Dynamic telecom network agent filtering

Abstract

A system, method, and computer program product are provided for implementing dynamic telecom network agent filtering. The method includes obtaining peripheral data associated with a user equipment (UE) enabled with respect to a telecom network associated with a telecom network hardware device. The peripheral data is mapped with a predefined filter selection model, and a filtering agent model is selected. Similarly, a filtering agent associated with the UE is selected, and environmental characteristics associated with the telecom network are obtained. A location of the UE is tagged, and the filtering agent is pushed to the UE. A network command associated with execution of the filtering agent is generated, and the filtering agent is executed with respect to the UE. In response, noise and distortion occurring during operation of the UE with respect to the telecom network is reduced.

Description

Technical Field

[0001] The present invention generally relates to a method for filtering noise and distortion occurring within a telecommunication network, and in particular, to a filtering agent model and selection of a filtering agent, tagging of the location of a user equipment, activation of the filtering agent, and a method and associated system for improving telecommunication network technology associated with reduction of noise and distortion occurring during operation of a user equipment with respect to a telecommunication network.

Summary of the Invention

[0002] A first aspect of the present invention is a telecom network hardware device comprising a processor coupled to a computer-readable memory unit, wherein the memory unit, when executed by the processor, performs the following steps: acquiring peripheral data associated with user equipment (UE) activated with respect to the telecom network associated with the telecom network hardware device by the processor executing software code with respect to the virtual network function (VNF) and physical network function (PNF) of the telecom network hardware device; mapping the peripheral data using a predefined filter selection model by the processor; selecting a filtering agent model from a pool of model resources in response to the result of the mapping by the processor executing the predefined filter selection model; and, in response to the execution of the filtering agent model, the processor extracts from the VNF with respect to the telecom network Steps include: selecting a filtering agent associated with noise and distortion reduction associated with the UE; the processor acquiring environmental characteristics associated with the telecom network via a plurality of Global Positioning System (GPS) sensors; the processor tagging a specified location of the UE, activated with respect to the telecom network, based on the environmental characteristics; the processor pushing the filtering agent to the UE, where the filtering agent is stored in the temporary memory space of the UE's operating system; the processor generating a network command associated with the execution of the filtering agent; the processor executing the filtering agent with respect to the UE in response to the activation of the network command, where the execution activates the filtering agent with respect to the UE over a specified time frame;The present invention provides a telecom network hardware device having instructions for implementing a dynamic telecom network agent filtering method, which includes a step of reducing noise and distortion that occurs during the operation of the UE with respect to the telecom network, in response to the results of the execution.

[0003] A second aspect of the present invention is a step of obtaining peripheral data associated with user equipment (UE) activated with respect to the telecom network associated with the telecom network hardware device by a processor of the telecom network hardware device that executes software code with respect to the virtual network function (VNF) and physical network function (PNF) of the telecom network hardware device; a step of the processor mapping the peripheral data using a predefined filter selection model; a step of the processor executing the predefined filter selection model selecting a filtering agent model from a pool of model resources in response to the result of the mapping; a step of the processor selecting filtering agents from the VNF associated with noise and distortion reduction associated with the UE with respect to the telecom network in response to the execution of the filtering agent model; and a step of the processor via a plurality of Global Positioning System (GPS) sensors A dynamic telecom network agent filtering method is provided, comprising the steps of: acquiring environmental characteristics associated with the telecom network; the processor tagging a specified location of the UE activated with respect to the telecom network based on the environmental characteristics; the processor pushing the filtering agent to the UE, where the filtering agent is stored in the temporary memory space of the UE's operating system; the processor generating a network command associated with the execution of the filtering agent; the processor executing the filtering agent with respect to the UE in response to the activation of the network command, where the execution activates the filtering agent with respect to the UE over a specified time frame; and the processor reducing noise and distortion that occurs during the operation of the UE with respect to the telecom network in response to the result of the execution.

[0004] A third aspect of the present invention is a computer program product comprising a computer-readable hardware storage device for storing computer-readable program code, wherein the computer-readable program code has an algorithm that, when executed by a processor of a telecom network hardware device, implements a dynamic telecom network agent filtering method, the method comprising: a step of obtaining peripheral data associated with user equipment (UE) activated with respect to a telecom network associated with the telecom network hardware device by the processor executing software code with respect to the virtual network function (VNF) and physical network function (PNF) of the telecom network hardware device; a step of mapping the peripheral data using a predefined filter selection model by the processor; and a step of the processor executing the predefined filter selection model according to the result of the mapping. The steps include: selecting a filtering agent model from a pool of model resources; selecting a filtering agent from the VNF associated with noise and distortion reduction for the UE with respect to the telecom network in response to the execution of the filtering agent model; obtaining environmental characteristics associated with the telecom network via a plurality of Global Positioning System (GPS) sensors; tagging a specified location of the UE enabled with respect to the telecom network based on the environmental characteristics; pushing the filtering agent to the UE, where the filtering agent is stored in the temporary memory space of the UE's operating system; and generating network commands associated with the execution of the filtering agent.The present invention provides a computer program product comprising the steps of: the processor executing the filtering agent with respect to the UE in response to the activation of the network command, wherein the execution activates the filtering agent with respect to the UE for a specified time frame; and the processor reducing noise and distortion that occur during the operation of the UE with respect to the telecom network in response to the result of the execution.

[0005] The present invention advantageously provides a simple method and associated system capable of filtering noise and distortion occurring within a telecom network. [Brief explanation of the drawing]

[0006] [Figure 1] The present invention provides a system for implementing an automated process for improving telecom network technology, which includes filtering agent models and selection of filtering agents, tagging the location of user equipment, activating filtering agents, and reducing noise and distortion that occurs during the operation of user equipment with respect to the telecom network.

[0007] [Figure 2] The present invention provides an algorithm detailing a process flow enabled by the system shown in Figure 1 for implementing an automated process to improve telecom network technology, which involves selecting a filtering agent model and filtering agent, tagging the location of user equipment, activating the filtering agent, and reducing noise and distortion that occurs during the operation of user equipment with respect to the telecom network.

[0008] [Figure 3] Figure 1 shows the internal structure diagram of the software / hardware according to an embodiment of the present invention.

[0009] [Figure 4] This invention illustrates a communication system that enables communication between a VNF and a PNF via the system shown in Figure 1, according to an embodiment of the present invention.

[0010] [Figure 5] This document details an algorithm that outlines a process flow for acquiring environmental characteristics and creating a noise profile according to an embodiment of the present invention.

