Patent Publication Number: US-2023136692-A1

Title: Dynamic telecom network agent filtering

Description:
BACKGROUND 
     The present invention relates generally to a method for filtering noise and distortion occurring within a telecom network an and in particular to a method and associated system for improving telecom network technology associated with selecting a filtering agent model and filtering agents, tagging a location of user equipment, activating the filtering agents, and reducing noise and distortion occurring during operation of the user equipment with respect to a telecom network. 
     SUMMARY 
     A first aspect of the invention provides a telecom network hardware device comprising a processor coupled to a computer-readable memory unit, the memory unit comprising instructions that when executed by the processor implements a dynamic telecom network agent filtering method comprising: retrieving, by the processor executing software code with respect to a virtual network function (VNF) and a physical network function (PNF) of the telecom network hardware device, peripheral data associated with user equipment (UE) enabled with respect to a telecom network associated with the telecom network hardware device; mapping, by the processor, the peripheral data with a pre-defined filter selection model; selecting from a pool of model resources, by the processor executing the pre-defined filter selection model and in response to results of the mapping, a filtering agent model; selecting, by the processor in response to executing the filtering agent model, filtering agents from the VNF and associated with noise and distortion reduction associated with the UE with respect to the telecom network; retrieving, by the processor via a plurality of global positioning system (GPS) sensors, environmental characteristics associated with the telecom network; tagging, by the processor based on the environmental characteristics, a specified location of the UE enabled with respect to the telecom network; pushing, by the processor, the filtering agents to the UE, wherein the filtering agents are stored within a temporary memory space of an operating system of the UE; generating, by the processor, network commands associated with execution of the filtering agents; executing, by the processor in response to enabling the network commands, the filtering agents with respect to the UE, wherein the executing activates the filtering agents with respect to the UE for a specified timeframe; and reducing, by the processor in response to results of the executing, noise and distortion occurring during operation of the UE with respect to the telecom network. 
     A second aspect of the invention provides a dynamic telecom network agent filtering method comprising: retrieving, by a processor of a telecom network hardware device executing software code with respect to a virtual network function (VNF) and a physical network function (PNF) of the telecom network hardware device, peripheral data associated with user equipment (UE) enabled with respect to a telecom network associated with the telecom network hardware device; mapping, by the processor, the peripheral data with a pre-defined filter selection model; selecting from a pool of model resources, by the processor executing the pre-defined filter selection model and in response to results of the mapping, a filtering agent model; selecting, by the processor in response to executing the filtering agent model, filtering agents from the VNF and associated with noise and distortion reduction associated with the UE with respect to the telecom network; retrieving, by the processor via a plurality of global positioning system (GPS) sensors, environmental characteristics associated with the telecom network; tagging, by the processor based on the environmental characteristics, a specified location of the UE enabled with respect to the telecom network; pushing, by the processor, the filtering agents to the UE, wherein the filtering agents are stored within a temporary memory space of an operating system of the UE; generating, by the processor, network commands associated with execution of the filtering agents; executing, by the processor in response to enabling the network commands, the filtering agents with respect to the UE, wherein the executing activates the filtering agents with respect to the UE for a specified timeframe; and reducing, by the processor in response to results of the executing, noise and distortion occurring during operation of the UE with respect to the telecom network. 
     A third aspect of the invention provides a computer program product, comprising a computer readable hardware storage device storing a computer readable program code, the computer readable program code comprising 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: retrieving, by the processor executing software code with respect to a virtual network function (VNF) and a physical network function (PNF) of the telecom network hardware device, peripheral data associated with user equipment (UE) enabled with respect to a telecom network associated with the telecom network hardware device; mapping, by the processor, the peripheral data with a pre-defined filter selection model; selecting from a pool of model resources, by the processor executing the pre-defined filter selection model and in response to results of the mapping, a filtering agent model; selecting, by the processor in response to executing the filtering agent model, filtering agents from the VNF and associated with noise and distortion reduction associated with the UE with respect to the telecom network; retrieving, by the processor via a plurality of global positioning system (GPS) sensors, environmental characteristics associated with the telecom network; tagging, by the processor based on the environmental characteristics, a specified location of the UE enabled with respect to the telecom network; pushing, by the processor, the filtering agents to the UE, wherein the filtering agents are stored within a temporary memory space of an operating system of the UE; generating, by the processor, network commands associated with execution of the filtering agents; executing, by the processor in response to enabling the network commands, the filtering agents with respect to the UE, wherein the executing activates the filtering agents with respect to the UE for a specified timeframe; and reducing, by the processor in response to results of the executing, noise and distortion occurring during operation of the UE with respect to the telecom network. 
