the performance data for a specific day of the week within the specified period and the performance data for a specific time period within the specified period.

8. The control device according to claim 6, wherein the calculated value of at least one of the performance data for the specified period includes the mean, sum, maximum, minimum, median, standard deviation, skewness, and slope when linearly regressed, of each of the multiple types of KPIs included in the performance data, and at least one of the sum, difference, product, and division of the multiple types of KPIs included in the performance data.

9. The control device according to claim 1, wherein the target cell identification unit identifies, among the plurality of cells, cells whose PUCCH SINR and DTX rates for a predetermined period satisfy the conditions as the target cells.

10. The control device according to any one of claims 1 to 9, wherein the change control unit changes the parameter settings of the radio base station at different times for a plurality of target cells among the plurality of target cells identified by the target cell identification unit, where the distance between the cells is shorter than a predetermined distance.

11. The control device according to any one of claims 1 to 9, wherein the target cell identification unit identifies the target cells by excluding cells in which the set parameter is higher than a predetermined level.

12. The control device according to any one of claims 1 to 9, wherein the target cell identification unit identifies the target cells by excluding cells in which the Uplink RSSI is higher than a predetermined threshold.

13. A control method performed by a computer, The acquisition stage involves obtaining performance data for each of multiple cells, A target cell identification step, in which a target cell is identified from the plurality of cells based on the performance data of the plurality of cells, A change control step involves changing the parameter settings of the wireless base station managing the target cell in order to increase the transmission power of the PUCCH of the user terminal in the target cell identified in the target cell identification step. A control method comprising the following features.

14. On the computer, The acquisition stage involves obtaining performance data for each of multiple cells, A target cell identification step, in which a target cell is identified from the plurality of cells based on the performance data of the plurality of cells, A change control step involves changing the parameter settings of the wireless base station managing the target cell in order to increase the transmission power of the PUCCH of the user terminal in the target cell identified in the target cell identification step. A program to execute.

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