Patent Publication Number: US-2022232399-A1

Title: Systems and methods for orchestration and optimization of wireless networks

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a Continuation of U.S. patent application Ser. No. 17/107,502, filed on Nov. 30, 2020, titled “SYSTEMS AND METHODS FOR ORCHESTRATION AND OPTIMIZATION OF WIRELESS NETWORKS,” the contents of which are herein incorporated by reference in their entirety. 
    
    
     BACKGROUND 
     Wireless networks, such as Long-Term Evolution (“LTE”) networks, Fifth Generation (“5G”) networks, or the like, may include radio access networks (“RANs”), via which user equipment (“UE”), such as mobile telephones or other wireless communication devices, may receive wireless service. RANs, and/or portions of RANs, may have different characteristics and/or may exhibit different performance metrics (e.g., latency, throughput, etc.). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an example overview of one or more embodiments described herein, in which a Global Optimization System (“GOS”) may determine a sector model, configuration framework, and/or set of actions to perform with respect to a given sector associated with a RAN of a wireless network; 
         FIG. 2  illustrates example configuration frameworks, sector models, and/or actions/parameters that may be generated, received, maintained, provided, etc. by a GOS of some embodiments; 
         FIGS. 3, 4A, and 4B  illustrate examples of respective configuration frameworks in accordance with some embodiments; 
         FIG. 5  illustrates example attributes associated with a particular sector model, and further illustrates an example associations between the sector model, configuration framework, and actions and/or parameters, in accordance with some embodiments; 
         FIGS. 6-8  illustrate an example determination of one or more sector models, configuration frameworks, and/or sets of actions to perform with respect to a given sector associated with a RAN of a wireless network; 
         FIG. 9  illustrates an example process for determining one or more sector models, configuration frameworks, and/or sets of actions to perform with respect to a given sector associated with a RAN of a wireless network, in accordance with some embodiments; 
         FIG. 10  illustrates an example environment in which one or more embodiments, described herein, may be implemented; 
         FIG. 11  illustrates an example arrangement of a radio access network (“RAN”), in accordance with some embodiments; 
         FIG. 12  illustrates an example arrangement of an Open RAN (“0-RAN”) environment in which one or more embodiments, described herein, may be implemented; and 
         FIG. 13  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 use of artificial intelligence/machine learning (“AUML”) techniques or other suitable techniques to model attributes, characteristics, key performance indicators (“KPIs”), and/or other information associated with various locations or regions associated with one or more RANs of a wireless network (e.g., a LTE network, a 5G network, and/or another type of network). As discussed herein, such locations or regions may be referred to as “sectors.” Further, in the examples discussed herein, sectors may include evenly distributed areas of a uniform shape (e.g., a hexagon). In practice, sectors may be arranged or defined differently. For example, in some embodiments, sectors may be defined with respect to the location of one or more base stations of a RAN (e.g., where a sector may be defined based on a coverage area of the one or more base stations and/or may be defined based on a physical location at which one or more antennas or other physical equipment of the base stations are installed), and/or may be defined independently of the location of the one or more base stations. 
     As described herein, one or more scores, metrics, etc. (referred to herein simply as “scores” for the sake of brevity) may be determined (e.g., using AI/ML techniques and/or other suitable techniques) based on service coverage (e.g., range or area of wireless service), service quality (e.g., Signal-to-Interference-and-Noise-Ratio (“SINR”), Channel Quality Indicator (“CQI”), latency, throughput, etc.), energy consumption metrics (e.g., measure of energy consumed over time), mobility metrics (e.g., quantity or proportion of UEs involved in a handover process), and/or other suitable metrics or information associated with base stations or other equipment associated with the RANs. In some embodiments, the scores may reflect an overall optimization score, which may reflect a holistic measure of how well a given sector is optimized. 
     As described herein, different sectors may be associated with different attributes, characterizations, categories, clusters, or the like. Embodiments herein may, for example, categorize a given sector as being associated with one or more sector models, where a sector model includes attributes, characteristics, etc. that may be compared to a given sector to determine whether the sector model applies to the sector. Further, particular sector models may be associated with configuration frameworks, which may specify weights and/or other information that may be used to generate an overall optimization score for a sector. For example, one particular configuration framework may weight energy savings metrics relatively heavily, while another configuration framework may weight energy savings metrics relatively lower than other types of metrics (e.g., coverage, quality, mobility, and/or other metrics). 
     Further, as described herein, particular configuration frameworks may be associated with particular sets of configuration parameters, attributes, and/or actions, which may be used by particular sectors in order to optimize the operation of the sectors in accordance with optimization goals that are reflected by the weights in the configuration frameworks. As described herein, the association between particular sector attributes, sector models, configuration frameworks, and/or associated actions may be generated and/or refined using one or more AI/ML techniques or other suitable techniques (e.g., deep learning, reinforced or unreinforced machine learning, neural networks, K-means clustering, regression analysis, and/or other suitable techniques). 
     As shown in  FIG. 1 , for example, geographical area (or region)  100  may be subdivided into a set of sectors  101 . The set of sectors  101  may include, as shown, sector  101 - 1 ,  101 - 2 , and one or more additional sectors that are not explicitly illustrated with a reference numeral. 
     Further in this example, each sector  101  may be associated with particular base stations  103 . For example, base station  103 - 1  may be located in one particular sector  101 , while base station  103 - 2  may be located in another sector  101 . Further, additional base stations  103  (e.g., base stations not explicitly illustrated with a reference numeral) may be present in geographical region  100 . That is, the location of each base station  103  may be within a particular geographical area (e.g., a hexagonal-shaped geographical area, in this example) that corresponds to a respective sector  101 . For the sake of example, each sector  101  is associated with at least one base station  103 . In practice, one or more sectors  101  may not include any base stations  103 . 
     As shown, Global Optimization System (“GOS”)  105  may receive (at  102 ) network KPIs and/or parameters associated with one or more sectors  101 . For example, Global Optimization System  105  may communicate with base stations  103  of sectors  101  and/or UEs located within such sectors  101  via an application programming interface (“API”), an X2 interface, and/or some other suitable communication pathway, in order to receive such information. For example, base stations  103  and/or UEs communicatively coupled to respective base stations  103  may “push” such information to Global Optimization System  105  (e.g., via the API) on a periodic or intermittent basis, upon the occurrence of trigger events (e.g., one or more Quality of Service (“QoS”) metrics exceeding a threshold value, a connection or disconnection of one or more UEs to one or more base stations  103 , and/or other events), and/or on some other basis. In some embodiments, Global Optimization System  105  may “pull” (e.g., request or otherwise obtain) such information from the UEs, base stations  103 , and/or other device or system that receives, collects, maintains, and/or provides such information. For example, Global Optimization System  105  may be communicatively coupled to a Service Capability Exposure Function (“SCEF”) of a core network associated with base stations  103 , a Network Exposure Function (“NEF”), and/or other suitable device, system, function, etc. 
     The received KPIs and/or parameters may include, for example, KPIs related to coverage, quality, energy consumption, mobility, and/or other suitable KPIs. Further, the received parameters may include, for example, configuration parameters, inter-sector information, locale features, and/or other suitable information indicating parameters and/or characteristics of a given sector  101 . More detailed examples of KPIs and/or parameters are described below. 
     As further shown, GOS  105  may determine (at  104 ) one or more sector models associated with respective sectors  101  based on the received KPIs and/or parameters. For example, as discussed below, GOS  105  may use AI/ML techniques or other suitable techniques to identify one or more sector models that includes KPIs and/or attributes that are similar to the KPIs and/or attributes (received at  102 ) associated with respective sectors  101 . For example, when determining whether KPIs and/or attributes of a given sector model is “similar” to KPIs and/or attributes of a given sector  101 , GOS  105  may generate one or more scores, classifiers, or the like, and/or may perform a suitable similarity analysis to determine a measure of similarity between KPIs and/or attributes of a set of sector models and KPIs and/or attributes of a given sector  101 . In some embodiments, GOS  105  may select a particular sector model if the measure of similarity exceeds a threshold measure of similarity. Additionally, or alternatively, GOS  105  may select a particular quantity of highest-scoring sector models (e.g., the highest scoring sector mode, the top three scoring sector models, etc.). In some embodiments, GOS  105  may select a particular quantity of highest-scoring sector models, so long as the scores associated with such sector models exceeds a threshold score (e.g., the top three scoring sector models so long as the top three scoring sector models exceed the threshold score, the top two scoring sector models if the third highest-scoring sector model is below the threshold score, etc.). 