[0011] [Figure 6A] An embodiment of the present invention illustrates a system that includes requirements evaluated at a predefined polling frequency via a service running within a wireless access network (i.e., a VNF). [Figure 6B] An embodiment of the present invention illustrates a system that includes requirements evaluated at a predefined polling frequency via a service running within a wireless access network (i.e., a VNF).

[0012] [Figure 7] Figure 1 shows a computer system used to implement an automated process for improving telecom network technology, which relates to the selection of filtering agent models and filtering agents, tagging the location of user equipment, activating filtering agents, and reducing noise and distortion that occurs during the operation of user equipment with respect to the telecom network, according to embodiments of the present invention.

[0013] [Figure 8] This document illustrates a cloud computing environment according to an embodiment of the present invention.

[0014] [Figure 9] This describes a set of functional abstraction layers provided by a cloud computing environment according to embodiments of the present invention. [Modes for carrying out the invention]

[0015] Figure 1 of the system shows a system 100 for improving telecom network technology associated with the selection of a filtering agent model and filtering agent 138, tagging the location of user equipment 133, activating the filtering agent 138, and reducing noise and distortion that occurs during the operation of user equipment 133 with respect to the telecom network 153, according to an embodiment of the present invention. A typical fifth-generation (5G) network is configured to enable a logical channeling process that includes dividing the frequency spectrum into categories for associated applications. Similarly, each category is further subdivided into multiple logical channels based on a dynamic runtime configuration on the radio interface. The aforementioned internal segmentation process can cause the radio interface to associate with multiple subdivided individual entries that hold traffic of packet data from user equipment (UE) to eNodeB (e.g., elements of an LTE radio access network) via the new radio interface, which are multiplexed before transmission to an S1 bearer (e.g., a connectivity provider). The logical channeling process can allow distortion and interference (e.g., noise) to be added to the 5G network. In addition, (due to the small cell architecture) if the user is determined to have exceeded the distance limit of a particular connectivity class, distortion in network traffic may increase, which may result in increased packet retransmission requirements. The aforementioned increase in distortion may not be directly eliminated due to the associated base carrier requirements. Additional factors contributing to distortion (in a 5G network) include natural phenomena such as rain, fog, or the presence of CO2 in the air. Distortion can be introduced into the 5G network within different channels associated with 5G radio operation. Alternatively, distortion can be introduced into the UE device if the user is located in a noisy area. Similarly, background noise can be introduced into the 5G network, which may require additional processing at the eNodeB.Next, interference data is formalized into packets and shared via the new radio (NR) interface, thereby increasing network traffic. Typical noise filtering techniques cannot enable uniform processing to prevent different types of noise coming from UE entities; therefore, the filtering process must be forwarded to the eNodeB, where eNodeB filters are used to reduce network traffic requirements.

[0016] The filtering requirements can vary depending on the type of interference and distortion introduced into the 5G network, and therefore the aforementioned filtering problems cannot be corrected through simple data filtering techniques. Similarly, in eNodeB and UE devices, the amount of filtering techniques may be limited because they can only enable lightweight processing power with respect to 5G core and edge cloud processors. In addition, much of the noise and distortion processing at the carrier endpoint may not be able to address the different types of errors added to various layers on the NR and UE. Therefore, system 100 is enabled to run processes within a 5G telecom network virtual network function (VNF) in cooperation with individual physical network functions to obtain the information necessary to build a multidimensional training model for the filtering agent.

[0017] The system 100 in Figure 1 includes a telecom network hardware device 139, a user equipment (UE) 133, a VNF 119, a PNF 122, peripheral data 122, a filtering selection model 135, and a filtering agent model / filtering agent 138, all interconnected through a telecom network (telecom) network 153. The telecom network hardware device 139 comprises a sensor 112, a circuit 127, and software / hardware 121. The UE 133 may comprise any type of communication device, including, in particular, a mobile device, a tablet computer, a laptop computer, or a smart device. Peripheral data may comprise any type of data associated with the UE 133. The telecom network hardware device 139 and the UE 133 may each comprise an embedded device. In this specification, an embedded device is defined as a dedicated device or computer comprising a combination of computer hardware and software (fixed capability or programmable) specifically designed to perform a dedicated function. A programmable embedded computer or device may have a dedicated programming interface. In one embodiment, the telecom network hardware device 139 and UE 133 may each include a dedicated hardware device having dedicated (non-general-purpose) hardware and circuitry (i.e., dedicated discrete non-general-purpose analog, digital, and logic-based circuits) that performs the processes described with respect to Figures 1 to 6B (independently or in combination).Dedicated discrete non-general-purpose analog, digital, and logic-based circuits (e.g., sensor 112, circuit / logic 127, software / hardware 121, etc.) are proprietary, specially designed components (e.g., dedicated integrated circuits, e.g., Application Specific Integrated Circuits (ASICs), etc.) designed solely for the implementation of automated processes to improve telecom network technology related to filtering agent model and filtering agent selection, user device location tagging, filtering agent activation, and reducing noise and distortion that occurs during the operation of user devices with respect to the telecom network. Sensor 112 may include any type of internal or external sensor, in particular, including GPS sensors, Bluetooth® beacon sensors, cell phone detection sensors, Wi-Fi® positioning detection sensors, triangulation detection sensors, activity tracking sensors, temperature sensors, ultrasonic sensors, light sensors, video retrieval devices, humidity sensors, voltage sensors, network traffic sensors, etc. Telecom network 117 may include, in particular, cellular / mobile networks, local area networks (LANs), wide area networks (LANs), etc. Network: May include any type of communication network, including WANs, the Internet, and wireless networks.

[0018] System 100 is configured to collect weather-related information and location-based data to construct location profiles for specified objects provided within each PNF 122. Similarly, System 100 is configured to supply information to VNF 119. In addition, System 100 is configured to collect all location-based requirements for noise and distortion filtering. The location-based requirements are used to construct a filtering selection model 135, which is implemented as a training corpus.

[0019] System 100 enables the dynamic content insight delivery platforms to interact with each other in order to obtain real-time location-specific information from PNF 122. The real-time location-specific information may particularly include weather-related data and real-time monitoring environment data including, in particular, fog, the presence of CO2 in the air, etc. Accordingly, the filtering agent requirements are updated for VNF 119. Proactive data obtained from resources located on the Internet is applied to obtain PNF serving data, such as information related to rain, workload and channeling balance information from each eNodeB within PNF 122, etc.

