DESC:RESERVATION OF RIGHTS
[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as but are 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 as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.

FIELD OF INVENTION
[0002] The embodiments of the present disclosure generally relate to systems and methods for determining geo spatial neighbouring base stations in a wireless telecommunications network while providing radio access technology (RAT) optimization/planning. More particularly, the present disclosure relates to a system and a method for determining spatial neighbourhood sectors in a network.

BACKGROUND
[0003] The following description of the related art is intended to provide background information pertaining to 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, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0004] Radio access technologies (RATs) like second generation/third generation (2G/3G) did not have any system to automatically define a network relationship and its neighbours. Further, configuring the neighbours list involved a tedious and a labour intensive process. The neighbour cell list would change very frequently for various reasons especially when new cells are incorporated. In Release 8 and onwards after development of a self-organizing network (SON), an automatic neighbour relation (ANR) has been introduced which incorporates addition, deletion, and updating the existing neighbour(s) list automatically based on a process. The process entails sending of a measurement command to a user equipment (UE) by the network for performing detection/measurement of cells around the UE. The UE detect/performs measurement on the cells and reports the measurement to the cells which sent the measurement command. Upon a reception of the measurement report, the cells extract cell information from the measurement report message and update a neighbour cell list. However, even after incorporating ANR and due to a multipath propagation of radio waves, many neighbourhood relations may have been created due to overshooting and undershooting problems of radio waves.
[0005] Further, such neighbourhood relations may create unnecessary handovers in network degrading key performance indicators (KPIs) and user experiences which may be solved through optimization. Therefore, ANR creates a “should be neighbours” versus an “engaged neighbours” gap and an identification strategy may be required to eliminate this gap. Further, currently in long term evolution-new radio (LTE-NR) interworking, many UEs are not able to report cell global identity (CGI) where ANR is not working. Also, LTE neighbours may have to be replicated that got created via ANR, again resulting in another gap. Additionally, proactive measures for correction and prevention of outage and backhaul may require manual tasks.
[0006] Further, a public land mobile network (PLMN) border site identification is extremely difficult to remotely incorporate parameter correction with correct border and accurate PLMN information. Swap detection and correction without going to a field, while investigating per site may create unnecessary costs. Also, wrong planning may result in two sites unnecessarily offloading a single site, adding a huge cost. Further, macro-micro optimizations may be used for offloading an environment sensing capability (ESC). Use of ANR neighbours may be misleading due to the gap and the ANR may malfunction at times due to bugs resulting in an absence of neighbourhood for small cells.
[0007] There is, therefore, a need in the art to provide a system and a method that can mitigate the problems associated with the prior arts.

OBJECTS OF THE INVENTION
[0008] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.
[0009] It is an object of the present disclosure to provide a system and a method for a framework that determines a large mesh of geo spatial neighbour base stations residing in a wireless network, where the framework utilizes information from geo spatial and angular coordinates of a mesh associated with the base stations and employs polygonal intersection mechanisms of geo-science in a cellular wireless network.
[0010] It is an object of the present disclosure to provide a system and a method that determines a spatial neighbourhood sector to sector relationship in a cellular wireless network.
[0011] It is an object of the present disclosure to provide a system and a method that addresses automatic neighbour relation (ANR) related issues, outage/backhaul issues, missing neighbours/optimization by offloading, provides swap detection and correction while removing redundancies among planned sites.