     As further discussed in more detail below, GOS  105  may further determine (at  104 ) one or more configuration frameworks for one or more sectors  101  based on the sector models identified with respect to respective sectors  101 . For example, as discussed below, particular sector models may be associated with particular configuration frameworks. In some embodiments, a given sector model may be associated with an affinity score for multiple configuration frameworks, where the affinity score indicates a measure of affinity, correlation, effectiveness, applicability, or the like of a given configuration framework to a given sector model. For example, the same configuration framework may be particularly applicable to one particular sector model (e.g., associated with a relatively high affinity score with respect to the particular sector model), while the same configuration framework may be less applicable to a different sector model (e.g., associated with a relatively low affinity score with respect to the other sector model). In some embodiments, GOS  105  may generate a configuration framework based on using AI/ML techniques or other suitable techniques to analyze the sector models, KPIs, and/or parameters associated with sector  101 , as well as analyzing previously generated configuration frameworks, to determine weights and/or other parameters of a configuration framework that is applicable to the particular sector  101 . 
     In some embodiments, GOS  105  may receive (at  102 ) KPIs and/or parameters over time, and may select (at  104 ) different sector models and/or configuration frameworks based on different KPIs and/or parameters received at different times and/or time periods. As one example, a particular sector  101  may exhibit a first set of KPIs and/or parameters (e.g., latency, throughput, quantity of connected UEs, and/or other KPIs or parameter) at times corresponding to a morning or afternoon weekday commute, and may exhibit a second set of KPIs and/or parameters at times corresponding to an evening or weekend. In this example, GOS  105  may determine (at  104 ) a first sector model (or set of sector models) and one or more associated configuration frameworks during morning or afternoon hours on weekdays, and may determine a second sector model (or set of sector models) and one or more associated configuration frameworks during evening hours and/or weekends. 
     GOS  105  may further output (at  106 ) information indicating the identified configuration frameworks to respective sectors  101 . For example, GOS  105  may provide the information to respective base stations  103  associated with sectors  101 , to a management device or system associated with one or more sectors  101 , and/or some other device or system. Additionally, or alternatively, GOS  105  may provide (at  106 ) information indicating one or more actions associated with the identified configuration frameworks. For example, in some embodiments, GOS  105  may determine, based on the sector model(s), KPIs, and/or parameters associated with a respective sector  101 , and further based on the identified configuration framework(s) selected for sector  101 , one or more actions to take to increase the overall optimization score associated with sector  101 . Additionally, or alternatively, each sector  101  may determine particular actions to take based on the received configuration framework information. As noted above, such actions may be selected and performed by network devices located in or serving sector  101  in order to increase the overall optimization score associated with sector  101 . For the sake of brevity, the performance of a given action by a network device located in or serving sector  101  will be referred to herein as sector  101  performing the action. 
     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 physical RAN resources, such as Physical Resource Blocks (“PRBs”), portions of radio frequency (“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 (“MR”), International Mobile Station Equipment Identity (“IMEI”), 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 sector  101 . 
     In some embodiments, such actions may include modifying one or more beamforming parameters associated with sector  101 . For example, sector  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 sector  101 . In some embodiments, sector  101  may modify a Multiple-Input Multiple-Output (“MIMO”) configuration associated with sector  101 , 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 sector  101 . 
     In some embodiments, such actions may include modifying parameters related to handovers and/or mobility. For example, sector  101  may modify a Neighbor Cell List (“NCL”) provided to UEs connected to a particular base station of sector  101 , 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. Such handover thresholds may refer to, for example, threshold measures of signal strength or quality, such as a Received Signal Strength Indicator (“RSSI”) value, a CQI value, a Signal-to-Interference-and-Noise-Ratio (“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 optimize the operation of sectors  101  (e.g., based on configuration frameworks identified with respect to sectors  101 ). In some embodiments, performing such actions may include performing multiple actions, such as multiple actions performed above. Such performance may be simultaneous, sequential, or on some other basis. 
     Respective sectors  101  may perform (at  108 ) the actions associated with respective configuration frameworks, and GOS  105  may continue to receive (at  102 ) up-to-date KPIs associated with sectors  101 . GOS  105  may, based on continuing to receive the up-to-date KPIs, modify the determination of sector models associated with a particular sector  101 , and/or may update an overall optimization score associated with the particular sector  101 . In some embodiments, GOS  105  may select a new set of actions and/or a different configuration framework for sector  101  based on the up-to-date KPIs. In some embodiments, GOS  105  may modify one or more sector models, configuration frameworks, and/or other information based on whether the performed (at  108 ) actions increased the overall optimization score associated with sector  101 , and/or based on how much the overall optimization score associated with sector  101  was modified based on the performance of the actions. 
     In some embodiments, while described in the context of being performed by GOS  105 , in some embodiments, one or more devices or systems associated with sectors  101  may perform one or more of the operations described above in lieu of, or in addition to, GOS  105 . For example, in some embodiments, one or more devices or systems of sector  101  may identify a particular action based on a given configuration framework and/or sector model, and/or based on continuing to monitor KPIs associated with sector  101  after performing (at  108 ) a particular action or set of actions. 
       FIG. 2  illustrates example configuration frameworks, sector models, and/or actions/parameters that may be generated, received, maintained, provided, etc. by GOS  105 . For example, GOS  105  may be associated with a set of configuration frameworks  201 , such as example configuration frameworks  201 - 1 ,  201 - 2 , and  201 -L. Further, GOS  105  may be associated with a set of sector models  203 , such as example sector models  203 - 1 ,  203 - 2 , and  203 -M. Additionally, GOS  105  may be associated with a set of actions/parameters  205 , such as example actions/parameters  205 - 1 ,  205 - 2 , and  205 -N. 
     GOS  105  may generate and/or modify configuration frameworks  201 , sector models  203 , and/or actions/parameters  205  based on AI/ML techniques or other suitable techniques. For example, GOS  105  may generate, modify, refine, etc. configuration frameworks  201 , sector models  203 , and/or actions/parameters  205  based on an evaluation of real-world data from sectors  101  and/or simulations of KPIs in a simulation and/or test environment. GOS  105  may further determine or identify correlations between respective configuration frameworks  201 , sector models  203 , and/or actions/parameters  205  using AI/ML techniques or other suitable techniques. 
       FIGS. 3, 4A, and 4B , discussed below, illustrate examples of respective configuration frameworks  201 .  FIG. 5 , also discussed below, illustrates example attributes associated with a particular sector model  203 , and further illustrates an example association between sector model  203 , configuration framework  201 , and/or actions/parameters  205 . 
     As shown in  FIG. 3 , for example, configuration framework  301  may include a set of KPIs, parameters, characteristics, etc.  303  (e.g., KPIs, parameters, characteristics, etc.  303 - 1  through  303 -Q). KPIs, parameters, characteristics, etc.  303  may include any suitable type of KPIs, parameters, characteristics, etc.  303  relating to actions to be performed in a network environment, in order to optimize considerations relating to KPIs, parameters, characteristics, etc.  303  in a manner similar to that described above. For the sake of brevity, sets of KPIs, parameters, characteristics, etc.  303  are sometimes referred to herein as “KPI categories  303 .” 
