Patent Publication Number: US-2023139135-A1

Title: Systems and methods for machine learning model augmentation using target distributions of key performance indicators in a wireless network

Description:
BACKGROUND 
     Wireless networks may be associated with adjustable parameters that may improve performance or reliability of the network, thereby enhancing the connectivity, performance, etc. of the wireless networks. Such adjustable parameters may include beamforming parameters, handover thresholds, and/or other types of parameters. A wireless network may use models, such as predictive models, to automatically make adjustments to one or more parameters in order to enhance the connectivity, performance, etc. of the wireless network without the need for manual adjustments by an operator associated with the wireless network. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an example overview of one or more embodiments described herein; 
         FIG.  2    illustrates an example predictive model in accordance with one or more embodiments described herein; 
         FIG.  3    illustrates an example distribution of Key Performance Indicators (“KPIs”) associated with a wireless network; 
         FIGS.  4  and  5    illustrate example target KPI distributions, in accordance with one or more embodiments described herein; 
         FIG.  6    illustrates an example process for generating, refining, and/or utilizing one or more models based on an augmentation of training data using target KPI distributions, in accordance with embodiments described herein; 
         FIG.  7    illustrates an example environment in which one or more embodiments, described herein, may be implemented; 
         FIG.  8    illustrates an example arrangement of a radio access network (“RAN”), in accordance with some embodiments; 
         FIG.  9    illustrates an example arrangement of an Open RAN (“O-RAN”) environment in which one or more embodiments, described herein, may be implemented; and 
         FIG.  10    illustrates example components of one or more devices, in accordance with one or more embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS 
     The following detailed description refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. 
     Embodiments described herein provide for the augmentation of one or more models, such as artificial intelligence/machine learning (“AI/ML”) models, predictive models, etc. based on target distributions of values associated with the models. In some embodiments, such models may be associated with a wireless network, and may associate a set of states to a set of values and/or to a set of actions. As discussed in more detail below, such states may include particular sets of configuration parameters associated with the wireless network, the values may include KPIs or other metrics associated with the wireless network, and the actions may include one or more actions to perform with respect to the wireless network when given a particular state and/or set of KPIs. For example, such actions may include modifications to the particular state (e.g., wireless network configuration parameters) when a particular set of KPIs are detected, where such modifications enhance, improve, or otherwise cause the KPIs to meet one or more thresholds or conditions. 
     For example, as shown in  FIG.  1   , Network Optimization System (“NOS”)  101  may receive (at  102 ) KPIs and/or configuration parameters associated with wireless network  103 . NOS  101  may receive such information from one or more elements of wireless network  103  via an application programming interface (“API”), a device or system that serves as an interface between wireless network and external systems (e.g., a Service Capability Exposure Function (“SCEF”) or a Network Exposure Function (“NEF”)), or some other device or system associated with wireless network  103 . Additionally, or alternatively, NOS  101  may receive some or all of such information from one or more other devices or systems that are external to wireless network  103  (e.g., proxies, application servers, databases, etc.). In some embodiments, NOS  101  may request the KPIs and/or configuration parameters (e.g., a “pull” technique, a “polling” technique, etc.), and/or may receive the KPIs and/or configuration parameters without specifically requesting such information (e.g., via a “push” technique, a “listening” technique, etc.). NOS  101  may receive the KPIs and/or configuration parameters over time on a periodic basis, an intermittent basis, an event-driven basis, and/or on some other suitable basis. 
     The KPIs may include any suitable metrics associated with wireless network  103 . For example, the KPIs may include metrics regarding communications between one or more base stations, RANs, or other wireless infrastructure equipment of wireless network  103  and one or more User Equipment (“UEs”). Such metrics may include radio frequency (“RF”) metrics, such as Received Signal Strength Indicator (“RSSI”) values, Channel Quality Indicator (“CQI”) values, Signal-to-Interference-and-Noise-Ratio (“SINR”) values, and/or other suitable metrics. The metrics between the wireless infrastructure equipment of wireless network  103  and one or more UEs may include performance metrics, such as latency, throughput, jitter, or other performance metrics. In some embodiments, the metrics between the wireless infrastructure equipment of wireless network  103  and one or more UEs may include other suitable metrics or KPIs, such as call drop rate, quantity of connected UEs at a given time or time period, quantity of handovers during a given time period, etc. 
     In some embodiments, the KPIs may include metrics regarding communications between elements of wireless network  103  and/or some other network, device, or system. For example, such metrics may include processing and/or queue time of particular types of traffic via particular network devices of wireless network  103 , latency of traffic via particular network devices of wireless network  103 , metrics related to categories or classes of traffic (e.g., associated with different Quality of Service (“QoS”) levels or network slices), etc. While example KPIs are discussed above, the KPIs (received at  102 ) may include other suitable KPIs associated with wireless network  103  or some other device or system. 
     The configuration parameters associated with wireless network  103  may include RAN or base station configuration parameters, such as beamforming parameters (e.g., azimuth angle, beam width, antenna power, etc.), Multiple-Input Multiple-Output (“MIMO”) parameters, Physical Resource Block (“PRB”) allocation parameters, traffic queueing parameters, access control parameters, handover thresholds, or other suitable RAN or base station configuration parameters. In some embodiments, the configuration parameters may include neighbor cell lists (“NCLs”), handover thresholds, routing parameters (e.g., routing tables, Domain Name System (“DNS”) tables, etc.), containerized virtual environment configuration parameters, power saving parameters, or any other suitable parameters of wireless network  103  that may be configured, adjusted, etc. 
     NOS  101  may receive (at  102 ) the KPIs and configuration parameters in such a manner that NOS  101  is able to determine a particular set of configuration parameters associated with wireless network  103  for a particular set of received KPIs. For example, the KPIs and/or configuration parameters may include one or more timestamps, device or network identifiers, or other suitable information based on which NOS  101  may correlate particular KPIs with particular sets of configuration parameters. Wireless network  103  may determine (at  104 ) a configuration parameter-specific association  105  of KPI distributions  107  to a set of configuration parameters  109 . In this manner, although one configuration parameter-specific association  105  of one KPI distribution  107  to one set of configuration parameters  109  is shown in  FIG.  1   , NOS  101  may determine (at  104 ) multiple configuration parameter-specific associations  105  that are each associated with different sets of configuration parameters  109 . 