SUMMARY
[0012] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0013] In an aspect, the present disclosure relates to a system for determining spatial neighbourhood sectors. The system includes a processor, and a memory operatively coupled to the processor, where the memory stores instructions to be executed by the processor. The processor receives an input from one or more base stations configured in a network. The input is based on a signal to interference and noise ratio (SINR) from the one or more base stations. The processor generates a source database and a target database based on the input from the one or more base stations. The source database includes identified one or more source base stations and the target database includes identified one or more target base stations associated with a geo-spatial sector. The processor computes a latitude and a longitude associated with the one or more source base stations and the one or more target base stations and determines if the one or more target base stations are within a predetermined distance from the one or more source base stations. The processor, in response to a positive determination, appends the source database with a buffer, wherein the buffer comprises the one or more target base stations within the predetermined distance. The processor categorizes the one or more source base stations and the one or more target base stations into one or more sectors based on the positive determination. The processor determines one or more polygon intersection of source and neighbouring beams associated with the one or more sectors. The processor determines a sequence of neighbouring base stations among the one or more target base stations based on the one or more polygon intersection of the source and neighbouring beams.
[0014] In an embodiment, in response to a negative determination, the processor may discard the one or more target base stations.
[0015] In an embodiment, the processor may identify the one or more base stations as the one or more source base stations based on the SINR and categorize the one or more source base stations into the source database.
[0016] In an embodiment, the processor may identify the one or more base stations as the one or more target base stations based on the SINR and categorize the one or more target stations into the target database.
[0017] In an embodiment, the processor may categorize the one or more target base stations into the target database based on a cellular circle data obtained from the one or more target base stations via a spatial neighbour process.
[0018] In an embodiment, the one or more polygon intersection of the source and neighbouring beams may include a source beam sweep based on a bearing computed from the latitude and the longitude associated with the one or more source base stations and the one or more target base stations. In an embodiment, the one or more polygon intersection of source and neighbouring beams may include a neighbouring beam sweep based on the bearing, wherein the neighbouring beam sweep may include an allowed target sector. In an embodiment, the one or more polygon intersection of source and neighbouring beams may include one or more facing sectors based on the allowed target sector and an angular direction target sector associated with the one or more target base stations.
[0019] In an aspect, the present disclosure relates to a method for determining spatial neighbourhood sectors. The method includes receiving, by a processor associated with a system, an input from one or more base stations configured in a network. The input is based on a SINR from the one or more base stations. The method includes generating, by the processor, a source database and a target database based on the input from the one or more base stations. The source database includes identified one or more source base stations and the target database includes identified one or more target base stations associated with a geo-spatial sector. The method includes computing, by the processor, a latitude and a longitude associated with the one or more source base stations and the one or more target base stations and determining if the one or more target base stations are within a predetermined distance from the one or more source base stations. The method includes, in response to a positive determination, appending, by the processor, the source database with a buffer. The buffer includes the one or more target base stations within the predetermined distance. The method includes categorizing, by the processor, the one or more source base stations and the one or more target base stations into one or more sectors based on the positive determination. The method includes determining, by the processor, one or more polygon intersection of source and neighbouring beams associated with the one or more sectors. The method includes determining, by the processor, a sequence of neighbouring base stations among the one or more target base stations based on the one or more polygon intersection of the source and neighbouring beams.
[0020] In an embodiment, the method may include discarding, by the processor, in response to a negative determination, the one or more target base stations.
[0021] In an embodiment, the method may include identifying, by the processor, the one or more base stations as the one or more source base stations based on the SINR and categorizing the one or more source base stations into the source database.
[0022] In an embodiment, the method may include identifying, by the processor, the one or more base stations as the one or more target base stations based on the SINR and categorizing the one or more target stations into the target database.
[0023] In an embodiment, the method may include categorizing, by the processor, the one or more target base stations into the target database based on a cellular circle data obtained from the one or more target base stations via a spatial neighbour process.
[0024] In an embodiment, the one or more polygon intersection of the source and neighbouring beams may include a source beam sweep based on a bearing computed from the latitude and the longitude associated with the one or more source base stations and the one or more target base stations. In an embodiment, the one or more polygon intersection of source and neighbouring beams may include a neighbouring beam sweep based on the bearing, wherein the neighbouring beam sweep may include an allowed target sector. In an embodiment, the one or more polygon intersection of source and neighbouring beams may include one or more facing sectors based on the allowed target sector and an angular direction target sector associated with the one or more target base stations.

BRIEF DESCRIPTION OF DRAWINGS
[0025] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems 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 the disclosure of electrical components, electronic components, or circuitry commonly used to implement such components.
[0026] FIG. 1 illustrates an example network architecture (100) for implementing a proposed system (106), in accordance with an embodiment of the present disclosure.
[0027] FIG. 2 illustrates an example block diagram (200) of a proposed system (106), in accordance with an embodiment of the present disclosure.
[0028] FIG. 3 illustrates an example diagram (300) of a super-system architecture of a geo-spatial process flow, in accordance with an embodiment of the present disclosure.
[0029] FIG. 4 illustrates an example flow diagram (400) of the geo-spatial process, in accordance with an embodiment of the present disclosure.
[0030] FIG. 5 illustrates an example flow diagram (500) of a break and build database associated with the geo-spatial process, in accordance with an embodiment of the present disclosure.
[0031] FIG. 6 illustrates an example flow diagram (600) for buffer zone creation during the geo-spatial process, in accordance with an embodiment of the present disclosure.
[0032] FIGs. 7A-7B illustrate example block diagrams (700A, 700B) for polygon intersection of source neighbour beams during the geo-spatial process, in accordance with embodiments of the present disclosure.
[0033] FIG. 8 illustrates an example graphical diagram (800) of geo-spatial sectors, in accordance with an embodiment of the present disclosure.
[0034] FIG. 9 illustrates an example computer system (900) in which or with which embodiments of the present disclosure may be implemented.
[0035] The foregoing shall be more apparent from the following more detailed description of the disclosure.