     In some embodiments, each KPI category  303  of configuration framework  301  may specify one or more KPIs, such as latency, throughput, jitter, quantity of active connections, quantity and/or proportion of dropped calls, energy consumption metrics, quantity or proportion of handovers into and/or out of a sector, durations of connections of UEs to base stations located in a sector, location information of UEs connected to a base station of a sector, and/or other suitable metrics. In some embodiments, each respective KPI category  303  may specify conditions, thresholds, or the like, based on which a given KPI category  303  may be applicable to a particular KPI or set of KPIs associated with a given sector. For example, KPI category  303 - 1  and KPI category  303 - 2  may both be associated with different value ranges for the same KPI. For example, KPI category  303 - 1  may be applicable to latency metrics if such latency metrics indicate a value of 100 milliseconds (“ms”) or lower, and KPI category  303 - 2  may be applicable to latency values of greater than 100 ms. 
     In some embodiments, KPI categories  303  may include thresholds based on which maximum and/or minimum scores may be determined. For example, assume that KPI category  303 - 1  is applicable to a latency metric. KPI category  303 - 1  may specify that latency values below a first threshold value, such as 30 ms, are associated with a maximum KPI score (e.g., 100 out of 100), may specify that latency values above a second threshold value (e.g., 300 ms) are associated with a minimum KPI score (e.g., 1 out of 100), etc. In this manner, certain KPIs may be prioritized by KPI category  303 , without allowing such KPIs to dominate the overall optimization score for a given sector. 
     As further shown, each respective KPI category  303  may be associated with a score weight. For example, KPI category  303 - 1  may be associated with a first weight W 1 , KPI category  303 - 2  may be associated with a second weight W 2 , KPI category  303 - 3  may be associated with a third weight W 3 , and so on. As noted above, the different weights may be used to prioritize certain KPIs or sets of KPIs more heavily than others. 
     For example, assume that configuration framework  301  has been selected with respect to a given sector  101 . Sector  101  may generate, determine, etc. a set of KPIs  305  over a given time period. Sector  101 , GOS  105 , and/or some other suitable device or system may perform an overall optimization score generation operation  307  with respect to sector  101  for the given time period, based on the set of KPIs  305  and the weights associated with respective KPIs. For example, assume that the set of KPIs  305  include latency metrics associated with sector  101  and energy consumption metrics associated with sector  101 . Further assume that latency is a particular KPI identified in KPI category  303 - 1 , and that energy consumption is a particular KPI identified in KPI category  303 - 2 . 
     When generating (at  307 ) an overall optimization score for sector  101 , a first KPI score may be generated based on the received latency metrics and a second KPI score may be generated based on the energy consumption metrics. In some embodiments, KPI score generation may be in a normalized manner (e.g., on a scale of 1-100 or some other suitable scale) for dissimilar or incongruous metrics. For example, a latency value of 10 ms may be associated with a KPI score of 92, and an energy consumption value of 10 kiloWatt-hours (“kWh”) may be associated with a KPI score of 15. In some embodiments, particular KPI categories  303  may specify formulas, rubrics, rules, or the like based on which respective KPI scores may be generated based on raw KPI values. These KPI scores may be further modified based on the respective weights associated with KPI categories  303 . For example, the first KPI score may be weighted according to weight W 1 , while the second KPI score may be weighted according to weight W 2 . In some embodiments, weighting the KPI scores may include multiplying the respective KPI scores by the weights in order to generated weighted KPI scores. In this manner, different KPIs or sets of KPIs may be factored or prioritized differently in the overall optimization score ultimately generated via overall optimization score operation  307 . 
     Overall optimization score operation  307  may include aggregating, combining, and/or performing other suitable computations on weighted KPI scores to generate overall optimization score  309  for sector  101 . As noted above, overall optimization score  309  may be generated and/or modified on an ongoing basis, as updated sector KPIs  305  are received or determined. 
       FIGS. 4A and 4B  illustrate example configuration frameworks  401  and  405 , in accordance with some embodiments. As shown in  FIG. 4A , configuration framework  401  may include KPI categories such as KPI categories  403 - 1  through  403 - 3 . 
     KPI category  403 - 1  may include, for example, KPIs related to coverage and/or quality, such as latency, throughput, jitter, SINR, CQI, RSSI, etc. In some embodiments, such KPIs may be specified as a function of location within a sector and/or distance from one or more base stations of a sector. For example, KPI category  403 - 1  may specify a first set of thresholds, values, formulas, etc. for calculating a KPI score relating to latency within 100 meters of a base station, and a second set of thresholds, values, formulas, etc. for calculating a KPI score relating to latency further than 100 meters from a base station. 
     KPI category  403 - 1  may, in some embodiments, include KPIs, metrics, parameters, etc. relating to RAN coverage within a given geographical area and/or sector  101 . For example, KPI category  403 - 1  may relate to areas within sector  101  that receive wireless coverage from base stations or other wireless network infrastructure located within or otherwise serving sector  101 . In some embodiments, KPI category  403 - 1  may include quality metrics as a function of distance or coverage (e.g., distance from a base station and/or other RF hardware), in that KPIs, metrics, etc. relating to signal quality may vary as a function of location (e.g., different levels or qualities of coverage may be available at different locations within sector  101 ). Such KPIs, metrics, etc. may include a RSRP value (e.g., a mean RSRP value over a given time period, a maximum RSRP value over a given time period, a minimum RSRP value over a given time period, etc.), a Reference Signal Received Quality (“RSRQ”) (e.g., a mean RSRQ value over a given time period, a maximum RSRQ value over a given time period, a minimum RSRQ value over a given time period, etc.), a Channel Quality Indicator CQI value (e.g., a mean CQI value over a given time period, a maximum CQI value over a given time period, a minimum CQI value over a given time period, etc.), an uplink (“UL”) power headroom value (e.g., a mean UL power headroom value over a given time period, a maximum UL power headroom value over a given time period, a minimum UL power headroom value over a given time period, etc.), an UL Physical Uplink Shared Channel (“PUSCH”) Signal-to-Interference-and-Noise-Ratio (“SINR”) value (e.g., a mean UL PUSCH value over a given time period, a maximum UL PUSCH value over a given time period, a minimum UL PUSCH value over a given time period, etc.), a UL Physical Uplink Control Channel (“PUCCH”) value (e.g., a mean UL PUCCH value over a given time period, a maximum UL PUCCH value over a given time period, a minimum UL PUCCH value over a given time period, etc.), quantity or percentage of samples with a given transmission (“Tx”) mode, MIMO utilization values, and/or other suitable KPIs, values, metrics, or the like. As noted above, such KPIs, values, metrics, or the like may be monitored, provided, etc. in a location-based manner. For example, performance KPIs associated with particular sectors  101  and/or sub-sectors (e.g., divided according to “bins” or categories based on distance and/or angle within a given sector  101 ) may be evaluated in accordance with one or more weights associated with KPI category  403 - 1 . 
     KPI category  403 - 2  may relate to metrics relating to mobility, such as quantity or proportion of handovers of UEs into or out of a sector, durations of connections between UEs and base stations located in a sector, durations that UE location information indicated that UEs were located within a sector, or the like. In some embodiments, such information may include, for example, whether a given UE is moving within sector  101 , moving between sectors, and/or is stationary. KPI category  403 - 2  may further include and/or may be based on trends, weights, constraints, predictions, etc. relating to mobility, such as whether a given UE is likely to enter, exit, traverse within, and/or remain stationary within sector  101 . For example, such trends, weights, predictions, etc. may be generated, derived, calculated, computed, etc. based on one or more AI/ML techniques or other suitable techniques. Such techniques may include deep learning, reinforced or unreinforced machine learning, neural networks, K-means clustering, regression analysis, and/or other suitable techniques, analyses, computations, or the like. In some embodiments, KPI category  403 - 2  may include KPIs, metrics, values, etc. relating to a quantity of inter-RAT handovers occurring in a particular geographical area (e.g., sector  101 ) or with respect to a respective base station over a given time period, quantity of inter-cell type sessions over a given time period, quantity of co-sector transitions over a given time period, quantity of intra-frequency handovers over a given time period, quantity of inter-frequency handovers over a given time period, quantity of blind redirections over a given time period, and/or other suitable KPIs, metrics, and/or other information. 
