SYSTEM AND METHOD FOR IDENTIFYING LOW TELECOMMUNICATION SERVICE UTILIZATION IN A PRE-DEFINED AREA - Patent application

Abstract

The present invention provides a system and method for identifying low telecommunication service utilization in a predefined area. The system receives data from a user regarding a set of telecommunication sites. The system divides each of the telecommunication sites into one or more spatial grids, the telecommunication sites including one or more macro sites. The system generates at least one spatially tagged measurement sample by a call log server including start and end times of each of the calls and data sessions of a subscriber associated with the macro site. The system marks one or more spatial grids as low utilization based on predefined conditions and clusters adjacent low utilization spatial grids in the predefined area to generate a larger cluster of low utilization grids representing low telecommunication utilization.

Description

[Technical field]

[0001] Reservation of Rights Portions of the disclosure of this patent document contain material that is subject to intellectual property rights, including but not limited to copyright, design, trademark, integrated circuit (IC) layout design, and / or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred to as the Owner). The Owner has no objection to the reproduction by anyone of the patent document or patent disclosure, as set forth in the Patent and Trademark Office patent file or records, but otherwise reserves all rights. All rights to such intellectual property are fully reserved by the Owner.

[0002] FIELD OF THE DISCLOSURE Embodiments of the present disclosure relate generally to telecommunications deployment. More particularly, the present disclosure relates to a system and method for identifying low telecommunications service utilization in a predefined area. [Background technology]

[0003] The following description of related art is intended to provide background information regarding the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, this section is intended to be used only to enhance the reader's understanding of the present disclosure and should not be understood as an admission of prior art.

[0004] Telecommunication operators typically designate a circular area of ​​a certain radius around an underutilized telecommunication site as an underutilized area or site based on one or more key performance indicators (KPIs) of that site. However, this coarse-grained approach cannot accurately mark an underutilized area because each of the sites contains multiple sectors, and each sector is made up of multiple cells, and thus, even if a site is underutilized due to a certain factor, a particular sector or a particular cell within a sector may be optimally utilized. Furthermore, a particular area that falls under overlapping telecommunication coverage may be served by different sites, and thus declaring a circular area as underutilized based on a low-utilization site in a central area does not pinpoint an accurate delineation of the underutilized area. There is no known method to accurately predict a telecommunication underutilization area. There is no known method to accurately predict a telecommunication underutilization area.

[0005] Therefore, there is a need in the art to provide a system and method that can overcome the shortcomings of the existing prior art and that can accurately identify one or more low utilization areas. Summary of the Invention [Problem to be solved by the invention]

[0006] Some of the objectives of the present disclosure are met by at least one embodiment herein in the areas listed below.

[0007] It is an object of the present disclosure to provide a system and method that helps telecommunication operators to accurately identify spatial clusters or underutilized areas of telecommunication assets that are deployed to provide services to subscribers in those areas.

[0008] It is an object of the present disclosure to provide a system and method for identifying low utilization spatial clusters so that telecommunication operators can direct increased subscriber numbers without compromising customer experience of telecommunication services.

[0009] The objective of the present disclosure is to provide a system and method that helps operators to identify underutilized areas based on morphological constraints, meaning that underutilized areas can be identified separately for dense urban, urban and rural morphologies.

[0010] It is an object of the present disclosure to provide a system and method that helps operators target specific morphologies in terms of priority. For example, operators may want to prioritize low utilization areas based on dense urban morphology.

[0011] The aim of the present invention is to optimise costs for network operators. [Means for solving the problem]

[0012] This section is provided to introduce certain objects and aspects of the disclosure in a simplified form, which are further described below in the Detailed Description. This Summary is not intended to identify key features or the scope of the claimed subject matter.

[0013] In one aspect, the present disclosure provides a system for identifying low telecommunication service utilization in a predefined area. The system includes one or more processors coupled to a memory. The memory stores instructions that, when executed by the one or more processors, cause the system to receive data from a user regarding a set of one or more telecommunication sites operating in the predefined area. Furthermore, the system may be configured to divide each of the one or more telecommunication sites into one or more spatial grids, the one or more telecommunication sites including one or more macro sites and at least one sector. Furthermore, the system may be configured to generate at least one spatially tagged measurement sample by a call log server. The at least one spatially tagged measurement sample may include start and end times of each of one or more calls and data sessions of one or more subscribers associated with the one or more macro sites. Finally, the system may be configured to mark one or more spatial grids as low utilization based on a predetermined condition. Furthermore, the system may be configured to cluster at least one adjacent low utilization spatial grid in the predefined area to generate one or more large clusters of low utilization grids representing low telecommunication utilization.

[0014] In one embodiment, the system may be configured to calculate a list of at least one under-utilized sector for one or more telecommunication sites operating in a predefined area based on at least one or more key performance indicators (KPIs) associated with a physical resource block (PRB).

[0015] In one embodiment, the system may be configured to map each of the at least one spatially tagged measurement sample to one or more spatial grids. Further, the system may be configured to calculate a list of unique sectors for each of the one or more spatial grids based on the mapped at least one spatially tagged measurement sample. Finally, the system may compare the list of unique sectors of the one or more spatial grids to the list of at least one low utilization sector to obtain one or more low utilization spatial grids.

