DESC:
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION
SYSTEM AND METHOD FOR NETWORK PLANNING
2. APPLICANT(S)
NAME NATIONALITY ADDRESS
JIO PLATFORMS LIMITED INDIAN OFFICE-101, SAFFRON, NR. CENTRE POINT, PANCHWATI 5 RASTA, AMBAWADI, AHMEDABAD 380006, GUJARAT, INDIA
3.PREAMBLE TO THE DESCRIPTION

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE NATURE OF THIS INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.

FIELD OF THE INVENTION
[0001] The present invention generally relates to network management, and more particularly relates to a system and a method for network planning.
BACKGROUND OF THE INVENTION
[0002] Network planning is a crucial aspect of ensuring efficient and reliable communication systems. Typically, network planning involves considering various factors such as current population density and geographical information of an area. These factors help in determining the optimal placement of network infrastructure, such as cell towers, fiber optic cables, and network nodes, to meet the existing demand for connectivity.
[0003] By utilizing the current population density and the geographical information, network planners can design networks that adequately serve the needs of the present population. This approach allows them to identify areas with high user concentrations and deploy network resources accordingly. Consequently, users in these areas experience satisfactory network performance and connectivity.
[0004] However, a potential challenge arises when planning networks solely based on current conditions without considering future growth and evolving technology trends. Over time, population density may increase due to factors such as urbanization, migration, or economic development. Additionally, advancements in technology and the emergence of new services and applications can lead to a surge in network traffic and demand.
[0005] Therefore, there is a need for an advancement of a system and method that can overcome at least one of the above shortcomings, particularly for network planning.
BRIEF SUMMARY OF THE INVENTION
[0006] One or more embodiments of the present disclosure provide a system and method for network planning.
[0007] In one aspect of the present invention, a system for network planning is provided. The system includes a collecting module configured to collect network planning data pertaining to at least one geographical area from one or more sources. The system further includes a network planning generator module configured to generate at least one network planning design based on the collected network planning data. The system further includes a transceiver configured to transmit the generated at least one network planning design to an interested entity.
[0008] In one embodiment, the network planning data collected pertaining to the at least one geographical area includes at least one of, subscriber density data, environmental characteristics, type of terrain, and geographic location data including at least one of, altitude, total area, and density.
[0009] In another embodiment, the system further includes a comparing module configured to compare the collected network planning data with historical data. Further, the system includes an extraction module configured to extract at least one of, patterns, trends and correlations pertaining to the collected network planning data based on comparing the collected network planning data with the historical data. Further, the system includes a prediction module configured to predict one or more network design parameters utilizing the extracted at least one of, patterns, trends and correlations pertaining to the collected network planning data.
[0010] In yet another embodiment, the predicted one or more network design parameters include at least one of, network traffic patterns, recommended types of transmission planning, estimates of future population growth, capacity planning, base station planning, pertaining to the at least one geographical area.
[0011] In yet another embodiment, the collected network planning data is compared with a similar type of historical data in order to generate valuable insights.
[0012] In another aspect of the present invention, a method for network planning is disclosed. The method includes the steps of collecting by the one or more processors network planning data pertaining to at least one geographical area from one or more sources. The method further includes generating by the one or more processors at least one network planning design based on the collected network planning data. The method further includes transmitting by the one or more processors the generated at least one network planning design to an interested entity.
[0013] Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all-inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0015] FIG. 1 is an exemplary block diagram of an environment for network planning, according to one or more embodiments of the present disclosure;
[0016] FIG. 2 is an exemplary block diagram of a system for network planning, according to one or more embodiments of the present disclosure;
[0017] FIG. 3 is a schematic representation of the present system of FIG. 1 workflow, according to one or more embodiments of the present disclosure;
[0018] FIG. 4 illustrates an exemplary scenario of network planning process within an at least one network planning design, according to one or more embodiments of the present disclosure; and
[0019] FIG. 5 is a flow diagram illustrating a method for network planning, according to one or more embodiments of the present disclosure.
[0020] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0021] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.
[0022] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0023] A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0024] As per various embodiments depicted, the present invention discloses the system and method for network planning. The network planning is usually done based on the current population density and the geographical information. By doing the system and method to predict and proactively fulfill the additional bandwidth and capacity requirements due to increased user density in future.
[0025] Referring to FIG. 1, FIG. 1 illustrates an exemplary block diagram of an environment 100 for network planning, according to one or more embodiments of the present disclosure. The environment 100 includes a network 105, a User Equipment (UE) 110, a server 115, and a system 120. The UE 110 aids a user to interact with the system 120 for transmitting the network planning data to one or more processors 205 (as shown in FIG. 2) to avail a network planning design. In one embodiment, the network planning design include, but not limited to a transmission planning, a capacity planning, and a base station location planning.
[0026] The terms “user,” and “subscriber,” and variations thereof, as used herein, are used interchangeably, without limiting the scope of the present disclosure.
