Abstract: A device may receive network data from base stations associated with a network and may aggregate the network data to generate aggregated network data. The device may remove data that satisfies a threshold from the aggregated network data to generate a reduced set of the aggregated network data. The device may divide the reduced set of the aggregated network data into groups of network factors associated with determining network parameters for the base stations. The device may apply weights to the network factors in the groups of network factors to generate groups of weighted network factors and may generate baselines for the network parameters based on the groups of weighted network factors. The device may compare the network data and the baselines to identify one or more anomalies in the network parameters and may perform one or more actions based on the one or more anomalies.

Abstract: A device may receive network data identifying key performance indicators associated with a base station and user equipment (UEs), UE data identifying UE data records and UE locations, and geographic data identifying a geographic area and features of the geographic area. The device may correlate the network data, the UE data, and the geographic data to generate correlated data. The device may process, the correlated data, with a plurality of machine learning models, to generate a corresponding plurality of results, and may evaluate the plurality of results, with prediction models, to generate a set of results. The device may compare classification cost function weighted predictions and the set of results to generate comparisons, and may select a machine learning model, for the geographic area and from the plurality of machine learning models, based on the comparisons. The device may implement the machine learning model for the geographic area.

Abstract: A method, a device, and a non-transitory storage medium are described in which a network performance spread service is provided. The service may include generating a dependency graph representative of a network and identifying current or prospective poor performance spread of network elements based on correlations between the network elements and performance data. The service may also include providing remedial services that address the poor performance spread in the network.

Abstract: A device may calculate a PRB seasonal strength based on PRB data from base stations, and may scale and normalize the PRB data based on the PRB seasonal strength. The device may calculate and combine FFTs for the normalized and scaled PRB data, may calculate a z-score for the combined FFT, and may calculate FFT IQRs for frequencies of the combined FFT. The device may filter the FFT IQRs based on the z-score, may process the FFT IQRs, with a clustering model, to identify clusters of periodic interference patterns, and may aggregate the clusters. The device may identify peak data in the PRB data, and may process the peak data, with a model, to determine a parameter for clustering. The device may process the PRB data, with the clustering model, to identify final clusters of periodic interference patterns, and may perform actions based on the final clusters.

Abstract: A device may determine that a first geographical area satisfies an area threshold. The first geographical area includes an estimated location of a source of interference associated with base stations. A first plurality of vertices, of the first geographical area, are based on locations of the base stations. The device may determine a second plurality of vertices. The second plurality of vertices are closer to the estimated location than the first plurality of vertices. The device may provide information regarding the second plurality of vertices and may obtain magnitude information indicating magnitudes of the interference measured at the second plurality of vertices. The device may determine a third plurality of vertices based on the magnitude information. The device may provide information regarding a second geographical area, formed by the third plurality of vertices, to cause a search of the second geographical area to identify the source of the interference.

Abstract: A management system may receive base station information regarding 5G base stations identified by a fixed wireless access (FWA) device. The base station information may identify the 5G base stations and sectors of the 5G base stations. The management system may provide a request for a quantity of devices connected to each of the sectors. The management system may receive device information indicating the quantity of devices connected to each sector of the sectors of the 5G base stations. The management system may determine loads of the 5G base stations based on the device information. The management system may provide load information regarding the loads of the 5G base stations. The load information may be provided to cause a 4G base station to determine the loads of the 5G base stations prior to causing a 5G base station to establish a connection with the FWA device.

Abstract: A device may receive antenna alert data associated with antennas of a plurality of base stations of a network, and may process the antenna alert data, with a correlation model, to generate a correlation heatmap matrix. The device may utilize a change point detection model to process the antenna alert data, associated with the correlation heatmap matrix, to determine change point metrics, and may calculate scores for the antenna alert data associated with the correlation heatmap matrix and the change point metrics. The device may perform one or more corrective actions based on the scores.

Abstract: A method may include identifying, in a network, a location of a Fifth Generation (5G) node and determining, based on the location of the 5G node and capabilities associated with the 5G node, a number of areas around the 5G node, wherein each area corresponds to a bin. The method may also include identifying, in at least some of the of bins, candidate anchor nodes for the 5G node and ranking the candidate anchor nodes based on parameters associated with the candidate anchor nodes. The method may further include selecting, for the 5G node, an anchor node from the candidate anchor nodes based on the ranking.

Abstract: A device may receive antenna alert data associated with antennas of a plurality of base stations of a network, and may process the antenna alert data, with a correlation model, to generate a correlation heatmap matrix. The device may utilize a change point detection model to process the antenna alert data, associated with the correlation heatmap matrix, to determine change point metrics, and may calculate scores for the antenna alert data associated with the correlation heatmap matrix and the change point metrics. The device may perform one or more corrective actions based on the scores.