[0016] In one embodiment, the predetermined condition for marking one or more spatial grids as low utilization is based on at least one non-zero sector that is part of at least one of the lists of unique sectors of the one or more spatial grids and a list of at least one low utilization sector of one or more telecommunication sites operating in the predefined area.

[0017] In one embodiment, the system may be configured to calculate one or more parameters for each of the clusters by aggregating data across one or more spatial grids that form part of each of the clusters.

[0018] In one embodiment, the system may be configured to calculate the list of at least one under-utilized sector based on a set of preconditions including a percentage of at least one spatially tagged measurement sample that is mapped to one or more spatial grids of at least one sector that is part of the grid sector list, and the overall list of at least one under-utilized sector in a predefined area is above a predetermined threshold.

[0019] In one embodiment, key performance indicators (KPIs) may be calculated for at least one sector including at least one of physical resource blocks (PRBs), number of active subscribers, average revenue per user (ARPU), total adds and drops.

[0020] In one embodiment, the system may be configured to compare the average PRB utilization with a predefined PRB utilization threshold to provide an inference of a list of at least one low utilization sector. The average PRB utilization may be calculated for the at least one sector during a busy hour of a day. If the predefined PRB utilization that may be calculated for the at least one sector is consistently below the predefined threshold for at least a first number of days in the second number of days, the corresponding cell is marked as low utilization.

[0021] In one embodiment, the system may be configured to generate at least one cluster for the list of low utilization sectors by calculating at least one parameter of the cluster by aggregating unique dominant sector IDs across one or more spatial grids that form part of the cluster.

[0022] In one embodiment, at least one spatially tagged measurement sample provides spatial location values ​​including at least one of latitude and longitude, a customer identifier, an International Mobile Subscriber Identity (IMSI), a serving cell identifier (CELLID), and a reference signal received power (RSRP) value.

[0023] In one embodiment, the predefined area of ​​the system may be filtered based on a predefined list of expected morphologies including at least one of urban, semi-urban, rural, and highway.

[0024] In one embodiment, users of the system may be network administrators, subscribers, and network operators.

[0025] In one aspect, the present disclosure relates to a user equipment (UE) operating in a low telecommunication coverage area. The UE may include one or more processors coupled to a memory that stores instructions that, when executed by the one or more processors, cause the UE to transmit data to a system regarding a set of one or more telecommunication sites operating in a predefined area. Additionally, the UE may execute the one or more instructions regarding responses received from the system corresponding to the one or more telecommunication sites.

[0026] In one aspect, the present disclosure relates to a method for identifying low telecommunication service utilization in a predefined area. The method includes receiving, by the system, data from a user regarding a set of one or more telecommunication sites operating in the predefined area. Further, the method includes dividing, by the system, each of the one or more telecommunication sites into one or more spatial grids, the one or more telecommunication sites including one or more macro sites and at least one sector. Further, the method includes generating, by the system, at least one spatially tagged measurement sample by a call log server. The at least one spatially tagged measurement sample may include start and end times of each of one or more calls and data sessions of one or more subscribers associated with the one or more macro sites. Finally, the method includes marking, by the system, one or more spatial grids as low utilization based on a predetermined condition, and clustering at least one adjacent low utilization spatial grid in the predefined area to generate one or more large clusters of low utilization grids representing low telecommunication utilization.

[0027] The accompanying drawings, which are incorporated herein and constitute a part of the present invention, depict exemplary embodiments of the disclosed methods and systems, with like reference numerals referring to the same parts throughout the different drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the invention. Some figures may use block diagrams to illustrate components and may not show the internal circuitry of each component. Those skilled in the art will appreciate that the inventions in such drawings include inventions in electrical or electronic components, or circuits commonly used to implement such components. [Brief description of the drawings]

[0028] [Figure 1] FIG. 1 illustrates an exemplary network architecture in which the proposed system of the present disclosure may be implemented or may be implemented with, in accordance with an embodiment of the present disclosure. [Figure 2A] FIG. 1 illustrates an exemplary representation of a proposed system (102) for identifying low telecommunication service usage in a predefined area, according to one embodiment of the present disclosure. [Figure 2B] 1 is an exemplary block diagram representation of a user equipment (UE) (106) for identifying low telecommunication service usage in a predefined area (110), according to one embodiment of the present disclosure. [Diagram 3] 3 illustrates an example flow diagram of a method (300) for calculating low utilization sectors in a predefined area at a certain time, according to one embodiment of the present disclosure. [Figure 4] FIG. 4 illustrates an exemplary representation of a flow diagram of a method (400) for computing a low utilization spatial grid at a given time for a telecommunication site of a predefined area, according to one embodiment of the present disclosure. [Diagram 5]FIG. 5 illustrates an example representation of a flow diagram of a method (500) for identifying larger clusters or areas of low telecommunication utilization based on low utilization grids identified in telecommunication sites of a predefined area, according to one embodiment of the present disclosure. [Figure 6] FIG. 1 illustrates an exemplary computer system in which embodiments of the present disclosure may be utilized in accordance with or with which embodiments of the present disclosure may be utilized. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] The above will become more apparent from the following more detailed description of the invention.