DEATILED DESCRIPTION
[0036] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0037] The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. 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 disclosure as set forth.
[0038] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[0039] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0040] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.
[0041] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0042] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0043] The present disclosure lays down a method and a framework for performing an efficient search of a large mesh of geo spatial neighbour base stations residing in a cellular wireless network of complex and large dimension. The present disclosure employs a highly efficient algorithm for a neighbourhood mesh discovery utilizing the known information of geospatial and angular coordinates of a mesh of base stations and employing polygonal intersection mechanisms of geoscience. The present disclosure aims to assist practicing wireless engineers to quickly discover gaps in “Should be Neighbours” versus “Engaged Neighbours” to aid in optimizing the network. The present disclosure also holds a potential application in futuristic machine to machine cognitive optimization for self-healing. Further, the present disclosure provides a large scale cellular network cognitive optimization framework for coverage, capacity, and customer experience. The present disclosure may be a base for automations which helps in reducing time and wastage of manpower.
[0044] Further, the present disclosure provides the following.
• Antenna swap detection and Azimuth correction automation.
• Inter public land mobile network (PLMN) mobility border site discovery.
• Inter PLMN call continuity audit and cognitive optimization.
• Coverage compensation of a wide cluster geography during sporadic power backout in pockets.
• Best neighbour discovery for enhancing mobility experience of users in a network.
• Inter System fourth generation- fifth generation (4G-5G), inter vendor mobility enhancement by efficient neighbour audit.
• Handling Macro-Micro-Pico neighbour issues for Het-net optimization for proper load balancing in a large scale.
• Enhancing capacity offload site planning process by predicting optimum site positioning.
[0045] Various embodiments of the present disclosure will be explained in detail with reference to FIGs. 1-9.
[0046] FIG. 1 illustrates an example network architecture (100) for implementing a proposed system (106), in accordance with an embodiment of the present disclosure.
[0047] As illustrated in FIG. 1, the network architecture (100) may include a system (106). The system (106) may be connected to one or more base stations (102-1, 102-2…102-N) via a network (104). It may be appreciated that the one or more base stations (102-1, 102-2…102-N) may be individually referred as the base station (102) and collectively referred as the base stations (102). In an embodiment, the system (106) may identify a number of geo spatial neighbour base stations residing in the network (104) by employing a highly efficient algorithm for the neighbourhood mesh discovery. Further, the system (106) may utilize known information of geospatial and angular coordinates associated with a mesh of the base stations (102). Further, the system (106) may employ a polygonal intersection mechanism of geoscience in the network (104).
[0048] In an embodiment, the network (104) may 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 (104) 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, or some combination thereof.
[0049] In an embodiment, the system (106) may digitally determine spatial neighbourhood sector to sector relationship with distance. Further, the system (106) may provide a flexible domain in search distance buffer and a number of relations while acting as a base for further automations. The system (106) may provide identification based on “Should be neighbours” versus “Engaged neighbours” and generate optimizations solving field related activities.
[0050] In an embodiment, the system (106) may receive an input from the one or more base stations (102) configured in the network (104). The input may be based on a signal to interference and noise ratio (SINR) from the one or more base stations (102). The system (106) may generate a source database and a target database based on the input from the one or more base stations (102). The source database may include identified one or more source base stations and the target database may include identified one or more target base stations associated with a geo-spatial sector.
[0051] In an embodiment, the system (106) may identify the one or more base stations (102) as the one or more source base stations based on the SINR and categorize the one or more source base stations into the source database. Further, the system (106) may identify the one or more base stations (102) as the one or more target base stations based on the SINR and categorize the one or more target stations into the target database. The system (106) may categorize the one or more target base stations into the target database based on a cellular circle data obtained from the one or more target base stations via a spatial neighbour process.
[0052] In an embodiment, the system (106) may compute a latitude and a longitude associated with the one or more source base stations and the one or more target base stations. The system (106) may determine if the one or more target base stations are within a predetermined distance from the one or more source base stations.
[0053] In an embodiment, the system (106) may in response to a positive determination, append the source database with a buffer. The buffer may include the one or more target base stations within the predetermined distance. The system (106) may in response to a negative determination, discard the one or more target base stations.
[0054] In an embodiment, the one or more polygon intersection of the source and neighbouring beams may include a source beam sweep based on a bearing computed from the latitude and the longitude associated with the one or more source base stations and the one or more target base stations. The one or more polygon intersection of source and neighbouring beams may include a neighbouring beam sweep based on the bearing. The neighbouring beam sweep may include an allowed target sector. The one or more polygon intersection of source and neighbouring beams may include one or more facing sectors based on the allowed target sector and an angular direction target sector associated with the one or more target base stations.
[0055] In an embodiment, the system (106) may categorize the one or more source base stations and the one or more target base stations into one or more sectors based on the positive determination. Further, the system (106) may determine the one or more polygon intersection of source and neighbouring beams associated with the one or more sectors.
