DESC:FORM 2
THE PATENTS ACT, 1970 (39 OF 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)

SYSTEM AND METHOD FOR DETERMINING INTERFERENCE IN A COMMUNICATION NETWORK

Jio Platforms Limited, an Indian company, having registered address at Office -101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India

The following specification describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
[0001] The embodiments of the present disclosure generally relate to the field of communication networks. More particularly, the present disclosure relates to a system and a method for determining interference in the communication network.
BACKGROUND OF THE INVENTION
[0002] The subject matter disclosed in the background section should not be assumed or construed to be prior art merely because of its mention in the background section. Similarly, any problem statement mentioned in the background section or its association with the subject matter of the background section should not be assumed or construed to have been previously recognized in the prior art.
[0003] With diverse and ever-increasing consumer demand for reliable network connectivity, there has been an expansion of network resources by telecom operators. To this end, physical network infrastructure such as transmission nodes are being dynamically placed throughout a network. Despite the expansion of the network, users continue to experience a degradation in performance of the communication network in terms of higher call drop rates, a degradation in quality of calls, an interference during calls, low internet speed, and a higher latency in the network. Additionally, ongoing dynamic changes in network conditions due to variability in network traffic, environmental interference and complex network topology, further impacts user experience.
[0004] For enhancing the user experience and optimizing performance of the network, telecom operators rely on traditional methods of network optimization based on network performance reports generated from data collected from the transmission nodes serving a plurality of User Equipment (UEs). However, a significant drawback in the traditional methods is that due to huge volume of the data generated by the transmission nodes and the UEs, crucial information about a specific transmission node to which the UEs are latched may get missed for processing. Without precise information, telecom operators face difficulties in identifying areas of poor coverage, high interference, high congestion, or frequent dropouts, thus impeding efforts to optimize the performance of the network effectively.
[0005] Furthermore, traditional methods may overlook the dynamic nature of network environments, where interference due to changes in user density, signal propagation, and network congestion require continuous monitoring and adaptation. Thus, the traditional methods may not provide an accurate analysis of areas in the communication network facing less coverage or degraded signal quality. Additionally, the traditional methods fail to provide the analysis in a readily comprehensible form making it difficult for the telecom operators to identify coverage areas and nodes in the communication network facing degraded performance.
[0006] In light of the above-mentioned limitations, there is a need for an effective analytical system and a method that is capable of analyzing huge volume of the data for determining quality of network and interference in the network experienced by the users.
SUMMARY
[0007] The following embodiments present a simplified summary in order to provide a basic understanding of some aspects of the disclosed invention. This summary is not an extensive overview, and it is not intended to identify key/critical elements or to delineate the scope thereof. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
[0008] In an embodiment, a method for determining interference in a communication network is described. The method comprises collecting, by an acquisition module from a crowd source data collection entity, crowd source data associated with a plurality of User Equipment (UEs). Further, the method comprises comparing, by a data processing module based on a reception of a user request for determining the severity level of interference, values of a plurality of samples in the crowd source data with a first pre-defined threshold range and a second pre-defined threshold range. Based on a result of the comparison, the method comprises generating, by the data processing module, visualization data representing a plot of the plurality of samples. Furthermore, the method comprises determining, by the data processing module, the severity level of the interference in a coverage area of the communication network based on the generated visualization data. Thereafter, the method comprises displaying, by a display control module on an application interface in a pre-defined data format, the determined severity level of the interference in a priority order.
[0009] In one aspect, the method further comprises determining, by the data processing module, one or more clusters of UEs among the plurality of UEs based on the plurality of samples using a Machine Learning (ML) model.
[0010] In one aspect, the plurality of samples is associated with at least two radio parameters of the crowd source data received in the user request, the pre-defined first threshold range is associated with a first radio parameter of the at least two radio parameters, and the pre-defined second threshold range is associated with a second radio parameter of the at least two radio parameters.
[0011] In one aspect, the method further comprises identifying, by the data processing module, a group of serving cells of a plurality of serving cells in the coverage area having the severity level of the interference indicating an area requiring improvement in the network coverage, or indicating an area having poor network coverage. Further, the method comprises acquiring, by the data processing module, performance metrics data from the identified group of serving cells. Furthermore, the method comprises performing, by the data processing module, an analysis on the acquired performance metrics data to identify a root cause of degradation in performance of the group of serving cells.
[0012] In one aspect, the method further comprises controlling, by the display control module, the application interface to display the root cause of degradation in the performance of the identified group of serving cells and display the visualization data in one or more graphical formats including a map layer, a graph, a plot, a scatter chart, and a pie chart.
[0013] In another embodiment, a system for determining interference in a communication network is described. The system comprises an acquisition module, a task execution module, a data processing module, and a display control module. The acquisition module is configured to collect, from a crowd source data collection entity, crowd source data associated with a plurality of User Equipment (UEs). Based on a reception of a user request for determining the severity level of interference, the data processing module is configured to compare values of a plurality of samples in the crowd source data with a first pre-defined threshold range and a second pre-defined threshold range. Further, the data processing module is configured to generate, based on a result of the comparison, visualization data representing a plot of the plurality of samples. Furthermore, the data processing module is configured to determine a severity level of the interference in a coverage area of the communication network based on the generated visualization data. Thereafter, the display control module is configured to display on an on an application interface in a pre-defined data format, the determined severity level of the interference in a priority order.
[0014] In one aspect, the data processing module is further configured to determine one or more clusters of UEs among the plurality of UEs based on the plurality of samples using a Machine Learning (ML) model.
[0015] In one aspect, the data processing module is configured to identify a group of serving cells of a plurality of serving cells in the coverage area having the severity level of the interference indicating an area requiring improvement in the network coverage, or indicating an area having poor network coverage. Furthermore, the data processing module is configured to acquire performance metrics data from the identified group of serving cells. Thereafter, the data processing module is configured to perform an analysis on the acquired performance metrics data to identify a root cause of degradation in performance of the group of serving cells.
[0016] In one aspect, the display control module is configured to control the application interface to display the root cause of degradation in the performance of the identified group of serving cells, and display the visualization data in one or more graphical formats including a map layer, a graph, a plot, a scatter chart, and a pie chart.
BRIEF DESCRIPTION OF DRAWINGS
[0017] Various embodiments disclosed herein will become better understood from the following detailed description when read with the accompanying drawings. The accompanying drawings constitute a part of the present disclosure and illustrate certain non-limiting embodiments of inventive concepts. Further, components and elements shown in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. For the purpose of consistency and ease of understanding, similar components and elements are annotated by reference numerals in the exemplary drawings.
[0018] FIG. 1 illustrates a block diagram depicting an exemplary environment of a system for determining interference in a communication network, in accordance with an embodiment of the present disclosure.
[0019] FIG. 2 illustrates a block diagram depicting a detailed system architecture of a server, in accordance with an embodiment of the present disclosure.
[0020] FIG. 3 illustrates graphical representation of aggregation of samples of crowd source data, in accordance with an embodiment of the present disclosure.
[0021] FIG. 4 illustrates a block diagram depicting an example system architecture of a Network Management Console (NMC), in accordance with an embodiment of the present disclosure.
[0022] FIG. 5 illustrates a scatter chart of Reference Signal Receive Power (RSRP) vs Signal to Interference plus Noise Ratio (SINR), in accordance with an embodiment of the present disclosure.
[0023] FIG. 6 illustrates a pie-chart of crowd source data depicting severity levels of the interference, in accordance with an embodiment of the present disclosure.
[0024] FIG. 7 illustrates a flow chart for a method for determining the interference in the communication network, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE INVENTION
[0025] Inventive concepts of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which examples of one or more embodiments of inventive concepts are shown. Inventive concepts may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Further, the one or more embodiments disclosed herein are provided to describe the inventive concept thoroughly and completely, and to fully convey the scope of each of the present inventive concepts to those skilled in the art. Furthermore, it should be noted that the embodiments disclosed herein are not mutually exclusive concepts. Accordingly, one or more components from one embodiment may be tacitly assumed to be present or used in any other embodiment.