     KPI category  403 - 3  may relate to metrics related to amounts or rates of energy consumption (e.g., electrical power usage) at one or more devices or systems of a given sector, such as rates of consumption (e.g., Watts, kiloWatts, etc.), amounts of consumption over time (e.g., Wh, kWh, etc.), and/or other metrics related to energy consumption. 
     Further, in the example of  FIG. 4A , KPI category  403 - 1  may be associated with a first weight W 1 , KPI category  403 - 2  may be associated with a second weight W 2 , and KPI category  403 - 3  may be associated with a third weight W 3 . In the example configuration framework  405  of  FIG. 4B , the same KPI categories  403 - 1 ,  403 - 2 , and  403 - 3  may be specified, but with different weights than the example configuration framework  401  of  FIG. 4A . For example, configuration framework  405  may specify that KPI category  403 - 2  is associated with a fourth weight W 4 , KPI category  403 - 3  is associated with a fifth weight W 5 , and KPI category  403 - 1  is associated with a sixth weight W 6 . 
       FIG. 5  illustrates example sector attributes, metrics, or the like that may be associated with particular sector models  203 , as well as an example association between sector model  203 , configuration framework  201 , and/or actions/parameters  205 . As shown, for example, example sector model  203  may include QoS metrics  501 , energy consumption metrics  503 , RAN configuration parameters  505 , inter-sector information  507 , locale features  509 , and/or one or more other types of information. 
     As discussed above, QoS metrics  501  may reflect QoS metrics associated with a particular sector  101  over a particular period of time, and energy consumption metrics  503  may indicate an amount of energy consumed at the particular sector  101  over the particular period of time. RAN configuration parameters  505  may include parameters such as an indication of quantity and/or position (e.g., geographical position) of physical infrastructure hardware (e.g., antennas, radios, data centers, or the like) associated with one or more RANs in sector  101 . In some embodiments, RAN configuration parameters  505  may indicate particular radio access technologies (“RATs”) implemented in sector  101  (e.g., a LTE RAT, a 5G RAT, etc.), beam configurations implemented in sector  101  (e.g., beam quantity, beam azimuth angles, beam width, beam transmission power, etc.), MIMO configuration information, and/or other suitable information. 
     Inter-sector information  507  may include information associated with sectors adjacent to or proximate to a given sector  101 . For example, inter-sector information  507  may include RAN parameters, QoS metrics, and/or energy consumption metrics, associated with sectors adjacent to or within a threshold distance of sector  101 . In some embodiments, inter-sector information  507  may include mobility information, which may be associated with mobility of UEs between sector  101  and neighboring sectors. For example, inter-sector information  507  may indicate that UEs that are located in sector  101  are likely to be stationary within sector  101  for a first duration of time (e.g., approximately one hour), and then that such UEs travel to a particular neighboring sector. As another example, inter-sector information  507  may indicate that UEs that are located in the neighboring sector are relatively likely to enter the particular sector  101 . 
     Locale features  509  may include information indicating attributes and/or features of the geographical area. For example, locale features  509  may include information relating to building layout and/or density, topographical features (e.g., mountains, valleys, forests, streams, etc.), weather-related information, air quality-related information (e.g., smog density, particulate density, fog density, etc.), and/or other factors that may affect energy consumption, QoS metrics, or other metrics. Locale features  509  may include geographical coordinates (e.g., latitude and longitude coordinates, Global Positioning System (“GPS”) coordinates, or the like) or other suitable location information, to indicate the geographical locations of respective features. 
     As described below with respect to  FIG. 6 , a given sector  101  may be associated with one or more sector models  203  based on a comparison of the above-described factors, and/or one or more other factors, of sector  101  to such factors associated with a set of candidate sector models  203 . Briefly, for example, GOS  105  may determine that a particular sector  101 , that exhibits a particular set of QoS metrics  501 , a particular set of energy consumption metrics  503 , and a first set of locale features  509  (e.g., urban features such as high-rise buildings) is associated with a first sector model  203 , while another sector  101 , that exhibits a similar set of QoS metrics  501  and a similar set of energy consumption metrics  503 , but a different second set of locale features  509  (e.g., rural features such as relatively flat areas with relatively low building density) is associated with a different second sector model  203 . Generally, a given sector model  203  may describe or reflect parameters, metrics, attributes, etc. of a given sector  101 . 
     As further shown, sector model  203  may be associated with one or more configuration frameworks  201 . For example, GOS  105  may use AI/ML techniques or other suitable techniques to determine that particular KPIs, attributes, etc. are more important for sectors  101  with particular attributes than for sectors with different attributes. As an example, GOS  105  may determine that sectors  101  having a first set of attributes should have mobility-related parameters prioritized (e.g., KPI category  403 - 2 ) and that energy consumption-related parameters (e.g., KPI category  403 - 3 ) are less of a priority, and may determine that sectors  101  having a second set of attributes should have coverage/quality-related parameters prioritized (e.g., KPI category  403 - 1 ). In this example, GOS  105  may determine that the first sector  101  is associated with a first configuration framework  201  and that the second sector  101  is associated with a second configuration framework  201 . 
     In some embodiments, additionally, or alternatively, GOS  105  may determine that the first and second sectors  101  are both associated with the first and second configuration frameworks  201 , but with different sector-framework affinity scores  511 . For example, the first sector  101  may have a relatively high sector-framework affinity score  511  with the first configuration framework  201  (e.g., based on the determination that configuration framework  201  prioritizes KPIs, metrics, or the like that are a priority for the first sector  101 ) and may have a relatively low sector-framework affinity score  511  with the second configuration framework  201 . On the other hand, the second sector  101  may have a relatively low sector-framework affinity score  511  with the first configuration framework  201  and a relatively high sector-framework affinity score  511  with the second configuration framework  201 . 
     GOS  105  may further generate, maintain, refine, etc. (e.g., using one or more AI/ML techniques or other suitable techniques) one or more associations between respective configuration frameworks  201  and one or more sets of actions/parameters  205 . For example, each configuration framework  201  may be associated with one or more sets of actions/parameters  205 , as each particular set of actions/parameters  205  may have been determined (e.g., based on real-world results and/or simulated results) as increasing an overall optimization score of one or more sectors  101  that match sector model  203 , where such overall optimization score is computed based on configuration framework  201 . As noted above, actions/parameters  205  may include modifying QoS parameters, modifying beamforming and/or other antenna parameters, modifying energy consumption parameters, modifying handover parameters, or other suitable actions. 
     GOS  105  may also determine framework-action affinity scores  513  between configuration framework  201  and respective sets of actions/parameters  205 . As similarly discussed above, framework-action affinity scores  513  may generally indicate how effective a given set of actions/parameters  205  are for increasing an overall optimization score of a particular sector  101 , given configuration framework  201  associated with sector  101 . Thus, multiple sets of actions/parameters  205  may be associated with a respective configuration framework  201 , and respective framework-action affinity scores  513  between configuration framework  201  and the sets of actions/parameters  205  may be used to ultimately determine which actions to take with respect to a given sector model  203 . 
     While  FIG. 5  provides examples of relationships between configuration frameworks  201 , sector models  203 , and actions/parameters  205 , in practice, other arrangements or relationships are possible. For example, in some embodiments, one or more affinity scores may be generated, maintained, refined, etc. between sector models  203  and sets of actions/parameters  205 . Such affinity scores between sector models  203  and actions/parameters  205  may be determined in addition to, or in lieu of, sector-framework affinity scores  511  and/or framework-action affinity scores  513 . For example, the same configuration framework  201  may be applied to two different sector models  203  for two different sectors  101 , and the resulting actions/parameters  205  may be different for the two different sectors  101 . 