[0027] Each of the UE 110 is configured to connect to the server 115 via the network 105. For the purpose of description and explanation, the description will be explained with respect to the UE 110, or to be more specific will be explained with respect to a first UE 110a, a second UE 110b, and a third UE 110c, and should nowhere be construed as limiting the scope of the present disclosure.
[0028] In an embodiment, each of the first UE 110a, the second UE 110b, and the third UE 110c is one of, but are not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device.
[0029] The network 105 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 105 may include, but is not limited to, a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a New Radio (NR), a Narrow Band Internet of Things (NB-IoT), an Open Radio Access Network (O-RAN), and the like.
[0030] The network 105 may also include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network 105 may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, a VOIP or some combination thereof.
[0031] The environment 100 includes the server 115 accessible via the network 105. The server 115 may include by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defense facility side, or any other facility that provides service.
[0032] The environment 100 further includes the system 120 communicably coupled to the server 115 and each of the first UE 110a, the second UE 110b, and the third UE 110c via the network 105. The system 120 is configured for the network planning. The system 120 is adapted to be embedded within the server 115 or is embedded as the individual entity. However, for the purpose of description, the system 120 is described as an integral part of the server 115, without deviating from the scope of the present disclosure.
[0033] Operational and construction features of the system 120 will be explained in detail with respect to the following figures.
[0034] Referring to FIG. 2, FIG. 2 illustrates an exemplary block diagram of the system 120 for network planning, according to one or more embodiments of the present disclosure. The system 120 includes one or more processors 205, a memory 210, and a user interface 215. The one or more processor 205, hereinafter referred to as the processor 205 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions. As per the illustrated embodiment, the system 120 includes one processor 205. However, it is to be noted that the system 120 may include multiple processors as per the requirement and without deviating from the scope of the present disclosure.
[0035] The information related to the network planning data may be provided or stored in the memory 210. Among other capabilities, the processor 205 is configured to fetch and execute computer-readable instructions stored in the memory 210. The memory 210 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 210 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROMs, FLASH memory, unalterable memory, and the like.
[0036] The information related to the network planning data may be rendered on the user interface 215. The user interface 215 includes a variety of interfaces, for example, interfaces for a Graphical User Interface (GUI), a web user interface, a Command Line Interface (CLI), and the like. The user interface 215 facilitates communication of the system 125. In one embodiment, the user interface 215 provides a communication pathway for one or more components of the system 125. Examples of such components include, but are not limited to, the UE 110 and a database 250.
[0037] The database 250 is configured to store the network planning data which is transmitted by the UE 110. Further, the database 250 provides structured storage, support for complex queries, and enables efficient data retrieval and analysis. The database 250 is one of, but is not limited to, one of a centralized database, a cloud-based database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache databases, and so forth. The foregoing examples of database types are non-limiting and may not be mutually exclusive e.g., a database can be both commercial and cloud-based, or both relational and open-source, etc.
[0038] Further, the processor 205, in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 205. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 205 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for processor 205 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 210 may store instructions that, when executed by the processing resource, implement the processor 205. In such examples, the system 120 may comprise the memory 210 storing the instructions and the processing resource to execute the instructions, or the memory 210 may be separate but accessible to the system 120 and the processing resource. In other examples, the processor 205 may be implemented by electronic circuitry.
[0039] In order for the system 120 for network planning, the processor 205 includes a collecting module 220, a network planning generator module 225, a comparing module 230, an extracting module 235, a predicting module 240, and a transceiver 245 communicably coupled to each other for network planning.
[0040] The collecting module 220, the network planning generator module 225, the comparing module 230, the extracting module 235, the predicting module 240, and the transceiver 245 in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 205. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 205 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 210 may store instructions that, when executed by the processing resource, implement the processor. In such examples, the system 120 may comprise the memory 210 storing the instructions and the processing resource to execute the instructions, or the memory 210 may be separate but accessible to the system 120 and the processing resource. In other examples, the processor 205 may be implemented by electronic circuitry.
[0041] The collecting module 220 is configured to collect the network planning data pertaining to an at least one geographical area from one or more sources. In an embodiment, the one or more sources include, but not limited to, Geographic Information System (GIS), and telecom infrastructure records. In an embodiment, the collected network planning data pertaining to the at least one geographical area includes at least one of, subscriber density data, environmental characteristics, type of terrain, and geographic location data including at least one of, altitude, total area, and density.
[0042] As per the above one embodiment, let us consider for example, the subscriber density data provides information about the number of users in specific regions within the area. The subscriber density data helps to identify the areas with high user concentrations to require enhanced network coverage and capacity. The environmental characteristics, such as climate conditions or potential sources of interference, are important to consider. For example, in coastal areas, a network planning operator may need to account for factors like saltwater corrosion when selecting equipment or deploying network infrastructure.