[0026] The following description presents various embodiments of the present disclosure. The embodiments disclosed herein are presented as teaching examples and are not to be construed as limiting the scope of the present disclosure. The present disclosure should in no way be limited to the illustrative implementations, drawings, and techniques illustrated below, including the exemplary design and implementation illustrated and described herein, but may be modified, omitted, or expanded upon without departing from the scope of the present disclosure.
[0027] The following description contains specific information pertaining to embodiments in the present disclosure. The detailed description uses the phrases “in some embodiments” which may each refer to one or more or all of the same or different embodiments. The term “some” as used herein is defined as “one, or more than one, or all.” Accordingly, the terms “one,” “more than one,” “more than one, but not all” or “all” would all fall under the definition of “some.” In view of the same, the terms, for example, “in an embodiment” refers to one embodiment and the term, for example, “in one or more embodiments” refers to “at least one embodiment, or more than one embodiment, or all embodiments.”
[0028] The term “comprising,” when utilized, means “including, but not necessarily limited to;” it specifically indicates open-ended inclusion in the so-described one or more listed features, elements in a combination, unless otherwise stated with limiting language. Furthermore, to the extent that the terms “includes,” “has,” “have,” “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.”
[0029] 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.
[0030] The description provided herein discloses exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the foregoing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing any of the exemplary embodiments. Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it may be understood by one of the ordinary skilled in the art that the embodiments disclosed herein may be practiced without these specific details.
[0031] 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 description and in the appended claims, the singular forms "a", "an", and "the" include plural forms unless the context of the invention indicates otherwise.
[0032] The terminology and structure employed herein are for describing, teaching, and illuminating some embodiments and their specific features and elements and do not limit, restrict, or reduce the scope of the present disclosure and the appended claims. Accordingly, unless otherwise defined, all terms, and especially any technical and/or scientific terms, used herein may be taken to have the same.
[0033] In order to facilitate an understanding of the disclosed invention, a number of terms are defined below.
[0034] Interference in a communication network refers to a phenomenon where a signal quality in the communication network is degraded due to the presence of other signals or noise. There are different types of interference such as co-channel interference, adjacent channel interference, co-site interference, inter-symbol interference, and multi-path interference.
[0035] Reference Signal Received Power (RSRP) may refer to an average power level of reference signals received from a specific serving cell, measured in dBm. The RSRP provides an indication of the signal strength and may be used to evaluate coverage quality. Value of the RSRP may assist in determining whether a User Equipment (UE) is located in the indoor environment, or the outdoor environment based on signal attenuation patterns.
[0036] Reference Signal Received Quality (RSRQ) may refer to a measure of the quality of the received reference signal, calculated as a ratio of the RSRP to total received power.
[0037] Signal-to-Interference-plus-Noise Ratio (SINR) may represent a ratio of a power of a useful signal to a power of interference plus background noise, typically expressed in decibels (dB). The SINR may determine a signal quality experienced by the UE, depending on the indoor environment or the outdoor environment of the UE.
[0038] A coverage area may refer to a geographical area within which a wireless communication node, such as a Base Station (BS), a small cell, a repeater, or an access point, is capable of providing reliable wireless communication services to UE. The boundaries of the coverage area may vary based on factors such as transmission power, antenna configuration, environmental conditions, and network topology.
[0039] A small cell may refer to a low-power cellular radio access node with a limited coverage area, typically ranging from 10 meters to a few hundred meters.
[0040] A macro cell may refer to a high-power cellular radio access nodes that provide wide-area coverage, typically with a range of several kilometers, and are often mounted on towers or tall buildings.
[0041] A micro cell may refer to medium-power access nodes with coverage areas smaller than the macro cells but larger than femto or pico cells, generally used in urban areas to support a higher user density.
[0042] International Mobile Subscriber Identity (IMSI) may refer to a unique numerical identifier assigned to a mobile subscriber within a communication network. The IMSI may be utilized to associate the data collected from the UE with a particular subscriber.
[0043] International Mobile Equipment Identity (IMEI) may refer to a unique numerical identifier assigned to mobile devices, used to identify valid devices on the network.
[0044] An object of the present disclosure is to provide a system and a method for analyzing quality of a communication network through crowd source data collected from user devices. Another object of the present disclosure is to provide a system and a method for facilitating optimization of network performance. Still another object of the present disclosure is to provide a system and a method for determining interference in the communication network using samples in the crowd source data. Yet another object of the present disclosure is to provide a system and a method for visualization of the interference in the communication network.
[0045] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings. FIG. 1 through FIG. 7, discussed below, and the one or more embodiments used to describe the principles of the present disclosure are by way of illustration only and should not be construed in any way to limit the scope of the present disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged system or device.
[0046] In the disclosure, various embodiments are described using terms used in communication standards (e.g., 3rd Generation Partnership Project (3GPP), Extensible Radio Access Network (xRAN), and Open-Radio Access Network (O-RAN)), but these are merely examples for description. Various embodiments of the disclosure may also be modified and applied to other communication systems.
[0047] FIG. 1 illustrates a block diagram depicting an exemplary environment of a system 100 for determining interference in a communication network, in accordance with an embodiment of the present disclosure. The embodiment of the system 100 shown in FIG. 1 is for illustration only. Other embodiments of the system 100 may be used without departing from the scope of this disclosure.
[0048] As shown in FIG. 1, the system 100 may include a plurality of nodes 110-1 through 110-n (cumulatively referred to as “nodes 110” and alternatively referred to as “serving cells 110”) connected to a plurality of UEs 120-1 through 120-n (cumulatively referred to as “UEs 120”), a server 140, a Distributed File System (DFS) 150, a crowd source data collection entity 160, and a Network Management Console (NMC) 170 (alternatively referred to as network management device 170 or client device 170). The components of the system 100 communicate with each other via a network 130.
[0049] The nodes 110 may include one of at least one BS, at least one relay, and at least one Distributed Unit (DU). The BS may be a network infrastructure that provides wireless access to one or more terminals. The base station provides coverage to a plurality of predetermined geographic areas based on distance over which a signal may be transmitted. Examples of the BS include, but are not limited to, the macro cell, the femtocell, wireless “Access Point (AP),” “evolved NodeB (eNodeB) (eNB),” “5th Generation (5G) node,” “next generation NodeB (gNB),” “wireless point,” “Transmission/Reception Point (TRP).” The BS may provide wireless access in accordance with wireless communication protocols, e.g., 5G/NR 3GPP New Radio interface/access (NR), LTE, LTE-A, High Speed Packet Access (HSPA), Wi-Fi 802.11a/b/g/n/ac, etc. Aspects of the present disclosure are intended to include, or otherwise cover, any technology (known or later developed) bearing same or similar characteristics as of the above-mentioned BS, without deviating from the scope of the present disclosure. The serving cells 110 may also include the small cells, the macro cells, the micro cells, the indoor serving cells, and the outdoor serving cells.
[0050] Typically, the term “UE” can refer to any component such as “mobile station,” “subscriber station,” “remote terminal,” “wireless terminal,” “receive point,” or “end user device.” The UE 120 may correspond to, but is not limited to, any of mobile devices, tablets, or other portable devices utilized by users to access services provided by the network 130. The UEs 120 may be served by one or more of the plurality of nodes 110.
[0051] The communication network may be divided into different coverage regions. Each coverage region may comprise multiple serving cells 110 and UEs 120. The UEs 120 may be served by one or more serving cells 110 in one or more coverage area defined to a predetermined geographic region based on a distance over which a signal may be transmitted. The coverage area of the serving cells 110 may have variations in radio environment associated with natural and man-made obstructions, and thus a variation in interference levels. Interference in the communication network may occur due to various reasons such as neighboring wireless networks, natural and man-made obstructions, or even the environment itself. An increased level of interference beyond a threshold range may cause a range of problems including decreased signal strength, increased bit error rate, and even complete loss of connectivity in the communication network.
[0052] The UEs 120 may communicate with the serving cells 110 to avail services of the serving cells 110 through the network 130. The network 130 may include wired connections, wireless connections such as a proprietary Internet Protocol (IP) network, Internet, or in accordance with other wireless communication standards such as Worldwide Interoperability for Microwave Access (WiMAX), Wi-Fi 802.11a/b/g/n/ac, or a combination of wired and wireless connections.
[0053] During the session, the UE 120 is configured to capture data including Key Performance Indicators (KPIs) of the communication network during the session. The KPIs may refer to quantifiable measures that reflect a behavioral state of the serving cells 110. The UE 120 sends the data to the crowd source data collection entity 160, periodically, on demand, after an event or in real time.
[0054] The data sent by each of the UE 120 may be referred to as crowd source data. The crowd source data may be related to the UE 120 latched to the serving cell 110 during the session. The crowd source data may include hardware configuration of the UE 120 such as a model number of the UE 120, IMEI of the UE 120, IMSI of the UE 120, volume of data transferred between the UE 120 and the node 110, nature of service availed, location of the UE 120, feedback provided by the users regarding their experience with the network 130, information associated with radio parameters or the KPIs of the communication network including a RSRP, a RSRQ, and a SINR. The location of the UE 120 may include latitude and longitude coordinates of the UE 120 captured by Global Positioning Sensors (GPS) provided in the UE 120 at a time of data collection.
[0055] The crowd source data collection entity 160 may be a server or a group of servers configured to collect and store the crowd source data. The group of servers may be one or more of a cloud-based server, an application server, a content server, a host server, a web server, a database server, or a server hosted over a desktop computer. The group of servers may be hosted locally or over a cloud network. In one embodiment, the crowd source data collection entity 160 may be a database configured to store the crowd source data and communicate with the server 140. Further, the crowd source data collection entity 160 is communicatively coupled with the server 140. The server 140 collects the crowd source data periodically or on demand from the crowd source data collection entity 160.
[0056] The NMC 170 may be any client device or an electronic device such as “user device”, “User Equipment (UE)”, “mobile station,” “subscriber station,” “remote terminal,” “wireless terminal,” or “receive point”, a desktop computer, a portable computing devices such as laptops, tablet computers, handheld computer, mobile phones, wearable computers, or any other device suitable to provide front end services. The server 140 is configured to receive from the NMC 170, a user request for determining a severity level of interference in a user specified coverage area of the communication network associated with at least two radio parameters included in the crowd source data.
[0057] The server 140 processes samples of the crowd source data, generates visualization data, and determines a severity level of interference in the communication network. Based on the determined severity level of interference in a group of serving cells, the server 140 acquires performance metrics data from the identified group of serving cells. Further, the server 140 performs a Root Cause Analysis (RCA) on performance metrics data collected from the serving cells 110 facing the interference. Based on the user request, the NMC 170 is configured to receive the visualization data generated by the server 140 for representation of a plurality of samples of the crowd source data corresponding to the at least two radio parameters
[0058] The server 140 may further be connected to a storage medium for storing and managing the crowd source data collected from the crowd source data collection entity 160. Storage medium may generally be one or more of, without limitation, disk drives, hard-disk arrays, solid state storage devices, Network Attached Storage (NAS) devices, tape libraries or other magnetic, non-tape storage devices, and optical media storage devices. In one embodiment, the storage medium may form part of the DFS 150. The DFS 150 may allow the server 140 seamless data access and retrieval as needed for processing and storage.
[0059] The DFS 150 is configured to provide a scalable and fault-tolerant storage system, capable of handling entire operation specific data across distributed clusters of files associated with the server 140. In other embodiments, the DFS 150 may be integrated within the server 140 for storing records of the crowd source data. The DFS 150 may also contain records of the severity levels of the interference in the communication network. The DFS 150 is further configured to be utilized for storage the plurality of performance metrics associated with the identified group of serving cells.
[0060] Although FIG. 1 illustrates one example of the system 100, various changes may be made to FIG. 1. Further, the system 100 may include any number of components in addition to the components shown in FIG. 1. For example, the communication network environment may include any number of serving cells 110 and any number of UEs 120 in any suitable arrangement. Further, the serving cells 110 may communicate directly with any number of UEs and provide the UEs with wireless broadband access to the network 130. Each of the serving cells 110 may also communicate directly with the server 140. The serving cells 110 may provide access to other or additional external networks, such as external telephone networks or other types of data networks. Further, various components in FIG. 1 may be combined, further subdivided, or omitted and additional components may be added according to particular needs.
[0061] FIG. 2 illustrates a block diagram depicting a detailed system architecture of the server 140, in accordance with an embodiment of the present disclosure. The embodiment of the server 140 as shown in FIG. 2 is for illustration only. However, the server 140 may come in a wide variety of configurations, and FIG. 2 does not limit the scope of the present disclosure to any particular implementation of the server 140.
[0062] As shown in FIG. 2, the server 140 includes one or more processors 202 (hereinafter may also be referred to as “processor 202” or “at least one processor 202”), an Input-Output (I/O) interface 204, a memory 206, a network communication module 208 (alternatively referred to as a control module 208 or a display control module 208), a communication interface 210, a database 212, and a plurality of modules/units 214 (collectively referred to as the modules 214). Components of the server 140 are coupled to each other via a communication bus 216.
[0063] The I/O interface 204 may include suitable logic, circuitry, interfaces, and/or codes that may be configured to receive input(s). For example, the I/O interface 204 may have an input interface and an output interface. The I/O interface 204 may be configured to enable the user to provide the user input(s) to trigger (or configure) the server 140 to perform various operations for determining the severity level of interference in the communication network and provide an output of the determined severity level to the NMC 170. Examples of the input interface may include, but are not limited to, a touch interface, a mouse, and a keyboard, and the output interface includes a digital display, an analog display, or a touch screen display. Aspects of the present disclosure are intended to include or otherwise cover any type of the I/O interface 204 including known, related art, and/or later developed technologies without deviating from the scope of the present disclosure.
[0064] The processor 202 may include various processing circuitry and communicates with the I/O interface 204, the memory 206, the network communication module 208, and the communication interface 210, the database 212, the module(s) 214 via the communication bus 216. Examples of the communication bus 216 may include, but are not limited to, a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), and a Front Side Bus (FSB). Aspects of the present disclosure are intended to include or otherwise cover any type of coupling means present or related to later developed technologies, that may be configured to for connect the processor 202 to the other subsystems of the server 140, as the communication bus 216, without deviating from the scope of the present disclosure.
[0065] The processor 202 may include various processing circuitry configured to execute instructions 206A (hereinafter also referred to as “a set of instructions 206A”) stored in the memory 206 and to perform various processes. The processor 202 may also include a plurality of processing engines i.e., information processing units for determining interference in the communication network. The processor 202 may be configured to handle a set of tasks or computations executed by the processor 202 in a distributed computing environment. For an example, the processor 202 is configured to execute programs and processes to execute instruction(s) or code(s) stored in the memory 206 pertaining to determination of the interference in the communication network. The processor 202 is further configured to move data into or out of the memory 206 as required by an execution process of the server 140.
[0066] The processor 202 may include one or a plurality of processors, including a general-purpose processor, such as, for example, and without limitation, a Central Processing Unit (CPU), an Application Processor (AP), a dedicated processor, a graphics-only processing unit such as a Graphics Processing Unit (GPU) or the like, a programmable logic device, or any combination thereof.
[0067] The memory 206 is configured to store a set of instructions required by the processor 202 for controlling overall operations of the server 140. A part of the memory 206 may include a RAM, a cache memory, or a ROM. The memory 206 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory 206 may, in some examples, be considered a non-transitory storage medium. The "non-transitory" storage medium is not embodied in a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted that the memory 206 is non-movable. In some examples, the memory 206 can be configured to store larger amounts of information. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache). The memory 206 can be an internal storage unit or it can be an external storage unit of the server 140, cloud storage, or any other type of external storage.
