Patent Publication Number: US-2022239395-A1

Title: Systems and methods for modification of radio access network parameters based on channel propagation models generated using machine learning techniques

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a Continuation of U.S. patent application Ser. No. 17/140,938, filed on Jan. 4, 2021, titled “SYSTEMS AND METHODS FOR MODIFICATION OF RADIO ACCESS NETWORK PARAMETERS BASED ON CHANNEL PROPAGATION MODELS GENERATED USING MACHINE LEARNING TECHNIQUES,” 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 channel quality metrics, which may include metrics related to signal strength, interference, or the like. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an example overview of one or more embodiments described herein, in which a RAN Optimization System (“ROS”) may determine a sector model, channel propagation model, 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 channel propagation models, sector models, and/or actions/parameters that may be generated, received, maintained, provided, etc. by a ROS of some embodiments; 
         FIG. 3  illustrates example attributes associated with a particular sector model, and further illustrates an example associations between the sector model, channel propagation model, and actions and/or parameters, in accordance with some embodiments; 
         FIGS. 4-6  conceptually illustrate example channel propagation information that may be included in, or reflected by, channel propagation models of some embodiments; 
         FIG. 7  illustrates an example of extended channel propagation information for geographical regions for which measured channel propagation information may be unavailable or insufficient; 
         FIG. 8  illustrates an example determination of one or more sector models associated with a given sector associated with a RAN of a wireless network, in accordance with some embodiments; 
         FIG. 9  illustrates an example determination of a particular set of actions to perform with respect to a given sector of a RAN based on a sector model and a channel propagation model associated with the sector, in accordance with some embodiments; 
         FIG. 10  illustrates an example implementation of one or more actions determined based on a sector model and a channel propagation model associated with a sector of a RAN, in accordance with some embodiments; 
         FIG. 11  illustrates an example process for modifying parameters of a RAN based on one or more channel propagation models identified with respect to one or more sectors of the RAN, in accordance with some embodiments; 
         FIG. 12  illustrates an example environment in which one or more embodiments, described herein, may be implemented; 
         FIG. 13  illustrates an example arrangement of a radio access network (“RAN”), in accordance with some embodiments; 
         FIG. 14  illustrates an example arrangement of an Open RAN (“O-RAN”) environment in which one or more embodiments, described herein, may be implemented; and 
         FIG. 15  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 (“AI/ML”) 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. 
     Embodiments described herein further provide for the use of AI/ML techniques or other suitable techniques to model, predict, and/or otherwise determine measures of radio frequency (“RF”) channel propagation characteristics for sectors having particular characteristics or attributes. For example, as discussed below, one or more channel propagation models may be generated based on measured RF propagation metrics for one or more sectors, and applied to sectors sharing similar attributes as the sectors based on which the channel propagation models were generated. As described herein, the association between particular sector models, channel propagation models, 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). 
     In this manner, RF channel propagation metrics may be predictively determined for sectors without the need to measure RF channel propagation metrics (e.g., metrics exhibited in real-world deployments and/or generated via RAN simulations). As such, RF channel quality for UEs receiving wireless service within particular locations of such sectors may be predicted and/or estimated without the need for determining RF channel metrics associated with the UEs. 
     Further, embodiments described herein may determine suitable remedial and/or performance-enhancing actions (referred to sometimes herein simply as “actions”) to perform to enhance RF channel quality for one or more sectors. The implementation of such actions may improve RF channel quality for UEs receiving wireless service within such sectors. Further, the automated determination and implementation of such actions may improve the operation of the RAN, without requiring human intervention to select and/or implement such actions. 
     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 discrete network infrastructure elements, such as 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, RAN Optimization System (“ROS”)  105  may receive (at  102 ) channel propagation metrics associated with one or more sectors  101 . The channel propagation metrics may include, for example, measures of signal quality, signal strength, propagation loss, or the like, at given sectors  101  and/or locations within sectors  101 . Such measures may include and/or may be based on, for example, Reference Signal Receive Power (“RSRP”) values, Received Signal Strength Indicator (“RSSI”) values, Reference Signal Received Quality (“RSRQ”) values, Signal-to-Interference-and-Noise-Ratio (“SINR”) values, Channel Quality Indicator (“CQI”) values, UE power headroom, or the like. 
     In some embodiments, such measures may be included in, and/or derived from, information generated by and/or received from UEs  107  located within sectors  101 . For example, a given UE  107  may scan for the presence of one or more base stations  103 , and may generate a measurement report including identifiers of detected base stations  103 , frequencies or frequency bands on which RF signals (e.g., pilot signals, reference signals, or other RF signals) were detected from base stations  103 , radio access technologies (“RATs”) associated with detected RF signals (e.g., a LTE RAT, a 5G RAT, or the like), and/or one or more measures of RF signal or RF channel quality associated with the detected RF signals. For example, a given measurement report may indicate that one or more reference signals were received from a particular base station  103 , and may indicate one or more channel propagation metrics (e.g., RSRP values, RSRQ values, SINR values, CQI values, or other values indicating characteristics of the detected RF signals) associated with the detected RF signals. Generally, “higher” channel propagation metrics may be associated with “stronger” or “less lossy” RF signals between a given UE  107  and base station  103  (e.g., channels  109 ), while “lower” channel propagation metrics may be associated with “weaker” or “more lossy” channels  109 . 
     A “channel,” as used herein, may refer to RF transmissions between a particular base station  103  and UE  107  (e.g., “downlink” RF signals transmitted from base station  103  to UE  107  and/or “uplink” RF signals transmitted from UE  107  to base station  103 ). For example, as shown in  FIG. 1 , UE  107 - 2  may be associated with channels  109 - 1  and  107 - 2  (indicated in the figure by dashed lines) between two respected base stations  103 . While not explicitly shown in this figure, other channels may be associated with UE  107 - 2  and one or more other base stations  103 . In some embodiments, a given UE  107 -base station  103  may be associated with multiple channels, such as one or more uplink channels and one or more downlink channels. In some embodiments, the channel(s)  109  between a given UE  107  and one or more base stations  103  may include a Physical Uplink Shared Channel (“PUSCH”), a Physical Uplink Control Channel (“PUCCH”), a Physical Downlink Shared Channel (“PDSCH”), a Physical Downlink Control Channel (“PDCCH”), and/or one or more other channels. In some embodiments, the channel(s)  109  between a given UE  107  and one or more base stations  103  may include different channels associated with different frequencies, frequency bands, and/or RATs. While example channels  109 - 1  and  109 - 2  are shown in  FIG. 1  between UE  107 - 2  and two base stations  103 , in practice other channels  109  may be present between other UEs  107  and other base stations  103 . 
     Relatively low channel propagation metrics (e.g., a relatively low RSRQ value, RSRP value, etc.) for a given channel  109  may indicate that the propagation of RF signals between a respective base station  103  and UE  107  is relatively weak, which may be caused by a relatively large distance between base station  103  and UE  107 , obstacles in a line of sight between base station  103  and UE  107  (e.g., buildings, topographical features, etc.), interference (e.g., colliding RF signals from other sources, such as other base stations  103 , drones, aviation equipment, or other RF-emitting devices), beamforming or other configuration parameters of base station  103  (e.g., directionality and/or transmit power associated with one or more antennas of base station  103 ), or other phenomena. 
     For example, a particular measurement report from a given UE  107  may indicate that UE  107  detected RF signals from one or more base stations  103  (e.g., via one or more respective channels  109  between UE  107  and the one or more base stations  103 ) of a given sector  101 . As an example, the measurement report from UE  107  may indicate a relatively low RSSI value, RSRP value, etc. associated with signals from base station  103 . Such values may be “relatively” low in that such values may be below a threshold value, and/or may be lower than an expected or threshold value (e.g., which may be determined based on a historical analysis of propagation metrics associated with sector  101 ). Further, such analysis may be performed based on location, where a first threshold value may be used at a first location (e.g., relatively close to base station  103 ), while a second threshold value may be used at a second location (e.g., farther away from base station  103 ). 