[0043] As per one or more embodiments, the type of terrain such as urban, suburban, or rural and the type of area helps to determine the specific network requirements. The urban areas with high population density may require a dense network of small cells for better coverage and capacity, while the rural areas may benefit from longer-range macro cells. The geographic location data including at least one of, altitude, total area, and density helps in understanding topographical features of the area. For instance, in hilly regions with varying altitudes, different transmission technologies like microwave links might be more suitable than installing fiber optic cables.
[0044] Accordingly, as per one embodiment, at least one network planning design is generated when the network planning data is collected by the collecting module 220.
[0045] The network planning generator module 225 is configured to generate the at least one network planning design based on the collected network planning data. For instance, based on the subscriber density and the geographic location data, the network planning generator module 225 generates the at least one network planning design indicating an increasing demand for network capacity in a particular urban area. The at least one generated network planning design includes, but not limited to, compare the collected network planning data, extract at least one of the patterns, the trends and the correlations, and predict the one or more network planning design parameters.
[0046] The comparing module 230 is configured to compare the collected network planning data with a historical data pertaining to the subscriber density and the geographic location data. In an embodiment, the collected network planning data is compared with a similar type of the historical data in order to generate valuable insights for extracting at least one of, patterns, trends and correlations by using the extracting module 235.
[0047] The extracting module 235 is configured to extract the at least one of the patterns, the trends and the correlations pertaining to the collected network planning data based on comparing the collected network planning data with the historical data. When the at least one of the patterns, the trends and the correlations are extracted, the predicting module 240 predicts the one or more network planning design parameters.
[0048] The predicting module 240 is configured to predict the one or more network planning design parameters utilizing the extracted at least one of, patterns, trends and correlations pertaining to the collected network planning data. In one embodiment, the one or more network planning design parameters are predicted by using an Artificial Intelligence/Machine Learning (AI/ML) model 405 (as shown in FIG. 4). In one embodiment, the predicted one or more network planning design parameters include at least one of, network traffic patterns, recommended types of transmission planning, estimates of future population growth, capacity planning, base station planning, pertaining to the at least one geographical area.
[0049] In one embodiment, the collected network planning data is fed into the AI/ML model 405 for network planning. The AI/ML model 405 utilizes sophisticated algorithms and statistical techniques to analyze and extract the network planning data. For example, the AI/ML model 405 can analyze patterns in the subscriber density data to identify the areas where network congestion is likely to occur in the future and analyze the geographic location data to determine suitable locations for deploying the base stations based on coverage gaps or population distribution.
[0050] The transceiver 245 is configured to transmit the at least one generated network planning design to an interested entity 410 (as shown in FIG. 4) by using when the one or more network planning design parameters to be predicted by the predicting module 240. In an embodiment, the interested entity 410 includes, but not limited to, a network planning operator. In one embodiment, the interested entity 410 receives the at least one generated network planning design from the AI/ML model 405 to plan for additional small cell deployments or upgrading existing infrastructure in that specific area. The at least one generated network planning design provides valuable insights and predictions that help guide the network planning process, for instance as described in FIG. 4. By doing so, the system 120 empowers the network planning operator with valuable information and insights to optimize the at least one network planning design and make informed decisions, ultimately leading to efficient and reliable network deployments, thereby reducing network congestion, improving network performance, and effectively addressing evolving needs of users.
[0051] FIG. 3 is a schematic representation of the system 120 in which various entities operations are explained, according to one or more embodiments of the present disclosure. Referring to FIG. 3, describes the system 120 for network planning. It is to be noted that the embodiment with respect to FIG. 3 will be explained with respect to the first UE 110a for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0052] As mentioned earlier in FIG.1, each of the first UE 110a, the second UE 110b, and the third UE 110c may include an external storage device, a bus, a main memory, a read-only memory, a mass storage device, communication port(s), and a processor. The exemplary embodiment as illustrated in the FIG. 3 will be explained with respect to the first UE 110a. The first UE 110a includes one or more primary processors 305 communicably coupled to the one or more processors 205 of the system 125.
[0053] The one or more primary processors 305 are coupled with a memory 310 for storing instructions which are executed by the one or more primary processors 305. Execution of the stored instructions by the one or more primary processors 305 enables the first UE 110a to transmit the network planning data to the one or more processors 205 in order to avail the network planning design.
[0054] As mentioned earlier in FIG.2, the one or more processors 205 of the system 120 is configured to collect the network planning data, generate the at least one network planning design, and the transmit the at least one generated network planning design to the interested entity.
[0055] As per the illustrated embodiment, the system 120 includes the one or more processors 205, the memory 210, and the user interface 215. The operations and functions of the one or more processors 205, the memory 210, and the user interface 215 are already explained in FIG. 2. For the sake of brevity, a similar description related to the working and operation of the system 120 as illustrated in FIG. 2 has been omitted to avoid repetition.
