System and method for determining congestion within a telecommunications deployment area

Abstract

The present disclosure relates to a system and method for determining congestion within a telecommunications deployment area. The system determines a cell congestion score for each cell for a predefined time interval based on an average throughput value for each cell and / or a PRB utilization KPI value for each cell, and collects spatial measurement samples from each cell corresponding to voice and / or data service sessions initiated by UEs in each cell. The system spatially maps the collected spatial measurement samples to a spatial grid of a predefined size within the area. The system determines a grid congestion score by calculating a weighted average of the sample congestion scores corresponding to the individual spatial measurement samples mapped to the corresponding spatial grid, determines an area congestion score based on the grid congestion score, averages the cell congestion score and the grid congestion score, and outputs an overall congestion score.

Description

Technical Field

[0001] Reservation of Rights A portion of the disclosure of this patent document includes subject matter that is the subject of intellectual property rights, including, but not limited to, copyrights, designs, trademarks, IC layout designs, and / or trade dress protection, which belongs to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred to as the owner). Since this patent document or this patent disclosure appears in the U.S. Patent and Trademark Office patent file or patent record, the owner does not object to the reproduction of this patent document or this patent disclosure by anyone, but reserves all other rights in all other respects. All rights to such intellectual property are fully reserved by the owner.

[0002] Embodiments of the present disclosure generally relate to wireless communication. More particularly, the present disclosure relates to systems and methods for determining congestion in a telecommunications deployment area.

Background Art

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

[0004] In general, with the emergence of wireless technologies such as Global System for Mobile Communications (GSM), Advanced Data Rate (EDGE) for GSM, High-Speed ​​Packet Access (HSPA), and Long-Term Evolution (LTE), all communications in wireless networks provide a variety of communication services, including voice, video, data, advertising, content, messaging, and broadcast. One example of such a network is Advanced Universal Terrestrial Radio Access (E-UTRA), which may be a radio access network standard intended to replace Universal Mobile Telecommunications System (UMTS) and High-Speed ​​Downlink Packet Access (HSDPA) / High-Speed ​​Uplink Packet Access (HSUPA) technologies, as specified in the Third Generation Partnership Project (3GPP) (registered trademark) Release 5 and later. E-UTRA may be the air interface for the 3GPP Long-Term Evolution (LTE) upgrade path for mobile networks. Unlike HSPA, LTE's E-UTRA may be an entirely new air interface system that is unrelated to and incompatible with Wideband Code Division Multiple Access (W-CDMA). W-CDMA may offer higher data rates and lower latency and is optimized for packet data. UMTS, the successor to GSM technology, currently supports various air interface standards, including Wideband Code Division Multiple Access (W-CDMA), Time Division Code Division Multiple Access (TD-CDMA), and Time Division Synchronous Code Division Multiple Access (TD-SCDMA). UMTS may also support enhanced 3G data communication protocols such as High Speed ​​Packet Access (HSPA), which provide higher data transfer rates and capacity to associated UMTS networks. Along with capacity and higher data transfer rates, there may be many issues related to cell and cell optimization.

[0005] Furthermore, in 5G cellular deployments, macrocells may be planned to provide coverage and capacity solutions across target areas, along with various small cells. Therefore, site-to-site distances may be smaller for the network. Additionally, a greater number of site / enode B may be needed to mitigate the ongoing data demands in emerging networks creating high-density to ultra-high-density wireless access networks within metropolitan areas. Congestion can be the most common and widespread problem in telecom deployments that degrade the customer experience over time. Moreover, congestion can vary geospatially across telecom deployments, and congestion within areas that are part of existing telecom coverage depends primarily on the geospatial density of users and the telecom coverage / capacity supplied to those areas. To address congestion issues, network operators may need insights into the congestion levels of underlying areas so that additional capacity and coverage can be supplied to those areas. Traditional methods of calculating congestion in telecom networks are based on calculating congestion levels in individual network elements. However, traditional methods may not be able to calculate a realistic congestion score for any given geographic area. This stems from the fact that an area may potentially be served by multiple telecom cells. Furthermore, the spatial density of users can vary considerably across different areas of the network.

[0006] Therefore, there is a need for progress in determining realistic congestion within telecommunications deployment areas, which are part of existing telecom deployments. This realistic congestion takes into account the fact that telecommunications deployment areas may potentially be served by multiple telecom cells of heterogeneous nature. Thus, calculating realistic congestion scores will provide telecommunications operators with an opportunity to gain insights into the congestion profile of the telecommunications area under consideration, and thus enable them to have solutions and preventive measures for the telecommunications area if the congestion score shows significant degradation in heterogeneous networks. [Overview of the Initiative] [Problems that the invention aims to solve]

[0007] Some of the purposes of this disclosure that are satisfied by at least one embodiment described herein are listed below.

[0008] In a general embodiment, this disclosure provides a system and method for determining congestion within a telecommunications deployment area.

[0009] In one embodiment, the disclosure helps telecommunications operators calculate congestion scores for the geographical areas covered by each telecommunications service.