     In some embodiments, and as further discussed below with respect to  FIG. 3 , ROS  105  may further receive and/or maintain attribute and/or characteristic information for one or more sectors  101 . Briefly, such attribute and/or characteristic information may include configuration parameters (e.g., beamforming configuration parameters, RF transmission power parameters, Multiple-Input Multiple-Output (“MIMO”) configuration parameters, or the like), physical network infrastructure parameters (e.g., antenna height, antenna location, etc.), locale features (e.g., building density, topographical information, or the like), and/or other types of information associated with respective sectors  101  and/or network infrastructure associated with respective sectors  101  (e.g., network infrastructure located within given sectors  101 , and/or providing wireless service to given sectors  101 ). 
     In some embodiments, ROS  105  may communicate with base stations  103  of sectors  101  and/or UEs  107  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  107  communicatively coupled to respective base stations  103  may “push” such information to ROS  105  (e.g., via the API) on a periodic or intermittent basis, upon the occurrence of trigger events (e.g., the detection of a reference signal from one or more base stations  103  by a UE  107  located within a given sector  101 , one or more Quality of Service (“QoS”) metrics exceeding a threshold value, a connection or disconnection of one or more UEs  107  to one or more base stations  103 , and/or other events), and/or on some other basis. In some embodiments, ROS  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, ROS  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. 
     As further shown, ROS  105  may determine (at  104 ) one or more sector models associated with respective sectors  101 , as well as channel propagation models associated with respective sectors  101 , based on the received channel propagation metrics. For example, as discussed below, ROS  105  may use AI/ML techniques or other suitable techniques to identify one or more sector models that includes attributes that are similar to the attributes associated with respective sectors  101 . For example, when determining whether attributes of a given sector model are “similar” to attributes of a given sector  101 , ROS  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 attributes of a set of sector models and attributes of a given sector  101 . In some embodiments, ROS  105  may select a particular sector model if the measure of similarity exceeds a threshold measure of similarity. Additionally, or alternatively, ROS  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, ROS  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, ROS  105  may further determine (at  104 ) one or more channel propagation models for one or more sectors  101  based on the sector models identified with respect to respective sectors  101 , as well as the channel propagation metrics received (at  102 ) with respect to the respective sectors  101 . Channel propagation models may indicate, for example, channel propagation metrics as a function of location within a given sector  101 . For example, determined channel propagation model for a given sector  101  may indicate that propagation metrics are relatively higher in regions that are closer to a particular base station  103  located in the given sector  101 , and that propagation metrics are relatively lower in regions that are farther away from the particular base station  103  located in sector  101 . As another example, a given channel propagation model may indicate that propagation metrics are relatively lower in a region where an obstacle such as a building is located in a line of sight between the region and a base station  103  located in sector  101 . Examples of channel propagation models are discussed in greater detail below with respect to  FIGS. 4-6 . 
     As additionally discussed below, ROS  105  may determine (at  104 ) one or more remedial and/or performance-enhancing actions to perform with respect to a given sector  101 . For example, ROS  105  may determine actions such as modifying RF parameters (e.g., RF transmission power, beamforming parameters, MIMO parameters, or the like) to improve channel propagation metrics in a given region, such as a region which has, or is expected to have (e.g., based on one or more predictive usage or load models) high demand for wireless service. As another example, the actions may include the modification of one or more handover thresholds, which may be used by base station  103  and/or UEs  107  to determine when to perform or request a handover from one base station  103  to another. In some embodiments, the actions may include any suitable action to enhance the operation of the RAN and/or of UEs  107  receiving wireless service from the RAN. 
     As noted above, the selection (at  104 ) of actions based not only on channel propagation metrics (e.g., based on channel propagation models), but also based on the characteristics and/or attributes of a sector  101  (e.g., based on sector models), may allow for channel propagation-enhancing solutions that are better tailored to sectors with particular configurations, attributes, or the like. Such solutions may be more likely to succeed and/or have more impact (e.g., increase of channel propagation metrics for channels  109  between base stations  103  and respective UEs  107 ) than actions selected solely based on the detection of particular channel propagation metrics in a given sector  101 . 
     In some embodiments, ROS  105  may receive (at  102 ) channel propagation metrics over time, and may select (at  104 ) different sets of actions (e.g., for particular sectors  101  and/or varying sets of sectors  101 ) based on different channel propagation metrics received at different times and/or time periods. As one example, a particular sector  101  may exhibit a first set of channel propagation metrics at times corresponding to a morning or afternoon weekday commute, and may exhibit a second set of channel propagation metrics at times corresponding to an evening or weekend. In this example, ROS  105  may determine (at  104 ) a first channel propagation model (or set of channel propagation models) and associated actions during morning or afternoon hours on weekdays, and may determine a second channel propagation model (or set of channel propagation models) and one or more associated actions during evening hours and/or weekends. 
     ROS  105  may further output (at  106 ) information indicating the identified actions to respective sectors  101 . ROS  105  may, for example, indicate the determined actions 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. For the sake of brevity, the performance of a given action by a network infrastructure element located in or serving sector  101  (e.g., base station  103  or some other suitable device or system) will be referred to herein as sector  101  “performing” the action. 
     Respective sectors  101  may perform (at  108 ) the indicated actions, and ROS  105  may continue to receive (at  102 ) up-to-date channel propagation metrics associated with sectors  101 . ROS  105  may, based on continuing to receive the up-to-date channel propagation metrics, modify the determination of channel propagation models associated with a particular sector  101 . In some embodiments, ROS  105  may select a new set of actions for sector  101  based on the up-to-date channel propagation metrics. In some embodiments, ROS  105  may modify one or more sector models, channel propagation models, and/or other information based on whether the performed (at  108 ) actions improved channel propagation metrics, and/or based on how much effect the actions had on such metrics. 
     While described in the context of being performed by ROS  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, ROS  105 . For example, in some embodiments, one or more devices or systems of sector  101  may identify a particular action based on a given sector model and/or channel propagation model, and/or based on continuing to monitor channel propagation metrics associated with sector  101  after performing (at  108 ) a particular action or set of actions. 
       FIG. 2  illustrates example interference models, sector models, and/or actions/parameters that may be generated, received, maintained, provided, etc. by ROS  105 . For example, ROS  105  may be associated with a set of sector models  201 , such as example sector models  201 - 1 ,  201 - 2 , and  201 -M. Further, ROS  105  may be associated with a set of channel propagation models  203 , such as example channel propagation models  203 - 1 ,  203 - 2 , and  203 -L. Additionally, ROS  105  may be associated with a set of actions/parameters  205 , such as example actions/parameters  205 - 1 ,  205 - 2 , and  205 -N. 
     ROS  105  may generate and/or modify sector models  201 , channel propagation models  203 , and/or actions/parameters  205  based on AI/ML techniques or other suitable techniques. For example, ROS  105  may generate, modify, refine, etc. sector models  201 , channel propagation models  203 , and/or actions/parameters  205  based on an evaluation of real-world data from sectors  101  and/or simulations of channel propagation metrics in a simulation and/or test environment. ROS  105  may further determine or identify correlations between respective sector models  201 , channel propagation models  203 , and/or actions/parameters  205  using AI/ML techniques or other suitable techniques. 
     For example, as shown in  FIG. 3 , sector model  201  may include RF and/or interference metrics  301  (referred to simply as “RF metrics  301 ” for the sake of brevity), Quality of Service (“QoS”) metrics  303 , energy consumption metrics  305 , RAN configuration parameters  307 , inter-sector information  309 , locale features  311 , and/or one or more other types of information. 