[0056] Further, the processor 205 includes the collecting module 220, the network planning generator module 225, the comparing module 230, the extracting module 235, the predicting module 240, and the transceiver 245. The operations and functions of the collecting module 220, the network planning generator module 225, the comparing module 230, the extracting module 235, the predicting module 240, and the transceiver 245 are already explained in FIG. 2. Hence, for the sake of brevity, a similar description related to the working and operation of the system 120 as illustrated in FIG. 2 has been omitted to avoid repetition. The limited description provided for the system 120 in FIG. 3, should be read with the description provided for the system 120 in the FIG. 2 above, and should not be construed as limiting the scope of the present disclosure.
[0057] FIG. 4 illustrates an exemplary scenario of network planning process within the at least one network planning design, according to one or more embodiments of the present disclosure.
[0058] The UE 110 is configured to transmit the network planning data to the one or more processors 205 to avail the network planning design. In one embodiment, the network planning design include, but not limited to transmission planning, capacity planning, and base station location planning.
[0059] At step 401, Transmission planning: Based on the predicted one or more network planning design parameters, the network planning operator assesses whether to install the fiber optic cables or the microwave links in specific geographical areas. The AI/ML model 405 generates the at least one generated network planning design by considering factors such as population density, terrain characteristics, and future demand projections.
[0060] For example, if the AI/ML model 405 predicts high population density in the urban area, indicating a significant need for data capacity, the network planning operator can opt for the fiber optic cables to provide high-bandwidth connectivity. In another embodiment, in remote rural areas with challenging terrain, where deploying the fiber may be cost-prohibitive, the network planning operator might choose the microwave links as a more feasible option.
[0061] At step 402, Base station location planning: Leveraging the predictions of future population density and geographical information, the network planning operator finalizes the optimal locations for deploying the base stations. The AI/ML model 405 projections provide insights into the areas that require enhanced coverage, allowing the network planning operator to strategically position the base stations for efficient network deployment.
[0062] Furthermore, the network planning operator considers the distance to be maintained between the base stations to ensure seamless handovers and optimal signal strength across the network and determines the total number of cell sites needed based on the population density, the anticipated traffic patterns, and the coverage requirements.
[0063] For instance, if the AI/ML model 405 predicts a significant increase in the population density in a suburban area over the next 20 years, the network planning operator can use the collected network planning data to identify suitable locations for the base stations in that area and estimate the number of cell sites required to handle the projected demand.
[0064] At step 403, Capacity planning: Considering the forecasted population density trend for the next 20 years, the network planning operator estimates the capacity and bandwidth requirements of each geographical location and helps ensure that the network infrastructure can accommodate the anticipated growth in users and data traffic.
[0065] The network planning operator considers the AI/ML model 405 predictions regarding the population density, demographic shifts, and technological advancements to estimate the capacity needed for each area, which includes evaluating factors such as the number of subscribers, their usage patterns, and the expected demand for bandwidth-intensive applications.
[0066] For example, if the AI/ML model 405 predicts a steady increase in population density in the urban area, the network planning operator can estimate the capacity required to support the growing number of users, including anticipated data rates, simultaneous connections, and quality of service requirements.
[0067] The geographical information and the subscriber density data is fed into the AI/ML model 405. The AI/ML model 405 transmits the at least one generated network planning design to the interested entity 410. The interested entity 410 receives the at least one generated network planning design from the AI/ML model 405 to plan for additional small cell deployments or upgrading existing infrastructure in that specific area. The at least one generated network planning design provides valuable insights and predictions that help guide the network planning process, for instance as described below.
[0068] By leveraging the at least one generated network design parameters, the network planning operator plans the transmission technologies, base station locations, and capacity requirements and ensures that the network infrastructure is optimized to meet the predicted demands and provide reliable and efficient connectivity for users in the designated geographical area. Thereby implementing the network planning design avoids congestion traffic, reduces space utilization in the memory and improves processing speed of services.
[0069] FIG. 5 is a flow diagram illustrating a method 500 for network planning, according to one or more embodiments of the present invention. For the purpose of description, the method 500 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0070] At step 505, the method 500 includes the step of collecting the network planning data pertaining to the at least one geographical area from one or more sources by the collecting module 220. In an embodiment, the one or more sources include, but not limited to, Geographic Information System (GIS), and telecom infrastructure records. In an embodiment, the collected network planning data pertaining to the at least one geographical area includes at least one of, subscriber density data, environmental characteristics, type of terrain, and geographic location data including at least one of, altitude, total area, and density.
[0071] As per the above illustrated embodiment, let us consider for example, the subscriber density data provides information about the number of users in specific regions within the area. The subscriber density data helps to identify the areas with high user concentrations to require enhanced network coverage and capacity. The environmental characteristics, such as climate conditions or potential sources of interference, are important to consider. For example, in coastal areas, a network planning operator may need to account for factors like saltwater corrosion when selecting equipment or deploying network infrastructure.
[0072] As per one or more embodiments, the type of terrain such as urban, suburban, or rural and the type of area helps to determine the specific network requirements. The urban areas with high population density may require a dense network of small cells for better coverage and capacity, while the rural areas may benefit from longer-range macro cells. The geographic location data including at least one of, altitude, total area, and density helps in understanding topographical features of the area. For instance, in hilly regions with varying altitudes, different transmission technologies like microwave links might be more suitable than installing fiber optic cables.