[0030] In the following description, for purposes of explanation, various specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it will be apparent that the embodiments of the present disclosure may be practiced without these specific details. Some of the features described below may be used independently of each other or with any combination of other features. Individual features may not address all of the above problems, or may address only some of the above problems. Some of the above problems may not be fully addressed by any of the features described herein.

[0031] The following description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the following description of exemplary embodiments provides those skilled in the art with an effective description for implementing the exemplary embodiments. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as described.

[0032] The present invention provides an efficient and reliable system and method that can accurately predict one or more low utilization areas that can be optimally targeted for subscriber growth. The system and method can enable identification of one or more areas of low telecommunication utilization. Telecommunication operators can then target one or more identified areas to drive further subscriber growth without compromising customer experience, ensuring optimal utilization and return on investment (ROI) of one or more deployed telecommunication assets.

[0033] FIG. 1 illustrates an exemplary network architecture in which the proposed system of the present disclosure may be implemented or may be implemented with, according to an embodiment of the present disclosure.

[0034] Referring to Figure 1, an example representation of a telecommunications deployment architecture (100) in a predefined area is shown, according to various aspects of the present disclosure. The telecommunications deployment architecture (100) may include a proposed system (102) capable of identifying or using one or more low utilization sites or cells in the predefined area. The predefined area may include, but is not limited to, urban, semi-urban, rural, highway, etc.

[0035] In one embodiment, but not limited to, the telecommunications deployment architecture (100) may include one or more computing devices / user equipment (UE) (106-1, 106-2, ... 106-N) associated with the system (102), the network (104), and one or more users (108-1, 108-2, ... 108-N). Those skilled in the art will appreciate that one or more computing devices (106-1, 106-2, ... 106-N) may be collectively referred to as a computing device (106) and individually referred to as a computing device (106). Similarly, one or more users (108-1, 108-2, ... 108-N) may be collectively referred to as a user (108) and individually referred to as a user (108). It should be understood that the terms "computing device" and "user equipment (UE)" may be used interchangeably throughout this disclosure.

[0036] In one embodiment, the users 108 may include, but are not limited to, a network administrator, a network operator, etc. Alternatively or additionally, the users 108 may include one or more subscribers, where one or more subscribers refer to people who can receive and access the services of a particular network.

[0037] In one embodiment, the computing device (106) may include, but is not limited to, a handheld wireless communication device (e.g., a mobile phone, a smartphone, a phablet device, etc.), a wearable computing device (e.g., a head-mounted display computing device, a head-mounted camera device, a wristwatch computing device, etc.), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playback device, a portable gaming system, and / or any other type of computing device with wireless communication capabilities, etc. In one embodiment, the computing device (106) may communicate with the system (102) via a set of executable instructions resident in any operating system. In one embodiment, the computing device (106) may include any electrical, electronic, electromechanical device or equipment, or a combination of one or more of the above devices, such as, but not limited to, a virtual reality (VR) device, an augmented reality (AR) device, a laptop, a general purpose computer, a desktop, a personal digital assistant, a tablet computer, a mainframe computer, or any other computing device, and the computing device may include one or more built-in or externally coupled accessories, including, but not limited to, a visual aid such as a camera, an audio aid, a microphone, an input device for receiving input from a user (108), such as a keyboard, a touchpad, a touchable screen, an electronic pen, etc. It may be understood that the computing device (106) is not limited to the mentioned devices, and various other devices may be used.

[0038] In another embodiment, the telecommunications deployment architecture (100) in the predefined area may also include one or more macro sites including at least one sector. In one embodiment, the predefined area may be divided into one or more spatial grids of equal and configurable size in area.

[0039] In one embodiment, the proposed system (102) may identify low telecommunication service utilization in a predefined area. The system (102) may be equipped with one or more processors (202) (shown in FIG. 2A ) that may enable the system (102) to receive data from users (108) regarding a set of one or more telecommunication sites operating in the predefined area. The system (102) may then extract a set of attributes from the received set of data or data packets, the set of attributes corresponding to parameters associated with one or more sectors.

[0040] In one embodiment, the system (102) may divide each of the one or more telecommunication sites into one or more spatial grids, each of which includes one or more macro sites and at least one sector. Each of the one or more spatial grids has a predefined size. Alternatively, the filtering of the one or more spatial grids may be based on the shape of the area represented by the one or more spatial grids.

[0041] In one embodiment, the network (104) may be communicatively coupled to at least one call log server (not shown) that may generate at least one spatially tagged measurement sample that includes start and end times of each of one or more calls and data sessions of one or more subscribers / users (108) associated with one or more macro sites.

[0042] In one embodiment, the network (104) may include, by way of example and 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 combinations thereof, one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, etc. The network (104) may also include, by way of example and not limitation, one or more of a wireless network, a wired network, the 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.

[0043] In an exemplary embodiment, the system (102) may collect at least one spatially tagged measurement sample from a subscriber's voice and data sessions initiated over the telecommunications network (104) and then map each of the spatially tagged measurement samples to one or more spatial grids. The at least one spatially tagged measurement sample provides spatial location values ​​in terms of latitude and longitude, a customer identifier, an International Mobile Subscriber Identity (IMSI), a Serving Cell Identifier (CELLID), a Reference Signal Received Power (RSRP) value, etc.