[0068] In an embodiment, the module(s) 214 may be implemented as a combination of hardware and software programming (for example, programmable instructions) to implement one or more functionalities of the server 140. In non-limiting examples, described herein, such combinations of hardware and software programming may be implemented in several different ways, without deviating from the scope of the present disclosure. The module(s) 214 may include suitable logic, circuitry, interfaces, and/or codes. For example, the programming for the module(s) 214 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the module(s) 214 may comprise a processing resource (for example, one or more processors), to execute such instructions. In an embodiment, the module(s) 214 may be combined to a single module or each module of the module(s) 214 may be further subdivided into different modules.
[0069] In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the module(s) 214. In such examples, the server 140 may also 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 server 140 and the processing resource. In other examples, the module(s) 214 may be implemented using an electronic circuitry.
[0070] In one or more embodiments, the module(s) 214 may include one or more modules such as an acquisition module 214-2, a task execution module 214-4, and a data processing module 214-6. Each of the module(s) 214 are communicatively coupled with each other.
[0071] In an aspect, the processor 202, using the acquisition module 214-2, is configured to receive the crowd source data from the crowd source data collection entity 160. Before processing the crowd source data for determining the interference in the communication network, the crowd source data is pre-processed by the processor 202 by a series of operations. The pre-processing operations may include data validation and data cleaning for obtaining accurate values of the samples in the crowd source data. The pre-processed data may then be utilized to correctly identify interference levels in the communication network corresponding to the serving cells 110.
[0072] The processor 202, using the task execution module 214-4, is configured to receive, from the NMC 170, the user request for determining a severity level of interference in the communication network associated with at least two radio parameters included in the crowd source data. The user request may include a user preference of the at least two radio parameters for determining the interference and a coverage area or a geographical location of interest for determining the interference. A first radio parameter of the at least two radio parameters may correspond to coverage in the communication network and a second radio parameter of the at least two radio parameters may correspond to signal quality in the communication network. Based on the user request, the at least two radio parameters are selected by the task execution module 214-4 from the crowd source data.
[0073] In an embodiment, for obtaining the at least two radio parameters from the user, processor 202, using the data processing module 214-6, controls the NMC 170 to display selectable options for selecting the at least two radio parameters from the crowd source data. The processor 202, using the data processing module 214-6, controls the NMC 170 to display a list of a plurality of radio parameters. From the displayed list of selectable options for the plurality of radio parameters, the user is prompted to select the at least two radio parameters. For example, the plurality of radio parameters may include, but not limited to, RSRP, RSRQ, SINR, and throughput.
[0074] In an embodiment, the samples of the crowd source data include the samples indicating values corresponding to one or more of the at least two radio parameters. For example, a first sample from a first UE among the UE 120 may correspond to a sample indicating value of the RSRP. A second sample from the first UE may correspond to a sample indicating value of the SINR. In another embodiment, a third sample from the first UE may correspond to a sample indicating both the value of the RSRP and the SINR. In response to the received user request, the processor 202, using the data processing module 214-6, compares the values of the plurality of samples in the crowd source data with a first pre-defined threshold range and a second pre-defined threshold range.
[0075] The first pre-defined threshold range is associated with the first radio parameter of the at least two radio parameters and the second pre-defined threshold range is associated with the second radio parameter of the at least two radio parameters. The first pre-defined threshold range associated with the first radio parameter has a first pre-defined minimum value of the first radio parameter and a first pre-defined maximum value of the first radio parameter. Similarly, the second pre-defined threshold range associated with the second radio parameter has a second pre-defined minimum value of the second radio parameter and a second pre-defined maximum value of the second radio parameter. The first pre-defined threshold range and the second pre-defined threshold range may be tuned as per requirement of a network operator.
[0076] The result of the comparison may indicate that values of a first set of samples amongst the plurality of samples lies within the first pre-defined threshold range and the second pre-defined threshold range. Further, the result of the comparison may indicate a value of a second set of samples amongst the plurality of samples that lies beyond/outside the first pre-defined threshold range and the second pre-defined threshold range. In particular, the data processing module 214-6, compares value of each sample corresponding to each of the at least two radio parameters with each of the first pre-defined minimum value, the first pre-defined maximum value, the second pre-defined minimum value, and the second pre-defined maximum value. The second set of samples having the second set of values corresponding to the at least two radio parameters are identified. Further, the data processing module 214-6 may also determine a count of the first set of samples and a count of the second set of samples.
[0077] Based on the results of the comparison, the processor 202, using the data processing module 214-6, is configured to generate the visualization data for representing a plot of values of the plurality of samples of the crowd source data corresponding to the at least two radio parameters. The visualization data represents the plot of the plurality of samples of the crowd source data corresponding to the at least two radio parameters over a map layer of the coverage area.
[0078] Further, based on the generated visualization data, processor 202, using the data processing module 214-6, is configured to determine the severity level of interference in the coverage area of the communication network. The severity level of interference in the coverage area of the communication network may be determined based on the plot of the plurality of samples of the crowd source data lying within or outside of the one or more of the first pre-defined threshold range and the second pre-defined threshold range. The severity level of the interference may indicate whether the UE 120 lies in one of a first severity level of the interference indicating an area receiving good network coverage lying within both the first pre-defined threshold range and the second pre-defined threshold range, a second severity level of the interference indicating an area having optimum network coverage lying within the second pre-defined threshold range and outside of the first pre-defined threshold range, a third severity level of the interference indicating an area requiring improvement in the network coverage lying within the first pre-defined threshold range and outside of the second pre-defined threshold range, and a fourth severity level of the interference indicating an area having poor network coverage lying outside both the first pre-defined threshold range and the second pre-defined threshold range.
[0079] The first severity level of interference may be an area receiving good network coverage with minimal severity level of interference. The second severity level of interference may be an area having optimum network coverage with low severity level of interference. The third severity level of interference may be an area requiring improvement in the network coverage with a medium severity level of interference, and the fourth severity level of interference may be an area an area having poor network coverage with a high severity level of interference. The severity levels may be relative indications of severity level with respect to each other and may be pre-defined with respect to one or more of the first pre-defined threshold range and the second pre-defined threshold range. Similarly, a plurality of severity levels may be pre-defined by the network operator based on fine-tuning of the first pre-defined threshold range and the second pre-defined threshold range.
[0080] For determining the severity level of the interference, the processor 202, using the data processing module 214-6, determines one or more clusters of UEs among the plurality of UEs 120 based on the plurality of samples in the generated visualization data. In an embodiment, for determining the one or more clusters of the UEs, the data processing module 214-6 may utilize a Machine Learning (ML) model. The ML model may comprise a machine learning component to perform a plurality of machine learning and deep learning operations on the plurality of samples of the crowd source data. The ML model may identify, based on one or more of a density or proximity of samples of the first set of samples and the second set of samples in the generated visualization data and the count of the samples in the first set of samples and the second set of samples. The ML model, may include, but not be limited to, one or more of cluster-based ML model, density-based ML models, proximity-based ML models, probability-based ML model, and neural networks. In another embodiment, the processor 202, using the data processing module 214-6, may apply one or more clustering techniques on the samples of the crowd source data in the generated visualization data, for determining the severity level of interference in the coverage area.
[0081] The processor 202, using the display control module 208, displays on a Graphical User Interface (GUI) (alternatively referred to as “an application interface”) of the NMC 170, in a pre-defined data format, the determined severity level of the interference in a priority order. The priority order may correspond to an order of the determined severity levels of the interference in an order of coverage areas requiring most attention from the network operators or in an order of coverage areas requiring least attention from the network operators. For example, the priority order may be in an order such as the fourth severity level having a first priority, the third severity level having a second priority, the second severity level having a third priority, and the first severity level having a fourth priority. The pre-defined data format may correspond to, but not limited to, tabular format or one or more graphical formats including a scatter chart, a pie chart, and a graph.