     The present invention advantageously provides a simple method and associated system capable of filtering noise and distortion occurring within a telecom network. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates a system for implementing an automated process for improving telecom network technology associated with selecting a filtering agent model and filtering agents, tagging a location of user equipment, activating the filtering agents, and reducing noise and distortion occurring during operation of the user equipment with respect to a telecom network, in accordance with embodiments of the present invention. 
         FIG.  2    illustrates an algorithm detailing a process flow enabled by the system of  FIG.  1    for implementing an automated process for improving telecom network technology associated with selecting a filtering agent model and filtering agents, tagging a location of user equipment, activating the filtering agents, and reducing noise and distortion occurring during operation of the user equipment with respect to a telecom network, in accordance with embodiments of the present invention. 
         FIG.  3    illustrates an internal structural view of the software/hardware of  FIG.  1   , in accordance with embodiments of the present invention. 
         FIG.  4    illustrates a communication system enabling communications between a VNF and PNFs enabled via the system of  FIG.  1   , in accordance with embodiments of the present invention. 
         FIG.  5    illustrates an algorithm detailing a process flow for retrieving environmental characteristics and creating a noise profile, in accordance with embodiments of the present invention. 
         FIGS.  6 A and  6 B  illustrate a system comprising requirements assessed with a predefined polling frequency via a service running within a radio access network (i.e., a VNF), in accordance with embodiments of the present invention. 
         FIG.  7    illustrates a computer system used by the system of  FIG.  1    for implementing an automated process for improving telecom network technology associated with selecting a filtering agent model and filtering agents, tagging a location of user equipment, activating the filtering agents, and reducing noise and distortion occurring during operation of the user equipment with respect to a telecom network, in accordance with embodiments of the present invention. 
         FIG.  8    illustrates a cloud computing environment, in accordance with embodiments of the present invention. 
         FIG.  9    illustrates a set of functional abstraction layers provided by cloud computing environment, in accordance with embodiments of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     System  FIG.  1    illustrates a system  100  for improving telecom network technology associated with selecting a filtering agent model and filtering agents  138 , tagging a location of user equipment  133 , activating the filtering agents  138 , and reducing noise and distortion occurring during operation of the user equipment  133  with respect to a telecom network  153 , in accordance with embodiments of the present invention. Typical fifth generation (5G) networks are configured to enable logical channeling processes that include dividing a frequency spectrum into categories for associated applications. Likewise, each category is further sub-divided into a number of logical channels based on a dynamic runtime configuration over a radio interface. The aforementioned internal segmentation process may cause a radio interface to associate with multiple sub-divided independent entities carrying packet data traffic from user equipment (UE) to an eNodeB (e.g., an element of an LTE radio access network) over a new radio interface that is multiplexed before transmission to a  51  bearer (e.g., a connectivity provider). Logical channeling processes may allow for distortion and interference (e.g., noise) to be added to a 5G network. Additionally (due to small cell architecture), if it has been determined that a user has crossed distance limits of a certain class of connectivity, then distortion within network traffic may increase which may lead to an increased packet retransmission requirement. The aforementioned increased distortion may not be directly removed because of associated base carrier requirements. An additional cause for distortion (within the 5G network) may include natural events such as, inter alia, like rain, fog, or Co2 presence within the air. Distortion may be injected into a 5G network within differing channels associated with 5G radio operations. Alternatively, distortion may be introduced within a UE device if a user is located at a noisy location. Likewise, background noise may be introduced into a 5G network thereby requiring additional processing at an eNodeB. Subsequently, interference data is formulated into packets and shared over a new radio (NR) interface thereby increasing network traffic. Typical noise filtering techniques are unable to enable a uniform process for preventing differing types of noise coming from UE entities and therefore the filtering processes must be transferred to an eNodeB such that eNodeB filters are used to reduce network traffic requirements. 
     The aforementioned filtering issues may not be rectified via simple data filtering techniques as requirements for filtering may differ for each type of interference and distortion introduced into a 5G network. Likewise, an amount of filtering techniques may be limited at an eNodeB and a UE device as they may only enable light weight processing capabilities with respect to a 5G core and an edge cloud processors. Additionally, a large portion of noise and distortion processing at transport endpoints may not be able to handle differing types of errors being added over various layers on NR and UE terminals. Therefore, system  100  is enabled to execute a process within a 5G telecom network virtual network function (VNF), collaborating with an individual physical network function to retrieve required information to build a multidimensional training model for filtering agents. 