       FIGS. 6-8  illustrate an example determination of one or more configuration frameworks  201  and sector models  203  for a particular sector  101 , and the performance of one or more actions  205  based on the determined configuration frameworks  201  and/or sector models  203 . As shown in  FIG. 6 , for example, GOS  105  may determine (at  602 ) parameters and/or attributes of sector  101 . As discussed above, such parameters and/or attributes may include QoS metrics  501 , energy consumption metrics  503 , RAN configuration parameters  505 , inter-sector information  507 , locale features  509 , and/or other suitable parameters, attributes, metrics, or the like. GOS  105  may further identify (at  604 ) one or more sector models  203  based on the determined parameters and/or attributes of sector  101 . 
     In this example, GOS  105  may determine that sector  101  is associated with a “highway” sector model  601 - 1  and a “media streaming” sector model  601 - 3 . As further shown, GOS  105  may not determine that sector  101  is associated with an example “office building” sector model  601 - 2 , or an example “dense buildings” sector model  601 - 4 . For example, GOS  105  may determine, based on a suitable similarity analysis of the parameters and/or attributes of sector  101 , that sector models  601 - 2  and  601 - 4  do not match (e.g., correspond with a measure of similarity above a threshold measure of similarity) sector models  601 - 2  and  601 - 4 , and/or that sector models  601 - 1  and  601 - 3  match (e.g., have a higher measure of similarity with) the parameters and/or attributes of sector  101  more closely. As discussed above, operations  602  and  604  may be performed on an ongoing basis, such that the selection of particular sector models  601  may change based on updated parameters and/or attributes received by GOS  105  over time. 
     As shown in  FIG. 7A , GOS  105  may identify (at  706 ) one or more configuration frameworks  201  that are associated with identified sector models  601 - 1  and  601 - 3 . For example, as discussed above, sector models  601 - 1  and  601 - 3  may each be associated with one or more configuration frameworks  201 . As also discussed above, one or more sector-framework affinity scores  511  between respective configuration frameworks  201  and sector models  601 - 1  and  601 - 3  may be used to determine the set of configuration frameworks  201  that are associated with sector models  601 - 1  and  601 - 3  (e.g., configuration frameworks  201  with the highest sector-framework affinity scores  511 , configuration frameworks  201  with sector-framework affinity scores  511  above a threshold score, etc.). In this example, sector model  601 - 1  is associated with configuration frameworks  201 - 1 ,  201 - 2 , and  201 - 3 , while sector model  601 - 3  is associated with configuration framework  201 - 3  and configuration framework  201 - 4 . 
     GOS  105  may select (at  708 ) a particular configuration framework  201  based on the identified sets of configuration frameworks  201 . In this example, GOS  105  may select (at  708 ) configuration framework  201 - 3  based on configuration framework  201 - 3  being the only respective configuration framework  201  that has been identified with respect to both sector models  601 - 1  and  601 - 3 . In some embodiments, GOS  105  may select configuration framework  201 - 3  based on a cumulative, aggregate, etc. sector-framework affinity score  511  associated with each configuration framework  201  with respect to each sector model  601 . In this example, configuration framework  201 - 3  have the highest cumulative, aggregate, etc. sector-framework affinity score  511  based on aggregating sector-framework affinity score  511  between configuration framework  201 - 3  and sector model  601 - 1 , and between configuration framework  201 - 3  and sector model  601 - 3 . While particular examples of the selection of configuration framework  201 - 3  are described above, in practice, GOS  105  may use any suitable selection process or criteria to select configuration framework  201 - 3  in the example of  FIG. 7A . 
       FIG. 7B  illustrates an example generation and/or selection of a particular configuration framework  201  to apply to sector  101 . For example, as similarly discussed above with respect to  FIG. 7A , GOS  105  may identify (at  706 ) one or more configuration frameworks  201  that are associated with identified sector models  601 - 1  and  601 - 3 . In this example, in lieu of selecting one of the identified configuration frameworks  201  (e.g., one of configuration frameworks  201 - 1  through  201 - 4 ), GOS  105  may select or generate a different configuration framework  201 - 5 . For example, GOS  105  may determine that a cumulative, aggregate, average, etc. of one or more of the sector-framework affinity scores  511  associated with the identified (at  706 ) configuration frameworks  201  does not meet a threshold score. Additionally, or alternatively, GOS  105  may compare sector models  601 - 1  and  601 - 3  and determine that a measure of similarity of these sector models  601  does not meet a threshold measure of similarity, and/or that a measure of dissimilarity of these sector models  601  meets a threshold measure of dissimilarity. 
     In some embodiments, GOS  105  may use one or more AI/ML techniques to select and/or generate configuration framework  201 - 5 . For example, GOS  105  may generate or select configuration framework  201 - 5  based on identifying features, attributes, etc. of one or more of configuration frameworks  201 - 1  through  201 - 4 , and identifying common features, attributes, etc. based on which configuration framework  201 - 5  may be generated or selected. In some embodiments, GOS  105  may additionally, or alternatively, generate and/or select configuration framework  201 - 5  based on features, attributes, or the like of sector models  601 - 1  and sector model  601 - 3 . 
     As shown in  FIG. 8 , GOS  105  may provide (at  812 ) the selected configuration framework  201  and/or one or more actions/parameters  205  associated with configuration framework  201 , to sector  101 . For example, as discussed above, configuration framework  201  may be associated with one or more sets of actions/parameters  205 , which may be identified by GOS  105 . In some embodiments, sector  101  may instead identify a set of actions/parameters  205  based on the provided configuration framework  201 . Sector  101  may further implement (at  814 ) the received actions and/or parameters. For example, sector  101  may modify antenna parameters (e.g., tilt angle, azimuth angle, etc.), QoS parameters (e.g., queue weights, resource allocation parameters, etc.), and/or other suitable actions and/or parameters as discussed above. Further, sector  101  may continue to “fine tune” the actions and/or parameters based on a continued monitoring of KPIs or other metrics associated with sector  101 , such that the actions/parameters  205  associated with sector  101  may be more precisely customized for the exact attributes, KPIs, etc. of sector  101  than configuration framework  201 . Additionally, or alternatively, GOS  105  may “fine tune” the actions and/or parameters, and/or modify associations (e.g., affinity scores) between sector  101  and one or more configuration frameworks  201 , sector models  203 , and/or actions/parameters  205 . GOS  105  may also refine associations between configuration frameworks  201 , sector models  203 , and/or actions/parameters  205  based on the continued monitoring. 
     While the examples above are provided in the context of configuration framework  201 , sector model  203 , and actions/parameters  205  being determined for a particular sector  101 , in practice, the same or similar operations may be performed (e.g., concurrently, synchronously, asynchronously, sequentially, etc.) with respect to multiple sectors  101 . Further, in some embodiments, a particular sector model  203  (and associated configuration frameworks  201  and/or actions/parameters  205 ) may be determined for an aggregate of multiple sectors (e.g., congruous sectors, adjacent sectors, sectors within a threshold proximity of each other, sectors within a given geographical region, etc.). Such aggregate may be referred to as a “super sector,” and/or the constituent sectors  101  may be referred to as “sub-sectors.” In such embodiments, a particular sector model  203  for a given super sector may be generated based on an aggregate, average, median, maximum, minimum, or other computed value associated with the KPIs, parameters, etc. of the sub-sectors. In some embodiments, sector model  203  for a given super sector may be based on the sector models  203  associated with respective sub-sectors. For example, sector model  203  for the super sector may be based on selecting one or more sector models  203  of the sub-sectors, and/or selecting or generating a different sector model  203  (e.g., in a manner similar to that described above with respect to  FIG. 7B ). 
     In some embodiments, the operations described above may be performed in an iterative and/or prioritized manner. For example, GOS  105  may rank a set of sectors  101  based on overall optimization scores, and may provide actions/parameters to sectors  101  in an order based on the ranking. For example, GOS  105  may select a lowest-scoring sector  101  and may perform operations described above in order to attempt to improve the overall optimization score associated with the lowest-scoring sector  101  (and may continue in this manner in order to address the particular sectors  101  most in need of remedial action). 
       FIG. 9  illustrates an example process  900  for determining one or more sector models  203 , configuration frameworks  201 , and/or sets of actions  205  to perform with respect to a given sector  101 . In some embodiments, some or all of process  900  may be performed by GOS  105 . In some embodiments, one or more other devices may perform some or all of process  900  in concert with, and/or in lieu of, GOS  105 , such as one or more devices or systems associated with one or more sectors  101 . 