[0010] In another aspect, this disclosure addresses how telecommunications operators can help plan for additional telecommunications capacity for their telecommunications areas, thereby improving the overall telecommunications experience for customers operating within those areas.

[0011] In another aspect, this disclosure helps telecommunications operators identify congestion as the root cause of customer dissatisfaction raised by customers operating within limited telecommunications areas.

[0012] In another aspect, this disclosure helps telecommunications operators construct time-based congestion profiles in telecommunications deployment areas.

[0013] In another aspect, the Disclosure provides a system and method for determining a cell congestion score for each cell in a telecommunications deployment area based on at least one of the average throughput value of each cell and one or more physical resource block (PRB) utilization key performance indicator (KPI) values ​​for each cell.

[0014] In another aspect, the disclosure provides a system and method for determining an area congestion score for a telecommunications deployment area based on a grid congestion score of a spatial grid determined within the telecommunications deployment area.

[0015] In another aspect, the disclosure provides a system and method for averaging cell congestion scores and grid congestion scores in a telecommunications deployment area to determine the exact overall congestion score of the telecommunications deployment area. [Means for solving the problem]

[0016] This section is provided to introduce, in a simplified form, some of the objects and embodiments of the invention, which are further described below in embodiments for carrying out the invention. This summary is not intended to identify the main features or scope of the claimed subject matter.

[0017] In one embodiment, the disclosure provides a system for determining congestion within a telecommunications deployment area. The system determines a cell congestion score for each cell within the telecommunications deployment area for a predetermined time interval based on at least one of the average throughput value of each cell and one or more physical resource block (PRB) utilization key performance indicator (KPI) values ​​for each cell. Furthermore, having determined the cell congestion score, the system collects one or more spatial measurement samples from each cell, corresponding to at least one of voice service sessions and data service sessions initiated by users of user equipment (UEs) connected to each cell. Furthermore, the system spatially maps the collected one or more spatial measurement samples to a spatial grid of a predetermined size within the telecommunications deployment area. The system then determines a grid congestion score by calculating one or more weighted averages of the sample congestion scores corresponding to the individual spatial measurement samples mapped to the corresponding spatial grids. The system also determines an area congestion score for the telecommunications deployment area based on the grid congestion scores of the spatial grids determined within the telecommunications deployment area, and averages the cell congestion score and grid congestion score within the telecommunications deployment area. Subsequently, the system outputs the overall congestion score for the telecommunications deployment area based on the determined area congestion score for the telecommunications deployment area.

[0018] In another aspect, the Disclosure further provides a method for determining congestion within a telecommunications deployment area. The method includes determining a cell congestion score for each cell within the telecommunications deployment area for a predetermined time interval based on at least one of the average throughput value of each cell and one or more physical resource block (PRB) utilization key performance indicator (KPI) values ​​for each cell. Furthermore, having determined the cell congestion score, the method includes collecting one or more spatial measurement samples from each cell corresponding to at least one of voice service sessions and data service sessions initiated by users of user equipment (UEs) connected to each cell. Furthermore, the method includes spatially mapping the collected one or more spatial measurement samples to a spatial grid of a predetermined size within the telecommunications deployment area. The method then includes determining a grid congestion score by calculating one or more weighted averages of sample congestion scores corresponding to the individual spatial measurement samples mapped to the corresponding spatial grids. Furthermore, the method includes determining an area congestion score for the telecommunications deployment area based on the grid congestion scores of the spatial grids determined within the telecommunications deployment area, and averaging the cell congestion score and grid congestion score within the telecommunications deployment area. Furthermore, the method includes outputting the overall congestion score for the telecommunications deployment area based on the determined area congestion score for the telecommunications deployment area.

[0019] The accompanying drawings incorporated herein and constituting part of the present invention illustrate exemplary embodiments of the methods and systems disclosed, and similar reference numerals refer to the same parts across different drawings. Components in the drawings are not necessarily to scale, but rather emphasized to clearly illustrate the principles of the present invention. Some drawings may use block diagrams to show components, and may not show the internal circuit configuration of each component. It will be understood by those skilled in the art that the invention of such drawings may include inventions of electrical components, electronic components, or circuit configurations commonly used to implement such components.

Brief Description of the Drawings

[0020] [Figure 1] A diagram showing an exemplary network architecture in which or with which the system of the present disclosure may be implemented to determine congestion in a telecommunications deployment area according to an embodiment of the present disclosure. [Figure 2] A diagram showing an exemplary depiction of a system for determining congestion in a telecommunications deployment area according to an embodiment of the present disclosure. [Figure 3] A diagram showing an exemplary method flowchart showing a method for determining congestion in a telecommunications deployment area according to an embodiment of the present disclosure. [Figure 4] A diagram showing an exemplary computer system in which or with which embodiments of the present invention may be utilized according to an embodiment of the present disclosure.

Modes for Carrying Out the Invention

[0021] The above should become clearer from the following more detailed description of the present invention.