[0056] In an embodiment, the system (106) may determine a sequence of neighbouring base stations among the one or more target base stations based on the one or more polygon intersection of the source and neighbouring beams.
[0057] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).
[0058] FIG. 2 illustrates an example block diagram (200) of a proposed system (106), in accordance with an embodiment of the present disclosure.
[0059] Referring to FIG. 2, the system (106) may comprise one or more processor(s) (202) that may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (106). The memory (204) 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 (204) may comprise any non-transitory storage device including, for example, volatile memory such as random-access memory (RAM), or non-volatile memory such as erasable programmable read only memory (EPROM), flash memory, and the like.
[0060] In an embodiment, the system (106) may include an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output (I/O) devices, storage devices, and the like. The interface(s) (206) may also provide a communication pathway for one or more components of the system (106). Examples of such components include, but are not limited to, processing engine(s) (208) and a database (210), where the processing engine(s) (208) may include, but not be limited to, a data ingestion engine (212) and other engine(s) (214). In an embodiment, the other engine(s) (214) may include, but not limited to, a data management engine, an input/output engine, and a notification engine.
[0061] In an embodiment, the processing engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system (106) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (106) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.
[0062] In an embodiment, the processor (202) may receive an input via the data ingestion engine (212). The input may be based on a SINR from the one or more base stations (102). The processor (202) may store the input in the database (210). The processor (202) may generate a source database and a target database based on the input from the one or more base stations (102). The source database may include identified one or more source base stations and the target database may include identified one or more target base stations associated with a geo-spatial sector.
[0063] In an embodiment, the processor (202) may identify the one or more base stations (102) as the one or more source base stations based on the SINR and categorize the one or more source base stations into the source database. Further, the processor (202) may identify the one or more base stations (102) as the one or more target base stations based on the SINR and categorize the one or more target stations into the target database. The processor (202) may categorize the one or more target base stations into the target database based on a cellular circle data obtained from the one or more target base stations via a spatial neighbour process.
[0064] In an embodiment, the processor (202) may compute a latitude and a longitude associated with the one or more source base stations and the one or more target base stations. The processor (202) may determine if the one or more target base stations are within a predetermined distance from the one or more source base stations.
[0065] In an embodiment, the processor (202) may in response to a positive determination, append the source database with a buffer. The buffer may include the one or more target base stations within the predetermined distance. The processor (202) may in response to a negative determination discard the one or more target base stations.
[0066] In an embodiment, the one or more polygon intersection of the source and neighbouring beams may include a source beam sweep based on a bearing computed from the latitude and the longitude associated with the one or more source base stations and the one or more target base stations. The one or more polygon intersection of source and neighbouring beams may include a neighbouring beam sweep based on the bearing. The neighbouring beam sweep may include an allowed target sector. The one or more polygon intersection of source and neighbouring beams may include one or more facing sectors based on the allowed target sector and an angular direction target sector associated with the one or more target base stations.
[0067] In an embodiment, the processor (202) may categorize the one or more source base stations and the one or more target base stations into one or more sectors based on the positive determination. Further, the processor (202) may determine the one or more polygon intersection of source and neighbouring beams associated with the one or more sectors.
[0068] In an embodiment, the processor (202) may determine a sequence of neighbouring base stations among the one or more target base stations based on the one or more polygon intersection of source and neighbouring beams.
[0069] Although FIG. 2 shows exemplary components of the system (106), in other embodiments, the system (106) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 2. Additionally, or alternatively, one or more components of the system (106) may perform functions described as being performed by one or more other components of the system (106).
[0070] FIG. 3 illustrates an example diagram (300) of a super-system architecture of a geo-spatial process flow, in accordance with an embodiment of the present disclosure.
[0071] As illustrated in FIG. 3, in an embodiment, the super-system architecture (300) may provide a geo spatial neighbourhood sector to sector relationship (302). The system (106) may provide a method for discovering spatial neighbourhood sector relations.
[0072] In an embodiment, the super-system architecture (300) may address automatic neighbour relation (ANR) (304) problems. This may include long term evolution-new radio (LTE-NR) interworking manual neighbour creation and determining should be neighbours versus engaged neighbours.
[0073] In an embodiment, the super-system architecture (300) may address macro-micro issues (306) including a missing neighbour issue with optimization/offloading.
[0074] In an embodiment, the super-system architecture (300) may address PLMN border site issues (308). This may include identification in bulk and parameter audit correction.
[0075] In an embodiment, the super-system architecture (300) may provide protective measures for corrections and prevention (310). This may address outage issues and backhaul issues.
[0076] In an embodiment, the super-system architecture (300) may provide swap detection and correction (312).
[0077] In an embodiment, the super-system architecture (300) may include remaining planning sites and capacity sites (314). This process may involve removal of redundancies in bulk.
[0078] FIG. 4 illustrates an example flow diagram (400) of the geo-spatial process, in accordance with an embodiment of the present disclosure.