     In some embodiments, a first configuration parameter-specific association  105  may be associated with a particular set of wireless network configuration parameters during a first time period or interval (e.g., weekdays), and a second configuration parameter-specific association  105  may be associated with the same particular set of wireless network configuration parameters during a second time period or interval (e.g., weekends). For example, the same configuration parameters  109  may be associated with different KPI distributions  107  based on factors other than configuration parameters  109 , such as UE density or location, types of traffic associated with wireless network  103  at different times, etc. In some embodiments, one or more other factors, in addition to or in lieu of time (e.g., weather, particulate matter metrics, topography, etc.), may be used to differentiate different configuration parameter-specific associations  105 . 
     KPI distribution  107  may include and/or may be represented by one or more plots, datasets, charts, curves, or the like that indicate an incidence of occurrence (e.g., “density”) of one or more particular KPI values. For example, a particular KPI distribution  107  may be based on a single KPI, which may include one or more raw values received from wireless network  103 . In some embodiments, a particular KPI distribution  107  may be computed or derived from multiple KPIs, and may include scores, averages, means, minimum values, maximum values, embedded values (e.g., low-dimensional representations), and/or other derived values. 
     NOS  101  may further identify (at  106 ) one or more associations  111  of modified configuration parameters  113  and associated target KPI distributions  115  based on the configuration parameter-specific association  105  of KPI distributions  107  to the set of configuration parameters  109 . For example, NOS  101  may maintain, receive, and/or otherwise determine a set of target KPI distributions  115  that include one or more pre-defined distributions. Target KPI distributions  115  may include, for example, one or more instances of a stable distribution, such as a Normal distribution, a Cauchy distribution, a Levy distribution, a Holtsmark distribution, a Pareto-Levy distribution, and/or some other distribution. In some embodiments, target KPI distributions  115  may include some other type of pre-defined distribution, a random distribution, a distribution determined using one or more AI/ML techniques, or some other suitable distribution. In some embodiments, target KPI distributions  115  may include one or more distributions determined using one or more Extreme Value Theory (“EVT”) techniques, such as a Block Maxima tech, a Peak Over Threshold tech, or some other suitable technique. The use of target KPI distributions  115  may facilitate the augmentation of one or more models, such as by identifying particular sets of configuration parameters  109  that yield KPIs that match or otherwise correlate to target KPI distributions  115 . Target distributions  115  may therefore be identified, selected, etc. based on “expected” or “standard” distributions which are relatively likely to occur in real-world or simulated scenarios. 
     In some embodiments, target distributions  115  may be generated and/or identified based on a randomness state model, such as a Mandelbrot randomness state model. For example, a given target distribution  115  may be associated with a given randomness state, such as “mild,” “slow,” “wild,” etc. In some embodiments, such states may have further granularity, such as “proper-mild,” “borderline mild,” “slow with finite and delocalized movements,” “slow with finite and localized movements,” “pre-wild,” “extreme,” and/or other suitable randomness states. 
     NOS  101  may generate respective sets of configuration parameters for wireless network  103  that yield one or more target KPI distributions  115 . For example, NOS  101  may perform modifications, such as incremental modifications, to one or more parameters of the set of configuration parameters  109  associated with wireless network  103 . Such modifications may be performed in real-world scenarios (e.g., by deploying sets of modified configuration parameters  113  to wireless network  103 ) and/or in one or more simulations (e.g., by configuring a test environment that models or simulates wireless network  103  according to one or more modified configuration parameters  113 ). NOS  101  may receive KPIs associated with wireless network  103  (e.g., as a result of deploying modified configuration parameters  113  to wireless network  103  and/or by simulating wireless network  103  according to the modified configuration parameters  113 , and may identify a distribution of the KPIs that result from the modified configuration parameters  113 . In situations where a given set of modified configuration parameters  113  yields KPIs that match a particular target KPI distribution  115 , NOS  101  may record (e.g., as a particular association  111  of modified configuration parameters  113  and associated target KPI distributions  115 ) information indicating that the given set of modified configuration parameters  113  are associated with the particular target KPI distribution. In this manner, NOS  101  may identify multiple sets of modified configuration parameters  113  that are respectively associated with particular target KPI distributions  115 . 
     In some embodiments, a set of KPIs (e.g., associated with a particular set of modified configuration parameters  113 ) may “match” a target KPI distribution if the distribution of the set of KPIs is the same as, or is within a threshold measure of similarity to, the given target KPI distribution. In some embodiments, NOS  101  may identify the similarity of a given KPI distribution (e.g., associated with a particular set of modified configuration parameters  113 ) based on attributes of curves and/or other representations of the set of KPIs and/or the one or more target KPI distributions  115  (referred to herein as “shape attributes”). For example, such shape attributes of a given KPI distribution may include or may be based on a “shift” or “location” attribute, which may be based on a mean, median, mode, etc. of the KPI distribution. Another shape attribute of a given KPI distribution may include or be based on a “width” or “scale” parameter, which may be based on a standard deviation, a variance, etc. of the KPI distribution. Another shape attribute of a given KPI distribution may include or be based on a “symmetry” or “skewness” parameter, which may be based on a density of the distribution above or below a mean, median, mode, etc. of the distribution. Another shape attribute of a given KPI distribution may include or be based on a “peakedness” or “Kurtosis” parameter, which may be based on an amount of data within tails of the distribution. Thus, different target KPI distributions  115  may have different values for these parameters and/or other parameters. 
     In this manner, NOS  101  may determine (at  106 ) multiple sets of modified configuration parameters  113  (e.g., modified based on performing incremental modifications to configuration parameters  109 , and/or otherwise different from configuration parameters  109 ) that are each associated with a particular target KPI distribution  115 . As such, as discussed below, when identifying a set of KPIs that matches a particular target KPI distribution  115 , NOS  101  may be able to identify one or more actions to perform (e.g., to improve the performance and/or reliability of wireless network  103 ). 
     In some embodiments, when determining (at  106 ) associations  111  of modified configuration parameters  113  and associated target KPI distributions  115 , NOS  101  may utilize a set of configuration parameter constraints  117 . Configuration parameter constraints  117  may, for example, include constraints, rules, policies, etc. based on which NOS  101  may simulate and/or otherwise identify sets of modified configuration parameters  113 . For example, a given set of configuration parameter constraints  117  may specify a maximum quantity of UEs that may be placed (e.g., in accordance with a simulation that allows placement of UEs) in a given geographical area, a minimum or maximum radio transmit power associated with one or more base stations of wireless network  103 , and/or other suitable constraints. In this manner, unrealistic circumstances or conditions may be avoided in an AI/ML procedure (e.g., the generation, refinement, training, etc. of a reinforcement model), and modified configuration parameters  113  may be more likely to match parameters that are attainable in real-world scenarios. 