[0022] In the following description, for purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. However, it will be apparent that embodiments of the present disclosure may be practiced without these specific details. Some of the features described below may each be used independently or in any combination with other features. Individual features may not address all of the problems described above, or may only address some of the problems described above. Some of the problems described above may not be fully addressed by any of the features described herein.

[0023] The following description merely provides exemplary embodiments and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the following description of the exemplary embodiments provides those skilled in the art with an enabling description for implementing the exemplary embodiments. It should be understood that various changes may be made to the functions and configurations of the elements without departing from the spirit and scope of the invention as described.

[0024] To provide a thorough understanding of the present embodiment, specific details are given in the following description. However, it will be understood by those skilled in the art that the present embodiment can be practiced without these specific details. For example, in order not to obscure the present embodiment with unnecessary details, circuits, systems, networks, processes, and other components may be shown as components in the form of block diagrams. In other cases, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary details in order to avoid obscuring the present embodiment.

[0025] Also, note that individual embodiments may be described as processes shown as flowcharts, flow diagrams, data flow diagrams, structure diagrams, or block diagrams. A flowchart may represent operations as sequential processes, but many of the operations may be executed in parallel or simultaneously. In addition, the order of the operations may be rearranged. A process ends when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, function, procedure, subroutine, subprogram, etc. When a process corresponds to a function, its end can correspond to the return of the function to the calling function or main function.

[0026] The terms “exemplary” and / or “demonstrative” are used herein to mean examples, cases, or illustrations. To avoid misunderstanding, 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 construed as being preferable or more advantageous than other aspects or designs, nor is it intended to exclude equivalent exemplary structures and techniques known to those skilled in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar terms are used in either the modes for carrying out the invention or the claims, such terms are intended to be inclusive—as with the term “comprising” as an open transition word—without excluding any additions or other elements.

[0027] Any reference throughout this specification to “one embodiment,” “an embodiment,” “an instance,” or “one instance” means that any particular feature, structure, or characteristic described in relation to this embodiment is included in at least one embodiment of the present invention. Therefore, the occurrence of the phrase “in one embodiment” or “in one embodiment” in various places throughout this specification does not necessarily all refer to the same embodiment. Furthermore, any particular feature, structure, or characteristic may be combined in any preferred manner within one or more embodiments.

[0028] The terms used herein are for the purpose of describing specific embodiments and are not intended to be limitations of the invention. Where used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that, where used herein, the terms “to provide” and / or “to provide” specify the presence of the described features, integers, steps, actions, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, actions, elements, components, and / or groups thereof. Where used herein, the terms “and / or” include any or all combinations of one or more of the related items listed.

[0029] Various embodiments of this disclosure provide systems and methods for determining congestion within telecommunications deployment areas. In one embodiment, the disclosure helps telecommunications operators calculate congestion scores for geographical areas covered by each telecommunications service. In another embodiment, the disclosure helps telecommunications operators plan additional telecommunications capacity for telecommunications areas, thereby addressing the improvement of the overall telecommunications experience for customers operating within those areas. In yet another embodiment, the disclosure helps telecommunications operators identify congestion as the root cause of customer dissatisfaction raised by customers operating within limited telecommunications areas. In yet another embodiment, the disclosure helps telecommunications operators construct time-based congestion profiles for telecommunications deployment areas. In yet another embodiment, the disclosure provides systems and methods for determining a cell congestion score for each cell within a telecommunications deployment area based on at least one of the average throughput value of each cell and one or more physical resource block (PRB) utilization key performance indicator (KPI) values ​​for each cell. In another aspect, the Disclosure provides a system and method for determining an area congestion score for a telecommunications deployment area based on grid congestion scores of spatial grids determined within the telecommunications deployment area. In yet another aspect, the Disclosure provides a system and method for averaging cell congestion scores and grid congestion scores within a telecommunications deployment area to determine an exact overall congestion score for the telecommunications deployment area.

[0030] Refer to Figure 1, which shows an exemplary network architecture (100) (also referred to as network architecture (100)) of a telecommunications congestion determination system in which the System (110) of the Disclosure may be implemented in or with the System (110) of the Disclosure according to embodiments of the Disclosure. As shown, the exemplary network architecture (100) may be equipped with a System (110) for determining congestion in a telecommunications deployment area. Furthermore, the network architecture (100) may include cells (101-1, 101-2, 101-3, ..., 101-N) (individually referred to as cellular towers (102) and collectively referred to as cellular towers (102)) (individually referred to as cells (101) and collectively referred to as cells (101)). Cells (101) may include, but are not limited to, macrocells, microcells, small cells, femtocells, picocells, etc., within the network architecture (100). A cellular tower (102) may include, but is not limited to, base stations (BS), mobile stations (MS), base station subsystems (BSS), transceiver base stations (BTS), base station controllers (BSC), advanced node B (e-node B), generation node B (g-node B), etc. An exemplary network architecture (100) may be an exemplary telecommunications deployment area. A cell (101) may provide wireless services to user equipment (UE) (104-1, 104-2, 104-3, ... 104-N) (individually referred to as user equipment (UE) (104) and collectively referred to as user equipment (UE) (104)) located within the telecommunications deployment area.In some implementations, UE(104) may include, but is not limited to, handheld wireless communication devices (e.g., mobile phones, smartphones, phablet devices, etc.), wearable computer devices (e.g., head-mounted display computer devices, head-mounted camera devices, wristwatch computer devices, etc.), Global Positioning System (GPS) devices, laptop computers, tablet computers, or other types of portable computers, media playback devices, portable gaming systems, home appliance devices, home monitoring devices, and / or any other types of computer devices having wireless communication capabilities.