[0079] As illustrated in FIG. 4, the flow diagram (400) of the geo-spatial process may include the following steps.
[0080] At step 402: The system (106) may create a cellular database.
[0081] At step 404: The system (106) may break and build a relevant database.
[0082] At step 406: The system (106) may create a buffer zone.
[0083] At step 408: The system (106) may determine polygon intersection of source and neighbour beams.
[0084] At step 410: The system (106) may enable sorting, sequencing, and ranging the list of neighbours.
[0085] At step 412: The system (106) may terminate the process.
[0086] These steps will be explained in further detail with reference to FIGs. 5-7.
[0087] FIG. 5 illustrates an example flow diagram (500) of a break and build database associated with the geo-spatial process, in accordance with an embodiment of the present disclosure.
[0088] As illustrated in FIG. 5, the flow diagram may include the following steps.
[0089] At step 502: The system (106) may create a cellular database.
[0090] At step 504: The system (106) may check if source nodes are of source circle filtered nodes from a source database.
[0091] At step 506: Based on a negative determination from step 504, the system (106) may filter the cellular database with source cellular circle nodes for the source database.
[0092] At step 508: Based on a positive determination from step 504, the system (106) may create a source database.
[0093] At step 510: The system (106) may check if target nodes are of target circle filtered nodes from a target database.
[0094] At step 512: Based on a negative determination from the step 510, the system (106) may filter the cellular database with border touching neighbouring cellular circles from a provided list for the target database.
[0095] At step 514: Based on a positive determination from step 510, the system (106) may create the target database.
[0096] At step 516: The system (106) may store the source database and the target database in a memory allocation.
[0097] At step 518: The system (106) may determine if a spatial neighbour process for all circle cellular data is completed. Based on a negative determination, the system (106) may go back to step 502.
[0098] At step 520: Based on a positive determination from step 518, the system (106) may terminate the process.
[0099] FIG. 6 illustrates an example flow diagram (600) for buffer zone creation during the geo-spatial process, in accordance with an embodiment of the present disclosure.
[00100] As illustrated in FIG. 6, in an embodiment, the flow diagram (600) may include the following steps.
[00101] At step 602: The system (106) may utilize the created source database and the target database.
[00102] At step 604: The system (106) may complete the target database.
[00103] At step 606: The system (106) may select source from the source database.
[00104] At step 608: The system (106) may check if ((LatA-LatB<0.045*N) and (LonA-LonB<0.045*N)).
[00105] At step 610: Based on a negative determination from step 608, the system (106) may discard a target sector associated with the target database.
[00106] At step 612: Based on a positive determination from step 608, the system (106) may add and append the target sector to a buffer zone database.
[00107] At step 614: The system (106) may sort ((LatA-LatB) + (LonA-LonB)) in a descending order and create a database for, for example, top 300 sectors.
[00108] At step 616: The system (106) may filter the top 300 sectors from the database.
[00109] At step 618: The system (106) may initiate a polygon intersection of source and neighbour beam process.
[00110] At step 620: The system (106) may provide sorting, sequencing, and ranging of a list of neighbours and go to step 606 to select a source from the source database.
[00111] Therefore, the system (106) may create a buffer zone where for every source node of a source cellular network region database, a buffer zone database of the target nodes from target cellular network region database may be created. Further, the system (106) may use a mathematical calculation per source sector as shown below.
if ((LatA-LatB<0.045*N) & (LonA-LonB<0.045*N)) then consider neighbour else ignore.
Where,
LatA is the latitude of source sector,
LonA is the longitude of source sector,
LatB is the latitude of target sector,
LonB is the longitude of target sector,
N is a constant multiplier of every 5 km, ranging from (1, 2, 3,4…. For 5 Km, 10 Km, 15 Km, 20 Km….). For 10 Km, the value of N may be 2 and that makes the constant value to be 0.09 in the mathematical calculation. This may be determined from a calculation where for every 5 km shift in Latitude or Longitude, a difference in Latitude, Longitude may be varied by a value 0.045. Therefore, top 300 neighbour sectors may be calculated as shown below.
((LatA-LatB) + (LonA-LonB)) may be sorted in a descending order and a database of top 300 sectors may be created.
[00112] The database of the selected sector may be provided to a next process of polygon intersection of source and neighbour beams followed by sorting, sequencing, and ranging the list of neighbours.
[00113] Based on the completion of the database, a list may be created and a new process may be started based on a next available source.
[00114] FIGs. 7A-7B illustrate example block diagrams (700A, 700B) for polygon intersection of source neighbour beams during the geo-spatial process, in accordance with embodiments of the present disclosure.
[00115] In an embodiment, a process of determining a polygon intersection of source neighbour beams by the system (106) may include calculations for a source sector beam and its 120-degree beam sweep. After successful detection of less than 300 target sectors for a particular site, the system (106) may determine a beam sweep of a source which may be calculated using such as a mathematical geosphere::bearing library function in R. Similarly, other packages can be used in different programming languages. Further, a direction of travel may be calculated by the system (106) and followed by the calculation for determining the beam sweep.
[00116] In an embodiment, bearing = geosphere::bearing (c(LatA, LonA), c(LatB, LonB)) for absolute bearing, if bearing<0 then bearing = bearing+360 else bearing = bearing 120-degree beam sweep both for the source may be calculated by a functionabsolute((bearing-AngularDirctionSourceSector)<60) where the value of 60 in the last formula may generate a 60 degree left and 60 degree right beam associated with the source, thereby forming the 120degree beam of the source beam. Further, the bearing may be calculated using the R geosphere::bearing library function and an angular direction of source sector, AngularDirctionSourceSector may be taken from the database of the source/target sector.
[00117] In an embodiment, a process of determining a polygon intersection of source neighbour beams by the system (106) may include creating a beam for neighbours intersection where the neighbours and their respective distances may be calculated allowed target beams may be calculated using an allowed target beam.