[0082] The network communication module 208 (alternatively referred to as display control module 208) may be an application console including suitable logic, circuitry, interfaces, and/or codes that may be configured to enable the server 140 to receive input(s) and/or render output(s) from the NMC 170. In some aspects of the present disclosure, the network communication module 208 may be a controlling engine to host a console on an external user device, for executing various operations of one or more computer executable applications by way of which a user can trigger the server 140 to identify the severity level of interference in the communication network.
[0083] In some other aspects of the present disclosure, the network communication module 208 may control the application interface on the NMC 170 for user interaction. The network communication module 208 controls display of output(s) information over the application interface related to the determined severity level of the interference in the pre-defined data format of the visualization data at the NMC 170. Further, the network communication module 208 controls the display of the visualization data. The application interface comprises one or more screens to enable the network operator to select the pre-defined data format and view the visualization data in the pre-defined data format. The displayed visualization data includes the plurality of samples plotted over a map layer of the geographic area corresponding to the severity level of interference. The displayed visualization day may also include the graph, the scatter chart, or the pie chart.
[0084] The processor 202, using the data processing module 214-6, may further determine the count of the first set of samples and the count of the second set of samples. The processor 202, using the display control module 208, displays the plurality of samples corresponding to the determined severity level of interference on the application interface in the pre-defined data format of the visualization data. Furthermore, the processor 202, using the display control module 208, displays on the application interface in the pre-defined data format, a percentage of the count of the first set of samples and the count of the second set of samples with respect to a total count of the plurality of samples corresponding to the determined severity level of interference. Furthermore, the display control module 208 is configured to control the NMC 170 to display, on the application interface, the root cause of degradation in the performance of the group of serving cells corresponding to the severity level of interference.
[0085] In one embodiment, the processor 202, using the display control module 208, controls the application interface of the NMC 170 to display selectable options such as a drop-down list of radio parameters such as RSRP, RSRQ, SINR, and throughput. From the drop-down list, the user is prompted to select the at least two radio parameters corresponding to the coverage and signal quality. For an example, the user may select the at least two radio parameters as the RSRP and the SINR. The network communication module 208 also controls the application interface to display to the user an option to input the geographic location for determining the severity of the interference in a specific coverage area the communication network.
[0086] In a non-limiting example, when the at least two radio parameters selected are RSRP and the SINR, the RSRP may indicated the coverage in the communication network and the SINR may indicate the signal quality in the communication network. Corresponding to the RSRP and SINR, the first pre-defined threshold range for the RSRP may be from -100dBm to -80dBm, where the first pre-defined minimum value is -100dBm and the first pre-defined maximum value is -80dBm. The second pre-defined threshold range for the SINR may be from 0 dB to 20 dB, where the second pre-defined minimum value is 0dB and the second pre-defined maximum value is 20dB.
[0087] The processor 202, using the data processing module 214-6, may determine the first set of samples having values of RSRP greater than -100dBm and values of SINR greater than 0dB, and values of RSRP lesser than -80dBm and values of SINR lesser than 20dB. Further, the processor 202, using the data processing module 214-6, may determine the second set of samples having values of RSRP lesser than -100dBm and values of SINR lesser than 20dB, values of RSRP lesser than -80dBm and values of SINR lesser than 0dB, and values of RSRP lesser than -100dBm and values of SINR lesser than 0dB, and values of RSRP greater than -80dBm and values of SINR greater than 20dB.
[0088] Based on the determined first set of samples and the second set of samples, the processor 202, using the data processing module 214-6, may generate the visualization data. The visualization data may include a plot of the first set of samples and the second set of samples on the map layer of the coverage area or in the graphical format. Based on the visualization data, the processor 202, using the data processing module 214-6, determines the severity level of an interference in the communication network by determining the one or more clusters of the UEs in which the first set of samples and the second set of samples are plotted in the visualization data. A detailed exemplary embodiment of the determination of the severity level of interference is described further below with respect to FIG. 5.
[0089] Based on the one or more clusters of the UEs, the processor 202, using the data processing module 214-6, may identify a group of serving cells in the coverage area corresponding to the samples of the crowd source data experiencing one of the severity levels of the interference. The group of serving cells may be identified based on identity information of the service cells 110 and a service area information of the service cells 110 serving the one or more clusters of the UEs. The processor 202, using the data processing module 214-6, may fetch the identity information of the service cells 110 and the service area information of the service cells 110 from the crowd source data collection entity 160 or the DFS 150. In an example, the processor 202, using the data processing module 214-6, may identify a group of serving cells in the coverage area having the third severity level of the interference or the fourth severity level of the interference. The processor 202, using the data processing module 214-6, may acquire performance metrics data corresponding to the identified group of serving cells directly from the serving cells 110 or via the crowd source data collection entity 160. The performance metrics data may include calculated values derived from the KPIs to evaluate whether the communication network is meeting operational objectives and/or customer service objectives set by a network operator.
[0090] In a non-limiting example, corresponding to the radio parameters RSRP and SINR, the processor 202, may acquire the performance metrics data of the identified group of serving cells. The performance metrics data may include RSRP distribution, SINR distribution, coverage gaps in the communication network, and network quality degradation.
[0091] The processor 202, using the data processing module 214-6, may then perform the RCA by analyzing the performance metrics data corresponding to the identified group of serving cells to ascertain the root cause of degradation in performance of the group of serving cells. The root cause of degradation may refer to an underlying problem or issue causing interference, including, but not limited to, the co-channel interference, the adjacent channel interference, the co-site interference, inter-symbol interference, and the multi-path interference, in the group of serving cells 110 and eventually causing service disruption for the UEs 120. To identify the root cause of degradation from the performance metrics data, the processor 202, may perform a series of operations on the performance metrics data. The series of operations may include analyzing trends in the performance metrics data, identification of anomalies in the performance metrics data, and correlation of the performance metrics with potential factors that may contribute to the identified anomalies.
[0092] The communication interface 210 may manage communications with the NMC 170, the network 130, the crowd source data collection entity 160, and the DFS 150. For example, the communication interface 210 may manage reception of the crowd source data from the UEs 120 by the server 140, directly or through the crowd source data collection entity 160. The communication interface 210 may include an electronic circuit specific to a standard that enables wired or wireless communication. The communication interface 210 is configured for communicating with external devices via one or more networks. Further, the communication interface 210 may also provide a communication pathway for one or more components of the server 140. Examples of such components include, but are not limited to, the module(s) 214, the database 212, and the DFS 150.
[0093] The database 212 may store the pre-processed crowd source data. Furthermore, the database 212 may store an outcome of the RCA of the serving cells 110 facing the severity level of interference in the communication network. The processor 202, may fetch from the DFS 150, the outcome of the RCA and the serving cells 110 facing the severity level of interference and send to the NMC 170. The database 212 may be accessed and updated by the processor 202. The database 212 may be implemented as one or more of centralized database, Relational Database Management System (RDBMS), Non-Relational Database Management System, Hierarchical Database Management System, Network Database Management System, an in-memory database including a distributed in-memory data storage, distributed database, or a distributed file system.
[0094] Although FIG. 2 illustrates one example of server 140, various changes may be made to FIG. 2. For example, the server 140 may include any number of components in addition to the components shown in FIG. 2. Further, various components in FIG. 2 may be combined, further subdivided, or omitted and additional components may be added according to particular needs.
[0095] In an alternate embodiment, each module/unit of the module(s)/unit(s) w14 (i.e., the acquisition module 214-2, the task execution module 214-4, and the data processing module 214-6) is configured to independently perform various operations of the processor 202, as described herein, without deviating from the scope of the present disclosure.
