DEVELOPMENT PLAN-BASED BASE STATION FORECASTING SYSTEM

Abstract

This invention relates to a system (1) developed to enable the analysis of urban development plans and plan notes, population and demographic projection data, existing base station location and network performance data and parcel ownership information used in base station planning in the telecommunications sector to determine in advance which regions will need base stations and which suitable parcels can be established for these stations.

Description

1 TARIFF DEVELOPMENT PLAN-BASED BASE STATION FORECASTING SYSTEM Technical Area This invention is used in urban planning for base stations in the telecommunications sector. zoning plans and plan notes, population and demographic projection data, existing basis Analysis of station location and network performance data, as well as land ownership information. by determining in which regions there will be a need for base stations in the future and A 10 that enables the pre-determination of suitable plots where stations can be established. It is related to the system. Previous Technique Predicting base station locations before deployment actually takes place is both technically advantageous. It also presents significant challenges due to socioeconomic uncertainties. Primarily, population... Because its intensity and mobility vary over time, predictions are made. It may become obsolete in a short time. Also, topography, building density and structure Physical environmental factors such as materials directly affect signal propagation. This complicates planning. In addition, future user behavior is a factor. data consumption habits and technological advancements (e.g., next-generation mobile) Communication standards) cannot be precisely predicted. Infrastructure costs, permit processes and the attitude of the local population toward base stations also affects the feasibility of the plans. These are other influencing factors. All these uncertainties necessitate early intervention. Positioning decisions carry both the risk of insufficient coverage and unnecessary investment. 25 This can lead to bringing it along with it. 2 Therefore, the city is used in base station planning in the telecommunications sector. zoning plans and plan notes, population and demographic projection data, existing basis Analysis of station location and network performance data, as well as land ownership information. by determining in which regions there will be a need for base stations in the future and A 5 that enables the pre-determination of suitable plots where stations can be established. It is understood that the system is needed. According to Chinese patent document CN119965847A, which is included in the prior art, In line with increasing load demand and distributed generation integration, the distribution network The company that planned the expansion and placed photovoltaic and energy storage systems in place. a method that optimizes selection and capacity under cost and technical constraints It is mentioned. Brief Description of the Invention The aim of this invention is to improve base station planning in the telecommunications sector. urban development plans and plan notes used, population and demographic projection data, Existing base station location and network performance data, and parcel ownership. By analyzing this data, we can determine which regions will need base stations in the future. that these stations would be formed and that suitable plots of land where these stations could be established had been identified in advance 20 The goal is to implement a system developed to enable its identification. Detailed Description of the Invention The “Zoning Plan Based Base Station 25” was developed to achieve the purpose of this invention. The "Predictive System" is shown in the attached figure; Figure 1 shows a schematic view of the system that is the subject of the invention. 3 The parts shown in the figure are individually numbered, and the corresponding numbers correspond to these numbers. It is given below. 1. System 5 2. Application 3. Server Urban planning used in base station planning in the telecommunications sector. plans and plan notes, population and demographic projection data, existing base station 10 By analyzing location and network performance data, as well as land ownership information. in which regions there will be a need for base stations in the future and where these stations will be located to ensure that suitable plots of land where it can be established are determined in advance The developed invention subject system (1); -Optimum base station site selection results are presented to the user via an interface 15 at least one application configured to enable transmission (2), - building density, building heights, types of use, and the relevant area. Approved zoning plans that include spatial restrictions in the form of areas closed to construction obtaining data from relevant sources, including population density, age distribution, mobility profiles, and Integrating demographic data in the form of time-based population changes into the flow 20 the preparation of elevation maps, terrain slopes, natural obstacles, and building contours. and the flow of topographic information through geographic information systems-based datasets transfer, locations of existing base stations, coverage areas, traffic loads, signal attenuation points, and intensity clocks from relevant sources. by taking them into account and including them in the flow, the ownership of the parcels where base stations can be established 25 its status, whether it is a public or private area, access conditions, and commercial incorporating usability information from relevant sources into the workflow, technical suitable locations but not legally or commercially suitable 4 elimination, normalization of all collected data, elimination of missing or inconsistent data. the extraction and generation of meaningful features for artificial intelligence models, different combining data from various sources under a common representation, region, nodes and modeling the structure of a graph consisting of edges, the potential of nodes base station points, and the spatial, traffic, and coverage relationships of the edges. 