US-20260128992-A1 - TELECOMMUNICATION NETWORK CAPACITY FORECASTING METHOD AND APPARATUS FOR IMPLEMENTING THE SAME

US20260128992A1US 20260128992 A1US20260128992 A1US 20260128992A1US-20260128992-A1

Abstract

A method includes receiving, at an apparatus, telecommunication network data including cumulative daily data and peak utilization data. A neural net-based time series model on the apparatus generates a forecasted number of users in a cell of the network and a forecasted traffic load in the cell of the network, each based on the cumulative daily data. A machine learning regression model on the apparatus calculates a predicted peak utilization of the cell of the network from the forecasted number of users and the forecasted traffic load of the cell.

Inventors

Sambeet KUMAR
Medithe MADHUKIRAN

Assignees

Rakuten Mobile, Inc.

Dates

Publication Date: 20260507
Application Date: 20241105

Claims (20)

1 . A method comprising: receiving, at an apparatus, telecommunication network data comprising cumulative daily data and peak utilization data; generating, from a neural net-based time series model on the apparatus, a forecasted number of users in a cell of the network and a forecasted traffic load in the cell of the network, each based on the cumulative daily data; and calculating, using a machine learning regression model on the apparatus, a predicted peak utilization of a cell of the network from the forecasted number of users and the forecasted traffic load of the cell.
2 . The method of claim 1 , further comprising: receiving, at the neural net-based time series model, a target increase in the forecasted number of users of the network; generating, from the neural net-based time series model: an estimated increase in the forecasted number of users based on the received target increase, and an updated forecasted number of users in the cell of the network and an updated forecasted traffic load in the cell of the network, each based on the estimated increase; and calculating, using the machine learning regression model, an updated predicted peak utilization of the cell from the updated forecasted number of users and the updated forecasted traffic load of the cell.
3 . The method of claim 1 , further comprising: receiving, at the neural net-based time series model, information corresponding to a scheduled event; using a similarity search algorithm on the apparatus to: identify a past event similar to the scheduled event based on the information; and based on the past event, identify a subset of the cells of the network corresponding to the scheduled event, wherein the subset of cells comprises the cell, and estimate, for each cell of the subset of cells, an increase in a forecasted number of cell users and an increase in a forecasted cell traffic load; and using the machine learning regression model to calculate, for each cell of the subset of cells, a predicted peak utilization of the corresponding cell from the corresponding increased forecasted number of cell users and increased forecasted cell traffic load, wherein the predicted peak utilization of the cell is an updated predicted peak utilization of the cell.
4 . The method of claim 1 , wherein the cell is a first roaming cell, and the method further comprises: using a similarity search algorithm on the apparatus to identify at least one most similar second roaming cell by performing an analysis of the first roaming cell and other roaming cells of the network proximate to non-roaming cells of the network; and using the neural net based time series model to, based on one or more key performance indicator (KPI) levels of the at least one most similar second roaming cell relative to one or more KPI levels of the corresponding at least one proximate non-roaming cell, estimate at least one KPI level of the one or more KPI levels of a planned non-roaming cell corresponding to the first roaming cell.
5 . The method of claim 1 , wherein the calculating the predicted peak utilization of the cell from the forecasted number of users and the forecasted traffic load comprises: predicting a downlink physical resource block (DL_PRB) utilization of the cell and a percentage of maximum allowed users of the cell; and adding a first product of the DL_PRB utilization and a first weight to a second product of the percentage of maximum allowed users and a second weight.
6 . The method of claim 5 , wherein the first weight is equal to 0.8, and the second weight is equal to 0.2.
7 . The method of claim 1 , further comprising: training the neural net-based time series model and/or the machine learning regression model on one or more time series datasets.
8 . The method of claim 1 , wherein each of the receiving the cumulative daily data and the receiving the peak utilization data comprises receiving key performance indicator (KPI) data.
9 . The method of claim 1 , further comprising: in response to the predicted peak utilization of the cell exceeding a threshold, performing a network resource configuration operation.
10 . A method comprising: receiving, at an apparatus, telecommunication network key performance indicator (KPI) data comprising cumulative daily data and peak utilization data; generating, from a neural net-based time series model on the apparatus, a forecasted number of users in a cell of the network and a forecasted traffic load in the cell of the network, each based on the cumulative daily data; calculating, using a machine learning regression model on the apparatus, a predicted peak utilization of the cell of the network from the forecasted number of users and the forecasted traffic load of the cell; and in response to the predicted peak utilization of the cell, performing a first network resource configuration operation.
11 . The method of claim 10 , further comprising: receiving, at the neural net-based time series model, a target increase in the forecasted number of users of the network; generating, from the neural net-based time series model: an estimated increase in the forecasted number of users based on the received target increase, and an updated forecasted number of users in the cell of the network and an updated forecasted traffic load in the cell of the network, each based on the estimated increase; calculating, using the machine learning regression model, an updated predicted peak utilization of the cell from the updated forecasted number of users and the updated forecasted traffic load for the cell; and in response to the updated predicted peak utilization of the cell, performing a second network resource configuration operation.