[00118] The allowed target beam = if else (bearing <180, absolute(180+ bearing), absolute( bearing-180)) where, bearing may be the bearing calculated for source. This allowed target beam may be calculated to restrict the sectors to those sectors which are facing only towards source sectors.
[00119] In an embodiment, a process of determining a polygon intersection of source neighbour beams by the system (106) may include finding facing neighbours. Till the previous step, the allowed target beam may be calculated and may be correlated to the target sectors. To find an intersection of allowed target beams and an azimuth of target sectors falling in that allowed target beams, sectors facing sectors of the neighbour(s) may be calculated as follows.
[00120] Facing_sec = ifelse(absolute(Allowed target beam - AngularDirctionTargetSector)<60, “Yes”, “No”). As Facing sectors may be simplified and addressed, the nearest facing neighbours may be determined by calculating a distance based on formulas below.
earthradius = 3443.89849
lat1 = LatA * pi / 180
lat2 = LatB * pi / 180
lon1 = LonA * pi / 180
lon2 = LonB * pi / 180
cosX = sin(lat1) * sin(lat2) + cos(lat1) * cos(lat2) * cos(lon1 - lon2)
Get_Distance = earthradius * acos(cosX), where LatA/LonA may be the latitude/longitude of the source sector and LatB/LonB may be the latitude/longitude of the target sector.
[00121] As illustrated in FIG. 7A, in an embodiment, the system (106) may utilize the following steps associated with the polygon intersection of source and neighbour beams.
[00122] At step 702: The system (106) may filter top 300 sectors from a database.
[00123] At step 704: The system (106) may calculate a source beam sweep.
[00124] At step 706: The system (106) may create a beam for neighbour intersection.
[00125] At step 708: The system (106) may find facing neighbours.
[00126] At step 710: The system (106) may enable sorting, sequencing, and ranging a list of neighbours.
[00127] At step 712: The system (106) may determine if last node is of a current cellular circle.
[00128] At step 714: Based on a negative determination from step 712, the system (106) may enable a buffer zone process and go to step 702.
[00129] At step 716: Based on a positive determination from step 712, the system (106) may further determine if the current circle is the last cellular circle.
[00130] At step 718: Based on a negative determination from step 716, the system (106) may break and build a relevant database and go to step 714.
[00131] In an embodiment, the system (106) may enable the sorting, sequencing and ranging the list of neighbours. Here, sorting of neighbours may be enabled based on their distances. Further, the neighbours may be sequenced in order. The programming framework used by the system (106) may include a data table library and base function. Further, a number of spatial neighbour sectors determined earlier may be used with a data.table::datatable introduction library for generating all the calculations.
[00132] As illustrated in FIG. 7B, in an embodiment, the system (106) may utilize the following steps associated with the polygon intersection of source and neighbour beams.
[00133] At step 720: The system (106) may determine the polygon intersection of source and neighbour beams.
[00134] At step 722: The system (106) may sort and sequence the neighbours by distance in a descending order.
[00135] At step 724: The system (106) may check a parameter to be taken by the neighbours.
[00136] At step 726: The system (106) may group and print the top neighbours with a distance based on the parameter.
[00137] At step 728: The system (106) may determine a last source node of a current cellular circle.
[00138] At step 730: Based on a negative determination from step 728, the system (106) may enable a buffer zone process and go to step 720.
[00139] At step 732: Based on a positive determination from step 728, the system (106) may determine if the current circle is the last cellular circle.
[00140] At step 734: Based on a negative determination from step 732, the system (106) may break and build a relevant database.
[00141] At step 736: The system (106) may terminate the process.
[00142] FIG. 8 illustrates an example graphical diagram (800) of geo-spatial sectors, in accordance with an embodiment of the present disclosure.
[00143] In an embodiment, the system (106) may generate a sector to sector output associated with the distances and a number of neighbours/base stations. FIG. 8 depicts a graphical view of a specific output of the system (106) where a geo spatial sector to sector relationship may be identified. Further, the system (106) may enlist all the 101-102 relations shown while discarding 103 in an outcome.
a. 101 is showing the sample source sector
b. 102 is showing the desired neighbour sector
c. 103 is showing some of the undesired sample sectors that may not be included as neighbours
[00144] FIG. 9 illustrates an exemplary computer system (900) in which or with which embodiments of the present disclosure may be implemented.
[00145] As shown in FIG. 9, the computer system (900) may include an external storage device (910), a bus (920), a main memory (930), a read-only memory (940), a mass storage device (950), a communication port(s) (960), and a processor (970). A person skilled in the art will appreciate that the computer system (900) may include more than one processor and communication ports. The processor (970) may include various modules associated with embodiments of the present disclosure. The communication port(s) (960) may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication ports(s) (960) may be chosen depending on a network, such as a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (900) connects.
[00146] In an embodiment, the main memory (930) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (940) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chip for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor (970). The mass storage device (950) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces).
[00147] In an embodiment, the bus (920) may communicatively couple the processor(s) (970) with the other memory, storage, and communication blocks. The bus (920) may be, e.g., a Peripheral Component Interconnect PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB, or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (970) to the computer system (900).
[00148] In another embodiment, operator and administrative interfaces, e.g., a display, keyboard, and cursor control device may also be coupled to the bus (920) to support direct operator interaction with the computer system (900). Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (960). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (900) limit the scope of the present disclosure.
[00149] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be implemented merely as illustrative of the disclosure and not as a limitation.