[0044] In one embodiment, the system (102) may mark one or more spatial grids as low utilization based on a predefined condition. The precondition may include, but is not limited to, a percentage of at least one spatially tagged measurement sample that is mapped to one or more spatial grids of at least one sector that is part of a grid sector list, and an overall list of at least one low utilization sector in a predefined area exceeds a predefined threshold. In one embodiment, the percentage of the total number of mapped samples of at least one or more sector grids across the low utilization sectors is then compared to a predefined threshold, e.g., 70%, and the percentage is calculated relative to the total number of spatially tagged measurement samples available for at least one sector grid. If the calculated percentage exceeds the predefined threshold, the at least one sector grid is declared as a "low utilization" grid. Furthermore, the system (102) may cluster at least one adjacent low utilization spatial grid in a predefined area to generate one or more large clusters of low utilization grids representing low telecommunication utilization. A cluster of lists of low utilization sectors may be obtained by calculating at least one parameter of the cluster and by aggregating unique dominant sector IDs across one or more spatial grids that form part of the cluster.

[0045] In one embodiment, the system (102) may calculate a list of at least one underutilized sector for one or more telecommunication sites operating in a predefined area based on at least one or more key performance indicators (KPIs) calculated for at least one sector. The KPIs may include, but are not limited to, physical resource blocks (PRBs), number of active subscribers, average revenue per user (ARPU), total adds, disconnections, etc.

[0046] In an exemplary embodiment, the system (102) may map each of the at least one spatially tagged measurement sample to one or more spatial grids. Additionally, the system (102) may calculate a list of unique sectors using the one or more spatial grids based on the mapped spatially tagged measurement samples. The system (102) may then compare the list of unique sectors of the one or more spatial grids to the at least one list of low utilization sectors to obtain one or more low utilization spatial grids.

[0047] In an exemplary embodiment, the system (102) may mark one or more spatial grids as low utilization based on a predefined condition being met by non-zero sectors that are part of at least one of the lists of unique sectors of the one or more spatial grids and a list of at least one low utilization sector of one or more telecommunication sites operating in the predefined area. Furthermore, the system (102) may be configured to cluster adjacent low utilization spaces in the predefined area to generate one or more large clusters of low utilization grids. Furthermore, the system (102) may calculate one or more parameters for each of the clusters by aggregating data across all of the one or more spatial grids that form part of the cluster. The system (102) may construct a concave boundary for each of the clusters to delineate each cluster as a spatial area representing low telecommunication utilization.

[0048] In an exemplary embodiment, the system (102) may compare an average PRB utilization to a PRB utilization threshold to provide an inference of a list of at least one low utilization sector, where the average PRB utilization is calculated for the at least one sector during a busy hour of a day, and where the calculated PRB utilization for the at least one sector is consistently below the 50% utilization threshold for at least a first number of days in the second number of days, the corresponding cell is marked as low utilization.

[0049] FIG. 2A illustrates an example representation (200) of a proposed system (102) for identifying low telecommunication service usage in a predefined area, according to one embodiment of the present disclosure.

[0050] As shown, the system (102) may include one or more processors (202), which may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuits, and / or any device that manipulates data based on operational instructions. Among other functions, the one or more processors (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204). The memory (204) may store one or more computer-readable instructions or routines that may be fetched and executed to create or share data units via a network service. The memory (204) may comprise any non-transitory storage device, including, for example, a volatile memory such as a RAM, or a non-volatile memory such as an EPROM, a flash memory, and the like.

[0051] In one embodiment, the system (102) may include an interface (206). The interface (206) may include various interfaces, such as interfaces for data input / output devices, referred to as I / O devices, storage devices, sensors, etc. The interface (206) may facilitate communication between the computing device (106) and various devices coupled thereto. The interface (206) may also provide a communication path to one or more components of the system (102). Examples of such components include, but are not limited to, a processing engine (208) and a database (210).

[0052] In one embodiment, the one or more processors (202) may be implemented as a combination of hardware and programming (e.g., programmable instructions) to implement one or more functions of the one or more processors (202). In the examples described herein, such a combination of hardware and programming may be implemented in a number of different ways. For example, the programming of the one or more processors (202) may be processor-executable instructions stored in a non-transitory machine-readable storage medium, and the hardware for the one or more processors (202) may comprise processing resources (e.g., one or more processors) for executing such instructions. In this example, the machine-readable storage medium may store instructions that, when executed by the processing resources, implement the one or more processors (202). In such an example, the system (102) may comprise a machine-readable storage medium that stores instructions and a processing resource for executing the instructions, or the machine-readable storage medium may be separate but accessible to the system (102) and the processing resources. In other examples, the one or more processors (202) may be implemented by electronic circuitry.

[0053] In one aspect, database (210) may comprise data that may be stored or generated as a result of functions implemented by any of the components of processor (202) and / or processing engine (208). In one embodiment, database (210) may include data processed by any or all components of system (102). By way of example and not limitation, database (210) may include at least one spatially tagged measurement sample corresponding to a predefined area that is fetched from a telecommunications core and stored for analysis.

[0054] In an exemplary embodiment, the processing engine (208) of the system (102) may include a data acquisition module (212), an under-utilized sector computing module (214), an under-utilized grid computing module (216), an under-utilized cluster computing module (218), and other modules / engines (220), which may further include, but are not limited to, a storage engine, a computing engine, or a signal generation engine.