[0073] As per the illustrated embodiment, at least one network planning design is generated when the network planning data is collected by the collecting module 220.
[0074] In one embodiment, the collected network planning data is fed into the AI/ ML model 405 for network planning. The AI/ML model 405 utilizes sophisticated algorithms and statistical techniques to analyze and extract the network planning data. For example, the AI/ML model 405 can analyze patterns in the subscriber density data to identify the areas where network congestion is likely to occur in the future and analyze the geographic location data to determine suitable locations for deploying the base stations based on coverage gaps or population distribution.
[0075] At step 510, the method 500 includes the step of generating the at least one network planning design based on the collected network planning data. For instance, based on the subscriber density and the geographic location data, the network planning generator module 225 generates the at least one network planning design indicating an increasing demand for network capacity in a particular urban area. The at least one generated network planning design further includes, but not limited to, compare the collected network planning data, extract at least one of the patterns, the trends and the correlations, and predict the one or more network planning design parameters.
[0076] The collected network planning data is compared with the historical data by the comparing module 230. In an embodiment, the collected network planning data is compared with a similar type of historical data in order to generate valuable insights for extracting at least one of, patterns, trends and correlations by using the extracting module 235.
[0077] The at least one of the patterns, the trends and the correlations pertaining to the collected network planning data are extracted based on comparing the collected network planning data with the historical data by the extracting module 235. When the at least one of the patterns, the trends and the correlations are extracted, the predicting module 240 is configured to predict the one or more network planning design parameters.
[0078] The one or more network planning design parameters are predicted utilizing the extracted at least one of, patterns, trends and correlations pertaining to the collected network planning data by the predicting module 240. In one embodiment, the one or more network planning design parameters are predicted by using the AI/ML model 405. In one embodiment, the predicted one or more network planning design parameters include at least one of, network traffic patterns, recommended types of transmission planning, estimates of future population growth, capacity planning, base station planning, pertaining to the at least one geographical area.
[0079] In one embodiment, the collected network planning data is fed into the AI/ML model 405 for network planning. The AI/ML model 405 utilizes sophisticated algorithms and statistical techniques to analyze and extract the network planning data. For example, the ML model can analyze patterns in the subscriber density data to identify the areas where network congestion is likely to occur in the future and analyze the geographic location data to determine suitable locations for deploying the base stations based on coverage gaps or population distribution.
[0080] The transceiver 245 is configured to transmit the at least one generated network planning design to the interested entity 410 by using when the one or more network planning design parameters are predicted by the predicting module 240.
[0081] At step 515, the method 500 includes the step of transmitting the at least one generated network planning design to the interested entity 410 by the transceiver 245. In an embodiment, the interested entity 410 includes, but not limited to, a network planning operator. In one embodiment, the interested entity 410 receives the at least one generated network planning design from the AI/ML model 405 to plan for additional small cell deployments or upgrading existing infrastructure in that specific area. The at least one generated network planning design provides valuable insights and predictions that help guide the network planning process. By doing so, the method 500 empowers the network planning operator with valuable information and insights to optimize the at least one network planning design and make informed decisions, ultimately leading to efficient and reliable network deployments, thereby reducing network congestion, improving network performance, and effectively addressing evolving needs of users.
[0082] The present invention further discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions. The computer-readable instructions are executed by a processor 205. The processor 205 is configured to collect a network planning data pertaining to at least one geographical area from one or more sources. The processor 205 is configured to generate an at least one network planning design based on the collected network planning data. Further, the processor 205 is configured to transmit the generated at least one network planning design to an interested entity.
[0083] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-5) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0084] The present disclosure incorporates technical advancement of collecting and analyzing a diverse range of data, including the subscriber density, the geographical information, and environmental characteristics, the system provides a comprehensive understanding of the specific area where the network planning is required. This enables the network planning operator to make data-driven decisions, resulting in optimized network designs that align with the area's unique requirements. The present disclosure generates accurate predictions and forecasting outputs by leveraging machine learning models. These outputs guide the network planning operator in crucial aspects such as the transmission planning, the base station location planning, and the capacity planning. As a result, the network planning operator can efficiently allocate resources, optimize network coverage, and estimate future capacity requirements. Lastly, the automation of network planning reduces manual effort, streamlines the process, and enhances overall efficiency. The network planning operator can make informed decisions quickly, resulting in cost savings and improved time-to-market for network deployments. Overall, the present disclosure provides a powerful solution for network planning that ensures optimal network performance, mitigates congestion risks, and creates a reliable and scalable communication infrastructure for the future.
[0085] The present invention offers multiple advantages over the prior art and the above listed are a few examples to emphasize on some of the advantageous features. The listed advantages are to be read in a non-limiting manner.