ADVANTAGES OF THE INVENTION
[00150] The present disclosure provides a system and a method with a framework for performing spatial search that generates a module to fit into a base of other automations.
[00151] The present disclosure provides a system and a method that provides a single solution for multiple problems associated with spatial neighbourhood sectors.
[00152] The present disclosure provides a system and a method that is fast, time efficient with 95 percent accuracy, thereby reducing manpower significantly and minimizing wastage.
[00153] The present disclosure provides a system and a method that is user friendly providing a configurable distance with an efficient search of a large mesh of geo spatial neighbour base stations.
[00154] The present disclosure provides a system and a method that provides identification of “should be neighbours” versus “engaged neighbours” and optimizes field related activities, enhancing customer experiences.
[00155] The present disclosure provides a system and a method that digitally determines a spatial neighbourhood sector to sector relationship with distance.
[00156] The present disclosure provides a system and a method with a flexible domain in determining a search distance buffer and a number of relations.
,CLAIMS:1. A system (106) for determining spatial neighbourhood sectors, the system (106) comprising:
a processor (202); and
a memory (204) operatively coupled with the processor (202), wherein said memory (204) stores instructions which, when executed by the processor (202), cause the processor (202) to:
receive an input from one or more base stations (102) configured in a network (104), wherein the input is based on a signal to interference and noise ratio (SINR) from the one or more base stations (102);
generate a source database and a target database based on the input from the one or more base stations, wherein the source database comprises identified one or more source base stations and the target database comprises identified one or more target base stations associated with a geo-spatial sector;
compute a latitude and a longitude associated with the one or more source base stations and the one or more target base stations and determine if the one or more target base stations are within a predetermined distance from the one or more source base stations;
in response to a positive determination, append the source database with a buffer, wherein the buffer comprises the one or more target base stations within the predetermined distance;
categorize the one or more source base stations and the one or more target base stations into one or more sectors based on the positive determination;
determine one or more polygon intersection of source and neighbouring beams associated with the one or more sectors; and
determine a sequence of neighbouring base stations among the one or more target base stations based on the one or more polygon intersection of the source and neighbouring beams.

2. The system (106) as claimed in claim 1, wherein, in response to a negative determination, the processor (202) is to discard the one or more target base stations.

3. The system (106) as claimed in claim 1, wherein the processor (202) is to identify the one or more base stations as the one or more source base stations based on the SINR and categorize the one or more source base stations into the source database.

4. The system (106) as claimed in claim 1, wherein the processor (202) is to identify the one or more base stations as the one or more target base stations based on the SINR and categorize the one or more target stations into the target database.

5. The system (106) as claimed in claim 4, wherein the processor (202) is to categorize the one or more target base stations into the target database based on a cellular circle data obtained from the one or more target base stations via a spatial neighbour process.

6. The system (106) as claimed in claim 1, wherein the one or more polygon intersection of the source and neighbouring beams comprises:
a source beam sweep based on a bearing computed from the latitude and the longitude associated with the one or more source base stations and the one or more target base stations;
a neighbouring beam sweep based on the bearing, wherein the neighbouring beam sweep comprises an allowed target sector; and
one or more facing sectors based on the allowed target sector and an angular direction target sector associated with the one or more target base stations.

7. A method for determining spatial neighbourhood sectors, the method comprising:
receiving by a processor (202), associated with a system (106), an input from one or more base stations (102) configured in a network (104), wherein the input is based on a signal to interference and noise ratio (SINR) from the one or more base stations (102);
generating, by the processor (202), a source database and a target database based on the input from the one or more base stations, wherein the source database comprises identified one or more source base stations and the target database comprises identified one or more target base stations associated with a geo-spatial sector;
computing, by the processor (202), a latitude and a longitude associated with the one or more source base stations and the one or more target base stations and determine if the one or more target base stations are within a predetermined distance from the one or more source base stations;
in response to a positive determination, appending by the processor (202), the source database with a buffer, wherein the buffer comprises the one or more target base stations within the predetermined distance;
categorizing, by the processor (202), the one or more source base stations and the one or more target base stations into one or more sectors based on the positive determination;
determining, by the processor (202), one or more polygon intersection of source and neighbouring beams associated with the one or more sectors; and
determining, by the processor (202), a sequence of neighbouring base stations among the one or more target base stations based on the one or more polygon intersection of the source and neighbouring beams.

8. The method as claimed in claim 7, comprising discarding, by the processor (202), in response to a negative determination, the one or more target base stations.

9. The method as claimed in claim 7, comprising identifying, by the processor (202), the one or more base stations as the one or more source base stations based on the SINR and categorizing the one or more source base stations into the source database.

10. The method as claimed in claim 7, comprising identifying, by the processor (202), the one or more base stations as the one or more target base stations based on the SINR and categorizing the one or more target stations into the target database.

11. The method as claimed in claim 10, comprising categorizing, by the processor (202), the one or more target base stations into the target database based on a cellular circle data obtained from the one or more target base stations via a spatial neighbour process.

12. The method as claimed in claim 7, wherein the one or more polygon intersection of the source and neighbouring beams comprises:
a source beam sweep based on a bearing computed from the latitude and the longitude associated with the one or more source base stations and the one or more target base stations;
a neighbouring beam sweep based on the bearing, wherein the neighbouring beam sweep comprises an allowed target sector; and
one or more facing sectors based on the allowed target sector and an angular direction target sector associated with the one or more target base stations.