[0055] In one embodiment, the data acquisition module (212) may include receiving data packets from the UE (106) relating to a set of one or more telecommunication sites operating in a predefined area.

[0056] In one embodiment, the low utilization sector calculation module (214) may calculate a list of low utilization sectors according to various aspects of the present disclosure. In one embodiment, the low utilization sector calculation module (214) may calculate, for each of at least one sector grid, a total number of mapped samples across all sectors that are part of the mapped spatially tagged measurement samples and are designated as "low utilization" sectors after calculating the low utilization sectors. Then, a percentage of the total number of mapped samples of at least one or more sector grids across the low utilization sectors is compared to a predefined threshold, e.g., 70%, and the percentage is calculated relative to the total number of mapped spatially tagged measurement samples available for at least one sector grid. If the calculated percentage exceeds the threshold, at least one sector grid is declared as a "low utilization" grid.

[0057] In one embodiment, the low utilization sector calculation module (214) may calculate areas for “low utilization” sector grids that together form a larger area. The “low utilization” sector grids are obtained by calculating a list of unique “low utilization” sector grids for each of the larger areas by merging the already calculated lists of “low utilization” sector grids for each of the “low utilization” sector grids that together form the larger area.

[0058] In one embodiment, the low utilization grid calculation module (216) may identify larger clusters or areas of low telecommunication utilization based on the identified low utilization grids at telecommunication sites in a predefined area. In one embodiment, the low utilization grid calculation module collects at least one spatially tagged measurement sample during a period of time from the database (210) and then calculates at least one low utilization sector grid in the predefined area.

[0059] In one embodiment, the low utilization cluster calculation module (218) may obtain at least one low utilization sector grid calculated by the low utilization grid calculation module (216) and then calculate low utilization larger clusters and associated unique sectors using each of the larger clusters in accordance with various aspects of the disclosure.

[0060] FIG. 2B illustrates an example block diagram representation of a user equipment (UE) (106) for identifying low telecommunication service usage in a predefined area, according to one embodiment of the present disclosure.

[0061] In one aspect, the UE (106) may comprise a processor (222). The processor (222) may be, but is not limited to, an edge-based processor. The processor (222) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuits, and / or any device that processes data based on operational instructions. Among other functions, the processor (222) may be configured to fetch and execute computer-readable instructions stored in a memory (224) of the UE (106). The memory (224) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium that may be fetched and executed to create or share data packets via a network service. The memory (224) may comprise any non-transitory storage device, including, for example, a volatile memory such as a RAM, or a non-volatile memory such as an EPROM, a flash memory, and the like.

[0062] In one embodiment, the UE (106) may include an interface (226). The interface (226) may comprise various interfaces, such as interfaces for data input / output devices, referred to as I / O devices, storage devices, etc. The interface (226) may facilitate communications for the UE (106). Examples of such components include, but are not limited to, a processing engine (228) and a database (230).

[0063] The processing engine (228) may be implemented as a combination of hardware and programming (e.g., programmable instructions) to implement one or more functions of the processing engine (228). In the examples described herein, such a combination of hardware and programming may be implemented in a number of different ways. For example, the programming of the processing engine (228) may be processor-executable instructions stored on a non-transitory machine-readable storage medium, and the hardware for the processing engine (228) may comprise processing resources (e.g., one or more processors) for executing such instructions. In this example, the machine-readable storage medium may store instructions that, when executed by the processing resources, implement the processing engine (228). In such an example, the UE (106) may comprise a machine-readable storage medium that stores instructions and the processing resources for executing the instructions, or the machine-readable storage medium may be separate but accessible to the UE (106) and the processing resources. In other examples, the processing engine (228) may be implemented by electronic circuitry.

[0064] In one aspect, database (230) may comprise data that may be stored or generated as a result of functions implemented by any of the components of processor (222) and / or processing engine (228). Additionally, database (230) may comprise transmitted data packets, data processed using one or more engines, calculation results, etc.

[0065] In an exemplary embodiment, the processing engine (228) of the user equipment (106) may include a data transmission module (232), a data management module (234), a mobility management module (236), a display module (238), and other modules (240), which may further include, but are not limited to, a storage engine, a computing engine, or a signal generation engine.

[0066] In one embodiment, the data transmission module 232 may include forwarding data packets at the user's request over a point-to-point or point-to-multipoint communication channel. The system 102 may receive data packets transmitted from the UE 106.

[0067] In one embodiment, the data management module 234 may include the collection, storage, analysis, and sharing of data within the network 104. The UE 106 may be configured to manage data received from the system 102, which may include identification of low sector areas, resource sharing, etc.

[0068] In one embodiment, the mobility management module (236) may enable tracking if the user (108) has permitted calls, SMS, and other UE services to be delivered.

[0069] In one embodiment, the display module 238 may enable presentation of information to the user 108. The system 102 provides the user 108, via the UE 106, with a low utilization grid representing low telecommunication utilization in a predefined area.

[0070] FIG. 3 illustrates an example flow diagram of a method (300) for calculating low utilization sectors in a predefined area at a certain time, according to one embodiment of the present disclosure.