     NOS  101  may generate or refine (at  108 ) one or more models  119  based on the parameter-specific associations of  105  and  111  of configuration parameters (e.g., configuration parameters  109  and modified configuration parameters  113 ) and KPI distributions (e.g., KPI distribution  107  and target KPI distributions  115 ). In this manner, model  119  may be “augmented” by one or more associations  111  of configuration parameters  113  and target KPI distributions  115 , as compared to a model that includes only an association  105  between configuration parameters  109  and KPI distribution  107 . 
     Model  119  may thus be more predictive, by virtue of increased generalization resulting from the inclusion of the augmented associations  111  of configuration parameters  113  and target KPI distributions  115 , as model  119  may be able to be tailored to fit a variety of scenarios (e.g., scenarios where measured KPIs matches a given target KPI distribution  115  and/or where a set of implemented configuration parameters of wireless network  103  matches a given set of modified configuration parameters  113 ). Further, as modified configuration parameters  113  may be based on configuration parameters  109  (e.g., based on performing iterative modifications to configuration parameters  109 ) and/or configuration parameter constraints  117 , the determination (at  106 ) of modified configuration parameters  113  may be less time- and/or processor-intensive, as the possible set of configuration parameters may be narrowed and/or constrained based on configuration parameters  109  and/or configuration parameter constraints  117 . Further, as KPIs that match a set of target KPI distributions  115  may be used to identify modified configuration parameters  113 , the possible set of configuration parameters may be narrowed and/or constrained such that the performance and/or other KPIs of wireless network  103  may be realistically identified, which may also be less time- and/or processor-intensive than identifying configuration parameters that would yield other KPI distributions. Such other KPI distributions may include fringe cases, outliers, and/or other unrealistic results. Paring down the possible set of KPI distributions in accordance with embodiments described herein may thus improve the efficiency and speed of one or more other procedures, such as a procedure of identifying configuration parameters that, when deployed to wireless network  103 , yield KPIs according to particular KPI distributions. 
     As noted above, a particular set of configuration parameters  109  and KPI distribution  107  (and/or modified configuration parameters  113  and target KPI distribution  115 ) may be associated with one or more actions. For example, as shown in  FIG.  2   , a first action  201  may be associated with configuration parameters  109  and KPI distribution  107 , a second action  203  may be associated with modified configuration parameters  113 - 1  and target KPI distribution  115 - 1 , a third action  205  may be associated with modified configuration parameters  113 - 2  and target KPI distribution  115 - 2 , and a fourth action  207  may be associated with modified configuration parameters  113 - 3  and target KPI distribution  115 - 3 . In some embodiments, NOS  101  may utilize supervised and/or unsupervised machine learning techniques, one or more other AI/ML techniques, a reinforcement learning technique, a Markov Decision Process, or other suitable procedure to associate a respective action with a respective set of configuration parameters  109 / 113  and/or KPI distribution  107 / 115 . For example, given a particular set of configuration parameters  109 / 113  and/or KPI distributions  107 / 115 , NOS  101  may identify a particular action  201 - 207  to perform with respect to wireless network  103 , where such action may improve or otherwise change KPIs associated with wireless network  103  (e.g., cause one or more KPIs to satisfy one or more thresholds, rules, conditions, constraints, etc.). 
     In some embodiments, such actions may include, for example, modifying QoS related parameters, such as modifying queue weights associated with the processing, transmitting, or otherwise handling traffic associated with particular QoS values, such as QoS Class Identifier (“QCI”) values, QoS Flow Identifier (“QFI”) values, priority values, and/or other suitable values or indicators. 
     In some embodiments, such actions may include modifying the availability or allocation of RAN resources, such as PRBs, portions of RF spectrum, or the like. In some embodiments, such modification may be on the basis of an identifier associated with a given UE, such as an International Mobile Subscriber Identity (“IMSI”), International Mobile Station Equipment Identity (WEI″), Globally Unique Temporary Identifier (“GUTI”), Subscription Permanent Identifier (“SUPI”), Internet Protocol (“IP”) address, Mobile Directory Number (“MDN”), or other suitable identifier. For example, a particular UE or set of UEs, such as UEs associated with first responders, government agencies, or some other suitable category, may be granted a larger allocation of available RF resources than other UEs. 
     In some embodiments, such actions may include implementing one or more energy-saving techniques, such as activating a cell suspend mode, modifying antenna transmission and/or reception parameters in the time and/or frequency domains, throttling one or more processors, entering a low-power mode, and/or otherwise reducing the amount of power (e.g., electrical power) consumed by one or more devices or systems that implement or are otherwise associated with wireless network  103 . 
     In some embodiments, such actions may include modifying one or more beamforming parameters associated with wireless network  103 . For example, NOS  101  may modify azimuth angle, tilt angle, beam width, antenna power, and/or other aspects of beamforming parameters associated with one or more antennas of wireless network  103 . In some embodiments, NOS  101  may modify a MIMO) configuration associated with wireless network  103 , such as activating or deactivating a MIMO mode, selecting one or more antennas to implement MIMO a given MIMO configuration, or other MIMO parameters associated with wireless network  103 . 
     In some embodiments, such actions may include modifying parameters related to handovers and/or mobility. For example, wireless network  103  may modify an NCL provided to UEs connected to a particular base station of wireless network  103 , which may affect how such UEs scan for or detect neighboring base stations. In some embodiments, such actions may include modifying handover-related parameters, such as handover thresholds used by UEs to determine whether such UEs should request a handover from base station to another base station and/or from one frequency band or RAT to another frequency band or RAT. Such handover thresholds may refer to, for example, threshold measures of signal strength or quality, such as an RSSI value, a CQI value, an SINR value, a Reference Signal Receive Power (“RSRP”) value, and/or some other suitable value. 
     While example actions are described above, in practice, other suitable actions may be performed in order to affect one or more KPIs of wireless network  103 . In some embodiments, actions  201 - 207  may include multiple actions, such as multiple actions discussed above and/or other actions. 
       FIG.  3    illustrates graph  300 , which may represent example KPI distribution  107 . While shown here as a graph, the information reflected by graph  300  may, in some embodiments, be notated or indicated in some other suitable fashion (e.g., a density distribution, a table, a chart, or some other suitable representation). As noted above, KPI distribution  107  may reflect KPIs measured or simulated with respect to wireless network  103  and/or some portion thereof, when wireless network  103  is associated with a particular set of configuration parameters  109 . KPI distribution  107  may be based on raw values for one or more KPIs, and/or may be based on computed or derived values based on one or more KPIs. In some embodiments, such computed or derived values may be reflected as one or more scores or other suitable values. The “density” for a particular KPI value or score may refer to a quantity of instances of that particular KPI value or score in the set of received KPIs with respect to wireless network  103 . 