12 . The method of claim 10 , further comprising: receiving, at the neural net-based time series model, information corresponding to a scheduled event; using a similarity search algorithm on the apparatus to identify a past event similar to the scheduled event based on the information; using the neural net-based time series model to, based on the past event, identify a subset of the cells of the network corresponding to the scheduled event, wherein the subset of cells comprises the cell, and estimate, for each cell of the subset of cells, an increase in a forecasted number of cell users and an increase in a forecasted cell traffic load; using the machine learning regression model to calculate, for each cell of the subset of cells, a predicted peak utilization of the corresponding cell from the corresponding increased forecasted number of cell users and increased forecasted cell traffic load, wherein the predicted peak utilization of the cell is an updated predicted peak utilization of the cell; and in response to the predicted peak utilization of each cell of the subset of cells, performing a second network resource configuration operation comprising adding a temporary cell to the subset of cells.
13 . The method of claim 10 , wherein the cell is a first roaming cell, and the method further comprises: using a similarity search algorithm on the apparatus to identify at least one most similar second roaming cell by performing analysis of the first roaming cell and other roaming cells of the network proximate to non-roaming cells of the network; using the neural net-based time series model to, based on one or more KPI levels of the at least one most similar second roaming cell relative to one or more KPI levels of the corresponding at least one proximate non-roaming cell, estimate at least one KPI level of the one or more KPI levels of a planned non-roaming cell corresponding to the first roaming cell; and in response to the estimated at least one KPI level of the planned non-roaming cell, performing a second network resource configuration operation.
14 . The method of claim 10 , wherein the calculating the predicted peak utilization of the cell from the forecasted number of users and the forecasted traffic load of the cell comprises: predicting a downlink physical resource block (DL_PRB) utilization of the cell and a percentage of maximum allowed users of the cell; and adding a first product of the DL_PRB utilization and a first weight to a second product of the percentage of maximum allowed users and a second weight.
15 . The method of claim 10 , wherein the performing the first network resource configuration operation is in response to the predicted peak utilization of the cell exceeding a cell capacity threshold.
16 . An apparatus comprising: a neural net-based time series model configured to: receive telecommunication network key performance indicator (KPI) data comprising cumulative daily data; and generate a forecasted number of users in a cell of the network and a forecasted traffic load in the cell of the network, each based on the cumulative daily data; and a machine learning regression model configured to calculate a predicted peak utilization of the cell of the network from the forecasted number of users and the forecasted traffic load for the cell.
17 . The apparatus of claim 16 , wherein the neural net-based time series model is further configured to: receive a target increase in the forecasted number of users of the network; generate an estimated increase in the forecasted number of users based on the received target increase; and generate an updated forecasted number of users in the cell of the network and an updated forecasted traffic load in the cell of the network, each based on the estimated increase, and the machine learning regression model is further configured to calculate an updated predicted peak utilization of the cell from the updated forecasted number of users and the updated forecasted traffic load of the cell.
18 . The apparatus of claim 16 , further comprising a similarity search algorithm configured to: receive information corresponding to a scheduled event; identify a past event similar to the scheduled event based on the information; and based on the past event, identify a subset of the cells of the network corresponding to the scheduled event, wherein the subset of cells comprises the cell, wherein the neural net-based time series model is further configured to estimate, for each cell of the subset of cells, an increase in a forecasted number of cell users and an increase in a forecasted cell traffic load, and the machine learning regression model is further configured to calculate, for each cell of the subset of cells, a predicted peak utilization of the corresponding cell from the corresponding increased forecasted number of cell users and increased forecasted cell traffic load, wherein the predicted peak utilization of the cell is an updated predicted peak utilization of the cell.
19 . The apparatus of claim 16 , wherein the cell is a first roaming cell, the apparatus further comprises a similarity search algorithm configured to identify at least one most similar second roaming cell by performing an analysis of the first roaming cell and other roaming cells of the network proximate to non-roaming cells of the network, and the neural net-based time series model is further configured to, based on one or more KPI levels of the at least one most similar second roaming cell relative to one or more KPI levels of the corresponding at least one proximate non-roaming cell, estimate at least one KPI level of the one or more KPI levels of a planned non-roaming cell corresponding to the first roaming cell.
20 . The apparatus of claim 16 , wherein the machine learning regression model is configured to calculate the predicted peak utilization of the cell from the forecasted number of users and the forecasted traffic load by: predicting a downlink physical resource block (DL_PRB) utilization of the cell and a percentage of maximum allowed users of the cell; and adding a first product of the DL_PRB utilization and a first weight to a second product of the percentage of maximum allowed users and a second weight.