     RF metrics  301  associated with a given sector  101  may include metrics related to the propagation of RF signals from network infrastructure within sector  101  (or providing service to sector  101 ). For example, RF metrics  301  may include RSRP values, RSRQ values, RSSI values, SINR values, CQI values, or other indicators of RF signal quality or strength. In some embodiments, RF metrics  301  may be determined by UEs  107  or other RF signal-receiving devices located within or near (e.g., within a threshold distance of) sector  101 . 
     In some embodiments, RF metrics  301  may be computed based on values provided by UEs  107 . For example, ROS  105 , a given base station  103 , and/or some other device or system may receive a RSRP value from a particular UE  107  (e.g., indicating a RSRP value associated with a particular channel  109  between UE  107  and a particular base station  103 ), and the RSRP value may be compared to a transmit power associated with the particular channel  109  (e.g., a power at which base station  103  transmitted a reference signal for which UE  107  measured or determined the RSRP value). The difference between the RSRP value and the transmit power may indicate a measure of path loss associated with the particular channel  109 . Further, path loss (and/or other channel propagation metrics) may be determined as a function of geographical location, such that the RF metrics  301  associated with sector  101  may be indicated or determined based on a geographical location at which a particular UE  107  (or other suitable device or system) that measured or determined a given set of RF metrics (e.g., channel propagation metrics) was located when measuring such metrics. 
     As noted above, ROS  105  may receive the measurement reports and/or other suitable RF metrics  301  from UEs  107  (e.g., via an API or other suitable communication pathway), and/or from base stations  103  (e.g., via an X2 interface or other suitable communication pathway, where base stations  103  may receive measurement reports from UEs via Radio Resource Control (“RRC”) messaging or some other suitable communication pathway). In some embodiments, ROS  105  may receive RF metrics  301  from some other device or system. 
     QoS metrics  303  may reflect QoS metrics associated with a particular sector  101  over a particular period of time. For example, QoS metrics  303  may include metrics relating to latency, bandwidth, jitter, packet loss, and/or other metrics related to network layer performance, application layer performance, or other “higher” layer performance (e.g., performance at a layer above a physical layer and/or a data link layer). QoS metrics  303  associated with a given sector  101  may be collected from and/or reported by UEs  107  receiving wireless service within sector  101  and/or from a base station  103  located within sector  101 , and/or may be received from base station  103  located in or providing wireless service to sector  101 . 
     Energy consumption metrics  305  may indicate an amount of energy consumed at the particular sector  101  over the particular period of time. For example, energy consumption metrics  305  may indicate an amount of electrical power (e.g., kilowatt-hours or some other measure of consumed power) consumed by network infrastructure elements (e.g., base stations  103 , data centers, routers, hubs, and/or other equipment) within or serving sector  101  over a given period of time. 
     RAN configuration parameters  307  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  307  may indicate particular 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. In some embodiments, some or all RAN configuration parameters  307  may be determined or maintained on a granular basis, such as a per-UE basis or some other basis. For example, in some embodiments, RAN configuration parameters  307  may include a transmission power associated with a particular channel  109 , a transmission power of RF signals sent from a particular base station  103  to a particular UE  107 , a transmission power of RF signals sent by base station  103  via one or more Physical Resource Blocks (“PRBs”), or on some other basis. 
     Inter-sector information  309  may include information associated with sectors adjacent to or proximate to a given sector  101 . For example, inter-sector information  309  may include RF metrics, 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  309  may include mobility information, which may be associated with mobility of UEs between sector  101  and neighboring sectors. For example, inter-sector information  309  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  309  may indicate that UEs that are located in the neighboring sector are relatively likely to enter the particular sector  101 . 
     Locale features  311  may include information indicating attributes and/or features of the geographical area. For example, locale features  311  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 RF metrics, energy consumption metrics, QoS metrics, or other metrics. Locale features  311  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, a given sector  101  may be associated with one or more sector models  201  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  201 . Briefly, for example, ROS  105  may determine that a particular sector  101 , that exhibits a particular set of RF metrics  301 , a particular set of QoS metrics  303 , a particular set of energy consumption metrics  305 , and a first set of locale features  311  (e.g., urban features such as high-rise buildings) is associated with a first sector model  201 , while another sector  101 , that exhibits a similar set of RF metrics  301 , a similar set of QoS metrics  303 , and a similar set of energy consumption metrics  305 , but a different second set of locale features  311  (e.g., rural features such as relatively flat areas with relatively low building density) is associated with a different second sector model  201 . 
     Channel propagation models  203  may indicate, predict, include, and/or be generated based on channel propagation metrics as a function of geographical location. For example, a particular channel propagation model  203 , associated with a particular sector  101 , may indicate channel propagation metrics as a function of a distance from the center of sector  101 , as a set of latitude and longitude coordinates (e.g., where such coordinates fall within coordinates defining a boundary of sector  101 ), as an index or identifier of discretely defined regions of sector  101 , or in some other suitable manner. 
       FIGS. 4-6  conceptually illustrate example channel propagation information that may be included in, or reflected by, channel propagation models  203  of some embodiments. For instance, as shown in  FIG. 4 , example channel propagation model  400  may include channel propagation metrics associated with a particular sector  101  on the basis of sub-sectors  401 , where a shape of sub-sectors  401  is defined as a hexagon, and/or having the same shape as sector  101  itself. While  FIG. 4  shows an expanded view of some sub-sectors  401  of sector  101 , in practice, additional sub-sectors  401  may be present in sector  101 . 
     In the examples herein, darker shading (e.g., as present in sub-sector  401 - 3 ) may represent relatively higher channel propagation metrics (e.g., higher signal strength, higher channel quality, lower interference, etc.), while lighter shading (e.g., as present in sub-sector  401 - 1 ) may represent relatively lower channel propagation metrics (e.g., lower signal strength, lower channel quality, higher interference, etc.). The shading of sub-sector  401 - 2  may indicate channel propagation metrics that are relatively higher than those associated with sub-sector  401 - 1 , and relatively lower than those associated with sub-sector  401 - 3 . 
     In some embodiments, the shading in the figures may represent average channel propagation metrics (e.g., averages of raw values) over time, maximum channel propagation metrics in a given time period, minimum channel propagation metrics in a given time period, and/or some other computed value based on channel propagation metrics received or determined over a given time period. In some embodiments, the shading may represent a score generated based on one or more channel propagation metrics. For example, ROS  105  may generate a channel propagation score based on RSRQ values, RSRP values, RSSI values, SINR values, and/or one or more other suitable channel propagation metrics as a function of geographical location (e.g., in this example, on a per-sub-sector  401  basis). 
       FIG. 5  provides another potential representation of channel propagation model  500 , in accordance with some embodiments. In this example, sub-sectors  501  (e.g., sub-sector  501 - 1 ,  501 - 2 , and  501 - 3 ) are geographical regions within sector  101  having a square shape (e.g., a grid pattern), and/or having a different shape than sector  101  itself. As similarly discussed above with respect to  FIG. 4 , the shading associated with various sub-sectors  501  may indicate channel propagation metrics associated with respective sub-sectors  501  (e.g., over a particular time period). 
       FIG. 6  provides another example representation of channel propagation model  600 , in accordance with some embodiments. In this example, sub-sectors  601  (e.g., sub-sector  601 - 1 ,  601 - 2 , and  601 - 3 ) are geographical regions within sector  101  having no particular pattern or relationship with each other. In this example, sub-sectors  601  are circles of varying sizes. In practice, sub-sectors  601  may be defined differently. In some embodiments, sub-sectors  601  may relate to regions defined or determined automatically (e.g., using one or more AI/ML techniques or other suitable techniques), such as regions in which relatively heavy UE traffic has been detected, regions relating to physical structures (e.g., office buildings, universities, etc.), regions within a particular proximity to a particular base station  103 , etc. As similarly discussed above with respect to  FIGS. 4 and 5 , the shading associated with various sub-sectors  601  may indicate channel propagation metrics associated with respective sub-sectors  601  (e.g., over a particular time period). 