     System  100  of  FIG.  1    includes a telecom network hardware device  139 , user equipment (UE)  133 , a VNF  119 , a PNF  122 , peripheral data  122 , filtering selection models  135 , and filtering agent models/filtering agents  138  interconnected through a telecommunication (telecom) network  153 . Telecom network hardware device  139  comprises sensors  112 , circuitry  127 , and software/hardware  121 . UE  133  may comprise any type of communication device including, inter alia, a mobile device, a tablet computer, a laptop computer, a smart device, etc. Peripheral data may include any type of data associated with UE  133 . Telecom network hardware device  139  and UE  133  each may comprise an embedded device(s). An embedded device is defined herein as a dedicated device or computer comprising a combination of computer hardware and software (fixed in capability or programmable) specifically designed for executing a specialized function. Programmable embedded computers or devices may comprise specialized programming interfaces. In one embodiment, telecom network hardware device  139  and UE  133  may each comprise a specialized hardware device comprising specialized (non-generic) hardware and circuitry (i.e., specialized discrete non-generic analog, digital, and logic-based circuitry) for (independently or in combination) executing a process described with respect to  FIGS.  1 - 6   . The specialized discrete non-generic analog, digital, and logic-based circuitry (e.g., sensors  112 , circuitry/logic  127 , software/hardware  121 , etc.) may include proprietary specially designed components (e.g., a specialized integrated circuit, such as for example an Application Specific Integrated Circuit (ASIC) designed for only implementing an automated process for improving telecom network technology associated with selecting a filtering agent model and filtering agents, tagging a location of user equipment, activating the filtering agents, and reducing noise and distortion occurring during operation of the user equipment with respect to a telecom network. Sensors  112  may include any type of internal or external sensors including, inter alia, GPS sensors, Bluetooth beaconing sensors, cellular telephone detection sensors, Wi-Fi positioning detection sensors, triangulation detection sensors, activity tracking sensors, a temperature sensor, an ultrasonic sensor, an optical sensor, a video retrieval device, humidity sensors, voltage sensors, network traffic sensors, etc. Telecom network  117  may include any type of communication network including, inter alia, a cellular/mobile network, a local area network, (LAN), a wide area network (WAN), the Internet, a wireless network, etc. 
     System  100  is configured to collect weather related information and location-based data for building a location profile for specified objects served within respective PNF  122 . Likewise, system  100  is configured to supply information to VNF  119 . Additionally, system  100  is configured to collect all location-based requirements for noise and distortion filtering. The location-based requirements are used for building filtering selection model  135  implemented as a training corpus. 
     System  100  enables dynamic content insight delivery platforms to engage each other for retrieving real time location specific information from PNF  122 . The real time location specific information may include, inter alia, weather related data and real time monitoring environmental data including, inter alia, a presence of fog, CO2 in the air, etc. Accordingly, filtering agent requirements are updated to VNF  119 . Proactive data retrieved from Internet situated resources are applied to retrieve PNF serving data such as rain related information, workload and channeling balancing information from a respective eNodeB within PNF  122 , etc. 
     Upon reception of the aforementioned information and data, filtering selection models  135  are invoked with attributes received from various resources. Filtering selection models  135  are enabled via functions of VNF  119 . Enabled filtering selection models  135  are configured to identify a requirement for a filtering algorithm and respective agents based on a situational (noise reduction) need. 
     If heavy rain is detected within a PNF serving region, distortion filters are selected with defined input parameters. A selected filter may be subsequently pushed to an edged entity based on a nature of an event. If distortion filters are selected, then the distortion filters may be pushed at eNodeB and related execution instructions may be transmitted to eNodeB over an S1 bearer. 
     Likewise, if noise or voice interference filters are required then they are pushed to eNodeB and executed over common control channel (CCCH) instructions utilizing S1 bearers and radio bearers to activate a signal. A related polling agent is configured to continuously validate a requirement validity and dissolve a filter with a feedback loop when a requirement is terminated. The feedback loop may be imposed with respect to a multidimensional model to improve related outcomes. 