       FIG.  4    illustrates graph  400 , which may include example KPI distribution  107  discussed above, as well as target KPI distribution  115 - 1 . As shown, target KPI distribution  115 - 1  may have one or more different shape attributes from KPI distribution  107 . For example, as shown, target KPI distribution  115 - 1  may be “shifted” to the left of graph  400 , as compared to a “location” of KPI distribution  107  within graph  400 . For example, a peak of target KPI distribution  115 - 1  may be associated with relatively lower KPI values and/or scores than a peak of KPI distribution  107 . As another example, target KPI distribution  115 - 1  may be “wider” than KPI distribution  107 , which may reflect a greater standard deviation or variance associated with target KPI distribution  115 - 1  than KPI distribution  107 . As another example, target KPI distribution  115 - 1  may be “skewed” less than KPI distribution  107 , as the distribution within tails of target KPI distribution  115 - 1  may be more even or “symmetrical” than the distribution within tails of KPI distribution  107 . Further, target KPI distribution  115 - 1  may be less “peaky” than KPI distribution  107 , as tails of target KPI distribution  115 - 1  may be denser with respect to a peak of target KPI distribution  115 - 1  than tails of KPI distribution  107  with respect to a peak of KPI distribution  107 . Further, target KPI distribution  115 - 1  may be less “peaky” than KPI distribution  107 , as a maximum density (e.g., “peak”) of target KPI distribution  115 - 1  may be lower than a maximum density of KPI distribution  107 . 
     As discussed above, NOS  101  may determine a particular set of configuration parameters  113 - 1  that, when deployed to wireless network  103 , yield KPIs that match target KPI distribution  115 - 1  within a threshold measure of similarity using any suitable similarity determination technique. As discussed above, such deployment may include modifying configuration parameters of wireless network  103  in a real-world scenario or in one or more simulated environments. In some embodiments, as noted above, determining configuration parameters  113 - 1  may include iteratively performing one or more modifications to configuration parameters  109 . In some embodiments, determining configuration parameters  113 - 1  may be independent of configuration parameters  109 . As discussed above, NOS  101  may generate or refine one or more models  119  to indicate that configuration parameters  113 - 1  are associated with target KPI distribution  115 - 1 . 
     Accordingly, in situations where NOS  101  or some other device or system is evaluating wireless network  103  and/or some other network, NOS  101  may predict that KPIs that are in accordance with target KPI distribution  115 - 1  may be expected when parameters of wireless network  103  match configuration parameters  113 - 1 , and/or or are similar to configuration parameters  113 - 1  within a threshold measure of similarity. Additionally, or alternatively, NOS  101  may predict or determine that the configuration parameters of wireless network  103  match configuration parameters  113 - 1  when identifying that a set of KPIs associated with wireless network  103  match target KPI distribution  115 - 1 . Additionally, or alternatively, when identifying that the configuration parameters of wireless network  103  match configuration parameters  113 - 1 , and/or when identifying that KPIs associated with wireless network  103  match target KPI distribution  115 - 1 , NOS  101  may determine that one or more actions, such as a particular action associated with configuration parameters  113 - 1  and/or target KPI distribution  115 - 1 , should be performed with respect to wireless network  103 . 
       FIG.  5    illustrates graph  500 , which may illustrate another target KPI distribution  115 - 2  for which one or more sets of configuration parameters associated with wireless network  103  may be determined. For example, as similarly discussed above, target KPI distribution  115 - 2  may have different shape attributes from KPI distribution  107  and/or from target KPI distribution  115 - 1 . NOS  101  may further identify, through real-world configuration modifications and/or via one or more simulations, that KPIs associated with wireless network  103  match, or are otherwise within a threshold measure of similarity to, target KPI distribution  115 - 2  when wireless network  103  is configured with configuration parameters  113 - 2 . In some situations, NOS  101  may identify multiple different sets of configuration parameters for which KPIs of wireless network  103  match target KPI distribution  115 - 2 . In this example, NOS  101  may also identify that when wireless network  103  is configured according to configuration parameters  113 - 3 , KPIs of wireless network  103  also match target KPI distribution  115 - 2 . In this manner, different possible configurations of wireless network  103  may be associated with the same KPI distribution. 
       FIG.  6    illustrates an example process  600  for generating, refining, and/or utilizing one or more models based on an augmentation of training data using target KPI distributions, in accordance with embodiments described herein. In some embodiments, some or all of process  600  may be performed by NOS  101 . In some embodiments, one or more other devices may perform some or all of process  600  in concert with, and/or in lieu of, NOS  101 . 
     As shown, process  600  may include receiving (at  602 ) information associating a set of KPIs with a set of network configuration parameters, network policies, etc. For example, as discussed above, NOS  101  may receive a set of KPIs for a given wireless network  103  and/or a portion thereof (e.g., a core portion of wireless network  103 , a RAN portion of wireless network  103 , and/or some other portion). NOS  101  may further receive an indication of network configuration parameters, associated with wireless network  103 , that correspond to the received set of KPIs. For example, such network configuration parameters may be network configuration parameters associated with one or more elements of wireless network  103  during a time period that corresponds to when the received KPIs were measured. Although discussed in terms of KPIs of wireless network  103 , in some embodiments, concepts similar to those described herein may apply to any suitable set of values that are associated with any suitable state. 
     Process  600  may further include identifying (at  604 ) one or more target KPI distributions for the KPIs associated with wireless network  103 . For example, as discussed above, NOS  101  may maintain a pre-determined set of target KPI distributions, may generate or modify a set of target KPI distributions based on one or more AI/ML techniques, and/or may otherwise receive or identify a set of target KPI distributions. In some embodiments, the target KPI distributions may have one or more different shape attributes than a KPI distribution associated with the KPIs received (at  602 ) with respect to wireless network  103 . 