[0020] Upon receiving the aforementioned information and data, the filtering selection model 135 is invoked using the attributes received from various resources. The filtering selection model 135 is activated via the functions of VNF 119. The activated filtering selection model 135 is configured to identify filtering algorithms and requirements for each agent based on the situational (noise reduction) needs.

[0021] If heavy rain is detected within the PNF serving area, a distortion filter is selected using the defined input parameters. The selected filter may then be pushed to the edge entities based on the nature of the event. If a distortion filter is selected, the distortion filter may be pushed to the eNodeB and the related execution instructions may be sent to the eNodeB via the S1 bearer.

[0022] Similarly, if noise or voice interference filters are required, they are pushed to the eNodeB and executed via a common control channel (CCCH) instruction using the S1 bearer and radio bearer to activate the signal. The relevant polling agent is configured to continuously enable the effectiveness of the requirements and clear the filters using a feedback loop when the requirements are finished. A feedback loop may be imposed with respect to the multidimensional model to improve the relevant results.

[0023] Figure 2 shows an algorithm that details the process flow enabled by the system 100 of FIG. 1 for implementing an automated process for improving telecommunication network technologies related to the filtering agent model and selection of a filtering agent, tagging of the location of a user equipment, activation of the filtering agent, and reduction of noise and distortion occurring during operation of the user equipment with respect to a telecommunication network according to an embodiment of the present invention. Each step of the algorithm of FIG. 2 is enabled by a computer processor executing computer code and may be executed in any order. Additionally, each step in the algorithm of FIG. 2 may be enabled and executed in combination by the telecommunication network hardware device 139 and the UE 133 of FIG. 1. At step 200, peripheral data is obtained by a telecommunication network hardware device executing software code with respect to the VNFs and PNFs of the telecommunication network hardware device. The peripheral data is associated with UEs enabled with respect to the telecommunication network associated with the telecommunication network hardware device. At step 202, the peripheral data is mapped using a predefined filter selection model. At step 204, a filtering agent model is selected from a pool of model resources via execution of the predefined filter selection model and in response to the result of the mapping of step 202. The selection of the filtering agent model may be executed with respect to the service orchestration layer of the VNFs and 5G network.

[0024] At step 208, a filtering agent is selected from the VNFs (via execution of the filtering agent model). The filtering agent is associated with reduction of noise and distortion associated with the UE with respect to the telecommunication network. The selection of the filtering agent may include executing a VNF multi-dimensional machine learning model based on the multi-level characteristics and attributes of the device.

[0025] In stage 210, environmental characteristics associated with the telecom network are acquired via multiple global positioning system (GPS) sensors. These environmental characteristics may be associated with resources related to the triggered PNF function.

[0026] In step 212, the designated locations of the UEs enabled for the telecom network are tagged based on environmental characteristics. In step 214, the filtering agent is pushed to the UE. The filtering agent is stored in the temporary memory space of the UE's operating system. Pushing the filtering agent to the UE may include: 1. Send the activation interaction code to the UE. 2. Activate the filtering agent to perform noise and distortion reduction (in response to the execution of the activation interaction code).

[0027] In step 216, a network command associated with the execution of the filtering agent is generated. In step 218 (in response to the activation of the network command), the filtering agent is executed with respect to the UE, thereby activating the filtering agent with respect to the UE for a specified time frame. In step 220, in response to the execution of step 218, noise and distortion occurring during the operation of the UE with respect to the telecom network are reduced. Noise and distortion reduction may include: 1. Evaluate the filtering agent requirements in relation to the predefined polling frequency associated with the services running within the VNF. 2. Provision each filtering agent (among the filtering agents) to the malfunctioning device of the UE in order to perform noise and distortion reduction.

[0028] In step 224 (after completing step 220), the filtering agent is removed from the UE.

[0029] Figure 3 shows an internal structure diagram of the software / hardware 121 (i.e., 121) of Figure 1 according to an embodiment of the present invention. The software / hardware 121 includes a mapping module 304, a selection module 305, an execution module 308, a noise and distortion reduction module 314, and a communication controller 312. The mapping module 304 includes dedicated hardware and software for controlling all functions related to the mapping stage in Figure 2. The selection module 305 includes dedicated hardware and software for controlling all functions related to the filter selection stage described with respect to the algorithm in Figure 2. The execution module 308 includes dedicated hardware and software for controlling all functions related to the filter agent execution stage in Figure 2. The noise and distortion reduction module 314 includes dedicated hardware and software for controlling all functions related to the noise and distortion reduction stage of the algorithm in Figure 2. The communication controller 312 is enabled to control all communication between the mapping module 304, the selection module 305, the execution module 308, and the noise and distortion reduction module 314.

[0030] Figure 4 shows a communication system 400 that enables communication between VNF402, activated via system 100 of Figure 1, and PNF404a and 404b (associated with peripheral information 408a and 408b and communications 410a and 410b), according to an embodiment of the present invention. The communication system 400 enables processes to be run within VNF402 to collect peripheral information 408a and 408b, such as details related to environmental noise and noise-related details at UE terminals for mapping using a predefined filter selection model. Peripheral information 408a and 408b may be associated with various levels of data in VNF402. Peripheral information 408a and 408b may be activated via the use of a VNF-PNF integrator service, thereby enabling the selection of a model from an available pool of resources. Similarly, the selected filtering agent (in VNF402) is pushed to the selected target endpoint (e.g., associated with PNF404a and 404b), and the associated activation interaction is sent to the end device (e.g., UE133 in Figure 1) to activate filtering for noise and distortion reduction.

[0031] Figure 5 shows an algorithm 500 detailing a process flow for acquiring environmental characteristics and creating a noise profile according to an embodiment of the present invention. In steps 502 and 504, environmental characteristics are acquired from the respective PNF functions of the GPS-based data resources. For example, a stream-based data collector may be enabled to acquire the GPS identity of the PNF boundary area. Similarly, associated noise and distortion cancellation filters from the 5G core network infrastructure are enabled via the execution of an in-band MAC-based protocol. In step 508, a filtering agent selection process is performed in the VNF function and the 5G service orchestration layer, thereby propagating filtering agents to be pushed to the PNF and logical endpoints (in step 510) based on the nature of the noise / distortion events. Similarly (in step 510), filters are pushed to UE devices experiencing noise on voice calls due to a noisy background. In steps 512 and 514, locations are tagged (with respect to the timeline) to create a noise profile. In response, once UE locations are detected within the defined boundary area (in step 518), noise-based filters are dynamically provisioned. For example, if a crowded retail store is detected as a location, a noise cancellation filter is provisioned during the initiation of a voice call. Similarly, a noise cancellation filter may be provisioned for natural noise initiation events. For example, (in the case of detected heavy rain or CO2 levels in the air) a CO2-based distortion filtering agent is required in eNodeB to obtain correct packet data decoding.