[0071] In one embodiment, the method (300) may include, in step (302), calculating daily PRB utilization for each of one or more macro sites in the predefined area for a predefined time period. In one embodiment, the PRB utilization is related to quality of service (QoS) management. As PRB utilization increases, resources may not be allocated to users of the cell in a timely and reliable manner. Therefore, the PRB utilization of each of one or more macro sites in the predefined area needs to be mapped to a predefined time period. For example, a subscriber in a "x" predefined area has been using "y" service since "second number of days".

[0072] In one embodiment, in step (304), the method may include iterating through each of the sectors in the predefined area to check whether one or more macro sites in the iterative sector have less than a predefined percentage of PRB utilization for at least a first predefined time of the predefined time. In an exemplary embodiment, the iteration ends if the PRB utilization is less than 50% for a first number of days of the second number of days.

[0073] In one embodiment, if any of the sectors satisfy the condition during the iteration, then in step (306), the method may include marking the same as one of the low utilization sectors in the predefined area. Once the iteration is completed, a list of low utilization sectors is available in the predefined area.

[0074] FIG. 4 illustrates an exemplary representation of a flow diagram of a method (400) for computing a low utilization spatial grid at a given time for a telecommunication site of a predefined area, according to one embodiment of the present disclosure.

[0075] In one embodiment, the method (400) may include, in step (402), dividing the predefined area into one or more spatial grids, e.g., rectangular grids of equal size, e.g., 50 x 50 meters. Alternatively or additionally, the one or more spatial grids may be filtered based on one or more morphological constraints, e.g., if only low utilization areas need to be found in urban areas, a grid with urban morphology is used for the low utilization calculation.

[0076] In another embodiment, the method (400) may further include, in step (404), collecting at least one spatially tagged measurement sample over a given time period, for example, a second number of days. The time period may be tagged with a sector identifier derived from a cell identifier attribute present in each of the at least one spatially tagged measurement sample. Then, in step (406), the method may include mapping each of the at least one spatially tagged measurement sample to one of the at least one sector grids considered for the low utilization calculation in step (402). The mapping is performed using latitude and longitude attributes present in each of the at least one spatially tagged measurement sample. If the at least one spatially tagged measurement sample cannot be mapped to any of the at least one sector grids under consideration, it is discarded for further calculation.

[0077] In one embodiment, the method (400) may further include performing an aggregation for each of the at least one sector grid using the non-zero mapped samples to count the number of at least one spatially tagged measurement sample mapped for each unique sector that is part of the mapped sample in step (408). After the aggregation, in step (410), the method may include calculating for each of the at least one sector grid, as shown in FIG. 3, the total number of mapped samples across all sectors that are part of the mapped at least one spatially tagged measurement sample and are designated as "low utilization" sectors after the algorithm for calculating low utilization sectors has already been performed. Then, a percentage of the total number of mapped samples of at least one or more sector grids across the low utilization sectors is compared to a predetermined threshold, for example 70%, and the percentage is calculated with respect to the total number of mapped at least one spatially tagged measurement samples available for the at least one sector grid. If the calculated percentage exceeds a threshold, then at least one sector grid is declared an "underutilized" grid.

[0078] FIG. 5 illustrates an example representation of a flow diagram of a method (500) for identifying larger clusters or areas of low telecommunication utilization based on low utilization grids identified in telecommunication sites of a predefined area, according to one embodiment of the present disclosure.

[0079] In one embodiment, the method (500) may include, in step (502), identifying at least one adjacent "low utilization" spatial grid. In step (504), the method may include drawing a concave boundary around the non-adjacent boundaries of all grids that belong to a neighboring group, and for each of the identified neighboring groups, a cluster is identified. Once larger areas of "low utilization" have been identified, in step (506), the method may include computing a list of unique "low utilization" sector grids for each of the larger areas by merging the already computed lists of "low utilization" sector grids for each of the "low utilization" sector grids that together form the larger area.

[0080] FIG. 6 illustrates an exemplary computer system (600) in accordance with or capable of utilizing embodiments of the present disclosure.

[0081] As shown in FIG. 6, the computer system (600) may include an external storage device (610), a bus (620), a main memory (630), a read-only memory (640), a mass storage device (650), a communication port (660), and a processor (670). Those skilled in the art will appreciate that the computer system (600) may include more than one processor and communication port. The processor (670) may include various modules related to the embodiments of the present disclosure. The communication port (660) may be an RS-232 port for use with a modem-based dial-up connection, a 10 / 100 Ethernet port, a gigabit or 10 gigabit port using copper or fiber, a serial port, a parallel port, or any other existing or future port. The communication port (660) may be selected depending on the network or any network to which the computer system (600) connects. The main memory (630) may be a random access memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (640) may be any static storage device. The mass storage device (650) may be any present or future mass storage solution that can be used to store information and / or instructions.

[0082] The bus (620) communicatively couples the processor (670) to other memory, storage, and communication blocks. Optionally, operator and management interfaces, such as a display, keyboard, and cursor control devices, may also be coupled to the bus (620) to support direct operator interaction with the computer system (600). Other operator and management interfaces may be provided through a network connection connected through a communication port (660). The above components are intended only to illustrate various possibilities. The foregoing exemplary computer system is not intended to limit the scope of the present disclosure.

[0083] Although considerable emphasis has been placed herein on the preferred embodiment, it should be understood that many embodiments may be made and many changes may be made in the preferred embodiment without departing from the principles of the invention. These and other changes in the preferred embodiment of the invention will be apparent to those skilled in the art from the disclosure herein, and it should be clearly understood that the foregoing description is to be implemented merely as an illustration and not as a limitation of the invention.