     The example channel propagation metrics reflected in  FIGS. 4-6  may each represent channel propagation metrics received or determined over particular time period (e.g., one day, one week, one month, etc.). Thus, a particular channel propagation model  203  may include multiple sets of channel propagation metrics (e.g., multiple instances of the representations shown in  FIGS. 4-6 ) for multiple time periods. That is, channel propagation model  203  may include a temporal element to reflect cyclical, periodic, intermittent, and/or otherwise repeating patterns or trends in channel propagation metrics within sector  101 . Additionally, or alternatively, sector  101  may be associated with multiple different channel propagation models  203 , where each of the channel propagation models  203  associated with sector  101  is further associated with a temporal condition, parameter, etc. 
     While the examples of  FIGS. 4-6  include representations of channel propagation metrics as a function of geographical location in two-dimensional space, in some embodiments, channel propagation models  203  may include channel propagation metrics as a function of geographical location in three-dimensional space. For example, the same two-dimensional sub-sector, region, etc. of a given sector  101  may be associated with two different sets of channel propagation metrics at different heights, altitudes, floors of a building, etc. 
     Further, while the examples of  FIGS. 4-6  use shading to represent channel propagation metrics, in practice, channel propagation metrics may be represented in some other way. For example, a three-dimensional plot may represent channel propagation metrics as a function of geographical location, where two axes of the three-dimensional plot represent latitude and longitude coordinates (or other descriptor of geographical location) and a third axis represents channel propagation metrics. For example, a higher value along the third axis may represent relatively higher channel propagation metrics at a given geographical location, and a lower value along the third axis may represent relatively lower channel propagation metrics at a given geographical location. 
     As noted above, channel propagation models  203  (e.g., the representations shown in  FIGS. 4-6 ) may be generated based on channel propagation metrics received from UEs  107 . However, situations may arise where certain portions of a particular sector  101  do not have any associated channel propagation metrics, or a relatively sparse amount of channel propagation metrics (e.g., fewer measured or derived values indicating channel propagation metrics than a threshold amount of values, fewer measured or derived values indicating channel propagation metrics than other portions of sector  101 , etc.). For example, as shown in  FIG. 7 , channel propagation model  701  for a given sector  101  may include channel propagation metrics for some sub-sectors of sector  101  (e.g., where such channel propagation metrics are determined in a manner similar to that described above). Channel propagation model  701  may further indicate a lack of channel propagation metrics for some of the sub-sectors. For example, such lack of channel propagation metrics for these sub-sectors may be based on UEs  107  not being physically located in these sub-sectors, UEs  107  being handed over to different sectors  101  (e.g., receiving wireless service from base stations  103  of other sectors  101 ), and/or for other reasons. 
     In accordance with some embodiments, ROS  105  may generate channel propagation model  703  based on channel propagation model  701 . Channel propagation model  703  may include estimated or predicted channel propagation metrics for the sub-sectors for which channel propagation metrics have not been determined. In some embodiments, ROS  105  may interpolate, extrapolate, and/or otherwise compute channel propagation metrics for these sub-sectors based on channel propagation metrics for sub-sectors for which channel propagation metrics have been determined. For example, ROS  105  may perform a regression analysis, smoothing, curve fitting, and/or other suitable analysis to generate channel propagation model  703  based on channel propagation model  701 . In some embodiments, ROS  105  may use one or more path loss models, AI/ML models, and/or other suitable types of models to compute, determine, estimate, etc. channel propagation metrics for the sub-sectors for which sufficient channel propagation metrics have not been received. 
       FIG. 8  illustrates an example determination of one or more sector models  201  for a particular sector  101 . As shown, ROS  105  may determine (at  802 ) parameters and/or attributes of sector  101 . As discussed above, such parameters and/or attributes may include RF metrics  301 , QoS metrics  303 , energy consumption metrics  305 , RAN configuration parameters  307 , inter-sector information  309 , locale features  311 , and/or other suitable parameters, attributes, metrics, or the like. ROS  105  may further identify (at  804 ) one or more sector models  201  based on the determined parameters and/or attributes of sector  101 . 
     In this example, ROS  105  may determine that sector  101  is associated with a “highway” sector model  801 - 1  and a “media streaming” sector model  801 - 3 . As further shown, ROS  105  may not determine that sector  101  is associated with an example “office building” sector model  801 - 2 , or an example “dense buildings” sector model  801 - 4 . For example, ROS  105  may determine, based on a suitable similarity analysis of the parameters and/or attributes of sector  101 , that sector models  801 - 2  and  801 - 4  do not match (e.g., correspond with a measure of similarity above a threshold measure of similarity) sector  101 , and/or that sector models  801 - 1  and  801 - 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  802  and  804  may be performed on an ongoing basis, such that the selection of particular sector models  801  may change based on updated parameters and/or attributes received by ROS  105  over time. 
     As shown in  FIG. 9 , ROS  105  may determine (at  902 ) a set of actions  205  for a given sector  101  based on one or more determined sector models  201  for sector  101 , as well as one or more determined channel propagation models  203  for sector  101 . ROS  105  may use one or more AI/ML techniques to determine an appropriate channel propagation model  203  (e.g., channel propagation model  203 - 2 , in this example) to represent the channel propagation metrics associated with sector  101  (e.g., as a function of geographical location within sector  101 ), where the selection is further based on attributes of sector  101  (e.g., sector model  201 ). 
     As noted above, ROS  105  may generate, maintain, refine, etc. (e.g., using one or more AI/ML techniques or other suitable techniques) one or more associations between respective channel propagation models  203  and one or more sets of actions/parameters  205 . In some embodiments, such associations may be multi-factor associations. For example, a first set of actions/parameters  205  may be associated with a particular channel propagation model  203  and a first sector model  201 , while a second set of actions/parameters  205  may be associated with the same channel propagation model  203  and a second sector model  201 . As another example, a first set of actions/parameters  205  may be associated with a first channel propagation model  203  and a particular sector model  201 , while a second set of actions/parameters  205  may be associated with a second channel propagation model  203  and the same sector model  201 . 
     For example, each sector model  201 -channel propagation model  203  pair 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 the performance (e.g., increasing channel propagation metrics) of one or more sectors  101  (and/or of particular sub-sectors of sectors  101 ) that match a particular sector  101  that is associated with a particular sector model  201  and channel propagation model  203 . As noted above, actions/parameters  205  may include modifying RF signal transmit power of one or more antennas and/or base stations  103  (e.g., on a per-UE basis and/or on some other basis), modifying beamforming parameters (e.g., modifying a coverage area of one or more base stations  103 ), and/or other suitable actions to enhance the propagation of RF signals to appropriate locations (e.g., sub-sectors or other locations at which UEs are located and/or are expected or predicted to be located). 
     In some embodiments, ROS  105  may also determine affinity scores and/or other correlations between sector models  201 , channel propagation models  203 , and respective sets of actions/parameters  205 . Such affinity scores may generally indicate how effective a given set of actions/parameters  205  are for enhancing channel propagation metrics in particular sector  101  (e.g., at particular sub-sectors), given sector model  201  and channel propagation model  203  associated with sector  101 . When selecting a particular channel propagation model  203  for sector  101  based on received or determined channel propagation metrics, ROS  105  may select such channel propagation model  203  based on affinities, scores, correlations, or the like between sector model  201  and channel propagation model  203 . 
     Similarly, when determining a particular set of actions/parameters  205  for a particular sector  101 , ROS  105  may select the particular set of actions/parameters  205  from candidate sets of actions/parameters  205  based on an affinity, score, correlation, etc. between sets of actions/parameters  205  and sector model  201  and/or channel propagation model  203 . 