Description

FIELD The present disclosure relates to capacity forecasting method in telecommunication applications and an apparatus for implementing the same. BACKGROUND The information disclosed in this background section is only for enhancement of understanding of the general background of the disclosure and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art. Telecommunication, e.g., cellular, systems serve an increasing number of users throughout expanding geographic areas. A given radio access network (RAN) includes a large number of cells having overlapping coverage areas and a variety of sizes and signal strengths. In some cases, network operators have roaming partnerships in which one network operator owns the infrastructure of a given cell, e.g., a cell tower and radio unit (RU), and leases out capacity to one or more roaming partners, sometimes referred to as greenfield operators. To serve an increasing number of users, a given network operator expands by adding cell capacity, for example by adding cells or by replacing a roaming cell with a non-roaming cell. SUMMARY The present disclosure is directed to forecasting telecommunication capacity by utilizing advanced machine learning algorithms and real-time data analytics, thereby providing telecommunication operators with precise and actionable insights into future network capacity requirements. In some embodiments, a method includes receiving, at an apparatus, telecommunication network data including cumulative daily data and peak utilization data. A neural net-based time series model on the apparatus generates a cumulative daily forecasted number of users and traffic load of a cell in the network, each based on the cumulative daily data. A machine learning regression model on the apparatus calculates a predicted peak utilization of a cell of the network from the cumulative daily forecasted number of users and the forecasted traffic load. In some embodiments, a method includes receiving, at an apparatus, telecommunication network key performance indicator (KPI) data including cumulative daily data and peak utilization data. A neural net-based time series model on the apparatus generates a cumulative daily forecasted number of users and traffic load of the cells in the network, each based on the cumulative daily data. A machine learning regression model on the apparatus calculates a predicted peak utilization of a cell of the network from the cumulative daily forecasted number of users and the forecasted traffic load. In response to the predicted peak utilization of the cell, a first network resource configuration operation is performed. In some embodiments, an apparatus includes a neural net-based time series model and a machine learning regression model. The neural net-based time series model is configured to receive telecommunication network KPI data including cumulative daily data and generate a cumulative daily forecasted number of users and traffic load of one or more cells in the network, each based on the cumulative daily data. The machine learning regression model is configured to calculate a predicted peak utilization of a cell of the network from the cumulative daily forecasted number of users and the forecasted traffic load. BRIEF DESCRIPTION OF THE DRAWINGS Features, aspects, and advantages of embodiments of the disclosure will be described below with reference to the accompanying drawings, in which like reference numerals denote like elements, and wherein: FIG. 1 is a diagram of example components of a telecommunication system, in accordance with some embodiments. FIG. 2 is a diagram of example capacity forecasting operations, in accordance with some embodiments. FIG. 3 is a diagram of example operations of a capacity forecasting method, in accordance with some embodiments. FIG. 4 is a diagram of example components of a capacity forecasting apparatus, in accordance with some embodiments. DETAILED DESCRIPTION The following detailed description of example embodiments refers to the accompanying drawings. The present disclosure provides illustrations and descriptions, but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible in light of the present disclosure or may be acquired from practice of the implementations. Further, one or more features or components of one embodiment may be incorporated into or combined with another embodiment (or one or more features of another embodiment). Additionally, the flowchart and description of operations provided below relate to at least one of the embodiments in the present disclosure. It should be noted that it is possible to make other embodiments that do not exactly match the flowchart and its description. It is understood that in other embodiments one or more operations may be omitted, one or more operations may be added, one