     Process  600  may additionally include modifying (at  606 ) network configuration parameters associated with the set of KPIs to generate sets of KPIs that correspond to the target KPI distributions. For example, NOS  101  may modify one or more configuration parameters that were received (at  602 ) with the set of KPIs. In some embodiments, NOS  101  may perform such modifications in an iterative process, in which NOS  101  performs multiple incremental modifications to a particular configuration parameter and/or to multiple configuration parameters, and determines KPIs associated with each set of incremental modifications. As such, operation  606  may be repeated multiple times, until the KPIs resulting from a particular set of modified configuration parameters matches a particular target KPI distribution. A “match” between a set of KPIs and a particular target distribution may be based on any suitable similarity measurement technique, which may indicate that a measure of similarity between the set of KPIs and the particular target distribution exceeds a threshold measure of similarity. In some embodiments, image recognition techniques may be used to compare a visual representation of KPI distributions, associated with KPIs resulting from modified configuration parameters, to a target KPI distribution. In some embodiments, the measure of similarity between two given distributions may include and/or may be based on a Wasserstein distance, a Kullback-Leibler divergence, or some other suitable measure of similarity. In some embodiments, some other suitable technique may be used to identify a match. As discussed above, the modifying (at  606 ) of network configuration parameters may be performed in a real-world network environment and/or in one or more simulated environments. 
     Process  600  may also include generating and/or refining (at  608 ) one or more predictive models based on the received set of KPIs and the generated sets of KPIs. For example, NOS  101  may maintain information associating the particular set of KPIs that match the target KPI distribution, to the set of modified configuration parameters from which such KPIs resulted. 
     Process  600  may further include associating (at  610 ) respective sets of KPIs and/or network parameters with one or more actions. For example, NOS  101  may use AI/ML techniques or other suitable techniques to identify particular actions to perform with respect to a given wireless network when configuration parameters of the wireless network match a set of configuration parameters of the one or more models, and/or when KPIs with respect to the given network match a set of KPIs of the one or more models. Examples of such actions are provided above. 
     Process  600  may additionally include identifying (at  612 ) a particular network matching a particular set of KPIs and/or network configuration parameters associated with the one or more predictive models. For example, NOS  101  may receive information regarding a given wireless network, such as wireless network  103  and/or some other wireless network, that includes KPIs and/or network configuration parameters of the given wireless network. NOS  101  may generate or identify a KPI distribution associated with the given wireless network, and may further identify a matching KPI distribution included in the one or more models. NOS  101  may accordingly predict, estimate, or otherwise identify one or more network configuration parameters of the given wireless network based on the one or more models, and/or may identify one or more actions to perform with respect to the wireless network based on the one or more models. 
     Process  600  may also include performing (at  614 ) one or more actions, as indicated by the one or more models, for the particular network. For example, NOS  101  may modify one or more network configuration parameters of the given wireless network based on the identified action(s) indicated in the one or more models. 
       FIG.  7    illustrates an example environment  700 , in which one or more embodiments may be implemented. In some embodiments, environment  700  may correspond to a Fifth Generation (“5G”) network, and/or may include elements of a 5G network. In some embodiments, environment  700  may correspond to a 5G Non-Standalone (“NSA”) architecture, in which a 5G radio access technology (“RAT”) may be used in conjunction with one or more other RATs (e.g., a Long-Term Evolution (“LTE”) RAT), and/or in which elements of a 5G core network may be implemented by, may be communicatively coupled with, and/or may include elements of another type of core network (e.g., an evolved packet core (“EPC”)). As shown, environment  700  may include UE  701 , RAN  710  (which may include one or more Next Generation Node Bs (“gNBs”)  711 ), RAN  712  (which may include one or more evolved Node Bs (“eNBs”)  713 ), and various network functions such as Access and Mobility Management Function (“AMF”)  715 , Mobility Management Entity (“MME”)  716 , Serving Gateway (“SGW”)  717 , Session Management Function (“SMF”)/Packet Data Network (“PDN”) Gateway (“PGW”)-Control plane function (“PGW-C”)  720 , Policy Control Function (“PCF”)/Policy Charging and Rules Function (“PCRF”)  725 , Application Function (“AF”)  730 , User Plane Function (“UPF”)/PGW-User plane function (“PGW-U”)  735 , Home Subscriber Server (“HSS”)/Unified Data Management (“UDM”)  740 , and Authentication Server Function (“AUSF”)  745 . Environment  700  may also include one or more networks, such as Data Network (“DN”)  750 . Environment  700  may include one or more additional devices or systems communicatively coupled to one or more networks (e.g., DN  750 ), such as NOS  101 . 
     The example shown in  FIG.  7    illustrates one instance of each network component or function (e.g., one instance of SMF/PGW-C  720 , PCF/PCRF  725 , UPF/PGW-U  735 , HSS/UDM  740 , and/or AUSF  745 ). In practice, environment  700  may include multiple instances of such components or functions. For example, in some embodiments, environment  700  may include multiple “slices” of a core network, where each slice includes a discrete set of network functions (e.g., one slice may include a first instance of SMF/PGW-C  720 , PCF/PCRF  725 , UPF/PGW-U  735 , HSS/UDM  740 , and/or AUSF  745 , while another slice may include a second instance of SMF/PGW-C  720 , PCF/PCRF  725 , UPF/PGW-U  735 , HSS/UDM  740 , and/or AUSF  745 ). The different slices may provide differentiated levels of service, such as service in accordance with different QoS parameters. 
     The quantity of devices and/or networks, illustrated in  FIG.  7   , is provided for explanatory purposes only. In practice, environment  700  may include additional devices and/or networks, fewer devices and/or networks, different devices and/or networks, or differently arranged devices and/or networks than illustrated in  FIG.  7   . For example, while not shown, environment  700  may include devices that facilitate or enable communication between various components shown in environment  700 , such as routers, modems, gateways, switches, hubs, etc. Alternatively, or additionally, one or more of the devices of environment  700  may perform one or more network functions described as being performed by another one or more of the devices of environment  700 . Devices of environment  700  may interconnect with each other and/or other devices via wired connections, wireless connections, or a combination of wired and wireless connections. In some implementations, one or more devices of environment  700  may be physically integrated in, and/or may be physically attached to, one or more other devices of environment  700 . 
     UE  701  may include a computation and communication device, such as a wireless mobile communication device that is capable of communicating with RAN  710 , RAN  712 , and/or DN  750 . UE  701  may be, or may include, a radiotelephone, a personal communications system (“PCS”) terminal (e.g., a device that combines a cellular radiotelephone with data processing and data communications capabilities), a personal digital assistant (“PDA”) (e.g., a device that may include a radiotelephone, a pager, Internet/intranet access, etc.), a smart phone, a laptop computer, a tablet computer, a camera, a personal gaming system, an IoT device (e.g., a sensor, a smart home appliance, or the like), a wearable device, an Internet of Things (“IoT”) device, a Machine-to-Machine (“M2M”) device, or another type of mobile computation and communication device. UE  701  may send traffic to and/or receive traffic (e.g., user plane traffic) from DN  750  via RAN  710 , RAN  712 , and/or UPF/PGW-U  735 . 