       FIG. 10  illustrates an example implementation of a particular set of actions  205  by a particular base station  103 . For example, base station  103  may receive (at  1002 ) a set of actions  205  from ROS  105 . For example, ROS  105  may have determined actions  205  based on one or more sector models  201  associated with a sector  101  in which base station  103  is located, channel propagation metrics received from UEs  107  located in sector  101  and/or from base station  103 , and/or one or more other factors. Additionally, or alternatively, base station  103  may determine a particular set of actions  205  to perform. For example, ROS  105  may provide one or more sector models  201  associated with sector  101  to base station  103  (and/or to some other device or system that communicates with base station  103 , such as an orchestration platform), and base station  103  may determine actions  205  based on the received sector models  201 . 
     In this example, assume that UE  107  is located in sub-sector  1003 - 1 , which may be a sub-sector of sector  101  in which base station  103  is located (and/or in which wireless service may be received from base station  103 ). Prior to the implementing (at  1004 ) of actions  205 , sub-sector  1003 - 1  may be a region in which relatively low channel propagation metrics (e.g., depicted in the figure as “low signal strength”) are associated. In some embodiments, the relatively low channel propagation metrics may be determined or indicated based on a particular channel propagation model  203  that has been identified with respect to base station  103  and/or sector  101  associated with base station  103 . That is, the “low signal strength” may be based on predicted or estimated values based on channel propagation model  203  in addition to, or in lieu of, real-world measured metrics. 
     Further, in this example, assume that base station  103 , ROS  105 , and/or some other device or system determines, estimated, predicts, etc. (e.g., based on one or more AI/ML, models, predictive models, threshold-based criteria, sector model  201  associated with sector  101 , and/or other suitable techniques) that channel propagation metrics associated with sub-sector  1003 - 1  are below a threshold, and/or that such channel propagation metrics should be increased. For example, it may be determined, estimated, predicted, etc. that demand for wireless service within sub-sector  1003 - 1  may increase and/or may be relatively high. For example, base station  103  may determine that sub-sector  1003 - 1  includes an office building in which a relatively large quantity of UEs  107  are located during particular hours of the day and/or days of the week. Accordingly, base station  103  may determine that channel propagation metrics associated with sub-sector  1003 - 1  should be increased to meet the estimated, predicted, etc. demand (e.g., to enhance the user experience of UEs  107  located within sub-sector  1003 - 1 ). 
     Accordingly, base station  103  may implement (at  1004 ) actions  205  to improve channel propagation metrics at sub-sector  1003 - 1 . For example, as discussed above, base station  103  may allocate additional power to RF transmissions directed toward sub-sector  1003 - 1 , may modify a beamforming configuration (e.g., tilt angle and/or azimuth angle) to point one or more antennas toward sub-sector  1003 - 1 , allocate additional power to RF transmissions directed toward particular UEs  107  located within sub-sector  1003 - 1  (e.g., on a per-PRB basis and/or some other basis), and/or may perform other suitable actions. 
     In some embodiments, performing (at  1004 ) actions  205  to increase channel propagation metrics associated with sub-sector  1003 - 1  may affect channel propagation metrics of one or more other sub-sectors. In this example, the actions taken to increase channel propagation metrics associated with sub-sector  1003 - 1  may decrease channel propagation metrics associated with sub-sector  1003 - 2 . For example, actions  205  may include pointing one or more antennas, which were previously pointed towards sub-sector  1003 - 2 , toward sub-sector  1003 - 1 . In some embodiments, base station  103 , ROS  105 , and/or some other device or system may determine that demand for wireless service is greater at sub-sector  1003 - 1  than at sub-sector  1003 - 2 , and/or that channel propagation metrics associated with sub-sector  1003 - 1  should be prioritized over channel propagation metrics associated with sub-sector  1003 - 2  based on one or more other factors. 
     While one example of actions  205  is provided in  FIG. 10 , as noted above, actions  205  may include any suitable actions to modify (e.g., increase) channel propagation metrics associated with a particular sector  101  or sub-sectors thereof. In some embodiments, actions  205  may include modifying handover thresholds in a particular sector  101  or sub-sectors thereof. For example, base station  103  may initiate a handover of a given UE  107  when base station  103  determines that UE  107  is located in, or is heading towards, a sub-sector that is associated with relatively low channel propagation metrics (e.g., as indicated by channel propagation model  203 ). 
       FIG. 11  illustrates an example process  1100  for modifying parameters of a RAN based on one or more channel propagation models identified with respect to one or more sectors  101  of the RAN, in accordance with some embodiments. In some embodiments, some or all of process  1100  may be performed by ROS  105 . In some embodiments, one or more other devices may perform some or all of process  1100  in concert with, and/or in lieu of, ROS  105 , such as base station  103 , a controller or orchestration platform associated with one or more network infrastructure elements associated with the RAN, and/or some other suitable device or system. 
     As shown, process  1100  may include generating, receiving, and/or modifying (at  1102 ) one or more sector models  201  based on metrics, parameters, etc. associated with one or more sectors  101  of a wireless network. For example, as discussed above, ROS  105  may use AI/ML techniques or other suitable techniques to generate and/or refine sector models  201 . For example, ROS  105  may evaluate metrics based on real-word and/or simulated metrics 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  201 . In one example, as referred to above, RF metrics  301  may be a factor based on which a particular sector model  201  is determined. In this manner, other sector attributes of a given sector  101  may be estimated, inferred, determined, etc. based on RF metrics  301  for the given sector  101 . For example, ROS  105  may determine that if RF metrics  301  for a first sector  101  are the same or are similar (e.g., at least a threshold measure of similarity based on a suitable similarity analysis) as RF metrics  301  for a second sector  101 , that the first and second sectors  101  have similar attributes. In this manner, ROS  105  may infer, estimate, identify, etc. attributes of a sector  101  with one or more unknown attributes by comparing RF metrics  301  (and/or other attributes) to another sector  101  with known attributes (e.g., the presence of tall buildings, topographical features, UE mobility patterns, etc.). 
     Process  1100  may further include generating, receiving, and/or modifying (at  1104 ) channel propagation models  203  indicating particular channel propagation metrics as a function of location. For example, as discussed above, ROS  105  may evaluate channel propagation metrics, such as RSRP values, RSRQ values, SINR values, CQI values, and/or other suitable values (e.g., values derived from or computed based on the above metrics and/or other metrics). Such channel propagation metrics may be received from UEs  107  connected to one or more base stations  103  (e.g., real-world measured or computed metrics), from one or more base stations  103  and/or other wireless network infrastructure elements, and/or may be generated or received based on a simulation of a RAN (e.g., in which channel conditions between one or more UEs  107  and one or more base stations  103  are simulated). 
     The channel propagation metrics may also include an indication of a geographical location at which such channel propagation metrics were measured and/or simulated. For example, the geographical location may be an absolute location (e.g., latitude and longitude coordinates and/or some other type of indicator of absolute location) and/or a location relative to a reference point (e.g., a distance and/or azimuth angle from a reference point, such as the location of a particular base station  103 , a particular reference point (e.g., the center, an edge, etc.) associated with a particular sector  101 , etc.). Example representations of channel propagation models  203  are provided above with respect to  FIGS. 4-6 , depicting particular channel propagation metrics as a function of location within a given sector  101 . 
     Process  1100  may additionally include determining (at  1106 ) associations between respective sector models  201  and channel propagation models  203 . For example, ROS  105  may perform one or more AI/ML techniques, such as deep learning, reinforced or unreinforced machine learning, neural networks, K-means clustering, tree-based ML techniques (e.g., XGBoost, random forest, and/or other tree-based techniques), regression analysis, and/or other suitable techniques to identify particular sets of channel propagation metrics as a function of location (e.g., as indicated by channel propagation models  203 ) with sectors  101  having particular attributes (e.g., as indicated by sector models  201 ). 
     Generally, sectors  101  with relatively similar attributes may exhibit similar channel propagation metrics as a function of location within such sectors  101 . For example, a first sector  101  with a ten-story building within 100 meters of a base station  103  implementing a 5G RAT may exhibit similar channel propagation metrics (e.g., as a function of location, such as a function of distance and/or angle from base station  103 ) as a second sector  101  that also includes a ten-story building within 100 meters of a base station  103  implementing a 5G RAT within the second sector  101 . As another example, a first sector  101  with a ten-story building within 100 meters of a base station  103  implementing a 5G RAT may exhibit similar channel propagation metrics (e.g., as a function of location, such as a function of distance and/or angle from base station  103 ) as a second sector  101  that includes a fifteen-story building within 150 meters of a base station  103  implementing a 5G RAT within the second sector  101 . As noted above, GOS  105  may utilize one or more suitable AI/ML techniques to determine a measure of correlation, similarity, etc. between sector models  201  associated with sectors  101  in order to determine channel propagation models  203  (or portions thereof) that are associated with multiple sectors  101  having relatively similar (e.g., within a threshold measure of similarity) attributes. 
     Process  1100  may also include determining (at  1108 ) a particular sector model  201  associated with a particular sector  101  based on attributes of the particular sector  101 . For example, ROS  105  may evaluate attributes of one or more sectors  101  to determine whether to perform actions to enhance channel propagation metrics with such sectors  101 . For example, such evaluation may be performed during a “network planning” phase, in which simulations are performed in a simulated environment to analyze various configurations or configuration changes to a RAN. As another example, such evaluation may be performed on a “live” or “deployed” RAN to identify sectors  101  that are exhibiting channel propagation metrics (or other metrics) below a threshold level. As yet another example, such evaluation may be performed on a “live” or “deployed” RAN to enhance the overall operation of the RAN (e.g., to enhance channel propagation metrics) as an ongoing process, even if channel propagation metrics associated with the RAN are above a threshold level. As noted above, ROS  105  may receive attributes, characteristics, etc. of particular sectors  101  of the RAN from base stations  103  within (or providing wireless service to) sectors  101 , UEs  107  within sectors  101 , an orchestration platform associated with sectors  101 , and/or some other device or system. Examples of types of attributes, characteristics, etc. are discussed above with respect to  FIG. 3 . 
     Process  1100  may further include selecting (at  1110 ) a particular channel propagation model for a particular sector  101  based on one or more sector models  201  determined for sector  101 . For example, ROS  105  may identify one or more channel propagation models  203  that were identified (e.g., at  1106 ) as being associated with one or more sector models  201  associated with sector  101 . In some embodiments, ROS  105  may select multiple channel propagation models  203  for a single sector  101 . For example, ROS  105  may determine that a given sector model  201  is associated with two example channel propagation models  203 : a first channel propagation model  203  for a first portion of sector  101  (e.g., a first set of sub-sectors), and a second channel propagation model  203  for a second portion of sector  101  (e.g., a second set of sub-sectors). In this manner, channel propagation models  203  may be used in a more granular manner than on a per-sector basis. 
     Process  1100  may additionally include determining (at  1112 ) a set of actions  205  based on the identified channel propagation model  203  and sector model  201  for sector  101 . For example, depending on the attributes, characteristics, etc. of sector  101 , different actions  205  may be possible and/or may yield different results in order to increase channel propagation metrics at particular portions of sector  101 . For example, one sector  101  may have available transmit power headroom to increase the power of RF transmissions to a particular portion of sector  101 , without reducing power of RF transmissions to other portions of sector  101 , while another sector  101  may not have such headroom available. In the latter example, one or more different or additional actions  205  may be determined, such as changing the directionality of one or more antennas to point towards a given portion of sector  101 . 
     As another example, such actions  205  may include the identification of increased demand for wireless service at a given location within sector  101 . For example, such increased demand may be determined based on an identification of a set of UEs  107  that are, or expected to be, at a given location within sector  101  (e.g., based on one or more predictive models of UE location, based on historical UE locations within sector  101 , based on the occurrence of a scheduled event within sector  101  (e.g., a sporting event, a bus departure, a concert, etc.), and/or based on some other type of estimation or prediction). ROS  105  may determine, based on such identification of increased demand, that channel propagation metrics associated with the given location within sector  101  should be increased. In some embodiments, ROS  105  may determine that such increase should be temporary (e.g., based on the end of a scheduled event, based on a set duration after the action  205  is performed, or some other temporary basis). For example, after action  205  is performed, ROS  105  may determine that action  205  should be partially or completely reverted at some later time. In some embodiments, as noted above, other actions  205  may be performed in order to enhance channel propagation metrics within a given sector  101 . 
     Process  1100  may also include implementing (at  1114 ) the set of actions  205  at sector  101 . For example, base station  103  and/or some other device or system within (or providing wireless service to) sector  101  may implement the identified set of actions. 
     As shown in  FIG. 11 , some or all of process  1100  may be performed and/or repeated iteratively. For example, some or all of operations  1108 - 1114  may be repeated and/or performed, in order to continuously (e.g., on an ongoing basis) remediate potential channel propagation issues, and/or otherwise enhance channel propagation metrics, within a given sector  101 . That is, the results of implementing (at  1114 ) particular actions in response to particular channel propagation models  203  associated with particular sector models  201  may be evaluated. Further, the associations between sector models  201 , channel propagation models  203 , and sets of actions/parameters  205  may be modified (e.g., strengthened or weakened) based on whether particular actions  205  improved channel propagation metrics within sector  101 . For example, if a particular action  205  increased channel propagation metrics at a given location within sector  101  (e.g., a location at which increased demand for wireless service is predicted or determined), an affinity score between particular action  205  and an appropriate sector model  201  and/or channel propagation model  203  for sector  101  may be increased. If, on the other hand, a particular action  205  decreased and/or did not affect channel propagation metrics within sector  101 , an affinity score between particular action  205  and an appropriate sector model  201  and/or channel propagation model  203  for sector  101  may be decreased, thus reducing or eliminating the likelihood that the same action  205  is selected in future instances of similar types of channel propagation metrics detected at sectors having similar attributes as particular sector  101 . 
       FIG. 12  illustrates an example environment  1200 , in which one or more embodiments may be implemented. In some embodiments, environment  1200  may correspond to a Fifth Generation (“5G”) network, and/or may include elements of a 5G network. In some embodiments, environment  1200  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  1200  may include UE  1201 , RAN  1210  (which may include one or more Next Generation Node Bs (“gNBs”)  1211 ), RAN  1212  (which may include one or more one or more evolved Node Bs (“eNBs”)  1213 ), and various network functions such as Access and Mobility Management Function (“AMF”)  1215 , Mobility Management Entity (“MME”)  1216 , Serving Gateway (“SGW”)  1217 , Session Management Function (“SMF”)/Packet Data Network (“PDN”) Gateway (“PGW”)-Control plane function (“PGW-C”)  1220 , Policy Control Function (“PCF”)/Policy Charging and Rules Function (“PCRF”)  1225 , Application Function (“AF”)  1230 , User Plane Function (“UPF”)/PGW-User plane function (“PGW-U”)  1235 , Home Subscriber Server (“HSS”)/Unified Data Management (“UDM”)  1240 , and Authentication Server Function (“AUSF”)  1245 . Environment  1200  may also include one or more networks, such as Data Network (“DN”)  1250 . Environment  1200  may include one or more additional devices or systems communicatively coupled to one or more networks (e.g., DN  1250 ), such as ROS  105 . 
     The example shown in  FIG. 12  illustrates one instance of each network component or function (e.g., one instance of SMF/PGW-C  1220 , PCF/PCRF  1225 , UPF/PGW-U  1235 , HSS/UDM  1240 , and/or  1245 ). In practice, environment  1200  may include multiple instances of such components or functions. For example, in some embodiments, environment  1200  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  1220 , PCF/PCRF  1225 , UPF/PGW-U  1235 , HSS/UDM  1240 , and/or  1245 , while another slice may include a second instance of SMF/PGW-C  1220 , PCF/PCRF  1225 , UPF/PGW-U  1235 , HSS/UDM  1240 , and/or  1245 ). 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. 12 , is provided for explanatory purposes only. In practice, environment  1200  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. 12 . For example, while not shown, environment  1200  may include devices that facilitate or enable communication between various components shown in environment  1200 , such as routers, modems, gateways, switches, hubs, etc. Alternatively, or additionally, one or more of the devices of environment  1200  may perform one or more network functions described as being performed by another one or more of the devices of environment  1200 . Devices of environment  1200  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  1200  may be physically integrated in, and/or may be physically attached to, one or more other devices of environment  1200 . 
     UE  1201  may include a computation and communication device, such as a wireless mobile communication device that is capable of communicating with RAN  1210 , RAN  1212 , and/or DN  1250 . UE  1201  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  1201  may send traffic to and/or receive traffic (e.g., user plane traffic) from DN  1250  via RAN  1210 , RAN  1212 , and/or UPF/PGW-U  1235 . 
     RAN  1210  may be, or may include, a 5G RAN that includes one or more base stations (e.g., one or more gNBs  1211 ), via which UE  1201  may communicate with one or more other elements of environment  1200 . UE  1201  may communicate with RAN  1210  via an air interface (e.g., as provided by gNB  1211 ). For instance, RAN  1210  may receive traffic (e.g., voice call traffic, data traffic, messaging traffic, signaling traffic, etc.) from UE  1201  via the air interface, and may communicate the traffic to UPF/PGW-U  1235 , and/or one or more other devices or networks. Similarly, RAN  1210  may receive traffic intended for UE  1201  (e.g., from UPF/PGW-U  1235 , AMF  1215 , and/or one or more other devices or networks) and may communicate the traffic to UE  1201  via the air interface. In some embodiments, base station  103  may be, may include, and/or may be implemented by one or more gNBs  1211 . 
     RAN  1212  may be, or may include, a LTE RAN that includes one or more base stations (e.g., one or more eNBs  1213 ), via which UE  1201  may communicate with one or more other elements of environment  1200 . UE  1201  may communicate with RAN  1212  via an air interface (e.g., as provided by eNB  1213 ). For instance, RAN  1210  may receive traffic (e.g., voice call traffic, data traffic, messaging traffic, signaling traffic, etc.) from UE  1201  via the air interface, and may communicate the traffic to UPF/PGW-U  1235 , and/or one or more other devices or networks. Similarly, RAN  1210  may receive traffic intended for UE  1201  (e.g., from UPF/PGW-U  1235 , SGW  1217 , and/or one or more other devices or networks) and may communicate the traffic to UE  1201  via the air interface. In some embodiments, base station  103  may be, may include, and/or may be implemented by one or more eNBs  1213 . 
     AMF  1215  may include one or more devices, systems, Virtualized Network Functions (“VNFs”), etc., that perform operations to register UE  1201  with the 5G network, to establish bearer channels associated with a session with UE  1201 , to hand off UE  1201  from the 5G network to another network, to hand off UE  1201  from the other network to the 5G network, manage mobility of UE  1201  between RANs  1210  and/or gNBs  1211 , and/or to perform other operations. In some embodiments, the 5G network may include multiple AMFs  1215 , which communicate with each other via the N14 interface (denoted in  FIG. 12  by the line marked “N14” originating and terminating at AMF  1215 ). 
     MME  1216  may include one or more devices, systems, VNFs, etc., that perform operations to register UE  1201  with the EPC, to establish bearer channels associated with a session with UE  1201 , to hand off UE  1201  from the EPC to another network, to hand off UE  1201  from another network to the EPC, manage mobility of UE  1201  between RANs  1212  and/or eNBs  1213 , and/or to perform other operations. 
     SGW  1217  may include one or more devices, systems, VNFs, etc., that aggregate traffic received from one or more eNBs  1213  and send the aggregated traffic to an external network or device via UPF/PGW-U  1235 . Additionally, SGW  1217  may aggregate traffic received from one or more UPF/PGW-Us  1235  and may send the aggregated traffic to one or more eNBs  1213 . SGW  1217  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  1210  and  1212 ). 
     SMF/PGW-C  1220  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  1220  may, for example, facilitate in the establishment of communication sessions on behalf of UE  1201 . In some embodiments, the establishment of communications sessions may be performed in accordance with one or more policies provided by PCF/PCRF  1225 . 
     PCF/PCRF  1225  may include one or more devices, systems, VNFs, etc., that aggregate information to and from the 5G network and/or other sources. PCF/PCRF  1225  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  1225 ). 
     AF  1230  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  1235  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  1235  may receive user plane data (e.g., voice call traffic, data traffic, etc.), destined for UE  1201 , from DN  1250 , and may forward the user plane data toward UE  1201  (e.g., via RAN  1210 , SMF/PGW-C  1220 , and/or one or more other devices). In some embodiments, multiple UPFs  1235  may be deployed (e.g., in different geographical locations), and the delivery of content to UE  1201  may be coordinated via the N9 interface (e.g., as denoted in  FIG. 12  by the line marked “N9” originating and terminating at UPF/PGW-U  1235 ). Similarly, UPF/PGW-U  1235  may receive traffic from UE  1201  (e.g., via RAN  1210 , SMF/PGW-C  1220 , and/or one or more other devices), and may forward the traffic toward DN  1250 . In some embodiments, UPF/PGW-U  1235  may communicate (e.g., via the N4 interface) with SMF/PGW-C  1220 , regarding user plane data processed by UPF/PGW-U  1235 . 
     HSS/UDM  1240  and AUSF  1245  may include one or more devices, systems, VNFs, etc., that manage, update, and/or store, in one or more memory devices associated with AUSF  1245  and/or HSS/UDM  1240 , profile information associated with a subscriber. AUSF  1245  and/or HSS/UDM  1240  may perform authentication, authorization, and/or accounting operations associated with the subscriber and/or a communication session with UE  1201 . 
     DN  1250  may include one or more wired and/or wireless networks. For example, DN  1250  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  1201  may communicate, through DN  1250 , with data servers, other UEs  1201 , and/or to other servers or applications that are coupled to DN  1250 . DN  1250  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  1250  may be connected to one or more devices, such as content providers, applications, web servers, and/or other devices, with which UE  1201  may communicate. 
     ROS  105  may include one or more devices, systems, VNFs, etc. that perform one or more operations described above. For example, ROS  105  may generate and/or maintain sector models  201 , channel propagation models  203 , and/or sets of actions and/or parameters  205 . Further ROS  105  may determine associations between respective sector models  201 , channel propagation models  203 , and/or sets of actions and/or parameters  205 . ROS  105  may identify particular sectors  101  to be remediated, improved, etc., and may identify sector models  201 , channel propagation models  203 , and/or actions  205  to perform with respect to such sectors  101 , as described above. ROS  105  may communicate with gNBs  911  and/or eNBs  913  via an X2 interface, may receive UE information and/or other network information from HSS/UDM  940  via a suitable API or other communication pathway, and/or may communicate with UEs  107  via gNBs  911  and/or eNBs  913 . 