[0084] Advantages of the present disclosure The present disclosure provides a unique and efficient system and method to help telecommunication operators accurately identify spatial clusters or underutilized areas of telecommunication assets deployed to serve subscribers in those areas.

[0085] The present disclosure identifies under-utilized spatial clusters and helps telecommunication operators direct dedicated marketing campaigns within the identified clusters to increase subscriber numbers without compromising subscriber experience of telecommunication services.

[0086] The present disclosure helps operators to identify underutilized areas based on morphological constraints, meaning that underutilized areas can be identified separately for dense urban, city and rural morphologies. This then helps operators to target specific morphologies in terms of priority. For example, each operator may want to prioritize underutilized areas based on dense urban morphology.

[0087] The present disclosure makes it easy for operators to target specific morphologies in terms of priority. For example, operators may want to prioritize low utilization areas based on dense urban morphology. [Explanation of symbols]

[0088] 100 Telecommunications Deployment Architecture 102 System 104 Network 106 User Equipment (UE) 106-1, 106-N Computing Devices / User Equipment (UE) 108 users 108-1, 108-N users 202 Processor 204 Memory 206 Interface 208 Processing Engine 210 Database 212 Data Acquisition Module 214 Low Utilization Sector Calculation Module 216 Low-utilization grid computing module 218 Low-utilization cluster computing module 220 Other Modules / Engines 222 processor 224 Memory 226 Interface 228 Processing Engine 230 Database 232 Data transmission module 234 Data Management Module 236 Mobility Management Module 238 Display Module 240 other modules 300 ways 400 ways 500 ways 600 Computer Systems 610 External Storage Device 620 Bus 630 Main memory 640 Read-Only Memory 650 Mass Storage Device 660 communication port 670 Processor

Claims

1. A system (102) for identifying low telecommunication service usage in a predefined area, comprising: and one or more processors (202) coupled to a memory (204), the memory (204) being configured to, when executed by the one or more processors (202), cause the system (102) to: receiving data packets from a user (108) relating to a set of one or more telecommunication sites operating in the predefined area; Dividing each of the one or more telecommunication sites into one or more spatial grids, the one or more telecommunication sites comprising one or more macro sites and at least one sector; generating at least one spatially tagged measurement sample by at least one call log server, the at least one spatially tagged measurement sample comprising start and end times of each of one or more calls and data sessions of one or more subscribers associated with the one or more macro sites; marking the one or more spatial grids as low utilization based on a predetermined condition and clustering at least one adjacent low utilization spatial grid in the predefined area to generate one or more large clusters of low utilization grids representing low telecommunication utilization; A system (102) storing instructions to cause the system to:

2. The system (102), 2. The system (102) of claim 1, configured to calculate a list of at least one underutilized sector of the one or more telecommunication sites operating in the predefined area based on at least one or more key performance indicators (KPIs) associated with a physical resource block (PRB).

3. The system (102), mapping each of the at least one spatially tagged measurement sample onto the one or more spatial grids; calculating a list of unique sectors for each of the one or more spatial grids based on the mapped at least one spatially tagged measurement sample; comparing the list of unique sectors of the one or more spatial grids with the list of at least one low utilization sector to obtain one or more low utilization spatial grids; The system (102) of claim 1 configured to:

4. 2. The system of claim 1, wherein the predetermined condition is based on at least one non-zero sector that is part of the at least one of the lists of unique sectors of the one or more spatial grids and the list of at least one low utilization sector of the one or more telecommunication sites operating in the predefined area.

5. 2. The system (102) of claim 1, configured to calculate one or more parameters for each of the clusters by aggregating data across the one or more spatial grids forming the portion of each of the clusters.

6. 2. The system (102) of claim 1, configured to calculate the list of at least one under-utilized sector based on a set of preconditions comprising a percentage of the at least one spatially tagged measurement sample that is mapped to the one or more spatial grids of the at least one sector that is part of a grid sector list, and an overall list of at least one under-utilized sector in the predefined area exceeds a predetermined threshold.

7. 3. The system of claim 2, wherein the key performance indicators (KPIs) calculated for the at least one sector comprise at least one of a physical resource block (PRB), a number of active subscribers, an average revenue per user (ARPU), and total adds and drops.

8. 2. The system of claim 1, further comprising: a processor configured to compare an average physical resource block (PRB) utilization with a predefined PRB utilization threshold to provide an inference for the list of at least one low-utilization sector, the average PRB utilization being calculated for the at least one sector during a busy hour of a day; and if the predefined PRB utilization calculated for the at least one sector is consistently below a predefined threshold for at least a first number of days in a second number of days, the corresponding cell is marked as low-utilization.

9. 2. The system of claim 1, configured to generate the cluster for the list of low utilization sectors by calculating at least one parameter of a cluster by aggregating unique dominant sector IDs across the one or more spatial grids that form the portion of the cluster.

10. 2. The system (102) of claim 1, wherein the at least one spatially tagged measurement sample provides a spatial location value comprising at least one of a latitude and longitude, a customer identifier, an International Mobile Subscriber Identity (IMSI), a serving cell identifier (CELLID), and a reference signal received power (RSRP) value.

11. 2. The system (102) of claim 1, wherein the predefined areas are filtered based on a predefined list of expected morphologies comprising at least one of urban, semi-urban, rural, and freeway.

12. 2. The system of claim 1, wherein the user is at least one of a network administrator, a network operator, and a subscriber.

13. A user equipment (UE) (106) operating in a low telecommunication coverage area, the UE (106) comprising: one or more processors (222) coupled to a memory (224), the memory (224) being configured to, when executed by the one or more processors (222), cause the UE (106) to: transmitting data to the system (102) regarding a set of one or more telecommunications sites operating in a predefined area; executing one or more instructions on a response received from the system (102) corresponding to the one or more telecommunication sites; A user equipment (UE) (106) stores instructions to cause the UE to:

14. 1. A method for identifying low telecommunication service usage in a predefined area, comprising: receiving, by the system (102), data from a user (108) regarding a set of one or more telecommunications sites operating in said predefined area; dividing, by the system (102), each of the one or more telecommunication sites into one or more spatial grids, the one or more telecommunication sites comprising one or more macro sites and at least one sector; generating, by said system (102), at least one spatially tagged measurement sample by at least one call log server, said at least one spatially tagged measurement sample comprising start and end times of each of one or more calls and data sessions of one or more subscribers associated with said one or more macro sites; marking (102) the one or more spatial grids as low-utilization based on a predefined condition and clustering at least one adjacent low-utilization spatial grid in the predefined area to generate one or more large clusters of low-utilization grids representing low telecommunication utilization; A method comprising:

15. The method further comprising:

15. The method of claim 14, further comprising: calculating, by the system (102), a list of at least one underutilized sector of the one or more telecommunication sites operating in the predefined area based on at least one or more key performance indicators (KPIs) associated with a physical resource block (PRB).

16. The method further comprising: mapping, by the system (102), each of the at least one spatially tagged measurement sample onto the one or more spatial grids; calculating, by the system (102), a list of unique sectors for each of the one or more spatial grids based on the mapped at least one spatially tagged measurement sample; comparing, by the system (102), the list of unique sectors of the one or more spatial grids with the list of at least one low utilization sector to obtain one or more low utilization spatial grids; The method of claim 14, further comprising:

17. 15. The method of claim 14, wherein the predetermined condition is based on at least one non-zero sector that is part of the at least one of the lists of unique sectors of the one or more spatial grids and the list of at least one low utilization sector of the one or more telecommunication sites operating in the predefined area.

18. 15. The method of claim 14, further comprising: calculating, by the system (102), one or more parameters for each of the clusters by aggregating data across the one or more spatial grids forming the portion of each of the clusters.

19. 15. The method of claim 14, further comprising: calculating, by the system (102), the list of at least one under-utilized sector based on a set of preconditions comprising a percentage of the at least one spatially tagged measurement sample that is mapped to the one or more spatial grids of the at least one sector that is part of a grid sector list, and wherein the overall list of at least one under-utilized sector in the predefined area exceeds a predetermined threshold.

20. 16. The method of claim 15, wherein the key performance indicators (KPIs) calculated for the at least one sector comprise at least one of a physical resource block (PRB), a number of active subscribers, an average revenue per user (ARPU), and total adds and drops.

21. 15. The method of claim 14, further comprising: comparing, by the system (102), an average physical resource block (PRB) utilization with a predefined PRB utilization threshold to provide an inference of the list of at least one low-utilization sector, wherein an average PRB utilization is calculated for the at least one sector during a busy hour of a day, and if the predefined PRB utilization calculated for the at least one sector is consistently below a predefined threshold for at least a first number of days in a second number of days, the corresponding cell is marked as low-utilization.

22. 15. The method of claim 14, further comprising: generating, by the system (102), at least one cluster for the list of low utilization sectors by aggregating unique dominant sector IDs across the one or more spatial grids forming the portion of the cluster, thereby calculating at least one parameter of the cluster.

23. 15. The method of claim 14, wherein the at least one spatially tagged measurement sample provides a spatial location value comprising at least one of a latitude and longitude, a customer identifier, an International Mobile Subscriber Identity (IMSI), a serving cell identifier (CELLID), and a Reference Signal Received Power (RSRP) value.

24. 15. The method of claim 14, wherein the predefined areas are filtered based on a predefined list of expected morphologies comprising at least one of urban, semi-urban, rural, and highway.

25. 15. The method of claim 14, wherein the user (108) is at least one of a network administrator, a network operator, and a subscriber.

26. receiving data packets from a user (108) relating to a set of one or more telecommunication sites operating in a predefined area; Dividing each of the one or more telecommunication sites into one or more spatial grids, the one or more telecommunication sites comprising one or more macro sites and at least one sector; generating at least one spatially tagged measurement sample by at least one call log server, the at least one spatially tagged measurement sample comprising start and end times of each of one or more calls and data sessions of one or more subscribers associated with the one or more macro sites; marking the one or more spatial grids as low utilization based on a predetermined condition and clustering at least one adjacent low utilization spatial grid in the predefined area to generate one or more large clusters of low utilization grids representing low telecommunication utilization; A non-transitory computer-readable medium comprising machine-executable instructions executable by a processor to perform the steps of:

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