     RAN  710  may be, or may include, a 5G RAN that includes one or more base stations (e.g., one or more gNBs  711 ), via which UE  701  may communicate with one or more other elements of environment  700 . UE  701  may communicate with RAN  710  via an air interface (e.g., as provided by gNB  711 ). For instance, RAN  710  may receive traffic (e.g., voice call traffic, data traffic, messaging traffic, signaling traffic, etc.) from UE  701  via the air interface, and may communicate the traffic to UPF/PGW-U  735 , and/or one or more other devices or networks. Similarly, RAN  710  may receive traffic intended for UE  701  (e.g., from UPF/PGW-U  735 , AMF  715 , and/or one or more other devices or networks) and may communicate the traffic to UE  701  via the air interface. 
     RAN  712  may be, or may include, a LTE RAN that includes one or more base stations (e.g., one or more eNBs  713 ), via which UE  701  may communicate with one or more other elements of environment  700 . UE  701  may communicate with RAN  712  via an air interface (e.g., as provided by eNB  713 ). For instance, RAN  710  may receive traffic (e.g., voice call traffic, data traffic, messaging traffic, signaling traffic, etc.) from UE  701  via the air interface, and may communicate the traffic to UPF/PGW-U  735 , and/or one or more other devices or networks. Similarly, RAN  710  may receive traffic intended for UE  701  (e.g., from UPF/PGW-U  735 , SGW  717 , and/or one or more other devices or networks) and may communicate the traffic to UE  701  via the air interface. 
     AMF  715  may include one or more devices, systems, Virtualized Network Functions (“VNFs”), etc., that perform operations to register UE  701  with the 5G network, to establish bearer channels associated with a session with UE  701 , to hand off UE  701  from the 5G network to another network, to hand off UE  701  from the other network to the 5G network, manage mobility of UE  701  between RANs  710  and/or gNBs  711 , and/or to perform other operations. In some embodiments, the 5G network may include multiple AMFs  715 , which communicate with each other via the N14 interface (denoted in  FIG.  7    by the line marked “N14” originating and terminating at AMF  715 ). 
     MME  716  may include one or more devices, systems, VNFs, etc., that perform operations to register UE  701  with the EPC, to establish bearer channels associated with a session with UE  701 , to hand off UE  701  from the EPC to another network, to hand off UE  701  from another network to the EPC, manage mobility of UE  701  between RANs  712  and/or eNBs  713 , and/or to perform other operations. 
     SGW  717  may include one or more devices, systems, VNFs, etc., that aggregate traffic received from one or more eNBs  713  and send the aggregated traffic to an external network or device via UPF/PGW-U  735 . Additionally, SGW  717  may aggregate traffic received from one or more UPF/PGW-Us  735  and may send the aggregated traffic to one or more eNBs  713 . SGW  717  may operate as an anchor for the user plane during inter-eNB handovers and as an anchor for mobility between different telecommunication networks or RANs (e.g., RANs  710  and  712 ). 
     SMF/PGW-C  720  may include one or more devices, systems, VNFs, etc., that gather, process, store, and/or provide information in a manner described herein. SMF/PGW-C  720  may, for example, facilitate the establishment of communication sessions on behalf of UE  701 . In some embodiments, the establishment of communications sessions may be performed in accordance with one or more policies provided by PCF/PCRF  725 . 
     PCF/PCRF  725  may include one or more devices, systems, VNFs, etc., that aggregate information to and from the 5G network and/or other sources. PCF/PCRF  725  may receive information regarding policies and/or subscriptions from one or more sources, such as subscriber databases and/or from one or more users (such as, for example, an administrator associated with PCF/PCRF  725 ). 
     AF  730  may include one or more devices, systems, VNFs, etc., that receive, store, and/or provide information that may be used in determining parameters (e.g., quality of service parameters, charging parameters, or the like) for certain applications. 
     UPF/PGW-U  735  may include one or more devices, systems, VNFs, etc., that receive, store, and/or provide data (e.g., user plane data). For example, UPF/PGW-U  735  may receive user plane data (e.g., voice call traffic, data traffic, etc.), destined for UE  701 , from DN  750 , and may forward the user plane data toward UE  701  (e.g., via RAN  710 , SMF/PGW-C  720 , and/or one or more other devices). In some embodiments, multiple UPFs  735  may be deployed (e.g., in different geographical locations), and the delivery of content to UE  701  may be coordinated via the N9 interface (e.g., as denoted in  FIG.  7    by the line marked “N9” originating and terminating at UPF/PGW-U  735 ). Similarly, UPF/PGW-U  735  may receive traffic from UE  701  (e.g., via RAN  710 , SMF/PGW-C  720 , and/or one or more other devices), and may forward the traffic toward DN  750 . In some embodiments, UPF/PGW-U  735  may communicate (e.g., via the N4 interface) with SMF/PGW-C  720 , regarding user plane data processed by UPF/PGW-U  735 . 
     HSS/UDM  740  and AUSF  745  may include one or more devices, systems, VNFs, etc., that manage, update, and/or store, in one or more memory devices associated with AUSF  745  and/or HSS/UDM  740 , profile information associated with a subscriber. AUSF  745  and/or HSS/UDM  740  may perform authentication, authorization, and/or accounting operations associated with the subscriber and/or a communication session with UE  701 . 
     DN  750  may include one or more wired and/or wireless networks. For example, DN  750  may include an Internet Protocol (“IP”)-based PDN, a wide area network (“WAN”) such as the Internet, a private enterprise network, and/or one or more other networks. UE  701  may communicate, through DN  750 , with data servers, other UEs  701 , and/or to other servers or applications that are coupled to DN  750 . DN  750  may be connected to one or more other networks, such as a public switched telephone network (“PSTN”), a public land mobile network (“PLMN”), and/or another network. DN  750  may be connected to one or more devices, such as content providers, applications, web servers, and/or other devices, with which UE  701  may communicate. 