       FIG. 13  illustrates an example Distributed Unit (“DU”) network  1300 , which may be included in and/or implemented by one or more RANs (e.g., RAN  1210 , RAN  1212 , or some other RAN). In some embodiments, a particular RAN may include one DU network  1300 . In some embodiments, a particular RAN may include multiple DU networks  1300 . In some embodiments, DU network  1300  may correspond to a particular gNB  1211  of a 5G RAN (e.g., RAN  1210 ). In some embodiments, DU network  1300  may correspond to multiple gNBs  1211 . In some embodiments, DU network  1300  may correspond to one or more other types of base stations of one or more other types of RANs. As shown, DU network  1300  may include Central Unit (“CU”)  1305 , one or more Distributed Units (“DUs”)  1303 - 1  through  1303 -N (referred to individually as “DU  1303 ,” or collectively as “DUs  1303 ”), and one or more Radio Units (“RUs”)  1301 - 1  through  1301 -M (referred to individually as “RU  1301 ,” or collectively as “RUs  1301 ”). 
     CU  1305  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. 12 , such as AMF  1215  and/or UPF/PGW-U  1235 ). In the uplink direction (e.g., for traffic from UEs  1201  to a core network), CU  1305  may aggregate traffic from DUs  1303 , and forward the aggregated traffic to the core network. In some embodiments, CU  1305  may receive traffic according to a given protocol (e.g., Radio Link Control (“RLC”)) from DUs  1303 , 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  1303 . 
     In accordance with some embodiments, CU  1305  may receive downlink traffic (e.g., traffic from the core network) for a particular UE  1201 , and may determine which DU(s)  1303  should receive the downlink traffic. DU  1303  may include one or more devices that transmit traffic between a core network (e.g., via CU  1305 ) and UE  1201  (e.g., via a respective RU  1301 ). DU  1303  may, for example, receive traffic from RU  1301  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  1303  may receive traffic from CU  1305  at the second layer, may process the traffic to the first layer, and provide the processed traffic to a respective RU  1301  for transmission to UE  1201 . 
     RU  1301  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  1201 , one or more other DUs  1303  (e.g., via RUs  1301  associated with DUs  1303 ), and/or any other suitable type of device. In the uplink direction, RU  1301  may receive traffic from UE  1201  and/or another DU  1303  via the RF interface and may provide the traffic to DU  1303 . In the downlink direction, RU  1301  may receive traffic from DU  1303 , and may provide the traffic to UE  1201  and/or another DU  1303 . 
     RUs  1301  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”)  1307 . For example, RU  1301 - 1  may be communicatively coupled to MEC  1307 - 1 , RU  1301 -M may be communicatively coupled to MEC  1307 -M, DU  1303 - 1  may be communicatively coupled to MEC  1307 - 2 , DU  1303 -N may be communicatively coupled to MEC  1307 -N, CU  1305  may be communicatively coupled to MEC  1307 - 3 , and so on. MECs  1307  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  1201 , via a respective RU  1301 . 
     For example, RU  1301 - 1  may route some traffic, from UE  1201 , to MEC  1307 - 1  instead of to a core network (e.g., via DU  1303  and CU  1305 ). MEC  1307 - 1  may process the traffic, perform one or more computations based on the received traffic, and may provide traffic to UE  1201  via RU  1301 - 1 . In this manner, ultra-low latency services may be provided to UE  1201 , as traffic does not need to traverse DU  1303 , CU  1305 , and an intervening backhaul network between DU network  1300  and the core network. In some embodiments, MEC  1307  may include, and/or may implement, some or all of the functionality described above with respect to ROS  105 . 
       FIG. 14  illustrates an example O-RAN environment  1400 , which may correspond to RAN  1210 , RAN  1212 , and/or DU network  1300 . For example, RAN  1210 , RAN  1212 , and/or DU network  1300  may include one or more instances of O-RAN environment  1400 , and/or one or more instances of O-RAN environment  1400  may implement RAN  1210 , RAN  1212 , DU network  1300 , and/or some portion thereof. As shown, O-RAN environment  1400  may include Non-Real Time Radio Intelligent Controller (“RIC”)  1401 , Near-Real Time RIC  1403 , O-eNB  1405 , O-CU-Control Plane (“O-CU-CP”)  1407 , O-CU-User Plane (“O-CU-UP”)  1409 , O-DU  1411 , O-RU  1413 , and O-Cloud  1415 . In some embodiments, O-RAN environment  1400  may include additional, fewer, different, and/or differently arranged components. 
     In some embodiments, some or all of the elements of O-RAN environment  1400  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  1400  may be implemented by, and/or communicatively coupled to, one or more MECs  1307 . 
     Non-Real Time RIC  1401  and Near-Real Time RIC  1403  may receive performance information (and/or other types of information) from one or more sources, and may configure other elements of O-RAN environment  1400  based on such performance or other information. For example, Near-Real Time MC  1403  may receive performance information, via one or more E2 interfaces, from O-eNB  1405 , O-CU-CP  1407 , and/or O-CU-UP  1409 , and may modify parameters associated with O-eNB  1405 , O-CU-CP  1407 , and/or O-CU-UP  1409  based on such performance information. Similarly, Non-Real Time MC  1401  may receive performance information associated with O-eNB  1405 , O-CU-CP  1407 , O-CU-UP  1409 , and/or one or more other elements of O-RAN environment  1400  and may utilize machine learning and/or other higher level computing or processing to determine modifications to the configuration of O-eNB  1405 , O-CU-CP  1407 , O-CU-UP  1409 , and/or other elements of O-RAN environment  1400 . In some embodiments, Non-Real Time RIC  1401  may generate machine learning models based on performance information associated with O-RAN environment  1400  or other sources, and may provide such models to Near-Real Time RIC  1403  for implementation. 
     O-eNB  1405  may perform functions similar to those described above with respect to eNB  1213 . For example, O-eNB  1405  may facilitate wireless communications between UE  1201  and a core network. O-CU-CP  1407  may perform control plane signaling to coordinate the aggregation and/or distribution of traffic via one or more DUs  1303 , which may include and/or be implemented by one or more O-DUs  1411 , and O-CU-UP  1409  may perform the aggregation and/or distribution of traffic via such DUs  1303  (e.g., O-DUs  1411 ). O-DU  1411  may be communicatively coupled to one or more RUs  1301 , which may include and/or may be implemented by one or more O-RUs  1413 . In some embodiments, O-Cloud  1415  may include or be implemented by one or more MECs  1307 , which may provide services, and may be communicatively coupled, to O-CU-CP  1407 , O-CU-UP  1409 , O-DU  1411 , and/or O-RU  1413  (e.g., via an O1 and/or O2 interface). 
       FIG. 15  illustrates example components of device  1500 . One or more of the devices described above may include one or more devices  1500 . Device  1500  may include bus  1510 , processor  1520 , memory  1530 , input component  1540 , output component  1550 , and communication interface  1560 . In another implementation, device  1500  may include additional, fewer, different, or differently arranged components. 
     Bus  1510  may include one or more communication paths that permit communication among the components of device  1500 . Processor  1520  may include a processor, microprocessor, or processing logic that may interpret and execute instructions. Memory  1530  may include any type of dynamic storage device that may store information and instructions for execution by processor  1520 , and/or any type of non-volatile storage device that may store information for use by processor  1520 . 
     Input component  1540  may include a mechanism that permits an operator to input information to device  1500  and/or other receives or detects input from a source external to  1540 , 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  1540  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  1550  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  1560  may include any transceiver-like mechanism that enables device  1500  to communicate with other devices and/or systems. For example, communication interface  1560  may include an Ethernet interface, an optical interface, a coaxial interface, or the like. Communication interface  1560  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  1500  may include more than one communication interface  1560 . For instance, device  1500  may include an optical interface and an Ethernet interface. 
     Device  1500  may perform certain operations relating to one or more processes described above. Device  1500  may perform these operations in response to processor  1520  executing software instructions stored in a computer-readable medium, such as memory  1530 . 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  1530  from another computer-readable medium or from another device. The software instructions stored in memory  1530  may cause processor  1520  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. 1-11 ), 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.