[0031] Furthermore, each cell (101) operating within the telecommunications deployment area may be connected to a system (110) which, by understanding the cells (101) operating within the telecommunications deployment area, collects spatial measurement samples (108) corresponding to one or more voice service sessions or one or more data service sessions initiated by a UE (102). Furthermore, the network architecture (100) may include a spatial grid (106) within the telecommunications deployment area to which the spatial measurement samples (108) are spatially mapped. The system (110) may collect spatial measurement samples (108) corresponding to a particular time interval. The spatial measurement sample (108) may include, but is not limited to, International Mobile Subscriber Identification Information (IMSI) (e.g., customer identifier), Cell Identification Information (ID) (e.g., macrocell / microcell / Wireless-Fidelity (Wi-Fi) identifier), Latitude / Longitude (e.g., estimated user location), Voice / Data flag, Session duration, Reference Signal Received Power (RSRP) (e.g., signal strength), Reference Signal Received Quality (RSRQ) (e.g., signal quality), and Signal-to-Interference Noise Ratio (SINR).

[0032] Furthermore, the network architecture (100) or telecommunications deployment area may include, but are not limited to, several layers (not shown in Figure 1), including network platforms (e.g., servers, databases), network infrastructure (e.g., fiber networks, cellular towers, cable networks, switches), computing devices (e.g., client devices, computers, smartphones, tablets), operating systems, and applications (e.g., social networking applications, e-commerce applications, third-party applications, operator applications, carrier applications). The network platform may provide content and services to the UE (104) through the network infrastructure and computing devices. Computing devices may include device hardware (e.g., computers, smartphones, tablets) and may be associated with a specific data plan provided by one or more network operators. In certain embodiments, the system (110) may collect data from the UE (104) or cellular tower (102) (e.g., application name, application type, duration, experience quality, network speed, latency, total amount of data delivered, signal strength, number of connected towers, signal stability status, network coverage, etc.). System (110) may use the collected data to monitor network performance, such as detecting network congestion or coverage problems. System (110) may provide network insights (e.g., congested areas, congestion alerts, coverage alerts, network speed, network latency, network performance, etc.) based on the collected data for optimizing the network infrastructure (not shown in Figure 1). System (110) may also provide feedback information (e.g., improvements in quality of experience (QoE), network speed, and latency) for optimization actions taken on the network infrastructure.

[0033] In certain embodiments, the system (110) may monitor communication network performance (e.g., network traffic congestion, network coverage issues) based on data from both the front-end (e.g., UE(104), applications, operating systems, websites, search engines, etc.) and back-end (e.g., network platforms, network infrastructure, servers, switches, databases, etc.) of the network architecture (100). In certain embodiments, the system (110) may collect user experience data (e.g., network speed, network latency, signal stability status) from both the front-end and back-end of the network architecture (100). In certain embodiments, the system (110) may use data collected from the front-end (e.g., applications) to generate optimization recommendations for the back-end network infrastructure and / or network platform. In certain embodiments, the system (110) may use data collected from the back-end (e.g., network platforms, network infrastructure) to generate optimization recommendations for the front-end user experience (e.g., applications, operating systems, UE(104), data plans, network speed, latency, etc.). In certain embodiments, the system (110) may determine one or more network coverage metrics (e.g., signal strength, number of connected towers, signal stability status) and compare the network coverage metrics to their respective thresholds to detect one or more network coverage problems. Furthermore, the system (110) may calculate key network key performance indicators (KPIs) related to each of the cells (101).

[0034] The system (110) may be further operablely coupled to computing devices associated with entities (not shown in Figure 1). Entities may include companies, organizations, network operators, vendors, universities, research facilities, business companies, defense facilities, or any other secure facilities. Furthermore, entities may analyze data or outputs from the system (110). In some implementations, the system (110) may also be associated with computing devices. Furthermore, the system (110) may also be communicably coupled to a UE (104) via a communication network of the network architecture (100).

[0035] Figure 1 shows an exemplary component of the network architecture (100), but in other implementations, the network architecture (100) may include fewer components, different components, differently configured components, or additional functional components than those shown in Figure 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).

[0036] In some implementations, the system (110) may be a standalone device and may be communicatively coupled to a computing device (not shown in Figure 1) and / or a centralized server (not shown in Figure 1). In other implementations, the system (110) may be associated with a computing device or a centralized server. The system (110) may be implemented in, but is not limited to, an electronic device, a mobile device, a wireless device, a wired device, or a server. Such a server may, but is not limited to, a standalone server, a remote server, a cloud server, a dedicated server, and the like.