       FIG.  8    illustrates an example Distributed Unit (“DU”) network  800 , which may be included in and/or implemented by one or more RANs (e.g., RAN  710 , RAN  712 , or some other RAN). In some embodiments, a particular RAN may include one DU network  800 . In some embodiments, a particular RAN may include multiple DU networks  800 . In some embodiments, DU network  800  may correspond to a particular gNB  711  of a 5G RAN (e.g., RAN  710 ). In some embodiments, DU network  800  may correspond to multiple gNBs  711 . In some embodiments, DU network  800  may correspond to one or more other types of base stations of one or more other types of RANs. As shown, DU network  800  may include Central Unit (“CU”)  805 , one or more Distributed Units (“DUs”) 803-1 through 803-N(referred to individually as “DU  803 ,” or collectively as “DUs  803 ”), and one or more Radio Units (“RUs”)  801 - 1  through  801 -M (referred to individually as “RU  801 ,” or collectively as “RUs  801 ”). 
     CU  805  may communicate with a core of a wireless network (e.g., may communicate with one or more of the devices or systems described above with respect to  FIG.  7   , such as AMF  715  and/or UPF/PGW-U  735 ). In the uplink direction (e.g., for traffic from UEs  701  to a core network), CU  805  may aggregate traffic from DUs  803 , and forward the aggregated traffic to the core network. In some embodiments, CU  805  may receive traffic according to a given protocol (e.g., Radio Link Control (“RLC”)) from DUs  803 , and may perform higher-layer processing (e.g., may aggregate/process RLC packets and generate Packet Data Convergence Protocol (“PDCP”) packets based on the RLC packets) on the traffic received from DUs  803 . 
     In accordance with some embodiments, CU  805  may receive downlink traffic (e.g., traffic from the core network) for a particular UE  701 , and may determine which DU(s)  803  should receive the downlink traffic. DU  803  may include one or more devices that transmit traffic between a core network (e.g., via CU  805 ) and UE  701  (e.g., via a respective RU  801 ). DU  803  may, for example, receive traffic from RU  801  at a first layer (e.g., physical (“PHY”) layer traffic, or lower PHY layer traffic), and may process/aggregate the traffic to a second layer (e.g., upper PHY and/or RLC). DU  803  may receive traffic from CU  805  at the second layer, may process the traffic to the first layer, and provide the processed traffic to a respective RU  801  for transmission to UE  701 . 
     RU  801  may include hardware circuitry (e.g., one or more RF transceivers, antennas, radios, and/or other suitable hardware) to communicate wirelessly (e.g., via an RF interface) with one or more UEs  701 , one or more other DUs  803  (e.g., via RUs  801  associated with DUs  803 ), and/or any other suitable type of device. In the uplink direction, RU  801  may receive traffic from UE  701  and/or another DU  803  via the RF interface and may provide the traffic to DU  803 . In the downlink direction, RU  801  may receive traffic from DU  803 , and may provide the traffic to UE  701  and/or another DU  803 . 
     RUs  801  may, in some embodiments, be communicatively coupled to one or more Multi-Access/Mobile Edge Computing (“MEC”) devices, referred to sometimes herein simply as “MECs”  807 . For example, RU  801 - 1  may be communicatively coupled to MEC  807 - 1 , RU  801 -M may be communicatively coupled to MEC  807 -M, DU  803 - 1  may be communicatively coupled to MEC  807 - 2 , DU  803 -N may be communicatively coupled to MEC  807 -N, CU  805  may be communicatively coupled to MEC  807 - 3 , and so on. MECs  807  may include hardware resources (e.g., configurable or provisionable hardware resources) that may be configured to provide services and/or otherwise process traffic to and/or from UE  701 , via a respective RU  801 . 
     For example, RU  801 - 1  may route some traffic, from UE  701 , to MEC  807 - 1  instead of to a core network (e.g., via DU  803  and CU  805 ). MEC  807 - 1  may process the traffic, perform one or more computations based on the received traffic, and may provide traffic to UE  701  via RU  801 - 1 . In this manner, ultra-low latency services may be provided to UE  701 , as traffic does not need to traverse DU  803 , CU  805 , and an intervening backhaul network between DU network  800  and the core network. In some embodiments, MEC  807  may include, and/or may implement, some or all of the functionality described above with respect to NOS  101 . 
       FIG.  9    illustrates an example O-RAN environment  900 , which may correspond to RAN  710 , RAN  712 , and/or DU network  800 . For example, RAN  710 , RAN  712 , and/or DU network  800  may include one or more instances of O-RAN environment  900 , and/or one or more instances of O-RAN environment  900  may implement RAN  710 , RAN  712 , DU network  800 , and/or some portion thereof. As shown, O-RAN environment  900  may include Non-Real Time Radio Intelligent Controller (“RIC”)  901 , Near-Real Time RIC  903 , O-eNB  905 ,  0 -CU-Control Plane (“O-CU-CP”)  907 ,  0 -CU-User Plane (“O-CU-UP”)  909 , O-DU  911 , O-RU  913 , and O-Cloud  915 . In some embodiments, O-RAN environment  900  may include additional, fewer, different, and/or differently arranged components. 
     In some embodiments, some or all of the elements of O-RAN environment  900  may be implemented by one or more configurable or provisionable resources, such as virtual machines, cloud computing systems, physical servers, and/or other types of configurable or provisionable resources. In some embodiments, some or all of O-RAN environment  900  may be implemented by, and/or communicatively coupled to, one or more MECs  807 . For example, in some embodiments, one or more elements of O-RAN environment  900  may receive configuration parameters from NOS  101 , and may implement the configuration parameters in order to modify the operation of one or more elements of O-RAN environment  900 . In some embodiments, one or more elements of O-RAN environment  900  may include and/or may be communicatively coupled to NOS  101 . 
     Non-Real Time RIC  901  and Near-Real Time RIC  903  may receive performance information (and/or other types of information) from one or more sources, and may configure other elements of O-RAN environment  900  based on such performance or other information. For example, Near-Real Time RIC  903  may receive performance information, via one or more E2 interfaces, from O-eNB  905 , O-CU-CP  907 , and/or O-CU-UP  909 , and may modify parameters associated with O-eNB  905 , O-CU-CP  907 , and/or O-CU-UP  909  based on such performance information. Similarly, Non-Real Time RIC  901  may receive performance information associated with O-eNB  905 ,  0 -CU-CP  907 , O-CU-UP  909 , and/or one or more other elements of O-RAN environment  900  and may utilize machine learning and/or other higher level computing or processing to determine modifications to the configuration of O-eNB  905 , O-CU-CP  907 , O-CU-UP  909 , and/or other elements of O-RAN environment  900 . In some embodiments, Non-Real Time RIC  901  may generate machine learning models based on performance information associated with O-RAN environment  900  or other sources, and may provide such models to Near-Real Time RIC  903  for implementation. 