5 to represent, graphic neural networks the interactions and coverage of neighboring areas. learning about their interactions, testing different base station placement scenarios based on criteria such as coverage quality, cost, capacity, and regulatory compliance. maximizing the reward function, making the model more efficient over time. learning settlement strategies, population growth, urbanization trends and traffic 10 Simulating future scenarios by taking changes into account, technical Locations that appear optimal in terms of rental costs, ease of access, and commercial aspects feasibility assessment and, as a result of all analyses, coverage, capacity, The most suitable base station locations that meet both cost and regulatory criteria have been identified. It includes at least one server (3) configured to enable its production. 15 The application (2) in the system (1) which is the subject of the invention does not require any communication protocol. to communicate with the server (3) and exchange data using It is being structured. The server (3) in the system (1) which is the subject of the invention, uses any communication protocol to communicate with application (2) and exchange data using It is structured. The server (3) does not produce decisions using artificial intelligence techniques, but ensuring data consistency and integrity to feed subsequent learning models A supporting pre-learning layer automatically processes data from different sources. 25 recognizing, classifying data types, and identifying temporal and spatial relationships between data. rule-driven machine learning that detects and correctly matches data. It is structured to include the components. Server (3), approved and draft city zoning plans, zoning plan revisions and plan notes, population and demographic projections data, GIS maps and topographic data, current base station locations and network data and data inputs in the form of land registry, cadastre and parcel ownership information the streaming of all data from sources using automatic or semi-automatic methods making the data timestamped and georeferenced, and placing the data in a central data lake 5 its integration in architecture and its output being geographically and temporally aligned raw datasets and a ready-made multi-source data pool for artificial intelligence models It is structured to enable the creation of the server (3), artificial intelligence, unsupervised learning and natural language processing techniques with numerical, textual and spatial By learning the implicit relationships between the data, decision support can be provided without human intervention. 10 Automatic generation of distinctive features that can be used in models It is structured to provide the raw architecture kept in the data lake. Server (3), population, GIS and network data, and textual zoning plan notes with common coordinates of the data. conversion to the system, correction of missing and inconsistent data, textual plan the analysis of the grades using natural language processing techniques, numerical and comparable 15 feature extraction and region-based numerical feature vectors and structured The model is configured to facilitate the creation of input datasets. Server (3) city with unsupervised learning based graphic spatial modeling technique. by learning about the dotting of its texture, through the relationships between the dots automatic discovery of spatial dependencies, regional feature vectors and 20 The urban structure is graphed using plot, road, settlement, and base station locations. converting it into a graph neural network, defining nodes and edges, unsupervised training of the network model and location-based environmental output. to enable the production of scores and spatial representations of situations is being configured. Server (3), reinforcement learning based location optimization 25 long-term technical and economic consequences of different location selections using this technique learning the most suitable strategies through simulation, spatial state vectors, network performance and cost data, and data in the form of ownership and commercial constraints. 6 Generating agent actions using the reward function and policy updates. learning how to do it and long-term strategies and optimize the output to enable the generation of location decisions and installation or postponement recommendations is structured. Server (3) uses supervised learning based time series models. 5. Learning from past data to understand the long-term effects of urban planning schemes. predicting, optimizing location decisions, historical population and traffic data, and Time series models are created using data in the form of zoning plan phase information. training, running scenario-based simulations, and publishing annual reports as output. The station is being configured to enable the generation of needs forecasts. Server (3), with integrated decision mechanism with artificial intelligence property and commercial planning 10 Combining outputs with a rule-based decision engine, optimized location. proposals, future needs projections and plot and property data Using data, locations are matched with commercial and legal constraints, and the output is... to ensure the production of prioritized parcel and investment plans It is structured. The server (3) produces 15 outputs as a result of the analyses performed. to ensure that the application (2) is transmitted to the relevant user via the interface It is being structured. Industrial Applicability Thanks to the system (1) which is the subject of the invention, base station in the telecommunications sector urban development plans and plan notes used in planning, population and demographics projection data, current base station location and network performance data Analyzing land ownership information to determine which areas will be suitable for base stations in the future. It was anticipated that there would be a need for these stations and that suitable plots of land where these stations could be built were identified in advance. This ensures identification. 7 Around these fundamental concepts, the subject of the invention is "Base Station Prediction Based on Development Plans". It is possible to develop a wide variety of applications related to the System (1)”, and the invention This cannot be limited to the examples described here, but is primarily stated in the claims. It is like that.