[0037] In one embodiment, the system (110) may include one or more processors coupled with memory, the memory may store instructions that, when executed by one or more processors, cause the system (110) to perform congestion determination in a telecommunications deployment area. An exemplary depiction of the system (110) for congestion determination in a telecommunications deployment area according to embodiments of the present disclosure is shown in Figure 2. In one embodiment, the system (110) may include one or more processors (202). One or more processors (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuit configurations, and / or any device that processes data based on operation instructions. Among the capabilities, one or more processors (202) may be configured to fetch and execute computer-readable instructions stored in the system (110)'s memory (204). Memory (204) may be configured to store one or more computer-readable instructions or routines that can be fetched and executed to create or share data packets over network services in a non-temporary computer-readable storage medium. Memory (204) may include any non-temporary storage device, such as volatile memory like RAM, or non-volatile memory like EPROM or flash memory.

[0038] In one embodiment, the system (110) may include an interface (206). The interface (206) may provide interfaces for various other interfaces, such as data input and output devices, storage devices, etc., referred to as I / O devices. The interface (206) may facilitate communication of the system (110). The interface (206) may also provide communication paths for one or more components of the system (110). Examples of such components include, but are not limited to, a processing unit / engine (208) and a database (210).

[0039] The processing unit / engine (208) may be implemented as a combination of hardware and programming (e.g., programmable instructions) for performing one or more functionalities of the processing engine (208). In the examples described herein, such a combination of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine (208) may be processor-executable instructions stored on a non-temporary machine-readable storage medium, and the hardware for the processing engine (208) 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 (208). In such an example, the system (110) may include a machine-readable storage medium for storing instructions and processing resources for executing instructions, or the machine-readable storage medium may be separate but accessible to the system (110) and the processing resources. In other examples, the processing engine (208) may be implemented by an electronic circuit configuration.

[0040] The processing engine (208) may include one or more modules / engines selected from any of the following: a decision module (212), a collection module (214), a mapping module (216), an output module (218), and other modules (220). The processing engine (208) may further be edge-based microservice event processing, but is not limited to such.

[0041] In one embodiment, the decision module (212) may determine a cell congestion score for each cell (101) in a telecommunications deployment area for a predetermined time interval based on at least one of the average throughput value of each cell (101) and one or more physical resource block (PRB) utilization key performance indicator (KPI) values ​​for each cell (101). In one embodiment, the cell congestion score may be either a single value determined for the entire predetermined time interval, or an array of cell congestion score values, each of which corresponds to a cell congestion score determined for one or more sub-intervals of the predetermined time interval. A sub-interval may be a divided, consecutive sub-interval of the overall predetermined time interval. For example, the cell congestion score may be either a single value calculated for the entire time interval "T", or an array of values, where each value corresponds to a cell congestion score calculated for a sub-interval of "T", when the overall time interval "T" is divided into consecutive sub-intervals, each sub-interval having a duration of "Tsub".

[0042] The cell congestion score may be calculated as a discrete value based on the average throughput of the cell and the PRB utilization rate KPI value for each cell (101). For example, if the average throughput is less than "512" Kbps and the PRB utilization rate is greater than 70%, the cell congestion score may be "100". Similarly, if the average throughput is between "512" Kbps and "1024" Kbps and the PRB utilization rate is greater than 70%, the cell congestion score may be "50". Furthermore, if the average throughput is between "1024" Kbps and "2048" Kbps and the PRB utilization rate is greater than 70%, the cell congestion score may be "25". Furthermore, if the average throughput exceeds "2048" Kbps, the cell congestion score may be "0".

[0043] In one embodiment, once the collection module (214) determines the cell congestion score, it may collect one or more spatial measurement samples (108) from each cell (101) corresponding to at least one of a voice service session and a data service session initiated by a user of a user device (104) connected to each cell (101). The spatial measurement samples (108) may include, but are not limited to, international mobile subscriber identification information (IMSI), cell identification information (ID), latitude / longitude, voice / data flag, session duration, reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference noise ratio (SINR), and the like.

[0044] In one embodiment, the mapping module (216) may spatially map one or more collected spatial measurement samples (108) to a spatial grid (106) of a predetermined size within the telecommunications deployment area. For example, each of the spatial measurement samples (108) collected for a time interval "T" is mapped to a spatial grid (106) of size "S" within the telecommunications deployment area. The grid size may be configurable. For example, the minimum grid size "S" may be configured as 10 × 10 meters.

[0045] In one embodiment, the decision module (212) may determine the grid congestion score by calculating one or more weighted averages of sample congestion scores corresponding to individual spatial measurement samples (108) mapped to the corresponding spatial grid (106). In one embodiment, each sample congestion score of a sample may be considered equivalent to the cell congestion score of the corresponding cell (101) calculated for a predetermined time interval in which the cell is identified by a cell identifier from the spatial measurement sample (108). In one embodiment, one or more weighted averages of sample congestion scores corresponding to individual spatial measurement samples are calculated as the ratio of the sample elapsed time in the predetermined time interval to the total elapsed time in the predetermined time interval for all samples mapped to the corresponding spatial grid (106).

[0046] In one embodiment, when the cell congestion score of a cell (101) represents an array of cell congestion score values ​​corresponding to one or more subintervals of a predetermined time interval, the sample congestion score corresponding to an individual spatial measurement sample (108) may be calculated as the average sum of the arrays of cell congestion score values ​​for all subintervals that are part of the sample session duration within the predetermined time interval. In another embodiment, the grid congestion score can be calculated as a weighted average of the congestion scores of individual spatial measurement samples (108) mapped to the corresponding grid, where the weight for each sample may be calculated as the ratio of the sample elapsed time in "T" to the total elapsed time in "T" for all samples mapped to the corresponding grid.

[0047] In one example, each sample congestion score can be considered equivalent to the cell congestion score of the corresponding cell (101) calculated for the overall time interval "T," where cell (101) is identified by a cell identifier from the spatial measurement sample (108). If the cell congestion score represents an array of congestion score values ​​corresponding to subintervals within the time interval "T," the sample congestion score may be calculated as the average sum of the score values ​​of all subintervals that are part of the sample session duration within the time interval "T."

[0048] In one embodiment, the decision module (212) may determine the area congestion score of the telecommunications deployment area based on the grid congestion score of the spatial grid (106) determined within the telecommunications deployment area, and may average the cell congestion score and grid congestion score in the telecommunications deployment area.

[0049] In one embodiment, the output module (218) may output the overall congestion score of the telecommunications deployment area based on the determined area congestion score of the telecommunications deployment area.

[0050] In one embodiment, the UE(104) or computing device (not shown in Figures 1 and 2) may communicate with the system(110) via a set of executable instructions residing on any operating system, including, but not limited to, Android®, iOS®, Kai OS®, etc. In one embodiment, the UE(104) may include, but not limited to, any electrical, electronic, electromechanical, or instrumental device, or one or more combinations of the above devices, such as a mobile phone, smartphone, virtual reality (VR) device, augmented reality (AR) device, laptop, general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device, and the computing device may include, but not limited to, one or more accessories built in internally or coupled externally, including visual assistance devices such as cameras, audio assistance, microphones, keyboards, touchpads, touch-enabled screens, and electronic pens, for receiving user input. It should be understood that the UE(104) is not limited to the devices described, and a variety of other devices may be used. A smart computing device may be one of several suitable systems for storing data and other personal / confidential information.

[0051] Figure 3 shows an exemplary method flowchart illustrating a method (300) for determining congestion in a telecommunications deployment area according to an embodiment of the present disclosure.

[0052] As shown in Figure 3, method (300) includes one or more blocks that represent a method for determining congestion in a telecommunications deployment area. Method (300) can be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions that perform functions or implement abstract data types.

[0053] The order in which Method (300) is described is not intended to be construed as limiting, and any number of Method blocks described can be combined in any order to implement Method (300). Additionally, individual blocks may be removed from the Method without departing from the scope of the subject matter described herein. Furthermore, Method (300) can be implemented in any suitable hardware, software, firmware, or a combination thereof.

[0054] In block (302), the method (300) may include having a processor (202) determine a cell congestion score for each cell (101) in a telecommunications deployment area for a predetermined time interval, based on at least one of the average throughput value of each cell (101) and one or more physical resource block (PRB) utilization key performance indicator (KPI) values ​​for each cell (101).

[0055] In block (304), the method (300) may include, once a cell congestion score is determined, having a processor (202) collect one or more spatial measurement samples (108) from each cell (101) corresponding to at least one of a voice service session and a data service session initiated by a user of a user equipment (UE) (104) connected to each cell (101).

[0056] In block (306), the method (300) may include spatially mapping one or more collected spatial measurement samples (108) to a spatial grid (106) of a predetermined size within a telecommunications deployment area by a processor (202).

[0057] In block (308), the method (300) may include a processor (202) determining a grid congestion score by calculating one or more weighted averages of sample congestion scores corresponding to individual spatial measurement samples (108) mapped to the corresponding spatial grid (106).

[0058] In block (310), the method (300) may include a processor (202) determining an area congestion score for a telecommunications deployment area based on grid congestion scores of spatial grids (106) determined within the telecommunications deployment area, and averaging the cell congestion score and grid congestion score within the telecommunications deployment area.

[0059] In block (312), the method (300) may include having the processor (202) output the entire congestion score of the telecommunications deployment area based on the determined area congestion score of the telecommunications deployment area.

[0060] Figure 4 shows an exemplary computer system (400) according to embodiments of the present disclosure in which embodiments of the present invention may be used in or in conjunction therewith.

[0061] As shown in Figure 4, the computer system (400) may include an external storage device (410), a bus (420), main memory (430), read-only memory (440), a mass storage device (450), a communication port (460), and a processor (470). Those skilled in the art will understand that the computer system may include two or more processors and communication ports. Examples of the processor (470) include, but are not limited to, an Intel® Itanium® or Itanium 2 processor, or an AMD® Opteron® or Athlon MP® processor, a Motorola® processor line, a FortiSOC® system-on-chip processor, or other future processors. The processor (470) may include various modules related to embodiments of the present invention. The communication port (460) may be any of the following: an RS-232 port for use with modem-based dial-up connections, 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 (460) may be selected depending on the network, such as a local area network (LAN), a wide area network (WAN), or any network to which the computer system will connect. The memory (430) may be random access memory (RAM) or any other dynamic storage device commonly known in the art. The read-only memory (440) may be any static storage device for storing static information, such as startup or BIOS instructions for the processor (470), such as a programmable read-only memory (PROM) chip, but not limited to these. The mass storage (450) may be any current or future mass storage solution that 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 (with internal or external, for example, Universal Serial Bus (USB) and / or Firewire interfaces), such as those available from Seagate (e.g., the Seagate Barracuda 782 family) or Hitachi (e.g., the Hitachi Deskstar 13K800), one or more optical disks, and independent disk redundant array (RAID) storage, such as arrays of disks (e.g., SATA arrays) available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc., and Enhance Technology, Inc.

[0062] The bus (420) connects the processor (470) to other memory, storage, and communication blocks in a communicative manner. The bus (420) may be other buses, such as the Peripheral Component Interconnection (PCI) / PCI Expansion (PCI-X) bus, Small Computer System Interface (SCSI), USB, etc., for connecting expansion cards, drives, and other subsystems, as well as the Front Side Bus (FSB) for connecting the processor (470) to software systems.

[0063] Optionally, operator and management interfaces, such as displays, keyboards, and cursor control devices, may also be coupled to the bus (420) to support direct operator interaction with the computer system. Other operator and management interfaces may be provided through network connections connected via a communication port (460). The external storage device (410) may be any type of external hard drive, floppy drive, IOMEGA® Zip drive, compact disc read-only memory (CD-ROM), rewritable compact disc (CD-RW), or digital video disc read-only memory (DVD-ROM). The components described above are intended to illustrate various possibilities only. The exemplary computer system described above should not limit the scope of this disclosure.

[0064] Various embodiments of this disclosure provide systems and methods for determining congestion within telecommunications deployment areas. In one embodiment, the disclosure helps telecommunications operators calculate congestion scores for geographical areas covered by each telecommunications service. In another embodiment, the disclosure helps telecommunications operators plan additional telecommunications capacity for telecommunications areas, thereby addressing the improvement of the overall telecommunications experience for customers operating within those areas. In yet another embodiment, the disclosure helps telecommunications operators identify congestion as the root cause of customer dissatisfaction raised by customers operating within limited telecommunications areas. In yet another embodiment, the disclosure helps telecommunications operators construct time-based congestion profiles for telecommunications deployment areas. In yet another embodiment, the disclosure provides systems and methods for determining a cell congestion score for each cell within a telecommunications deployment area based on at least one of the average throughput value of each cell and one or more physical resource block (PRB) utilization key performance indicator (KPI) values ​​for each cell. In another aspect, the Disclosure provides a system and method for determining an area congestion score for a telecommunications deployment area based on grid congestion scores of spatial grids determined within the telecommunications deployment area. In yet another aspect, the Disclosure provides a system and method for averaging cell congestion scores and grid congestion scores within a telecommunications deployment area to determine an exact overall congestion score for the telecommunications deployment area.

[0065] While preferred embodiments are given considerable emphasis in this specification, it will be understood that many embodiments can be made without departing from the principles of the present invention, and that many modifications can be made within the preferred embodiments. These and other modifications in preferred embodiments of the present invention will be apparent to those skilled in the art from the disclosure herein, so that it will be clearly understood that the above descriptive matters are implemented merely as examples of the present invention and not as limitations. [Explanation of Symbols]

[0066] 100 Telecommunications congestion determination system, network architecture 101 cells 102 Cellular Tower 104 User Equipment (UE) 106 Spatial Grid 108 Spatial Measurement Samples 110 System 202 processors 204 memory 206 Interfaces 208 Processing Units / Engines 210 Databases 212 Decision Module 214 Collection Module 216 Mapping Modules 218 Output Modules 220 Other Modules 400 Computer Systems 410 External storage devices 420 bus 430 Main Memory 440 Read-only memory 450 Mass Storage Devices 460 communication ports 470 processor

Claims

1. A system (110) for determining congestion within a telecommunications deployment area, Processor (202), The processor (202) is coupled to a memory (204), the memory (204) contains processor-executable instructions, and the processor-executable instructions are, when executed, transmitted to the processor (202), Based on the average throughput value of each cell (101) and at least one of the key performance indicator (KPI) values ​​of one or more physical resource block (PRB) utilization rates for each cell (101), the cell congestion score for each cell (101) in the telecommunications deployment area is determined for a predetermined time interval. Once the cell congestion score is determined, one or more spatial measurement samples (108) corresponding to at least one of the voice service sessions and data service sessions initiated by the user of the user equipment (UE) (104) connected to each cell (101) are collected from each cell (101). The collected one or more spatial measurement samples (108) are spatially mapped onto a spatial grid (106) of a predetermined size within the telecommunications deployment area. The grid congestion score is determined by calculating one or more weighted averages of sample congestion scores corresponding to individual spatial measurement samples (108) mapped to the corresponding spatial grid (106). Based on the grid congestion score of the spatial grid (106) determined within the telecommunications deployment area, the area congestion score of the telecommunications deployment area is determined, and the cell congestion score and the grid congestion score in the telecommunications deployment area are averaged. Based on the determined area congestion score of the telecommunications deployment area, the overall congestion score of the telecommunications deployment area is output. System (110).

2. The system (110) according to claim 1, wherein the spatial measurement sample (108) comprises at least one of the following: international mobile subscriber identification information (IMSI), cell identification information (ID), latitude / longitude, voice / data flag, session duration, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal-to-interference noise ratio (SINR).

3. The system (110) according to claim 1, wherein the cell congestion score is either a single value determined for the default time interval as a whole, or an array of cell congestion score values, each value corresponding to the cell congestion score determined for one or more sub-intervals of the default time interval, and the sub-intervals are divided, consecutive sub-intervals of the overall default time interval.

4. The system (110) according to claim 3, wherein the cell congestion score of a cell (101) represents the array of cell congestion score values ​​corresponding to one or more sub-intervals of the predetermined time interval, and the sample congestion score corresponding to an individual spatial measurement sample (108) is calculated as the average sum of the array of cell congestion score values ​​for all sub-intervals that are part of the sample session duration in the predetermined time interval.

5. The system (110) according to claim 1, wherein the sample congestion score of each of the one or more spatial measurement samples is deemed equivalent to the cell congestion score of the corresponding cell (101), calculated for the predetermined time interval in which the cell (101) is identified from the cell identifier of the spatial measurement sample (108).

6. The system (110) according to claim 1, wherein the one or more weighted average of sample congestion scores corresponding to individual spatial measurement samples is calculated as the ratio of the sample elapsed time in the predetermined time interval to the total elapsed time in the predetermined time interval for all samples mapped to the corresponding spatial grid (106).

7. A method for determining congestion within a telecommunications deployment area, The steps include: determining the cell congestion score of each cell (101) in a telecommunications deployment area for a predetermined time interval by a processor (202) based on the average throughput value of each cell (101) and at least one of the physical resource block (PRB) utilization key performance indicator (KPI) values ​​of each cell (101); Once the cell congestion score is determined, the processor (202) collects one or more spatial measurement samples (108) from each cell (101) that correspond to at least one of the voice service sessions and data service sessions initiated by the user of the user equipment (UE) (104) connected to each cell (101). The steps include: spatially mapping one or more collected spatial measurement samples (108) to a spatial grid (106) of a predetermined size within the telecommunications deployment area using the processor (202); The processor (202) determines the grid congestion score by calculating one or more weighted averages of sample congestion scores corresponding to individual spatial measurement samples (108) mapped to the corresponding spatial grid (106). The steps include: determining an area congestion score for the telecommunications deployment area using the processor (202) based on the grid congestion score of the spatial grid (106) determined within the telecommunications deployment area; and averaging the cell congestion score and the grid congestion score in the telecommunications deployment area. The steps include: outputting the entire congestion score of the telecommunications deployment area by the processor (202) based on the determined area congestion score of the telecommunications deployment area; A method for providing this.

8. The method according to claim 7, wherein the spatial measurement sample (108) comprises at least one of the following: international mobile subscriber identification information (IMSI), cell identification information (ID), latitude / longitude, voice / data flag, session duration, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal-to-interference noise ratio (SINR).

9. The method according to claim 7, wherein the cell congestion score is either a single value determined for the default time interval as a whole, or an array of cell congestion score values, each value corresponding to the cell congestion score determined for one or more sub-intervals of the default time interval, and the sub-interval is a divided, consecutive sub-interval of the overall default time interval.

10. The method according to claim 9, wherein when the cell congestion score of a cell (101) represents the array of cell congestion score values ​​corresponding to one or more sub-intervals of the predetermined time interval, the sample congestion score corresponding to an individual spatial measurement sample (108) is calculated as the average sum of the array of cell congestion score values ​​for all sub-intervals that are part of the sample session duration in the predetermined time interval.

11. The method according to claim 7, wherein the sample congestion score of each of the one or more spatial measurement samples is deemed equivalent to the cell congestion score of the corresponding cell (101) calculated for the predetermined time interval in which the cell (101) is identified from the cell identifier of the spatial measurement sample (108).

12. The method according to claim 7, wherein the one or more weighted average of sample congestion scores corresponding to individual spatial measurement samples is calculated as the ratio of the sample elapsed time in the predetermined time interval to the total elapsed time in the predetermined time interval for all samples mapped to the corresponding spatial grid (106).

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