     O-eNB  905  may perform functions similar to those described above with respect to eNB  713 . For example, O-eNB  905  may facilitate wireless communications between UE 1uu and a core network. O-CU-CP  907  may perform control plane signaling to coordinate the aggregation and/or distribution of traffic via one or more DUs  803 , which may include and/or be implemented by one or more O-DUs  911 , and O-CU-UP  909  may perform the aggregation and/or distribution of traffic via such DUs  803  (e.g., O-DUs  911 ). O-DU  911  may be communicatively coupled to one or more RUs  801 , which may include and/or may be implemented by one or more O-RUs  913 . In some embodiments, O-Cloud  915  may include or be implemented by one or more MECs  807 , which may provide services, and may be communicatively coupled, to O-CU-CP  907 , O-CU-UP  909 , O-DU  911 , and/or O-RU  913  (e.g., via an O1 and/or O2 interface). 
       FIG.  10    illustrates example components of device  1000 . One or more of the devices described above may include one or more devices  1000 . Device  1000  may include bus  1010 , processor  1020 , memory  1030 , input component  1040 , output component  1050 , and communication interface  1060 . In another implementation, device  1000  may include additional, fewer, different, or differently arranged components. 
     Bus  1010  may include one or more communication paths that permit communication among the components of device  1000 . Processor  1020  may include a processor, microprocessor, or processing logic that may interpret and execute instructions. In some embodiments, processor  1020  may be or may include one or more hardware processors. Memory  1030  may include any type of dynamic storage device that may store information and instructions for execution by processor  1020 , and/or any type of non-volatile storage device that may store information for use by processor  1020 . 
     Input component  1040  may include a mechanism that permits an operator to input information to device  1000  and/or other receives or detects input from a source external to  1040 , such as a touchpad, a touchscreen, a keyboard, a keypad, a button, a switch, a microphone or other audio input component, etc. In some embodiments, input component  1040  may include, or may be communicatively coupled to, one or more sensors, such as a motion sensor (e.g., which may be or may include a gyroscope, accelerometer, or the like), a location sensor (e.g., a Global Positioning System (“GPS”)-based location sensor or some other suitable type of location sensor or location determination component), a thermometer, a barometer, and/or some other type of sensor. Output component  1050  may include a mechanism that outputs information to the operator, such as a display, a speaker, one or more light emitting diodes (“LEDs”), etc. 
     Communication interface  1060  may include any transceiver-like mechanism that enables device  1000  to communicate with other devices and/or systems. For example, communication interface  1060  may include an Ethernet interface, an optical interface, a coaxial interface, or the like. Communication interface  1060  may include a wireless communication device, such as an infrared (“IR”) receiver, a Bluetooth® radio, or the like. The wireless communication device may be coupled to an external device, such as a remote control, a wireless keyboard, a mobile telephone, etc. In some embodiments, device  1000  may include more than one communication interface  1060 . For instance, device  1000  may include an optical interface and an Ethernet interface. 
     Device  1000  may perform certain operations relating to one or more processes described above. Device  1000  may perform these operations in response to processor  1020  executing software instructions stored in a computer-readable medium, such as memory  1030 . A computer-readable medium may be defined as a non-transitory memory device. A memory device may include space within a single physical memory device or spread across multiple physical memory devices. The software instructions may be read into memory  1030  from another computer-readable medium or from another device. The software instructions stored in memory  1030  may cause processor  1020  to perform processes described herein. Alternatively, hardwired circuitry may be used in place of or in combination with software instructions to implement processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software. 
     The foregoing description of implementations provides illustration and description, but is not intended to be exhaustive or to limit the possible implementations to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the implementations. 
     For example, while series of blocks and/or signals have been described above (e.g., with regard to  FIGS.  1 - 6   ), the order of the blocks and/or signals may be modified in other implementations. Further, non-dependent blocks and/or signals may be performed in parallel. Additionally, while the figures have been described in the context of particular devices performing particular acts, in practice, one or more other devices may perform some or all of these acts in lieu of, or in addition to, the above-mentioned devices. 
     The actual software code or specialized control hardware used to implement an embodiment is not limiting of the embodiment. Thus, the operation and behavior of the embodiment has been described without reference to the specific software code, it being understood that software and control hardware may be designed based on the description herein. 
     In the preceding specification, various example embodiments have been described with reference to the accompanying drawings. It will, however, be evident that various modifications and changes may be made thereto, and additional embodiments may be implemented, without departing from the broader scope of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative rather than restrictive sense. 
     Even though particular combinations of features are recited in the claims and/or disclosed in the specification, these combinations are not intended to limit the disclosure of the possible implementations. In fact, many of these features may be combined in ways not specifically recited in the claims and/or disclosed in the specification. Although each dependent claim listed below may directly depend on only one other claim, the disclosure of the possible implementations includes each dependent claim in combination with every other claim in the claim set. 
     Further, while certain connections or devices are shown, in practice, additional, fewer, or different, connections or devices may be used. Furthermore, while various devices and networks are shown separately, in practice, the functionality of multiple devices may be performed by a single device, or the functionality of one device may be performed by multiple devices. Further, multiple ones of the illustrated networks may be included in a single network, or a particular network may include multiple networks. Further, while some devices are shown as communicating with a network, some such devices may be incorporated, in whole or in part, as a part of the network. 
     To the extent the aforementioned implementations collect, store, or employ personal information of individuals, groups or other entities, it should be understood that such information shall be used in accordance with all applicable laws concerning protection of personal information. Additionally, the collection, storage, and use of such information can be subject to consent of the individual to such activity, for example, through well known “opt-in” or “opt-out” processes as can be appropriate for the situation and type of information. Storage and use of personal information can be in an appropriately secure manner reflective of the type of information, for example, through various access control, encryption and anonymization techniques for particularly sensitive information. 
     No element, act, or instruction used in the present application should be construed as critical or essential unless explicitly described as such. An instance of the use of the term “and,” as used herein, does not necessarily preclude the interpretation that the phrase “and/or” was intended in that instance. Similarly, an instance of the use of the term “or,” as used herein, does not necessarily preclude the interpretation that the phrase “and/or” was intended in that instance. Also, as used herein, the article “a” is intended to include one or more items, and may be used interchangeably with the phrase “one or more.” Where only one item is intended, the terms “one,” “single,” “only,” or similar language is used. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise.