Decision Support System for Mobile Network Operators

Abstract

The invention is a decision support system that analyzes traffic load, radio resource utilization, and existing equipment capacity at sites to enable mobile network operators to make more accurate, faster, and cost-effective investment decisions, thereby identifying where capacity increases are needed and how these needs should be met.

Description

1 TARIFF Decision Support System for Mobile Network Operators TECHNICAL AREA 5 The invention is generally applicable to 4G, 5G, and future 6G mobile communication systems. capacity planning of radio access networks, support for investment decisions and a for mobile network operators developed to provide network optimization It is related to decision support systems. 10 The invention is particularly useful for enabling mobile network operators to make more accurate, faster, and more efficient investment decisions. traffic load in the fields, radio sources to ensure cost-effective acquisition By analyzing usage and available equipment capacity, we can determine which areas require capacity. that an increase is needed and by what method this need should be met 15 It is related to a decision support system that determines this. STATE OF THE ART Today, telecommunications operators need proactive and data-driven capacity planning. Decision support systems play an important role in this regard. Increased mobile data traffic, The widespread adoption of 5G and the more efficient use of existing radio resources, sites based not only on their current loads but also on future traffic and capacity This requires management according to their needs. Therefore, artificial intelligence and machine learning... Learning-assisted capacity analysis systems; identify capacity bottlenecks in advance. by identifying which area should be invested in, when, and using which method. to determine what is needed, reduce unnecessary hardware investments and network It contributes to the more efficient use of resources. In current technology, investment planning and capacity increase in mobile communication networks 30 Decisions are generally made based on historical traffic data, specific thresholds, and manual engineering. This is carried out through evaluations. This approach, in the network dynamic changes and the combined effect of multiple parameters should not be taken into sufficient consideration. is unable to do so. Furthermore, existing systems often only address insufficient capacity. 2 It identifies the deficiency, but there is no detailed information on how this deficiency can be remedied. It is unable to provide comparative recommendations. It compares the performance and performance of different investment options. Since cost impacts are often assessed individually and manually, optimal Decision-making becomes more difficult, and unnecessary or delayed investments occur. This can be done. This situation leads to both inefficient use of resources and 5 This negatively impacts the user experience. Based on research conducted under the known state of the art, US20230164049A1 Application number [number] was found. The purpose of the application is mobile communication. Predicting future traffic demand and capacity needs on the network 10 The aim is to assist in capacity planning by analyzing historical traffic data. site / location information, events, weather, population, transportation and live data, etc. It collects data from various sources and uses machine learning to predict future traffic and networks. It predicts its performance. As a result of the prediction, its capacity may be insufficient. Cells or infrastructure elements are being identified; reallocation of resources, traffic 15 actions such as routing, capacity planning, adding / changing equipment suggestions can be made and performance if the suggested changes are implemented. This allows comparison with the current situation. Thus, the operator's capacity problems can be identified. anticipating its occurrence and proactively planning network resources The aim is to estimate future capacity needs with this application. and simulation and forecasting mechanisms for capacity adjustment It offers; however, different capacity increase alternatives are considered in terms of cost and expected capacity. together based on multiple criteria such as gain and performance impact a clear decision that automatically determines the optimal investment option by evaluating it It does not present an optimization mechanism. 25 In conclusion, a decision support system for mobile network operators Improvements are being made, therefore the aforementioned disadvantages are being eliminated. There is a need for new structures that will remove existing systems and provide solutions. It is heard. 30 THE PURPOSE OF THE INVENTION 3 The present invention meets the aforementioned requirements and overcomes all the disadvantages. a for mobile network operators that eliminates and brings some additional advantages It is related to decision support systems. The main purpose of the invention is to enable mobile network operators to make more accurate investment decisions. 5 traffic load in the fields, radio to enable quick and cost-effective acquisition By analyzing resource utilization and available equipment capacity, in which fields there is a need for increased capacity and what method should be used to meet this need. The goal is to provide a decision support system that determines what is needed. One aim of the invention is to enable 4G, 5G, and future 6G mobile communication systems, capacity planning of radio access networks, support for investment decisions and The goal is to provide network optimization. Another purpose of the invention is to reduce traffic congestion in the field, the use of radio sources, 15 analyze current hardware capacity and service performance indicators together The aim is to ensure that capacity requirements are determined in a multi-dimensional way. The structural and characteristic features and all the advantages of the invention are given in the figures below. And thanks to the detailed explanation written with references to these figures, it becomes clearer. 20 This will be understood as such. Therefore, the evaluation should also be based on these forms and details. This should be done taking the explanation into consideration. BRIEF DESCRIPTION OF THE FIGURES The advantages of the current invention, along with its structure and additional elements, can be best utilized in 25 years. For understanding, it should be evaluated together with the figures explained below. is necessary. Figure 1. The invention describes a block of a decision support system for mobile network operators. This is a diagram view. REFERENCE NUMBERS 1. Addition module 4 2. Analysis module 3. Prediction module 4. Scenario creation module 5. Optimization module DETAILED EXPLANATION OF THE INVENTION This detailed explanation describes the invention as a decision support tool for mobile network operators. The preferred structures of the system contribute not only to a better understanding of the subject. It is explained in a way that is directed towards and does not create any limiting effect. 10 The invention, whose block diagram view is given in Figure 1, enables mobile network operators. to enable investment decisions to be made more accurately, quickly and cost-effectively traffic load in the fields, radio resource usage and available equipment By analyzing its capacity, we can determine which areas need increased capacity and 15 a decision support that determines how this need should be met It is a system. In 4G, 5G, and future 6G mobile communication systems, radio access capacity planning of networks, support for investment decisions and network It was developed to provide optimization. The subject of the invention is a decision support system.  