REFERENCE NUMERALS
[0086] Environment - 100;
[0087] Network - 105;
[0088] User Equipment - 110;
[0089] Server - 115;
[0090] System - 120;
[0091] One or more processor -205;
[0092] Memory – 210;
[0093] User interface– 215;
[0094] Collecting Module- 220;
[0095] Network Planning Generator Module- 225;
[0096] Comparing Module - 230;
[0097] Extracting Module - 235;
[0098] Predicting Module – 240;
[0099] Transceiver – 245;
[00100] Database - 250;
[00101] One or more primary processors – 305;
[00102] Memory of user equipment – 310;
[00103] AI/ML model-405;
[00104] Interested entity- 410.

,CLAIMS:CLAIMS
We Claim:
1. A method (500) for network planning, the method (500) comprises the steps of:
collecting (505), by one or more processors (205), a network planning data pertaining to an at least one geographical area from one or more sources;
generating (510), by the one or more processors (205), an at least one network planning design based on the collected network planning data; and
transmitting (515), by the one or more processors (205), the generated at least one network planning design to an interested entity (410).

2. The method (500) as claimed in claim 1, wherein the network planning data collected pertaining to the at least one geographical area includes at least one of, subscriber density data, environmental characteristics, type of terrain, and geographic location data including at least one of, altitude, total area and density.

3. The method (500) as claimed in claim 1, wherein the step of, generating, by the one or more processors (205), the at least one network planning design based on the collected network planning data, includes the steps of:
comparing, by the one or more processors (205), the collected network planning data with a historical data;
extracting, by the one or more processors (205), at least one of, patterns, trends and correlations pertaining to the collected network planning data based on comparing the collected network planning data with the historical data; and
predicting, by the one or more processors (205), one or more network design parameters utilizing the extracted at least one of, patterns, trends and correlations pertaining to the collected network planning data.

4. The method (500) as claimed in claim 3, wherein the predicted one or more network design parameters include at least one of, network traffic patterns, recommended types of transmission planning, estimates of future population growth, capacity planning, base station planning, pertaining to the at least one geographical area.

5. The method (500) as claimed in claim 3, wherein the collected network planning data is compared with a similar type of the historical data in order to generate valuable insights.

6. A system (120) for network planning, the system (120) comprising:
a collecting module (220) configured to collect, a network planning data pertaining to an at least one geographical area from one or more sources;
a network planning generator module (225) configured to generate, an at least one network planning design based on the collected network planning data; and
a transceiver (245) configured to transmit, the generated at least one network planning design to an interested entity.

7. The system (120) as claimed in claim 7, wherein the network planning data collected pertaining to the at least one geographical area includes at least one of, subscriber density data, environmental characteristics, type of terrain, and geographic location data including at least one of, altitude, total area, and density.

8. The system (120) as claimed in claim 7, wherein the system (120) further comprising:
a comparing module (230) configured to compare, the collected network planning data with a historical data;
an extracting module (235) configured to extract, at least one of, patterns, trends and correlations pertaining to the collected network planning data based on comparing the collected network planning data with the historical data; and
a predicting module (240) configured to predict, one or more network design parameters utilizing the extracted at least one of, patterns, trends and correlations pertaining to the collected network planning data.

9. The system (120) as claimed in claim 7, wherein the predicted one or more network design parameters include at least one of, network traffic patterns, recommended types of transmission planning, estimates of future population growth, capacity planning, base station planning, pertaining to the at least one geographical area.

10. The system (120) as claimed in claim8, wherein the collected network planning data is compared with a similar type of the historical data in order to generate valuable insights.

11. A User Equipment (UE) (110), comprising:
one or more primary processors (305) communicatively coupled to one or more processors (205), the one or more primary processors (305) coupled with a memory (310), wherein said memory (310) stores instructions which when executed by the one or more primary processors (305) causes the UE (110) to:
transmit, network planning data to the one or more processors (205) in order to avail a network planning design; and
wherein the one or more processors (205) is configured to perform the steps as claimed in claim 1.