     As shown, process  900  may include generating, receiving, and/or modifying (at  902 ) one or more sector models  203  based on metrics, parameters, etc. associated with one or more sectors  101  of a wireless network. For example, as discussed above, GOS  105  may use AI/ML techniques or other suitable techniques to generate and/or refine sector models  203 . For example, GOS  105  may evaluate metrics based on real-word and/or simulated KPIs and/or attributes of one or more sectors  101  in order to generate one or more clusters, classifications, or the like which may be reflected by sector models  203 . 
     Process  900  may further include identifying (at  904 ) one or more configuration frameworks  201  associated with sector models  203 . For example, as discussed above, GOS  105  may identify configuration frameworks  201 , which may include weights for particular KPIs, attributes, or the like. The weights may be used when evaluating overall effectiveness, yield, etc. of given sectors  101 . For example, as discussed above, the weights may be applied to KPIs associated with sectors  101  in order to generate an overall optimization score associated with sectors  101 . In some embodiments, as noted above, GOS  105  may determine one or more affinity scores between particular sector models  203  and configuration frameworks  201 , which may indicate the effectiveness, correlation, etc. of a given configuration framework  201  to a given sector model  203 . 
     Process  900  may additionally include receiving (at  906 ) metrics, KPIs, attributes, or the like associated with a particular sector  101 . For example, GOS  105  may receive the metrics, KPIs, attributes, or the like from one or more devices or systems located in sector  101 , one or more devices or systems that provide service to UEs located in sector  101 , one or more devices or systems associated with a core network to which network infrastructure associated with sector  101  is communicatively coupled, or some other suitable device or system. As discussed above, the metrics, KPIs, attributes, etc., may include, for example, QoS metrics  501 , energy consumption metrics  503 , RAN configuration parameters  505 , inter-sector information  507 , locale features  509 , and/or other suitable information. 
     Process  900  may also include determining (at  908 ) a particular sector model  203  based on the received metrics, KPIs, attributes, etc. For example, as discussed above, GOS  105  may use one or more AI/ML techniques to determine an association, correlation, or the like between the received metrics, KPIs, attributes, etc. and metrics, KPIs, attributes, etc. associated with sector model  203 . For example, GOS  105  may select a particular sector model  203  from a set of candidate sector models  203 , and/or may generate a new sector model  203  based on the received metrics, KPIs, attributes, etc. 
     Process  900  may further include selecting (at  910 ) a particular configuration framework  201  based on the determined sector model  203 . For example, as discussed above, GOS  105  may select configuration framework  201  based on respective sector-framework affinity scores  511  between sector model  203  and a set of candidate configuration frameworks  201 , and/or may select or generate configuration framework  201  based on sector model  203  (e.g., as similarly discussed above with respect to  FIGS. 7A and/or 7B ). 
     Process  900  may additionally include identifying (at  912 ) a set of actions  205  to perform based on the selected particular configuration framework  201 . For example, as discussed above, GOS  105  may select actions/parameters  205  based on respective framework-action affinity scores  513  between configuration framework  201  and a set of candidate actions/parameters  205 , and/or may otherwise select or generate actions/parameters  205  based on sector model  203 . As noted above, actions/parameters  205  may include QoS actions or parameters, antenna actions or parameters, energy consumption actions or parameters, and/or other suitable actions and/or parameters. 
     Process  900  may also include implementing (at  914 ) the identified set of actions. For example, as discussed above, sector  101  may make one or more adjustments to parameters, physical devices (e.g., antennas), or the like based on the identified set of actions/parameters  205 . 
     Process  900  may additionally include determining (at  916 ) an overall optimization score for sector  101  based on the selected sector model  203  and actions/parameters  205 . For example, GOS  105  may determine the overall optimization score based on KPIs received after actions/parameters  205  are implemented (at  914 ), and further based on weights specified by configuration framework  201 . In this manner, GOS  105  may evaluate the effectiveness, accuracy, etc. of the determined configuration framework  201 , sector model  203 , and/or actions/parameters  205  with respect to sector  101 . 
     As shown in  FIG. 9 , some or all of process  900  may be performed and/or repeated iteratively. For example, operations  902 ,  904 ,  906 , and/or  912  may be repeated and/or performed based on the determined (at  916 ) overall optimization score, which may be updated over time based on real-time and/or near real-time KPIs of sector  101 . In this manner, the respective correlations between sector  101 , configuration framework  201 , sector model  203 , and/or actions/parameters  205  may continue to be refined, and sector  101  may be optimized in an automated manner without the need for manual intervention, thereby enhancing the user experience of users receiving service in sector  101 . 
     Further, in some embodiments, some or all of process  900  may be concurrently performed as separate processes on separate devices or systems of a network. For example, GOS  105  may concurrently perform some or all of process  900  at multiple sectors  101 . In some embodiments, GOS  105  may perform some or all of process  900  at different levels of hierarchy, which as discussed above may include determining sector models  203  for super sectors, as well as associated configuration frameworks  201  and actions and/or parameters  205 . For example, various orchestration, automation, deployment, etc. systems may implement actions and/or parameters  205  at virtualized or containerized instances of network functions. One example of such an orchestration, automation, deployment, etc. is the open-source Kubernetes system. One or more orchestration systems may accordingly adjust parameters associated with a RAN, which may be implemented according to an O-RAN environment (e.g., using a xApp deployment technique, a rApp deployment technique, or some other suitable deployment technique) in some embodiments, as described below. 
     Accordingly, the analysis and/or adjustment of parameters associated with sectors  101 , super sectors, and/or other levels of hierarchy in the network may be performed according to different levels of granularity by, or in concert with, a suitable orchestration system. In some embodiments, for example, GOS  105  may identify a super sector that is exhibiting sub-optimal KPIs, and may further identify a particular sector  101  within the super sector that is exhibiting sup-optimal KPIs (e.g., an overall optimization score below a threshold optimization score). In such a situation, GOS  105  may perform actions and/or modify parameters associated with sector  101 , without needing to necessarily perform actions and/or modify parameters associated with other constituent sectors  101  of the super sector. In some embodiments, as also discussed above GOS  105  may perform some or all of process  900  in a prioritized manner, in order to address low-scoring or lowest-scoring sectors  101  (or other delineations of the wireless network) and improve the performance of such sectors  101 . 
       FIG. 10  illustrates an example environment  1000 , in which one or more embodiments may be implemented. In some embodiments, environment  1000  may correspond to a 5G network, and/or may include elements of a 5G network. In some embodiments, environment  1000  may correspond to a 5G Non-Standalone (“NSA”) architecture, in which a 5G RAT may be used in conjunction with one or more other RATs (e.g., a 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  1000  may include UE  1001 , RAN  1010  (which may include one or more Next Generation Node Bs (“gNBs”)  1011 ), RAN  1012  (which may include one or more one or more evolved Node Bs (“eNBs”)  1013 ), and various network functions such as Access and Mobility Management Function (“AMF”)  1015 , Mobility Management Entity (“MME”)  1016 , Serving Gateway (“SGW”)  1017 , Session Management Function (“SMF”)/Packet Data Network (“PDN”) Gateway (“PGW”)-Control plane function (“PGW-C”)  1020 , Policy Control Function (“PCF”)/Policy Charging and Rules Function (“PCRF”)  1025 , Application Function (“AF”)  1030 , User Plane Function (“UPF”)/PGW-User plane function (“PGW-U”)  1035 , Home Subscriber Server (“HSS”)/Unified Data Management (“UDM”)  1040 , and Authentication Server Function (“AUSF”)  1045 . Environment  1000  may also include one or more networks, such as Data Network (“DN”)  1050 . Environment  1000  may include one or more additional devices or systems communicatively coupled to one or more networks (e.g., DN  1050 ), such as GOS  105 . 