[0032] Figures 6A and 6B (combined) show a system 600 according to an embodiment of the present invention, which includes requirements 602 evaluated at a predefined polling frequency 604 via a service running within a wireless access network 608 (i.e., a VNF). The system 600 is configured to provision a filtering agent to degraded devices to optimize network utilization and avoid packed retransmission errors. Interfaces for transmitting various types of network traffic are utilized so that the selection of filtering agents is performed within the wireless access network 608. Similarly, filters are selected using a VNF multidimensional machine learning model with respect to multilevel features and attributes in a common filter repository. In addition, input data attributes (i.e., associated with weather, location, and additional device-specific characteristics) are applied to the associated datasets. The selected filtering agents are then pushed to edge devices as follows: 1. Based on the noise / distortion requirements, a suitable filter is detected in the wireless access network 608. 2. A logical channel is created to transfer the machine learning model to the edge location. 3. Edge positions are identified based on the filter type. For example, a distortion filter has edged loc=eNodeB.

[0033] In addition, a network command is generated at the target location to issue the execution of the selected filter, so that a CCCH frame is formed on the radio interface and transmitted over NR. The target device is then configured to receive incoming filters in a temporary space of the device operating system according to the NR interface. The incoming filters are executed, and the selected filtering agents are activated over a specified timeline, thereby saving computational requirements at the target location and processing power on the terminal device. After filtering, all further packet data transmission processes are performed. A polling thread for the verification manager is activated in the PNF and configured to poll for event verification. Similarly, when the current requirements are complete, the event manager triggers a signal. The incoming filters are then cleared (i.e., deleted).

[0034] Figure 7 shows a computer system 90 (e.g., telecom network hardware devices 139 and UE 133 in Figure 1) used by or included in the system 100 of Figure 1 for implementing an automated process to improve telecom network technology associated with selecting filtering agent models and filtering agents, tagging the location of user equipment, activating filtering agents, and reducing noise and distortion that occurs during the operation of user equipment with respect to the telecom network, according to an embodiment of the present invention.

[0035] Each aspect of the present invention may take the form of a hardware embodiment overall, a software embodiment overall (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware embodiments, and all such embodiments may generally be referred to herein as “circuits,” “modules,” or “systems.”

[0036] The present invention may be a system, method, and / or a computer program product. The computer program product may include a computer-readable storage medium (or a set of mediums) having computer-readable program instructions for causing a processor to execute an aspect of the present invention.

[0037] A computer-readable storage medium can be a tangible device capable of holding and storing instructions for use by an instruction execution device. A computer-readable storage medium may be, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any preferred combination thereof. A non-exhaustive list of more specific examples of computer-readable storage media includes: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically encoded devices such as punch cards or grooved structures on which instructions are recorded, and any preferred combination thereof. As used herein, computer-readable storage media themselves are not considered to be radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or transient signals such as electrical signals transmitted through wires.

[0038] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to each computing / processing device, or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface within each computing / processing device receives computer-readable program instructions from the network and transfers them for storage in a computer-readable storage medium within each computing / processing device.

[0039] The computer-readable program instructions for performing the operations of the present invention may be source code or object code written in any combination of one or more programming languages, including assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or object-oriented programming languages ​​such as Smalltalk®, C++, Spark, and R, and conventional procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer-readable program instructions may run as a standalone software package, either entirely on the user's computer, partially on the user's computer, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or wide area network (WAN), or the connection may be to an external computer (for example, via the Internet using an Internet Service Provider). In some embodiments, to carry out aspects of the present invention, an electronic circuit including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA) may execute computer-readable program instructions to customize the electronic circuit by utilizing state information of computer-readable program instructions.

[0040] Each aspect of the present invention is described herein with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It will be understood that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0041] Computer-readable program instructions may be provided to a processor of a general-purpose computer, a dedicated computer, or other programmable data processing device for creating a machine, thereby creating means for implementing functions / operations specified in one or more blocks of a flowchart and / or block diagram, through which instructions executed via the processor of the computer or other programmable data processing device. These computer-readable program instructions may also be stored in a computer-readable storage medium that can instruct a computer, a programmable data processing device, and / or other device to function in a particular manner, thereby providing a manufactured article containing instructions for implementing modes of functions / operations specified in one or more blocks of a flowchart and / or block diagram.

[0042] Computer-readable program instructions may be loaded into a computer, another programmable data processing device, or another device to perform a series of operational steps on the computer, another programmable device, or another device to produce a computer implementation process, thereby enabling the instructions executed on the computer, another programmable device, or another device to implement the functions / operations specified in one or more blocks of a flowchart and / or block diagram.

[0043] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of the system, method, and computer program product according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of instructions containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions described in the blocks may be performed in an order different from the order shown in the drawings. For example, two blocks shown consecutively may actually be implemented as a single step, executed simultaneously, substantially simultaneously, partially or entirely, with overlapping timelines, or blocks may be executed in reverse order depending on the functionality involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs a specified function or operation, or a combination of dedicated hardware and computer instructions.

[0044] The computer system 90 shown in Figure 7 includes a processor 91, an input device 92 coupled to the processor 91, an output device 93 coupled to the processor 91, and memory devices 94 and 95 coupled to the processor 91, respectively. The input device 92 may be, in particular, a keyboard, mouse, camera, touchscreen, etc. The output device 93 may be, in particular, a printer, plotter, computer screen, magnetic tape, removable hard disk, floppy disk, etc. The memory devices 94 and 95 may be, in particular, a hard disk, floppy disk, magnetic tape, optical storage such as a compact (CD) or digital video disc (DVD), dynamic random access memory (DRAM), read-only memory (ROM), etc. The memory device 95 includes computer code 97. The computer code 97 includes a filtering agent model and selection of filtering agents, tagging the location of user equipment, activating filtering agents, and an algorithm (e.g., the algorithm in Figure 2) for improving telecom network technology related to reducing noise and distortion that occurs during the operation of user equipment with respect to the telecom network. Processor 91 executes computer code 97. Memory device 94 contains input data 96. Input data 96 contains inputs required by computer code 97. Output device 93 displays the output from computer code 97. Either or both of memory devices 94 and 95 (or one or more additional memory devices such as read-only memory device 85) may be used as a computer-readable medium (or computer-readable medium or program storage device) having an algorithm (e.g., the algorithm in Figure 2), computer-readable program code embodied internally, and / or other data stored internally, the computer-readable program code including computer code 97. Generally, the computer program product (or alternatively manufactured article) of the computer system 90 may include a computer-readable medium (or program storage device).