[0096] In one embodiment, the displayed visualization data may correspond to a graphical representation of aggregation of values of the samples of the radio parameters derived from the crowd source data validated by the processor 202 for visualization of severity level of interference in the communication network in a geographical region to be analyzed. FIG. 3 illustrates the graphical representation 300 of aggregation of the samples of the crowd source data, in accordance with an embodiment of the present disclosure. As illustrated in FIG. 3, a plurality of nodes 301-1 through 301-n (same as the nodes 110) serving the UEs 120 operating in the geographic region identified by the processor 202 corresponding to the medium severity level of interference and/or the high severity level of interference, may be plotted. The identified plurality of nodes 301-1 through 301-n may be plotted over a spatial map layer of the geographic region of the communication network to be analyzed. The spatial map layer may also depict the plurality of samples of the crowd source data indicated by color-coded dots. The color-coded dots may represent the severity level of interference experienced by a specific UE 120 operating at a location in the geographic region. Through the generated map layer, the network operator may easily identify at a glance, the geographic regions in which the one or more clusters of the UEs 120 are facing medium severity level of interference and/or the high severity level of interference.
[0097] FIG. 4 illustrates a block diagram depicting an example system architecture of the NMC 170, in accordance with an embodiment of the present disclosure. The embodiment of the system architecture of the of the NMC 170 as shown in FIG. 4 is for illustration only. However, the NMC 170 may come in a wide variety of configurations, and FIG. 4 does not limit the scope of the present disclosure to any particular system architecture of the NMC 170.
[0098] As shown in FIG. 4, the NMC 170 (alternatively referred to as “user device” or the “client device”) includes one or more processors 402 (hereinafter also referred to as “processor 402”), a memory 404, an interface(s) 406, a communication unit 408, and a processing engine(s)/unit(s) 410. These components may be in electronic communication via one or more buses (e.g., communication bus 412). Although not shown in FIG. 4, the NMC 170 may also include a touchscreen, and a display. For the sake of convenience, the term “client device” used herein refers to an electronic device such as the NMC 170 that wirelessly accesses the server 140 via the network 130.
[0099] The one or more components of the NMC 170 are communicatively coupled with the processor 402 (described below) for accessing different functionalities of the system 100. The processor 402 may include various processing circuitry and configured to execute programs or computer readable instructions stored in the memory 404. The processor 402 may also include an intelligent hardware device including a general-purpose processor, such as, for example, and without limitation, a Central Processing Unit (CPU), an Application Processor (AP), a dedicated processor, or the like, a microcontroller, a Field-Programmable Gate Array (FPGA), a programmable logic device, a discrete hardware component, or any combination thereof. In some cases, the processor 402 may be configured to operate a memory array using a memory controller. In some cases, a memory controller may be integrated into the processor 402. The processor 402 may be configured to execute computer-readable instructions stored in a memory (e.g., the memory 404) to cause the NMC 170 to perform various functions (e.g., displaying the generated visualization data received from the server 140).
[0100] The memory 404 is communicatively coupled to the processor 402. A part of the memory 404 may include a RAM, and another part of the memory 404 may include a flash memory or other ROM. The memory 404 is configured to store a set of instructions required by the processor 402 for controlling overall operations of the NMC 170. The memory 404 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of EPROM or EEPROM memories. In addition, the memory 404 may, in some examples, be considered a non-transitory storage medium. The "non-transitory" storage medium is not embodied in a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted that the memory 404 is non-movable. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in the RAM or cache). The memory 404 can be an internal storage unit or it can be an external storage unit of the NMC 170, cloud storage, or any other type of external storage.
[0101] More specifically, the memory 404 may store computer-readable instructions including instructions that, when executed by a processor (e.g., the processor 402) cause the NMC 170 to perform various functions described herein. In some cases, the memory 404 may contain, among other things, a BIOS which may control basic hardware or software operation such as the interaction with peripheral components or devices.
[0102] The interface 406 (same as the application interface or the GUI) may include suitable logic, circuitry, a variety of interfaces, and/or codes that may be configured to receive input(s) and present output(s) on the application interface of the NMC 170. The variety of interfaces may include interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. For example, the I/O interface may have an input interface and an output interface. The interface 406 may facilitate communication of the NMC 170 with various devices and systems connected to it. The interface 406 may also provide a communication pathway for one or more components of the NMC 170. Examples of such components include, but are not limited to, the processing Engine(s)/Unit(s) 410.
[0103] In one or more embodiments, processing Engine(s)/Unit(s) 410 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the NMC 170. In non-limiting 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)/Unit(s) 410 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor 402 may comprise a processing resource (for example, one or more processors), to execute such instructions.
[0104] In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing Engine(s)/Unit(s) 410. In such examples, the NMC 170 may also 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 NMC 170 and the processing resource. In other examples, the processing Engine(s)/Unit(s) 410 may be implemented using an electronic circuitry.
[0105] In one or more embodiments, processing Engine(s)/Unit(s) 410 may include one or more Engine(s)/Unit(s) selected from any of an input unit 410-2, a display control unit 410-4, and other Engines/Unit(s) (not shown).
[0106] In an embodiment, the processor 402, using the input unit 410-2, is configured to receive, from the server 140, the user request for determining interference in the communication network. The processor 402 may control, via the display control unit 410-4, the interface 406 of the NMC 170 to display the list of selectable options corresponding to the plurality of the radio parameters for receiving a selection of the at least two radio parameters. The processor 402, using the input unit 410-2, is configured to receive, from the server 140, the visualization data corresponding to the selection of the at least two radio parameters. Further, the processor 402, using the input unit 410-2, is configured to receive, from the server 140, the determined severity level of the interference in the pre-defined data format. The processor 402 may control, via the display control unit 410-4, the interface 406 of the NMC 170 to display the visualization data received from the server 140.
[0107] In one embodiment, the processor 402, using the display control unit 410-4, renders the visualization data in one or more pictorial format, graphical format, and tabular format. The processor 402, may also control, via the display unit 410-4, the interface 406 to display the visualization data including the identity information of the group of serving cells and the serving area information associated with the group of serving cells along with the level of the interference, the severity level of interference in the priority order, and the root cause of degradation in the performance of the group of serving cells. The visualization data may also include statistics corresponding to the percentage of samples of the UEs 120 facing low severity level of interference, minimal severity level of interference, medium severity level of interference and/or the high severity level of interference.
[0108] The processor 402 is configured to control the interface 406 to provide an intuitive control to the network administrators to manage display of graphical elements such as charts, diagrams, or highlighted text in the visualization data that enhance the comprehension and presentation of the visualization data.
[0109] The communication unit 408 may include one or more antennas, one or more of Radio Frequency (RF) transceivers, a transmit processing circuitry, and a receive processing circuitry. The communication unit 408 may be configured to receive incoming signals, such as signals transmitted by the server 140, and the NMC 170. The communication unit 408 may down-convert the incoming signals to generate baseband signals which may be sent to the receiver processing circuitry. The receiver processing circuitry may transmit the processed baseband signals to the processor 402 for further processing. The transmit processing circuitry may receive analog or digital data from the processor 402 and may encode, multiplex, and/or digitize the outgoing baseband data to generate processed baseband signals. The communication unit 408 may further receive the outgoing processed baseband from the transmit processing circuitry and up-converts the baseband signals to Radio Frequency (RF) signals that may be transmitted to the server 140.
[0110] The NMC 170 may be deployed as a software application on a dedicated server, a cloud-based solution, or a hybrid system, depending on the communication network requirements. The NMC 170 may be utilized by the network operators of a network operations team for receiving the root cause analysis of degradation in the performance of the group of serving cells. Additionally, the NMC 170 may be integrated with network monitoring tools, database management systems, and security modules to provide a holistic view of the performance metrics analysis of the crowd source data in form of the visualization data or the network performance report.
[0111] Although FIG. 4 illustrates one example of NMC 170, various changes may be made to FIG. 4. For example, various components in FIG. 4 could be combined, further subdivided, or omitted, and additional components could be added according to particular needs. As a particular example, the processor 402 may be divided into multiple processors, such as one or more CPUs and one or more GPUs. Further, while FIG. 4 illustrates the NMC 170 configured as a mobile telephone or smartphone, the NMC 170 may also be configured to operate as other types of mobile or stationary devices.
[0112] In an alternate embodiment, each engine/module of the processing Engine(s)/module(s) 410 is configured to independently perform various operations of the processor 402, as described herein, without deviating from the scope of the present disclosure. Additionally, different engines/modules shown in Fig. 4 may be split into two or more engines/modules each operating independently in communication with one another, optionally in a distributive manner, with shared responsibilities. Furthermore, multiple instances of the engines/modules may be implemented for identification of serving cells having degraded performance or multiple modules can be combined into a single engine/module to perform all corresponding functions described herein.
[0113] In another embodiment, the processor 202 is configured to display the severity level of interference in the pre-defined data format by plotting a scatter chart of the plurality of samples corresponding to the RSRP and the SINR. FIG. 5 illustrates a scatter chart 500 of RSRP vs SINR, in accordance with an embodiment of the present disclosure. As illustrated in FIG. 5, horizontal axis may represent RSRP values of the UEs 120, measured in decibels-milliwatts (dBm), and vertical axis may represent SINR values of the UEs 120, measured in decibels (dB). The processor 202, using the data processing module 214-6, may generate the visualization data by analyzing co-relation between the values of the RSRP and the values of the SINR, and plot the plurality of samples of the crowd source data. The color-coded dots representing samples of the crowd source data validated by the processor 202, based on the determined the severity level of the interference in the communication network, may be plotted in the scatter chart 500. The scatter chart 500 may also include a line extrapolated between a pre-defined threshold value of the RSRP and a pre-defined threshold value of the SINR, for ease of analysis by the network operator. A count of samples of the crowd source data falling above or below the line in a quadrant may indicate a count of UEs 120 experiencing different severity levels of the interference.
[0114] As illustrated in FIG. 5, dots in region indicated as (1) may represent UEs 120 having values of RSRP greater than -100 dBm but lesser than -80 dBm and values of SINR greater than 0 dB but lesser than 20 dB. The region (1) may thus indicate the area receiving good network coverage (indicated as good coverage area). Dots in region indicated as (2) may represent UEs 120 having values of RSRP greater than -100 dBm and values of SINR lesser than 0 dB. The region (2) may thus indicate the area requiring improvement in the network coverage (indicated as interference area). Dots in region indicated as (3) represent UEs 120 having values of RSRP lesser than -100 dBm and values of SINR greater than 0 dB. The region (3) may thus indicate the area having optimum network coverage (indicated as improvement area). Dots in region indicated as (4) represent UEs 120 having values of RSRP lesser than -100 dBm and values of SINR lesser than 0 dB. The region (4) may thus indicate the area having poor network coverage (indicated as poor coverage area).
[0115] In yet another embodiment, the processor 202 may be configured to control, using the control module 208, the application interface to display the severity level of interference in the pre-defined data format by plotting a pie-chart of the crowd source data. FIG. 6 illustrates a pie-chart 600 of crowd source data depicting the severity levels of interference, in accordance with an embodiment of the present disclosure. As illustrated in FIG. 6, the pie-chart may represent percentage of the samples of total plurality of samples of crowd source data determined by the processor 202 having different severity levels of the interference. The processor 202, using the visualization data, may generate the pie-chart for making a comparison of a geographical area to be analyzed in terms of UEs 120 in an area facing different severity levels of the interference.
[0116] As illustrated in FIG. 6, 50% of the samples of the crowd source data lie in the area receiving good network coverage (indicated as good coverage area). 20% of the samples of the crowd source data lie in the area requiring improvement in the network coverage (indicated as interference area). 10% of the samples of the crowd source data lie in the area having optimum network coverage (indicated as improvement area). 20% of the samples of the crowd source data lie in the area having poor network coverage (indicated as poor coverage area).
[0117] FIG. 7 illustrates a flow chart of a method 700 for determining the interference in the communication network, in accordance with an embodiment of the present disclosure. Although the flow diagram 700 comprises a series of operation steps indicated by steps 702 through 712, in some embodiments, the flow diagram 700 may include additional steps, fewer steps or steps in different order than those depicted in Fig. 7. In other embodiments, the steps 702-712 may be combined or may be performed in parallel. The flow diagram 700 starts at steps 702. The flow diagram 700 described herein is a process executed by the processor 202 to determine the severity level of the interference in the coverage area of the communication network.
[0118] At step 702, the processor 202, using the acquisition module 214-2, collects the crowd source data from the crowd source data collection entity 160 and stores the crowd source data in the DFS 150. The crowd source data may include, among other information, the coverage area for determining the interference and information related to radio parameters of the nodes 110 including the throughput, the RSRP, the RSRQ, and the SINR.
[0119] At step 704, the processor 202, using the task execution module 214-4, receives from the NMC 170, a user request for determining a severity level of interference in the communication network associated with at least two radio parameters included in the crowd source data. Selection of the at least two radio parameters may be based on a user requirement of assessment of the quality of the communication network. The requirement may be determined by the network operator and input into the server 140 through the NMC 170. The user request may also include a geographical area for determining the severity level of interference. In an embodiment, the user request may include a preference of the pre-defined format of the network operator for viewing the generated visualization data. In one embodiment, the at least two radio parameters may include values of the RSRP and the values of the SINR from the crowd source data. The information corresponding at least two parameters is analyzed by the processor 202 for determining the severity level of interference in communication network.
[0120] At step 706, the processor 202 compares, using the data processing module 214-6 based on a reception of the user request for determining the severity level of interference, values of the plurality of samples in the crowd source data with the first pre-defined threshold range and the second pre-defined threshold range. In a non-limiting example, the processor 202, using the data processing module 214-6, compares values of the crowd source data corresponding to the RSRP and the SINR with pre-defined threshold ranges of the RSRP and the SINR respectively.
[0121] At step 708, the processor 202, using the task execution module 214-4, generates the visualization data representing the plot of the plurality of samples of the crowd source data corresponding to the at least two radio parameters, based on the results of the comparison. In one embodiment, the visualization data may be generated in one or more graphical formats. The one or more graphical formats of the visual representation may include the map, the graph, the plot, the scatter chart, and the pie chart of the samples of the crowd source data. In another embodiment, the samples of the crowd source data may be layered over the map of the geographical location.
[0122] At step 710, the processor 202, using the data processing module 214-6, determines the severity level of the interference in the coverage area of the communication network based on the generated visualization data. The severity level may indicate a minimal severity, low severity, medium severity, and high severity. The processor 202 utilizes the plot of the first set of samples and the second set of samples on the map layer to determine the one or more clusters of the UEs. Based on the one or more clusters of the UEs, the processor 202 determines the severity level of the interference in the communication network by identifying whether the coverage area corresponds to the area receiving good network coverage with the low severity level, the area having network coverage requiring improvement with the minimal severity, the area having high interference with the medium severity, and the area having poor network coverage with the high severity.
[0123] In another embodiment, the processor 202, using the data processing module 214-6, may apply the one or more clustering techniques on the samples of the crowd source data plotted in the visualization data. The processor 202 then identifies the serving cells 110 serving the UEs 120 in the one or more clusters. The processor 202 acquires performance metrics data from the identified group of serving cells, and performs an analysis on the acquired performance metrics data to identify the root cause of degradation in performance of the group of serving cells. The generated visualization data and the root cause of degradation in performance of the group of serving cells are transmitted to the NMC 170 by the processor 202.
[0124] At step 712, the processor 202, using the display control module 208, displays the determined severity level of interference in the priority order on the application interface. The processor 202 may display the determined severity level of interference in the pre-defined format. Further, the processor 202 may control the application interface of the NMC 170 to display the generated visualization data. Furthermore, the processor 202 may control the application interface of the NMC 170 to display the root cause of degradation in performance of the group of serving cells.
[0125] Now, referring to the technical abilities and advantageous effect of the present disclosure, operational advantages that may be provided by one or more embodiments may include determining interference in the communication network and other poor performing nodes in the communication network based on a graphical analysis of values of the radio parameters. The present disclosure enables the network operations team to visualize an analysis of a large volume of the crowd source data to identify interference levels in the communication network.
[0126] The present disclosure further enables the network operation team to analyze and perform a quick corrective action on the serving cells experiencing interference in the network for enhancement of the user experience. Another notable advantage offered by the present disclosure is that the disclosed system and the method is that enables visualization of an outcome of the analysis of the communication network corresponding to the severity level of interference for improving network coverage and interference in a form desired by the network operator based on geographical areas, number of samples, and a preference of format of viewing the visualization data and the outcome.
[0127] Embodiments of the present technology may be described herein with reference to flowchart illustrations of methods and systems according to embodiments of the technology, and/or procedures, algorithms, steps, operations, formulae, or other computational depictions, which may also be implemented as computer program products. In this regard, each block or step of the flowchart, and combinations of blocks (and/or steps) in the flowchart, as well as any procedure, algorithm, step, operation, formula, or computational depiction can be implemented by various means, such as hardware, firmware, and/or software including one or more computer program instructions embodied in computer-readable program code. As will be appreciated, any such computer program instructions may be executed by one or more computer processors, including without limitation a general-purpose computer or special purpose computer, or other programmable processing apparatus to perform a group of operations comprising the operations or blocks described in connection with the disclosed methods.
[0128] Further, these computer program instructions, such as embodied in computer-readable program code, may also be stored in one or more computer-readable memory or memory devices (for example, the memory 206) that can direct a computer processor or other programmable processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory or memory devices produce an article of manufacture including instruction means which implement the function specified in the block(s) of the flowchart(s).
[0129] It will further be appreciated that the term “computer program instructions” as used herein refer to one or more instructions that can be executed by the one or more processors (for example, the processor 202) to perform one or more functions as described herein. The instructions may also be stored remotely such as on a server, or all or a portion of the instructions can be stored locally and remotely.
[0130] Those skilled in the art will appreciate that the methodology described herein in the present disclosure may be carried out in other specific ways than those set forth herein in the above disclosed embodiments without departing from essential characteristics and features of the present invention. The above-described embodiments are therefore to be construed in all aspects as illustrative and not restrictive.
[0131] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Any combination of the above features and functionalities may be used in accordance with one or more embodiments.
[0132] In the present disclosure, each of the embodiments has been described with reference to numerous specific details which may vary from embodiment to embodiment. The foregoing description of the specific embodiments disclosed herein may reveal the general nature of the embodiments herein that others may, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications are intended to be comprehended within the meaning of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and is not limited in scope.
LIST OF REFERENCE NUMERALS
[0133] The following list is provided for convenience and in support of the drawing figures and as part of the text of the specification, which describe innovations by reference to multiple items. Items not listed here may nonetheless be part of a given embodiment. For better legibility of the text, a given reference number is recited near some, but not all, recitations of the referenced item in the text. The same reference number may be used with reference to different examples or different instances of a given item. The list of reference numerals is:
100 – Communication system
110 – Serving cell/Node
120 – User Equipment (UE)
130 – Network
140 – Server
150 – Distributed File System
160 – Crowd source data collection entity
170 – Network Management Console
202 – Processor
204 – I/O Interface
206 – Memory
206A - Instructions
208 – Network Communication Module/Control Module
210 – Communication Interface
212 – Database
214 – Module(s)
214-2 – Acquisition module
214-4 – Task execution module
214-6 – Data processing module
216 - Communication Bus
300 - Graphical representation of aggregation of the samples of the crowd source data
400 – System architecture of Network Management Console (NMC)
402 – Processor
404 – Memory
406 – Interface
408 – Communication unit
410 – Processing Engine(s)/Unit(s)
410-2 – Input Unit
410-4 – Display Control Unit
412 – Communication Bus
500 - Scatter chart of RSRP vs SINR
600 - Pie-chart of crowd source data depicting severity levels of interference
700 - Flow chart for a method for determining interference in the communication network

,CLAIMS:WE CLAIM:

1. A method (700) for determining interference in a communication network, the method (700) comprising:
collecting, by an acquisition module (214-2) from a crowd source data collection entity, crowd source data associated with a plurality of User Equipment (UEs) (120);
comparing, by a data processing module (214-6) based on a reception of a user request for determining the severity level of interference, values of a plurality of samples in the crowd source data with a first pre-defined threshold range and a second pre-defined threshold range;
generating, by the data processing module (214-6) based on a result of the comparison, visualization data representing a plot of the plurality of samples;
determining, by the data processing module (214-6), a severity level of the interference in a coverage area of the communication network based on the generated visualization data; and
displaying, by a display control module (208) on an application interface in a pre-defined data format, the determined severity level of the interference in a priority order.
2. The method (700) as claimed in claim 1, further comprising determining, by the data processing module (214-6), one or more clusters of UEs among the plurality of UEs (120) based on the plurality of samples using a Machine Learning (ML) model.
3. The method (700) as claimed in claim 1, wherein
the plurality of samples is associated with at least two radio parameters of the crowd source data received in the user request,
the pre-defined first threshold range is associated with a first radio parameter of the at least two radio parameters, and
the pre-defined second threshold range is associated with a second radio parameter of the at least two radio parameters.
4. The method (700) as claimed in claim 1, further comprising:
identifying, by the data processing module (214-6), a group of serving cells of a plurality of serving cells (110) in the coverage area having the severity level of the interference indicating an area requiring improvement in a network coverage, or indicating an area having a poor network coverage;
acquiring, by the data processing module (214-6), performance metrics data from the identified group of serving cells; and
performing, by the data processing module (214-6), an analysis on the acquired performance metrics data to identify a root cause of degradation in performance of the group of serving cells.
5. The method (700) as claimed in claim 4, further comprising controlling, by the display control module (208), the application interface for:
displaying the root cause of degradation in the performance of the identified group of serving cells; and
displaying the visualization data in one or more graphical formats including the plot of the plurality of samples in one or more of a map layer, a graph, a scatter chart, and a pie chart.
6. A system (100) for determining interference in a communication network, the system (100) comprising:
an acquisition module (214-2) configured to collect, from a crowd source data collection entity, crowd source data associated with a plurality of User Equipment (UEs) (120);
a data processing module (214-6) configured to:
compare, based on a reception of a user request for determining the severity level of interference, values of a plurality of samples in the crowd source data with a first pre-defined threshold range and a second pre-defined threshold range;
generate, based on a result of the comparison, visualization data representing a plot of the plurality of samples;
determine a severity level of the interference in a coverage area of the communication network based on the generated visualization data; and
a display control module (208) configured to display, on an application interface in a pre-defined data format, the determined severity level of the interference in a priority order.
7. The system (100) as claimed in claim 6, wherein the data processing module (214-6) is further configured to determine one or more clusters of UEs among the plurality of UEs (120) based on the plurality of samples using a Machine Learning (ML) model.
8. The system (100) as claimed in claim 6, wherein
the plurality of samples is associated with at least two radio parameters of the crowd source data received in the user request,
the pre-defined first threshold range is associated with a first radio parameter of the at least two radio parameters, and
the pre-defined second threshold range is associated with a second radio parameter of the at least two radio parameters.
9. The system (100) as claimed in claim 6, wherein the data processing module (214-6) is further configured to:
identify a group of serving cells of a plurality of serving cells (110) in the coverage area having the severity level of the interference indicating an area requiring improvement in a network coverage, or indicating an area having a poor network coverage;
acquire performance metrics data from the identified group of serving cells; and
perform an analysis on the acquired performance metrics data to identify a root cause of degradation in performance of the group of serving cells.
10. The system (100) as claimed in claim 9, wherein the display control module (208) is further configured to control the application interface to:
display the root cause of degradation in the performance of the identified group of serving cells; and
display the visualization data in one or more graphical formats including the plot of the plurality of samples in one or more of a map layer, a graph, a scatter chart, and a pie chart.