     The example shown in  FIG. 10  illustrates one instance of each network component or function (e.g., one instance of SMF/PGW-C  1020 , PCF/PCRF  1025 , UPF/PGW-U  1035 , HSS/UDM  1040 , and/or  1045 ). In practice, environment  1000  may include multiple instances of such components or functions. For example, in some embodiments, environment  1000  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  1020 , PCF/PCRF  1025 , UPF/PGW-U  1035 , HSS/UDM  1040 , and/or  1045 , while another slice may include a second instance of SMF/PGW-C  1020 , PCF/PCRF  1025 , UPF/PGW-U  1035 , HSS/UDM  1040 , and/or  1045 ). The different slices may provide differentiated levels of service, such as service in accordance with different Quality of Service (“QoS”) parameters. 
     The quantity of devices and/or networks, illustrated in  FIG. 10 , is provided for explanatory purposes only. In practice, environment  1000  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. 10 . For example, while not shown, environment  1000  may include devices that facilitate or enable communication between various components shown in environment  1000 , such as routers, modems, gateways, switches, hubs, etc. Alternatively, or additionally, one or more of the devices of environment  1000  may perform one or more network functions described as being performed by another one or more of the devices of environment  1000 . Devices of environment  1000  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  1000  may be physically integrated in, and/or may be physically attached to, one or more other devices of environment  1000 . 
     UE  1001  may include a computation and communication device, such as a wireless mobile communication device that is capable of communicating with RAN  1010 , RAN  1012 , and/or DN  1050 . UE  1001  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 Mobile-to-Mobile (“M2M”) device, or another type of mobile computation and communication device. UE  1001  may send traffic to and/or receive traffic (e.g., user plane traffic) from DN  1050  via RAN  1010 , RAN  1012 , and/or UPF/PGW-U  1035 . 
     RAN  1010  may be, or may include, a 5G RAN that includes one or more base stations (e.g., one or more gNBs  1011 ), via which UE  1001  may communicate with one or more other elements of environment  1000 . UE  1001  may communicate with RAN  1010  via an air interface (e.g., as provided by gNB  1011 ). For instance, RAN  1010  may receive traffic (e.g., voice call traffic, data traffic, messaging traffic, signaling traffic, etc.) from UE  1001  via the air interface, and may communicate the traffic to UPF/PGW-U  1035 , and/or one or more other devices or networks. Similarly, RAN  1010  may receive traffic intended for UE  1001  (e.g., from UPF/PGW-U  1035 , AMF  1015 , and/or one or more other devices or networks) and may communicate the traffic to UE  1001  via the air interface. In some embodiments, sector  101  may be implemented by and/or otherwise associated with one or more gNBs  1011 . For example, in some embodiments, base station  103  may be, or may include, gNB  1011 . 
     RAN  1012  may be, or may include, a LTE RAN that includes one or more base stations (e.g., one or more eNBs  1013 ), via which UE  1001  may communicate with one or more other elements of environment  1000 . UE  1001  may communicate with RAN  1012  via an air interface (e.g., as provided by eNB  1013 ). For instance, RAN  1010  may receive traffic (e.g., voice call traffic, data traffic, messaging traffic, signaling traffic, etc.) from UE  1001  via the air interface, and may communicate the traffic to UPF/PGW-U  1035 , and/or one or more other devices or networks. Similarly, RAN  1010  may receive traffic intended for UE  1001  (e.g., from UPF/PGW-U  1035 , SGW  1017 , and/or one or more other devices or networks) and may communicate the traffic to UE  1001  via the air interface. In some embodiments, sector  101  may be implemented by and/or otherwise associated with one or more eNBs  1013 . For example, in some embodiments, base station  103  may be, or may include, eNB  1013 . 
     AMF  1015  may include one or more devices, systems, Virtualized Network Functions (“VNFs”), etc., that perform operations to register UE  1001  with the 5G network, to establish bearer channels associated with a session with UE  1001 , to hand off UE  1001  from the 5G network to another network, to hand off UE  1001  from the other network to the 5G network, manage mobility of UE  1001  between RANs  1010  and/or gNBs  1011 , and/or to perform other operations. In some embodiments, the 5G network may include multiple AMFs  1015 , which communicate with each other via the N14 interface (denoted in  FIG. 10  by the line marked “N14” originating and terminating at AMF  1015 ). 
     MME  1016  may include one or more devices, systems, VNFs, etc., that perform operations to register UE  1001  with the EPC, to establish bearer channels associated with a session with UE  1001 , to hand off UE  1001  from the EPC to another network, to hand off UE  1001  from another network to the EPC, manage mobility of UE  1001  between RANs  1012  and/or eNBs  1013 , and/or to perform other operations. 
     SGW  1017  may include one or more devices, systems, VNFs, etc., that aggregate traffic received from one or more eNBs  1013  and send the aggregated traffic to an external network or device via UPF/PGW-U  1035 . Additionally, SGW  1017  may aggregate traffic received from one or more UPF/PGW-Us  1035  and may send the aggregated traffic to one or more eNBs  1013 . SGW  1017  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  1010  and  1012 ). 
     SMF/PGW-C  1020  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  1020  may, for example, facilitate in the establishment of communication sessions on behalf of UE  1001 . In some embodiments, the establishment of communications sessions may be performed in accordance with one or more policies provided by PCF/PCRF  1025 . 
     PCF/PCRF  1025  may include one or more devices, systems, VNFs, etc., that aggregate information to and from the 5G network and/or other sources. PCF/PCRF  1025  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  1025 ). 
     AF  1030  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  1035  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  1035  may receive user plane data (e.g., voice call traffic, data traffic, etc.), destined for UE  1001 , from DN  1050 , and may forward the user plane data toward UE  1001  (e.g., via RAN  1010 , SMF/PGW-C  1020 , and/or one or more other devices). In some embodiments, multiple UPFs  1035  may be deployed (e.g., in different geographical locations), and the delivery of content to UE  1001  may be coordinated via the N9 interface (e.g., as denoted in  FIG. 10  by the line marked “N9” originating and terminating at UPF/PGW-U  1035 ). Similarly, UPF/PGW-U  1035  may receive traffic from UE  1001  (e.g., via RAN  1010 , SMF/PGW-C  1020 , and/or one or more other devices), and may forward the traffic toward DN  1050 . In some embodiments, UPF/PGW-U  1035  may communicate (e.g., via the N4 interface) with SMF/PGW-C  1020 , regarding user plane data processed by UPF/PGW-U  1035 . 
     HSS/UDM  1040  and AUSF  1045  may include one or more devices, systems, VNFs, etc., that manage, update, and/or store, in one or more memory devices associated with AUSF  1045  and/or HSS/UDM  1040 , profile information associated with a subscriber. AUSF  1045  and/or HSS/UDM  1040  may perform authentication, authorization, and/or accounting operations associated with the subscriber and/or a communication session with UE  1001 . 
     DN  1050  may include one or more wired and/or wireless networks. For example, DN  1050  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  1001  may communicate, through DN  1050 , with data servers, other UEs  1001 , and/or to other servers or applications that are coupled to DN  1050 . DN  1050  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  1050  may be connected to one or more devices, such as content providers, applications, web servers, and/or other devices, with which UE  1001  may communicate. 
     GOS  105  may include one or more devices, systems, VNFs, or the like that perform one or more operations described above. For example, GOS  105  may generate, receive, refine, etc. one or more models and/or correlations thereof, apply the models to real-world scenarios (e.g., to sectors  101  based on KPIs, parameters, etc. associated with sectors  101 ), and determine one or more actions to perform based on the determined models for such real-world scenarios. GOS  105  may, as discussed above, receive suitable information from one or more devices or systems associated with sectors  101  (e.g., UE  1001 , gNB  1011 , eNB  1013 , AMF  1015 , MME  1016 , HSS/UDM  1040 , a SCEF, a NEF, and/or one or more other suitable devices or systems). 