[0045] In some embodiments, rather than being stored in and accessed from a hard drive, optical disk, or other writable, rewritable, removable hardware memory device 95, the stored computer program code 84 (e.g., including algorithms) may be stored in a static, non-removable, read-only storage medium, such as a read-only memory (ROM) device 85, or may be accessed directly from such static, non-removable, read-only medium by the processor 91. Similarly, in some embodiments, the stored computer program code 97 may be stored as computer-readable firmware 85, or may be accessed directly from such firmware 85 by the processor 91 rather than from a more dynamic or removable hardware data storage device 95, such as a hard drive or optical disk.

[0046] Furthermore, any of the components of the present invention may be created, integrated, hosted, maintained, deployed, managed, and serviced by a service provider that provides filtering agent models and filtering agent selection, user device location tagging, filtering agent activation, and improvements to telecom network technology associated with reducing noise and distortion that occurs during the operation of user devices with respect to the telecom network. Accordingly, the present invention discloses processes for deploying, creating, integrating, hosting, maintaining, and / or integrating a computing infrastructure, including integrating computer-readable code into a computer system 90, the code being capable of performing methods for enabling processes for improving filtering agent models and filtering agent selection, user device location tagging, filtering agent activation, and improvements to telecom network technology associated with reducing noise and distortion that occurs during the operation of user devices with respect to the telecom network. In another embodiment, the present invention provides a business method for performing each process stage of the present invention on a subscription fee basis, an advertising fee basis, and / or a commission basis. In other words, a service provider such as Solution Integrator may offer to enable processes for improving telecom network technology, which includes filtering agent model and filtering agent selection, tagging the location of user equipment, activating filtering agents, and reducing noise and distortion that occurs during the operation of user equipment with respect to the telecom network. In this case, the service provider may create, maintain, and support a computer infrastructure that runs the process stages of the present invention for one or more customers. In return, the service provider may receive payments from customers who have entered into subscription and / or commission agreements, and / or from the sale of advertising space to one or more third parties.

[0047] Figure 7 shows a computer system 90 as a specific hardware and software configuration, but any hardware and software configuration known to those skilled in the art may be used in conjunction with the specific computer system 90 in Figure 7 for the purposes described above. For example, memory devices 94 and 95 may be part of a single memory device rather than separate memory devices. Cloud computing environment

[0048] While this disclosure includes a detailed description of cloud computing, it should be understood that the implementation of the teachings described herein is not limited to cloud computing environments. Rather, embodiments of the present invention can be implemented in conjunction with any other type of computing environment that is currently known or may be developed in the future.

[0049] Cloud computing is a service delivery model that enables convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and deployed with minimal management effort or interaction with service providers. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.

[0050] The characteristics are as follows:

[0051] On-demand self-service: Cloud consumers can unilaterally provision computing power, such as server time and network storage, automatically as needed, without requiring human interaction with service providers.

[0052] Broad network access: Capabilities are available over the network and accessed through standard mechanisms that facilitate use by heterogeneous thin-client or thick-client platforms (e.g., mobile phones, laptops, and PDAs®).

[0053] Resource pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with various physical and virtual resources dynamically allocated and reallocated according to demand. Consumers generally do not have control or knowledge of the exact location of the resources provided, but they may be able to specify the location at a higher level of abstraction (e.g., country, state, or data center), thus demonstrating location independence.

[0054] Rapid resilience: Capabilities are provisioned quickly and flexibly, sometimes automatically, allowing for rapid scaling out or rapid release and rapid scaling in. To consumers, the available capacity for provisioning often appears unlimited and can be purchased in any quantity at any time.

[0055] Measurement Services: Cloud systems automatically control and optimize resource usage by leveraging measurement capabilities appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts) at a certain level of abstraction. Resource usage can be monitored, controlled, and reported, thereby providing transparency to both service providers and consumers.

[0056] The service model is as follows:

[0057] Software as a Service (SaaS): The capability offered to consumers is the use of a provider's applications running on cloud infrastructure. These applications are accessible from various client devices through thin client interfaces such as web browsers (e.g., web-based email). Consumers do not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, storage, or even individual application capabilities, with the conceivable exception of limited, user-specific application configuration settings.

[0058] Platform as a Service (PaaS): The capability offered to consumers is the ability to deploy applications they have created or acquired, written using programming languages ​​and tools supported by the provider, onto a cloud infrastructure. Consumers do not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but they have control over the deployed applications and, in some cases, the configuration of the application host environment.

[0059] Infrastructure as a Service (IaaS): The ability provided to consumers is to provision processing, storage, networking, and other basic computing resources, allowing consumers to deploy and run any software, including operating systems and applications. Consumers do not manage or control the underlying cloud infrastructure, but they have control over the operating system, storage, and deployed applications, and in some cases, limited control over selected networking components (e.g., host firewalls).

[0060] The deployment model is as follows:

[0061] Private Cloud: Cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may reside on-premises or off-premises.

[0062] Community Cloud: A cloud infrastructure is shared by multiple organizations to support a specific community that shares common interests (e.g., mission, security requirements, policies, and compliance considerations). The community cloud may be managed by those organizations or third parties and may reside on-premises or off-premises.

[0063] Public cloud: Cloud infrastructure is made available to the general public or large industry groups and is owned by organizations that sell cloud services.

[0064] Hybrid Cloud: This cloud infrastructure is a complex of two or more clouds (private, community, or public) that remain unique entities but are joined together by standardized or proprietary technologies that enable data and application portability (e.g., cloud bursting for load balancing across clouds).

[0065] Cloud computing environments are service-oriented, emphasizing statelessness, low coupling, modularity, and semantic interoperability. At the core of cloud computing lies an infrastructure that includes a network of interconnected nodes.