Claims

8 REQUESTS 1. Cities used in base station planning in the telecommunications sector. zoning plans and plan notes, population and demographic projection data, existing Base station location and network performance data, and plot ownership 5 By analyzing this data, we can determine which regions will need base stations in the future. that these stations would be formed and that suitable plots of land where these stations could be established were identified in advance. enabling its determination; -Optimum base station site selection results via an interface at least one application configured to ensure that it is delivered to the user (2) 10 including and - building density, building heights, types of use, and the relevant area. Approved zoning plan that includes spatial restrictions in the form of areas closed to construction. obtaining plans from relevant sources, population density, age distribution, demographic mobility profiles and time-based population changes 15 integrating data into the flow, elevation maps, terrain slope, natural Geographic information systems for obstacles, building contours, and topographic information streaming via base station-based datasets their locations, coverage areas, traffic loads, signal attenuation 20 points and traffic density data were included in the flow by taking them from the relevant sources. the ownership status of the plots where base stations can be built, whether it is a public or private area, access conditions, and commercial incorporating availability information from relevant sources into the workflow, technically feasible but legally or commercially impractical eliminating locations, normalizing all collected data, missing 25 or filtering out inconsistent data and making it meaningful for artificial intelligence models. generating attributes, providing a common representation of data from different sources. the merging of a graph consisting of regions, nodes, and edges under one heading 9 modeling the structure, the nodes as potential base station points, The edges should represent spatial, traffic, and coverage relationships in the graphic. neural networks and the interactions and coverage of neighboring areas learning how to cover by testing different base station placement scenarios The award is based on criteria such as quality, cost, capacity, and regulatory compliance. maximizing the function, making the model more efficient over time learning settlement strategies, population growth, urbanization trends and traffic Simulating future scenarios by taking changes into account, Locations that appear technically optimal, in terms of rental costs and ease of access. and its commercial feasibility is evaluated, and as a result of all analyses, 10 the most suitable option that meets the criteria of coverage, capacity, cost and regulation all together configured to produce base station locations as output. a system characterized by at least one server (3) (1).

2. To communicate with the server (3) using any communication protocol and 15 The application is characterized by (2) which is structured to perform data exchange. A system like the one in Request 1 (1).

3. Communicate with the application (2) using any communication protocol. and characterized by the server (3) configured to carry out data exchange 20 A system like the one in Request 1 or 2 (1).

4. Artificial intelligence techniques that do not generate decisions but rather follow-up learning models. a supporting front end that ensures data consistency and integrity in order to feed it. The learning layer automatically recognizes data from different sources, 25 classifying data types and identifying temporal and spatial relationships between data. rule-based machine learning that detects and performs correct data matching Characterized by the server (3) which is configured to include learning components. a system like any of the above-mentioned requests (1).

5. Approved and draft city development plans, development plan revisions, and plan notes, population and demographic projection data, GIS maps and topographic data, 5 current base station location and network data and land registry, cadastre and parcel Data entries in the form of property information are automatically obtained from relevant sources or the data is streamed using semi-automatic methods, and all data is timestamped and making the data geographically referenced, in a central data lake architecture combining and outputting geographically and temporally aligned raw data 10 sets and a ready-made multi-source data pool for artificial intelligence models characterized by the server (3) configured to enable its creation a system like any of the above requests (1).

6. Artificial intelligence, with unsupervised learning and natural language processing techniques, enables numerical, 15 By learning about the implicit relationships between textual and spatial data, humans distinctive features that can be used in decision support models without intervention configured to enable the automatic generation of attributes as in any of the above requests characterized by the server (3) a system (1). 20 7. Raw zoning, population, GIS and network data, and textual zoning data are held in the data lake. converting the plan notes and data to a common coordinate system, missing and Correction of inconsistent data, natural language processing of textual plan notes. the analysis using the technique, numerical and comparable features 25 extraction and region-based numerical feature vectors and structured configured to enable the creation of model input datasets. as in any of the above requests characterized by the server (3) a system (1). 11 8. City modeling using unsupervised learning-based graphic spatial modeling techniques. by learning about the dotting of its texture, through the relationships between the dots automatic detection of spatial dependencies, regional characteristics Using vectors and parcel, road, settlement and base station locations, city 5 converting the structure to a graph structure, nodes and edges the definition, unsupervised training of the graphical neural network model, and as output location-based environmental scores and spatial status representations characterized by the server (3) configured to enable its production a system like any of the above requests (1). 10 9. Different locations using reinforcement learning-based location optimization techniques. by simulating the long-term technical and economic consequences of the elections learning appropriate strategies, spatial state vectors, network Performance and cost data, and data in the form of ownership and commercial constraints 15 using agent actions, reward function and policy updating and learning long-term strategies and outputs optimized location decisions and installation or postponement recommendations characterized by the server (3) configured to enable its production a system like any of the above requests (1). 20 10. Using supervised learning-based time series models to analyze past data by learning how to predict the long-term effects of urban development plans, Optimized location decisions, historical population and traffic data, and zoning. Time series models are created using data in the form of plan stage information. training, running scenario-based simulations, and output for the year to enable the generation of base station requirements estimates any of the above requests characterized by the configured server (3) a system like one of them (1). 12 11. Artificial intelligence integrated with property and commercial planning, decision-making mechanisms and rules. combining the outputs with a supported decision engine, optimized location recommendations, future needs projections, and plot and property Using data in the form of data, locations are subject to commercial and legal restrictions. 5 matching and prioritizing plots and investment plans as outputs characterized by the server (3) configured to enable its production a system like any of the above requests (1).

12. The outputs produced as a result of the analyses performed can be accessed through the application (2) interface. with the server (3) configured to ensure that it is forwarded to the relevant user a system like any of the above characterized demands (1).

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