Base station connected to radio access network in mobile communication network or traffic volume from cells, number of users, radio resource usage data relating to rate, capacity, data speed or performance indicators at least one network data collection module that continuously collects (1) 25  Traffic and performance collected by the mentioned collection module (1) receiving data and processing this data at each base station or cell with existing radio, carrier, bandwidth or hardware capacity by evaluating together the current capacity utilization level and resources An analysis that determines usage and capacity shortages 30 module (2),  Historical and current traffic, capacity utilization, and performance data by analyzing future traffic for each base station or cell increase, capacity utilization level or potential capacity a forecasting module that estimates the need for a specific time interval (3),  Capacity requirement data obtained by the mentioned forecasting module (3) base stations where capacity shortages are predicted by analysis. or adding additional carriers for cells, increasing bandwidth, 5 Upgrading radio or equipment capacity, traffic redirection, different alternative investment scenarios including capacity expansion methods a scenario creation module (4)  The investment generated by the mentioned scenario creation module (4) is 10 scenarios, the capacity increase they will provide, user performance the effect, improvement in resource utilization, investment cost or existing by comparing them in terms of their impact on the use of hardware resources optimal capacity increase under defined technical or financial constraints scenario an optimization module (5) 15 It includes. The invention concerns a decision support system located within a mobile communication network. To determine the current and future capacity needs of the station and cells, and 20 the most suitable investment option to meet the capacity requirement It works to determine this automatically. A sample of the system that is the subject of the invention is shown. In the application, data is primarily obtained from each base station and cell in the network. traffic, capacity, radio resource utilization, hardware capacity, and performance data. through the collection module (1) continuously or at specific time intervals 25 It collects data. In this context, during the data collection process, for example, per cell Downstream and upstream traffic volume, number of active users, physical resource blocks utilization rate, available bandwidth, number of carriers, radio resource utilization rate, user data rate, spectral efficiency, signal quality and similar performance 30 radio and equipment units located at the base station related to the indicators Capacity usage information is collected. Thus, the system only monitors the amount of traffic. not, but an up-to-date one that also takes into account the impact of traffic on existing network resources. It creates a dataset. Data obtained by the collection module (1) The analysis module (2) processes each base station. 6 and the current traffic load for the cell, the radio resources used, and the available resources. by evaluating the hardware capacity together and assessing the current capacity utilization status. This determines the situation. In this context, even though there is high traffic in a cell, the radio... sufficient resources or high use of radio resources However, there are different situations such as the fact that the current hardware capacity has not yet been reached. 5 They are separated from each other. Thus, capacity shortage is only a single use. not depending on the rate, but on traffic load, radio resource utilization, available equipment. by evaluating capacity and performance indicators together This is determined. As a result of the analysis, the available capacity utilization for each cell is calculated. level, amount of available capacity, resource utilization efficiency and capacity 10 It is determined whether or not there is a bottleneck. The current situation obtained. The results are calculated by the forecast module (3) together with historical data. This module is being evaluated. It provides history for each base station and cell. traffic trends, current traffic levels, capacity utilization changes, and By analyzing temporal changes in performance indicators, we can predict the future 15 It estimates the potential increase in traffic and the resulting capacity needs. The forecasting process results in, for example, the available capacity over a specific time period. The cells expected to be overwhelmed are identified, and the estimated traffic level for each cell is determined. An estimated capacity utilization rate is generated. Thus, the system only not the cells that are currently experiencing capacity problems, but the cells that currently have a capacity of 20 While currently sufficient, it may experience capacity shortages in the future due to increased traffic. It also determines the expected cells. Capacity by the estimation module (3) After identifying the cells that are expected to develop a deficiency, for the relevant cells The scenario creation module (4) is activated. The scenario creation module (4), the existing hardware configuration of each cell, available radio resources, capacity 25 multiple It creates a capacity increase scenario. These scenarios, for example, to the existing cell Adding additional carriers, increasing available bandwidth, radio unit or replacing the existing equipment with higher-capacity equipment Redirecting radio sources or traffic to neighboring cells is possible. 30 reallocation of the spectrum or capacity requirement with existing cells In cases where the capacity cannot be met, different capacities such as installing a new base station may be considered. It may include methods for increasing returns. For each investment scenario created, technical and economic consequences resulting from the implementation of the scenario 7 is calculated by the optimization unit (5). In this context, each the estimated capacity increase that the scenario will provide to meet the estimated traffic demand level, change in radio resource utilization, current hardware capacity while determining the change in usage and its impact on user performance, The investment cost required for the relevant scenario is also calculated. Thus, the system, 5 For example, not just the scenario that provides more capacity, but also the necessary capacity. the scenario that provides this and does so with a lower investment cost It can determine the technical and economic results obtained by the optimization unit (5). The results are determined based on established capacity, performance, and cost criteria. By evaluating the alternatives created, the most suitable investment scenario is selected from among 10 options. It selects the capacity requirements of a scenario during this evaluation. Despite meeting the demand, it creates a high investment cost compared to lower cost options. However, a scenario that provides insufficient capacity is separated from the others. Thus The decision is not based solely on the amount of capacity increase or solely on the investment cost, meeting capacity needs, maintaining or improving performance and 15 joint evaluation of the criteria for reducing investment costs This proposal is based on the current capacity situation and the projected capacity. needs, chosen capacity expansion method, expected capacity gain, and investment. This information is presented along with details such as cost. Thus, the system determines the capacity in a cell. not only identifying the inadequacy, but also determining that the capacity deficiency is 20 from existing resources, from the use of radio resources, or from hardware? whether it is due to capacity or future traffic increase evaluating and selecting the most suitable among alternative solutions to this problem It determines the solution. The decision support system described in this invention can only identify capacity gaps. not only that, but by creating multiple investment scenarios to address this deficiency It compares these based on performance and cost criteria. For example, new adding a carrier, upgrading existing equipment, installing new site equipment, or small Alternatives such as cell implantation are being evaluated and the most suitable solution is 30 This approach is recommended to move investment decisions away from a static and one-dimensional structure. by creating a dynamic, predictive and comparative decision-making mechanism. It transforms [the system] so that operators can achieve the highest performance at the lowest cost. It can identify the investments that will ensure this growth. 35

Claims

8 REQUESTS 1. Radio access in 4G, 5G, and future 6G mobile communication systems. capacity planning of networks, support for investment decisions and network Decision support system 5 for mobile network operators enabling optimization Its characteristic is;  Base station connected to radio access network in mobile communication network or traffic volume from cells, number of users, radio resource usage data relating to rate, capacity, data speed or performance indicators at least one network data collection module (1) that continuously collects, 10  Traffic and performance collected by the mentioned collection module (1) receiving data and processing this data at each base station or cell with existing radio, carrier, bandwidth or hardware capacity by evaluating together the current capacity utilization level and resources An analysis that determines usage and capacity shortages 15 module (2),  Historical and current traffic, capacity utilization, and performance data by analyzing future traffic for each base station or cell increase, capacity utilization level or potential capacity a forecasting module that estimates the need for a specific time interval 20 (3),  Capacity requirement data obtained by the mentioned forecasting module (3) base stations where capacity shortages are predicted by analysis. or adding additional carriers for cells, increasing bandwidth, radio or equipment capacity upgrade, traffic redirection, 25 different alternative investment scenarios including capacity expansion methods a scenario creation module (4)  The investment created by the mentioned scenario creation module (4) their scenarios, the capacity increase they will provide, user performance 30 the effect, improvement in resource utilization, investment cost or existing by comparing them in terms of their impact on the use of hardware resources optimal capacity increase under defined technical or financial constraints an optimization module (5) 35

Images