[0066] Referring here to Figure 8, an exemplary cloud computing environment 50 is shown. As illustrated, the cloud computing environment 50 includes one or more cloud computing nodes 10 that can communicate with local computing devices used by cloud consumers, such as a personal digital assistant (PDA) or cellular phone 54A, a desktop computer 54B, a laptop computer 54C, and / or an automotive computer system 54N. The nodes 10 can communicate with each other. They can be grouped physically or virtually within one or more networks, such as a private cloud, community cloud, public cloud, or hybrid cloud, or a combination thereof, as described above in this specification (not illustrated). This enables the cloud computing environment 50 to provide infrastructure, platform, and / or software as a service, which does not require cloud consumers to maintain resources on their local computing devices for that purpose. The types of computing devices 54A, 54B, 54C, and 54N shown in Figure 8 are for illustrative purposes only, and it should be understood that the computing node 10 and the cloud computing environment 50 can communicate with any type of computer device and any type of network and / or network-addressable means of communication (for example, using a web browser).

[0067] Referring now to Figure 9, a set of functional abstraction layers provided by the cloud computing environment 50 (see Figure 8) is shown. It should be understood in advance that the components, layers, and functions shown in Figure 9 are intended to be illustrative only and that embodiments of the present invention are not limited thereto. As illustrated, the following layers and corresponding functions are provided:

[0068] The hardware and software layer 60 includes hardware and software components. Examples of hardware components include a mainframe 61; a RISC (Reduced Instruction Set Computer) architecture-based server 62; a server 63; a blade server 64; a storage device 65; and network and networking components 66. In some embodiments, the software components include network application server software 67 and database software 68.

[0069] The virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71; virtual storage 72; virtual networks 73 including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.

[0070] In one example, the management layer 80 may provide the following functions: Resource provisioning 81 provides dynamic procurement of computing and other resources used to perform tasks within the cloud computing environment. Metering and pricing 82 provides cost tracking as resources are used within the cloud computing environment and billing or invoicing for the consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification of cloud consumers and tasks, as well as protection of data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 87 allocates and manages cloud computing resources to ensure that the required service levels are met. Service level agreement (SLA) planning and execution 88 pre-arranges and procures cloud computing resources for which future requirements are anticipated in accordance with the SLA.

[0071] The workload layer 101 provides examples of functionality that may be available in a cloud computing environment. Examples of workloads and functions that may be provided from this layer include: mapping and navigation 102; software development and lifecycle management 103; virtual classroom education delivery 133; data analysis processing 134; transaction processing 106; and improving network switching techniques related to detecting the operational status of ports and generating actions associated with the operational status of data packets arriving at ports; and improving telecom network techniques related to filtering agent model and filtering agent selection, user device location tagging, filtering agent activation, and reducing noise and distortion that occurs during the operation of user devices with respect to the telecom network 107.