       FIG.  2    illustrates an algorithm detailing a process flow enabled by system  100  of  FIG.  1    for implementing an automated process for improving telecom network technology associated with selecting a filtering agent model and filtering agents, tagging a location of user equipment, activating the filtering agents, and reducing noise and distortion occurring during operation of the user equipment with respect to a telecom network, in accordance with embodiments of the present invention. Each of the steps in the algorithm of  FIG.  2    may be enabled and executed in any order by a computer processor(s) executing computer code. Additionally, each of the steps in the algorithm of  FIG.  2    may be enabled and executed in combination by telecom network hardware device  139  and UE  133   133  of  FIG.  1   . In step  200 , peripheral data is retrieved by a telecom network hardware device executing software code with respect to a VNF and a PNF of the telecom network hardware device. The peripheral data is associated with UE enabled with respect to a telecom network associated with the telecom network hardware device. In step  202 , the peripheral data is mapped with a pre-defined filter selection model. In step  204 , a filtering agent model is selected from a pool of model resources via execution of the pre-defined filter selection model and in response to results of the mapping of step  202 . Selecting the filtering agent model may be executed with respect to a VNF and service orchestration layer of a 5G network. 
     In step  208 , filtering agents are selected (via execution of the filtering agent model) from the VNF. The filtering agents are associated with noise and distortion reduction associated with the UE with respect to the telecom network. Selecting the filtering agents may include executing a VNF multidimensional machine learning model based on multilevel features and attributes of devices. 
     In step  210 , environmental characteristics associated with the telecom network are retrieved via a plurality of global positioning system (GPS) sensors. The environmental characteristics may be associated with resources related to triggered PNF functions. 
     In step  212 , a specified location of the UE enabled with respect to the telecom network is tagged based on the environmental characteristics. In step  214 , the filtering agents are pushed to the UE. The filtering agents are stored within a temporary memory space of an operating system of the UE. Pushing the filtering agents to the UE may include: 
     1. Transmitting activation interaction code to the UE.
 
2. Activating (in response to execution of the activation interaction code) the filtering agents for executing noise and distortion reduction.
 
     In step  216 , network commands associated with execution of the filtering agents are generated. In step  218  (in response to enabling the network commands), the filtering agents are executed with respect to the UE thereby activating the filtering agents with respect to the UE for a specified timeframe. In step  220 , noise and distortion occurring during operation of the UE with respect to the telecom network is reduced in response to execution of step  218 . Reducing the noise and distortion may include: 
     1. Assessing requirements of the filtering agents with respect to a predefined polling frequency associated with a service running within the VNF.
 
2. Provisioning a respective filtering agent (of the filtering agents) to a malfunctioning device of the UE for executing the noise and distortion reduction.
 
     In step  224  (after completing execution of step  220 ), the filtering agents are removed from UE. 
       FIG.  3    illustrates an internal structural view of software/hardware  121  (i.e.,  121 ) of  FIG.  1   , in accordance with embodiments of the present invention. Software/hardware  121  includes a mapping module  304 , a selection module  305 , an execution module  308 , a noise and distortion reduction module  314 , and communication controllers  312 . Mapping module  304  comprises specialized hardware and software for controlling all functions related to the mapping steps of  FIG.  2   . Selecting module  305  comprises specialized hardware and software for controlling all functionality related to the filter selection steps described with respect to the algorithm of  FIG.  2   . Execution module  308  comprises specialized hardware and software for controlling all functions related to the filter agent execution steps of  FIG.  2   . Noise and distortion reduction module  314  comprises specialized hardware and software for controlling all functions related to the noise and distortion reduction steps of the algorithm of  FIG.  2   . Communication controllers  312  are enabled for controlling all communications between mapping module  304 , selection module  305 , execution module  308 , and noise and distortion reduction module  314 . 
       FIG.  4    illustrates a communication system  400  enabling communications between a VNF  402  and PNFs  404   a  and  404   b  (associated with peripherical information  408   a  and  408   b  and communications  410   a  and  410   b ) enabled via system  100  of  FIG.  1   , in accordance with embodiments of the present invention. Communication system  400  enables a process running within VNF  402  for collecting peripherical information  408   a  and  408   b  such as, inter alia, environmental noise related details and noise related details at a UE terminal for mapping with a pre-defined filter selection model. Peripherical information  408   a  and  408   b  may be associated with various levels of data at VNF  402 . Peripherical information  408   a  and  408   b  may be enabled via usage of VNF-PNF integrator services thereby allowing a model to be selected from available pool of resources. Likewise, selected filtering agents (at VNF  402 ) are pushed to selected target endpoints (e.g., associated with PNF  404   a  and  404   b ) and associated activation interactions are transmitted to an end device (e.g., UE  133  of  FIG.  1   ) to activate a filter for noise and distortion reduction. 