       FIG. 11  illustrates an example Distributed Unit (“DU”) network  1100 , which may be included in and/or implemented by one or more RANs (e.g., RAN  1010 , RAN  1012 , or some other RAN). In some embodiments, a particular RAN may include one DU network  1100 . In some embodiments, a particular RAN may include multiple DU networks  1100 . In some embodiments, DU network  1100  may correspond to a particular gNB  1011  of a 5G RAN (e.g., RAN  1010 ). In some embodiments, DU network  1100  may correspond to multiple gNBs  1011 . In some embodiments, DU network  1100  may correspond to one or more other types of base stations of one or more other types of RANs. As shown, DU network  1100  may include Central Unit (“CU”)  1105 , one or more Distributed Units (“DUs”)  1103 - 1  through  1103 -N (referred to individually as “DU  1103 ,” or collectively as “DUs  1103 ”), and one or more Radio Units (“RUs”)  1101 - 1  through  1101 -M (referred to individually as “RU  1101 ,” or collectively as “RUs  1101 ”). 
     CU  1105  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. 10 , such as AMF  1015  and/or UPF/PGW-U  1035 ). In the uplink direction (e.g., for traffic from UEs  1001  to a core network), CU  1105  may aggregate traffic from DUs  1103 , and forward the aggregated traffic to the core network. In some embodiments, CU  1105  may receive traffic according to a given protocol (e.g., Radio Link Control (“RLC”)) from DUs  1103 , 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  1103 . 
     In accordance with some embodiments, CU  1105  may receive downlink traffic (e.g., traffic from the core network) for a particular UE  1001 , and may determine which DU(s)  1103  should receive the downlink traffic. DU  1103  may include one or more devices that transmit traffic between a core network (e.g., via CU  1105 ) and UE  1001  (e.g., via a respective RU  1101 ). DU  1103  may, for example, receive traffic from RU  1101  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  1103  may receive traffic from CU  1105  at the second layer, may process the traffic to the first layer, and provide the processed traffic to a respective RU  1101  for transmission to UE  1001 . 
     RU  1101  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  1001 , one or more other DUs  1103  (e.g., via RUs  1101  associated with DUs  1103 ), and/or any other suitable type of device. In the uplink direction, RU  1101  may receive traffic from UE  1001  and/or another DU  1103  via the RF interface and may provide the traffic to DU  1103 . In the downlink direction, RU  1101  may receive traffic from DU  1103 , and may provide the traffic to UE  1001  and/or another DU  1103 . 
     RUs  1101  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”)  1107 . For example, RU  1101 - 1  may be communicatively coupled to MEC  1107 - 1 , RU  1101 -M may be communicatively coupled to MEC  1107 -M, DU  1103 - 1  may be communicatively coupled to MEC  1107 - 2 , DU  1103 -N may be communicatively coupled to MEC  1107 -N, CU  1105  may be communicatively coupled to MEC  1107 - 3 , and so on. MECs  1107  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  1001 , via a respective RU  1101 . 
     For example, RU  1101 - 1  may route some traffic, from UE  1001 , to MEC  1107 - 1  instead of to a core network (e.g., via DU  1103  and CU  1105 ). MEC  1107 - 1  may process the traffic, perform one or more computations based on the received traffic, and may provide traffic to UE  1001  via RU  1101 - 1 . In this manner, ultra-low latency services may be provided to UE  1001 , as traffic does not need to traverse DU  1103 , CU  1105 , and an intervening backhaul network between DU network  1100  and the core network. In some embodiments, MEC  1107  may include, and/or may implement, some or all of the functionality described above with respect to GOS  105 . 
       FIG. 12  illustrates an example 0-RAN environment  1200 , which may correspond to RAN  1010 , RAN  1012 , and/or DU network  1100 . For example, RAN  1010 , RAN  1012 , and/or DU network  1100  may include one or more instances of O-RAN environment  1200 , and/or one or more instances of O-RAN environment  1200  may implement RAN  1010 , RAN  1012 , DU network  1100 , and/or some portion thereof. As shown, O-RAN environment  1200  may include Non-Real Time Radio Intelligent Controller (“RIC”)  1201 , Near-Real Time RIC  1203 , O-eNB  1205 , O-CU-Control Plane (“O-CU-CP”)  1207 , O-CU-User Plane (“O-CU-UP”)  1209 , O-DU  1211 , O-RU  1213 , and O-Cloud  1215 . In some embodiments, O-RAN environment  1200  may include additional, fewer, different, and/or differently arranged components. 
     In some embodiments, some or all of the elements of O-RAN environment  1200  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  1200  may be implemented by, and/or communicatively coupled to, one or more MECs  1107 . 
     Non-Real Time RIC  1201  and Near-Real Time RIC  1203  may receive performance information (and/or other types of information) from one or more sources, and may configure other elements of O-RAN environment  1200  based on such performance or other information. For example, Near-Real Time MC  1203  may receive performance information, via one or more E2 interfaces, from O-eNB  1205 , O-CU-CP  1207 , and/or O-CU-UP  1209 , and may modify parameters associated with O-eNB  1205 , O-CU-CP  1207 , and/or O-CU-UP  1209  based on such performance information. Similarly, Non-Real Time MC  1201  may receive performance information associated with O-eNB  1205 , O-CU-CP  1207 , O-CU-UP  1209 , and/or one or more other elements of O-RAN environment  1200  and may utilize machine learning and/or other higher level computing or processing to determine modifications to the configuration of O-eNB  1205 , O-CU-CP  1207 , O-CU-UP  1209 , and/or other elements of O-RAN environment  1200 . In some embodiments, Non-Real Time RIC  1201  may generate machine learning models based on performance information associated with O-RAN environment  1200  or other sources, and may provide such models to Near-Real Time RIC  1203  for implementation. 
     O-eNB  1205  may perform functions similar to those described above with respect to eNB  1013 . For example, O-eNB  1205  may facilitate wireless communications between UE  1001  and a core network. O-CU-CP  1207  may perform control plane signaling to coordinate the aggregation and/or distribution of traffic via one or more DUs  1103 , which may include and/or be implemented by one or more O-DUs  1211 , and O-CU-UP  1209  may perform the aggregation and/or distribution of traffic via such DUs  1103  (e.g., O-DUs  1211 ). O-DU  1211  may be communicatively coupled to one or more RUs  1101 , which may include and/or may be implemented by one or more O-RUs  1213 . In some embodiments, O-Cloud  1215  may include or be implemented by one or more MECs  1107 , which may provide services, and may be communicatively coupled, to O-CU-CP  1207 , O-CU-UP  1209 ,  0 -DU  1211 , and/or O-RU  1213  (e.g., via an O1 and/or O2 interface). 
       FIG. 13  illustrates example components of device  1300 . One or more of the devices described above may include one or more devices  1300 . Device  1300  may include bus  1310 , processor  1320 , memory  1330 , input component  1340 , output component  1350 , and communication interface  1360 . In another implementation, device  1300  may include additional, fewer, different, or differently arranged components. 
     Bus  1310  may include one or more communication paths that permit communication among the components of device  1300 . Processor  1320  may include a processor, microprocessor, or processing logic that may interpret and execute instructions. Memory  1330  may include any type of dynamic storage device that may store information and instructions for execution by processor  1320 , and/or any type of non-volatile storage device that may store information for use by processor  1320 . 
     Input component  1340  may include a mechanism that permits an operator to input information to device  1300  and/or other receives or detects input from a source external to  1340 , 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  1340  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  1350  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  1360  may include any transceiver-like mechanism that enables device  1300  to communicate with other devices and/or systems. For example, communication interface  1360  may include an Ethernet interface, an optical interface, a coaxial interface, or the like. Communication interface  1360  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  1300  may include more than one communication interface  1360 . For instance, device  1300  may include an optical interface and an Ethernet interface. 
     Device  1300  may perform certain operations relating to one or more processes described above. Device  1300  may perform these operations in response to processor  1320  executing software instructions stored in a computer-readable medium, such as memory  1330 . 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  1330  from another computer-readable medium or from another device. The software instructions stored in memory  1330  may cause processor  1320  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. 1-9 ), 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.