[0072] While embodiments of the present invention have been described herein for illustrative purposes, many modifications and changes will become apparent to those skilled in the art. Accordingly, the appended claims are intended to encompass all such modifications and changes as being true to the spirit and scope of the invention. 。 [Item 1] A telecom network hardware device comprising a processor coupled to a computer-readable memory unit, wherein the computer-readable memory unit, when executed by the processor: A step in which the processor, which executes software code with respect to the virtual network function (VNF) and physical network function (PNF) of the telecom network hardware device, acquires peripheral data associated with user equipment (UE) activated with respect to the telecom network associated with the telecom network hardware device; The processor performs the step of mapping the peripheral data using a predefined filter selection model; A step in which the processor, which executes the predefined filter selection model, selects a filtering agent model from a pool of model resources in response to the results of the mapping; The processor, in response to the execution of the filtering agent model, selects filtering agents from the VNF that are associated with noise and distortion reduction with respect to the telecom network and associated with the UE; The processor acquires environmental characteristics associated with the telecom network via multiple Global Positioning System (GPS) sensors; The processor tags the designated locations of the UEs that have been activated with respect to the telecom network, based on the environmental characteristics; The processor pushes the filtering agent to the UE, where the filtering agent is stored in the temporary memory space of the UE's operating system; The processor generates network commands associated with the execution of the filtering agent; The processor performs the filtering agent with respect to the UE in response to the activation of the network command, wherein the execution activates the filtering agent with respect to the UE for a specified time frame; and The processor, in response to the results of the execution, reduces noise and distortion that occur during the operation of the UE with respect to the telecom network. The instructions include a dynamic telecom network agent filtering method that includes Telecom network hardware devices. [Item 2] The aforementioned dynamic telecom network agent filtering method is: After the reduction is completed, the processor removes the filtering agent from the user device. A telecom network hardware device as described in item 1, further comprising the features described above. [Item 3] The step of pushing the filtering agent to the UE is: The step of sending an activation interaction code to the UE; and Steps to activate the filtering agent for performing the noise and distortion reduction in response to the execution of the activation interaction code: A telecom network hardware device as described in item 1, having the following features. [Item 4] The telecom network hardware device described in item 1, wherein the aforementioned environmental characteristics are associated with resources related to the triggered PNF function. [Item 5] The telecom network hardware device described in item 1, wherein the step of selecting the filtering agent model is performed with respect to the VNF and the service orchestration layer of the 5G network. [Item 6] The aforementioned dynamic telecom network agent filtering method is: The processor evaluates the requirements of the filtering agent with respect to a predefined polling frequency associated with the service running within the VNF; and In order to perform the reduction, the processor provisions each of the filtering agents to the malfunctioning device of the UE. A telecom network hardware device as described in item 1, further comprising the features described above. [Item 7] The aforementioned step of selecting the filtering agent is: The stage of running a VNF multidimensional machine learning model based on the multilevel features and attributes of the device. A telecom network hardware device as described in item 1, having the following features. [Item 8] A step in which the processor of the telecom network hardware device, which executes software code relating to the virtual network function (VNF) and physical network function (PNF) of the telecom network hardware device, obtains peripheral data associated with user equipment (UE) activated with respect to the telecom network associated with the telecom network hardware device; The processor performs the step of mapping the peripheral data using a predefined filter selection model; A step in which the processor, which executes the predefined filter selection model, selects a filtering agent model from a pool of model resources in response to the results of the mapping; The processor, in response to the execution of the filtering agent model, selects filtering agents from the VNF that are associated with noise and distortion reduction with respect to the telecom network and associated with the UE; The processor acquires environmental characteristics associated with the telecom network via multiple Global Positioning System (GPS) sensors; The processor tags the designated locations of the UEs that have been activated with respect to the telecom network, based on the environmental characteristics; The processor pushes the filtering agent to the UE, where the filtering agent is stored in the temporary memory space of the UE's operating system; The processor generates network commands associated with the execution of the filtering agent; The processor performs the filtering agent with respect to the UE in response to the activation of the network command, wherein the execution activates the filtering agent with respect to the UE for a specified time frame; and The processor, in response to the results of the execution, reduces noise and distortion that occur during the operation of the UE with respect to the telecom network. A dynamic telecom network agent filtering method comprising the following features. [Item 9] After the reduction is completed, the processor removes the filtering agent from the user device. The dynamic telecom network agent filtering method described in item 8 further comprises the following: [Item 10] The step of pushing the filtering agent to the UE is: The step of sending an activation interaction code to the UE; and Steps to activate the filtering agent for performing the noise and distortion reduction in response to the execution of the activation interaction code: A dynamic telecom network agent filtering method as described in item 8, comprising: [Item 11] The dynamic telecom network agent filtering method described in item 8, wherein the aforementioned environmental characteristics are associated with resources related to the triggered PNF function. [Item 12] The dynamic telecom network agent filtering method according to item 8, wherein the step of selecting the filtering agent model is performed with respect to the service orchestration layer of the VNF and the 5G network. [Item 13] The processor evaluates the requirements of the filtering agent with respect to a predefined polling frequency associated with the service running within the VNF; and In order to perform the reduction, the processor provisions each of the filtering agents to the malfunctioning device of the UE. The dynamic telecom network agent filtering method described in item 8 further comprises the following: [Item 14] The aforementioned step of selecting the filtering agent is: The stage of running a VNF multidimensional machine learning model based on the multilevel features and attributes of the device. A dynamic telecom network agent filtering method as described in item 8, comprising: [Item 15] The further step comprises providing at least one support service for at least one of the creation, integration, hosting, maintenance, and deployment of computer-readable code in the telecom network hardware device, wherein the computer-readable code is executed by the processor to implement the steps of: acquiring the peripheral data, mapping the data, selecting the filtering agent model, selecting the filtering agent, acquiring the environmental characteristics, tagging the data, pushing the data, generating the data, executing the data, and reducing the data. The dynamic telecom network agent filtering method described in item 8. [Item 16] A computer program product comprising a computer-readable hardware storage device for storing computer-readable program code, wherein the computer-readable program code, when executed by the processor of a telecom network hardware device, has an algorithm that implements a dynamic telecom network agent filtering method, and the dynamic telecom network agent filtering method is: A step in which the processor, which executes software code with respect to the virtual network function (VNF) and physical network function (PNF) of the telecom network hardware device, acquires peripheral data associated with user equipment (UE) activated with respect to the telecom network associated with the telecom network hardware device; The processor performs the step of mapping the peripheral data using a predefined filter selection model; A step in which the processor, which executes the predefined filter selection model, selects a filtering agent model from a pool of model resources in response to the results of the mapping; The processor, in response to the execution of the filtering agent model, selects filtering agents from the VNF that are associated with noise and distortion reduction with respect to the telecom network and associated with the UE; The processor acquires environmental characteristics associated with the telecom network via multiple Global Positioning System (GPS) sensors; The processor tags the designated locations of the UEs that have been activated with respect to the telecom network, based on the environmental characteristics; The processor pushes the filtering agent to the UE, where the filtering agent is stored in the temporary memory space of the UE's operating system; The processor generates network commands associated with the execution of the filtering agent; The processor performs the filtering agent with respect to the UE in response to the activation of the network command, wherein the execution activates the filtering agent with respect to the UE for a specified time frame; and The processor, in response to the results of the execution, reduces noise and distortion that occur during the operation of the UE with respect to the telecom network. A computer program product that has [certain characteristics]. [Item 17] The aforementioned dynamic telecom network agent filtering method is: After the reduction is completed, the processor removes the filtering agent from the user device. A computer program product as described in item 16, further comprising the features described therein. [Item 18] The step of pushing the filtering agent to the UE is: The step of sending an activation interaction code to the UE; and Steps to activate the filtering agent for performing the noise and distortion reduction in response to the execution of the activation interaction code: A computer program product as described in item 16, which has the following characteristics. [Item 19] The computer program product described in item 16, wherein the aforementioned environmental characteristics are associated with resources related to the triggered PNF function. [Item 20] The computer program product described in item 16, wherein the step of selecting the filtering agent model is performed with respect to the VNF and the service orchestration layer of the 5G network.

Claims

1. A telecom network hardware device comprising a processor coupled to a computer-readable memory unit, wherein the computer-readable memory unit, when executed by the processor: A step of obtaining peripheral data associated with user equipment (UE) activated with respect to the telecom network associated with the telecom network hardware device, which includes details related to environmental noise and noise-related details in the UE, by the processor executing software code with respect to the virtual network function (VNF) and physical network function (PNF) of the telecom network hardware device; A step of mapping the peripheral data using a predefined filter selection model configured by the processor to identify requirements for a filtering agent for noise reduction, wherein the filter selection model identifies requirements for a filtering agent for noise reduction; The processor, which executes the predefined filter selection model, selects a filtering agent model in response to the results of the mapping, for selecting a filtering agent from a pool of model resources in which available filtering agent models are pooled; The processor, in response to the execution of the filtering agent model, selects filtering agents from the VNF that are associated with noise and distortion reduction with respect to the telecom network and associated with the UE; The processor acquires environmental characteristics, including weather-related data, via multiple Global Positioning System (GPS) sensors, linked to the telecom network; The processor performs the step of tagging a specified location of the UE, which is enabled with respect to the telecom network, with respect to a timeline, based on the environmental characteristics, in order to create a noise profile; In the step where the processor pushes the filtering agent to the UE, the filtering agent is stored in the temporary memory space of the UE's operating system; The processor generates network commands associated with the execution of the filtering agent; The processor performs the filtering agent with respect to the UE in response to the activation of the network command, wherein the execution activates the filtering agent with respect to the UE for a specified time frame; and The processor, in response to the results of the execution, reduces noise and distortion that occur during the operation of the UE with respect to the telecom network. The instructions include a dynamic telecom network agent filtering method that includes Telecom network hardware devices.

2. The aforementioned dynamic telecom network agent filtering method is: After the reduction is completed, the processor removes the filtering agent from the user device. The telecom network hardware device according to claim 1, further comprising the following:

3. The step of pushing the filtering agent to the UE is: The steps include: sending an activation interaction code, which is code that activates a filtering agent when executed by the UE, to the UE; and Steps to activate the filtering agent for performing the noise and distortion reduction in response to the execution of the activation interaction code: A telecom network hardware device according to claim 1 or 2, having the following features.

4. The telecom network hardware device according to claim 1 or 2, wherein the environmental characteristics are associated with a resource related to a triggered PNF function.

5. The telecom network hardware device according to claim 1 or 2, wherein the step of selecting the filtering agent model is performed with respect to the VNF and the service orchestration layer of the 5G network.

6. The aforementioned dynamic telecom network agent filtering method is: The processor evaluates the requirements of the filtering agent with respect to a predefined polling frequency associated with the service running within the VNF; and In order to perform the reduction, the processor provisions each of the filtering agents to the malfunctioning device of the UE. The telecom network hardware device according to claim 1 or 2, further comprising the following:

7. The aforementioned step of selecting the filtering agent is: The stage of running a VNF multidimensional machine learning model based on the device's multilevel features and attributes. A telecom network hardware device according to claim 1 or 2, having the following features.

8. A step of acquiring peripheral data associated with user equipment (UE) activated with respect to the telecom network associated with the telecom network hardware device, which includes details related to environmental noise and noise-related details in the UE, by the processor of the telecom network hardware device executing software code with respect to the virtual network function (VNF) and physical network function (PNF) of the telecom network hardware device; A step of mapping the peripheral data using a predefined filter selection model configured by the processor to identify requirements for a filtering agent for noise reduction, wherein the filter selection model identifies requirements for a filtering agent for noise reduction; The processor, which executes the predefined filter selection model, selects a filtering agent model in response to the results of the mapping, for selecting a filtering agent from a pool of model resources in which available filtering agent models are pooled; The processor, in response to the execution of the filtering agent model, selects filtering agents from the VNF that are associated with noise and distortion reduction with respect to the telecom network and associated with the UE; The processor acquires environmental characteristics, including weather-related data, via multiple Global Positioning System (GPS) sensors, linked to the telecom network; The processor performs the step of tagging a specified location of the UE, which is enabled with respect to the telecom network, with respect to a timeline, based on the environmental characteristics, in order to create a noise profile; In the step where the processor pushes the filtering agent to the UE, the filtering agent is stored in the temporary memory space of the UE's operating system; The processor generates network commands associated with the execution of the filtering agent; The processor performs the filtering agent with respect to the UE in response to the activation of the network command, wherein the execution activates the filtering agent with respect to the UE for a specified time frame; and The processor, in response to the results of the execution, reduces noise and distortion that occur during the operation of the UE with respect to the telecom network. A dynamic telecom network agent filtering method comprising the following features.

9. After the reduction is completed, the processor removes the filtering agent from the user device. The dynamic telecom network agent filtering method according to claim 8, further comprising:

10. The step of pushing the filtering agent to the UE is: The steps include: sending an activation interaction code, which is code that activates a filtering agent when executed by the UE, to the UE; and Steps to activate the filtering agent for performing the noise and distortion reduction in response to the execution of the activation interaction code: A dynamic telecom network agent filtering method according to claim 8 or 9, comprising:

11. The dynamic telecom network agent filtering method according to claim 8 or 9, wherein the environmental characteristics are associated with a resource related to a triggered PNF function.

12. The dynamic telecom network agent filtering method according to claim 8 or 9, wherein the step of selecting the filtering agent model is performed with respect to the VNF and the service orchestration layer of the 5G network.

13. The processor evaluates the requirements of the filtering agent with respect to a predefined polling frequency associated with the service running within the VNF; and In order to perform the reduction, the processor provisions each of the filtering agents to the malfunctioning device of the UE. The dynamic telecom network agent filtering method according to claim 8 or 9, further comprising:

14. The aforementioned step of selecting the filtering agent is: The stage of running a VNF multidimensional machine learning model based on the device's multilevel features and attributes. A dynamic telecom network agent filtering method according to claim 8 or 9, comprising:

15. The further step comprises providing at least one support service for at least one of the creation, integration, hosting, maintenance, and deployment of computer-readable code in the telecom network hardware device, wherein the computer-readable code is executed by the processor to implement the steps of: acquiring the peripheral data, mapping the data, selecting the filtering agent model, selecting the filtering agent, acquiring the environmental characteristics, tagging the data, pushing the data, generating the data, executing the data, and reducing the data. The dynamic telecom network agent filtering method according to claim 8 or 9.

16. To the processor of telecom network hardware devices: A procedure for obtaining peripheral data associated with a user equipment (UE) activated with respect to the telecom network associated with the telecom network hardware device, by the processor executing software code with respect to the virtual network function (VNF) and physical network function (PNF) of the telecom network hardware device, the peripheral data including details related to environmental noise and noise-related details in the UE; A procedure for mapping the peripheral data using a predefined filter selection model configured by the processor to identify requirements for a filtering agent for noise reduction, wherein the filter selection model identifies requirements for a filtering agent for noise reduction; A step by which the processor, which executes the predefined filter selection model, selects a filtering agent model to select a filtering agent from a pool of model resources in which available filtering agent models are pooled, in response to the result of the mapping; The process by which the processor selects, in response to the execution of the filtering agent model, filtering agents from the VNF associated with noise and distortion reduction for the UE with respect to the telecom network; The processor performs a procedure for acquiring environmental characteristics, including weather-related data, associated with the telecom network via multiple Global Positioning System (GPS) sensors; The process by which the processor tags a specified location of the UE, which is enabled with respect to the telecom network, with respect to a timeline, based on the environmental characteristics, in order to create a noise profile; The processor performs a step of pushing the filtering agent to the UE, where the filtering agent is stored in the temporary memory space of the UE's operating system; A procedure by which the processor generates network commands associated with the execution of the filtering agent; The processor performs the following steps in response to the activation of the network command: to execute the filtering agent with respect to the UE, wherein the execution activates the filtering agent with respect to the UE for a specified time frame; and The processor provides a procedure for reducing noise and distortion that occur during the operation of the UE with respect to the telecom network, in response to the result of the execution. A computer program designed to execute something.

17. To the aforementioned processor: After the reduction is complete, the processor performs a procedure to remove the filtering agent from the user device. A computer program according to claim 16 for further execution of the above.

18. The procedure for pushing the filtering agent to the UE is: A procedure to send an activation interaction code, which is code that activates a filtering agent when executed by the UE, to the UE; and A procedure for activating the filtering agent to perform the noise and distortion reduction in response to the execution of the activation interaction code. A computer program according to claim 16, having the following characteristics.

19. The computer program according to claim 16, wherein the environmental characteristics are associated with resources related to the triggered PNF function.

20. The computer program according to claim 16, wherein the step of selecting the filtering agent model is performed with respect to the VNF and the service orchestration layer of the 5G network.

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