       FIG.  5    illustrates an algorithm  500  detailing a process flow for retrieving environmental characteristics and creating a noise profile, in accordance with embodiments of the present invention. In steps  502  and  504 , environmental characteristics from GPS based data resources respective PNF functions are retrieved. For example, stream based data collectors may be enabled to retrieve a GPS identity of a PNF boundary region. Likewise, an associated noise and distortion cancellation filter from a 5G core network infrastructure is enabled via execution of in band MAC based protocols. In step  508 , a filtering agent selection process is performed at a VNF function and a service orchestration layer of 5G thereby propagating filtering agents to be pushed (in step  510 ) to the PNF and a logical endpoint based on a nature of a noise/distortion event. Likewise (in step  510 ), filter is pushed to UE devices experiencing noise over voice calls due to a noisy background. In steps  512  and  514 , a location is tagged (with respect to a timeline) to create a noise profile. In response, noise-based filters are dynamically provisioned when a UE location is detected (in step  518 ) within a defined boundary region. For example, if a crowded retail facility is detected as a location, then a noise cancellation filter is provisioned during initiation of a voice call. Likewise, a noise cancellation filter may be provisioned with respect to natural noise initiating events. For example (with respect to detected heavy rain or CO2 values in the air) Co2 based distortion filtering agents are required at an eNodeB to obtain correct packet data decoding. 
       FIGS.  6 A and  6 B  (in combination) illustrate a system  600  comprising requirements  602  assessed with a predefined polling frequency  604  via a service running within a radio access network  608  (i.e., a VNF), in accordance with embodiments of the present invention. System  600  is configured to provision a respective filtering agent to a degraded device to optimize network utilization and avoid packed retransmission errors. Interfaces are utilized to transmit various types of network traffic such that filtering agent selection is performed at radio access network  608 . Likewise, filters are selected using a VNF multidimensional machine learning model with respect to multilevel features and attributes within a common filter repository. Additionally, input data attributes (i.e., associated with weather, location, and additional device specific characteristics) are applied to associated datasets. Subsequently, selected filtering agents are pushed to an edged device as follows: 
     1. Suitable filters are detected at radio access network  608  based on a requirement of a noise/distortion situation.
 
2. Logical channels are created to transfer the machine learning model at an edged location
 
3. An edged location is identified based on a type of filer. For example, a distortion filter comprises an edged loc=eNodeB.
 
     Additionally, network commands are generated to issue an execution of a selected filter at a target location such that CCCH frames are formed over a radio interface and transmitted over an NR. Subsequently, a target device is configured to listen to an NR interface and receive an incoming filter in a temporary space of a device operating system. The incoming filter is executed and a selected filtering agent is activated for a specified timeline thereby saving a computation requirement at the target location and saving processing power at a terminal device. All further packet data transmission processes are performed after filter processing. A polling thread for a validation manager is activated at a PNF and is configured to poll for event validity. Likewise, an event manager triggers a signal when a current requirement is completed. Subsequently, the incoming filter is dissolved (i.e., deleted). 
       FIG.  7    illustrates a computer system  90  (e.g., telecom network hardware device  139  and UE  133  of  FIG.  1   ) used by or comprised by the system  100  of  FIG.  1    for improving telecom network technology associated with selecting a filtering agent model and filtering agents, tagging a location of user equipment, activating the filtering agents, and reducing noise and distortion occurring during operation of the user equipment with respect to a telecom network, in accordance with embodiments of the present invention. 
     Aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module,” or “system.” 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, 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 suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing apparatus receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, spark, R language, or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, device (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing device, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing device, or other device to cause a series of operational steps to be performed on the computer, other programmable device or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable device, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The computer system  90  illustrated in  FIG.  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  each coupled to the processor  91 . The input device  92  may be, inter alia, a keyboard, a mouse, a camera, a touchscreen, etc. The output device  93  may be, inter alia, a printer, a plotter, a computer screen, a magnetic tape, a removable hard disk, a floppy disk, etc. The memory devices  94  and  95  may be, inter alia, a hard disk, a floppy disk, a magnetic tape, an optical storage such as a compact disc (CD) or a digital video disc (DVD), a dynamic random access memory (DRAM), a read-only memory (ROM), etc. The memory device  95  includes a computer code  97 . The computer code  97  includes algorithms (e.g., the algorithms of  FIG.  2   ) for improving telecom network technology associated with selecting a filtering agent model and filtering agents, tagging a location of user equipment, activating the filtering agents, and reducing noise and distortion occurring during operation of the user equipment with respect to a telecom network. The processor  91  executes the computer code  97 . The memory device  94  includes input data  96 . The input data  96  includes input required by the computer code  97 . The output device  93  displays output from the computer code  97 . Either or both memory devices  94  and  95  (or one or more additional memory devices Such as read only memory device  85 ) may include algorithms (e.g., the algorithms of  FIG.  2   ) and may be used as a computer usable medium (or a computer readable medium or a program storage device) having a computer readable program code embodied therein and/or having other data stored therein, wherein the computer readable program code includes the computer code  97 . Generally, a computer program product (or, alternatively, an article of manufacture) of the computer system  90  may include the computer usable medium (or the program storage device). 
     In some embodiments, rather than being stored and accessed from a hard drive, optical disc or other writeable, rewriteable, or removable hardware memory device  95 , stored computer program code  84  (e.g., including algorithms) may be stored on a static, nonremovable, read-only storage medium such as a Read-Only Memory (ROM) device  85 , or may be accessed by processor  91  directly from such a static, nonremovable, read-only medium. Similarly, in some embodiments, stored computer program code  97  may be stored as computer-readable firmware  85 , or may be accessed by processor  91  directly from such firmware  85 , rather than from a more dynamic or removable hardware data-storage device  95 , such as a hard drive or optical disc. 
     Still yet, any of the components of the present invention could be created, integrated, hosted, maintained, deployed, managed, serviced, etc. by a service supplier who offers to improve telecom network technology associated with selecting a filtering agent model and filtering agents, tagging a location of user equipment, activating the filtering agents, and reducing noise and distortion occurring during operation of the user equipment with respect to a telecom network. Thus, the present invention discloses a process for deploying, creating, integrating, hosting, maintaining, and/or integrating computing infrastructure, including integrating computer-readable code into the computer system  90 , wherein the code in combination with the computer system  90  is capable of performing a method for enabling a process for improving telecom network technology associated with selecting a filtering agent model and filtering agents, tagging a location of user equipment, activating the filtering agents, and reducing noise and distortion occurring during operation of the user equipment with respect to a telecom network. In another embodiment, the invention provides a business method that performs the process steps of the invention on a subscription, advertising, and/or fee basis. That is, a service supplier, such as a Solution Integrator, could offer to enable a process for improving telecom network technology associated with selecting a filtering agent model and filtering agents, tagging a location of user equipment, activating the filtering agents, and reducing noise and distortion occurring during operation of the user equipment with respect to a telecom network. In this case, the service supplier can create, maintain, support, etc. a computer infrastructure that performs the process steps of the invention for one or more customers. In return, the service supplier can receive payment from the customer(s) under a subscription and/or fee agreement and/or the service supplier can receive payment from the sale of advertising content to one or more third parties. 
     While  FIG.  7    shows the computer system  90  as a particular configuration of hardware and software, any configuration of hardware and software, as would be known to a person of ordinary skill in the art, may be utilized for the purposes stated supra in conjunction with the particular computer system  90  of  FIG.  7   . For example, the memory devices  94  and  95  may be portions of a single memory device rather than separate memory devices. 
     Cloud Computing Environment 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling 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 released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring now to  FIG.  8   , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A,  54 B,  54 C and  54 N shown in  FIG.  12    are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG.  9   , a set of functional abstraction layers provided by cloud computing environment  50  (see  FIG.  8   ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG.  9    are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     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 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  87  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  88  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  101  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  102 ; software development and lifecycle management  103 ; virtual classroom education delivery  133 ; data analytics processing  134 ; transaction processing  106 ; and for improving network switching technology associated with detecting operational states for ports, generating actions associated with the operational states with respect to data packets arriving at the ports, and improving telecom network technology associated with selecting a filtering agent model and filtering agents, tagging a location of user equipment, activating the filtering agents, and reducing noise and distortion occurring during operation of the user equipment with respect to a telecom network  107 . 
     While embodiments of the present invention have been described herein for purposes of illustration, 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 fall within the true spirit and scope of this invention.