Abstract: A device may include a processor configured to receive a report from a fixed wireless access device indicating that no anchoring relationship exists between a Fourth Generation (4G) base station and a Fifth Generation (5G) base station to which the fixed wireless access device is attempting to connect. The device may be further configured to instruct a network management system to create the anchoring relationship between the 4G base station and the 5G base station; and update an anchoring relationships database with information relating to the created anchoring relationship, wherein the anchoring relationships database stores information relating to anchoring relationships between base stations in a radio access network.

Abstract: A device may receive time domain PRB data associated with a plurality of base stations of a network, and may transform the time domain PRB data into frequency domain PRB data. The device may identify, based on the frequency domain PRB data, periodic interference patterns associated with the network, and may process periodic data associated with the periodic interference patterns, with a clustering model, to identify clusters of periodic interference patterns. The device may utilize a time offset analysis to link clusters and identify a transient periodic interferer, and may process cluster data associated with the clusters and the transient periodic interferer, and public transportation data, with a machine learning model, to predict a route associated with the transient periodic interferer. The device may perform one or more actions based on the route associated with the transient periodic interferer.

Abstract: Disclosed are systems and methods for a robust Self-Organizing Network (SON) framework that quantifies SON applications' control and management of a network into key performance indicators (KPI) that are leveraged to determine the impact of a SON's application effectiveness in regulating network parameters, which then dictates how the SON application operates. The disclosed framework is configured to receive multiple data streams from existing data sources, determine the performance of a node on a network, and then automatically perform SON operations based therefrom. The disclosed framework can utilize this information to predict additional and/or future opportunities for SON automation on the network.

Abstract: A method, a device, and a non-transitory storage medium are described in which a network performance spread service is provided. The service may include generating a dependency graph representative of a network and identifying current or prospective poor performance spread of network elements based on correlations between the network elements and performance data. The service may also include providing remedial services that address the poor performance spread in the network.

Abstract: A system may receive, from a network, an estimate of coordinates of a location of a potential interference source; determine, based in the estimate, whether the system is within a first threshold distance from the location; if the system is with the first threshold distance from the location, obtain real-time interference data from the network; determine, based on the real-time interference data, whether a source of interference exists near a first location that is within a second distance from a second location specified by the real-time interference data; and send a reply that indicates a result of the determination to the network.

Abstract: A device may receive network data from base stations associated with a network and may aggregate the network data to generate aggregated network data. The device may remove data that satisfies a threshold from the aggregated network data to generate a reduced set of the aggregated network data. The device may divide the reduced set of the aggregated network data into groups of network factors associated with determining network parameters for the base stations. The device may apply weights to the network factors in the groups of network factors to generate groups of weighted network factors and may generate baselines for the network parameters based on the groups of weighted network factors. The device may compare the network data and the baselines to identify one or more anomalies in the network parameters and may perform one or more actions based on the one or more anomalies.

Abstract: Disclosed are systems and methods for a robust Self-Organizing Network (SON) framework that quantifies SON applications' control and management of a network into key performance indicators (KPI) that are leveraged to determine the impact of a SON's application effectiveness in regulating network parameters, which then dictates how the SON application operates. The disclosed framework is configured to receive multiple data streams from existing data sources, determine the performance of a node on a network, and then automatically perform SON operations based therefrom. The disclosed framework can utilize this information to predict additional and/or future opportunities for SON automation on the network.

Abstract: A method, a device, and a non-transitory storage medium are described in which a network performance spread service is provided. The service may include generating a dependency graph representative of a network and identifying current or prospective poor performance spread of network elements based on correlations between the network elements and performance data. The service may also include providing remedial services that address the poor performance spread in the network.

Abstract: Systems and methods provide automated generation of neighbor frequency lists for configuration of frequency objects for wireless stations. A computing device selects a target sector carrier of a wireless station of multiple wireless stations in a radio access network and identifies, based on distances from the wireless station, neighboring sector carriers of the target sector carrier. The computing device filters the neighboring sector carriers based on an azimuth of the target sector carrier to form a filtered set of neighboring sector carriers. The computing device calculates a probability of neighboring frequencies for the target sector carrier based on locations of the filtered set of neighboring sector carriers and generates, based on the calculating, a neighbor frequency list for the target sector carrier. The neighbor frequency list is used to configure frequency objects, for the target sector carrier, that ensure seamless handovers within the radio access network.

Abstract: A device may receive network data from base stations associated with a network and may aggregate the network data to generate aggregated network data. The device may remove data that satisfies a threshold from the aggregated network data to generate a reduced set of the aggregated network data. The device may divide the reduced set of the aggregated network data into groups of network factors associated with determining network parameters for the base stations. The device may apply weights to the network factors in the groups of network factors to generate groups of weighted network factors and may generate baselines for the network parameters based on the groups of weighted network factors. The device may compare the network data and the baselines to identify one or more anomalies in the network parameters and may perform one or more actions based on the one or more anomalies.

Abstract: A device may receive network data from base stations associated with a network and may aggregate the network data to generate aggregated network data. The device may remove data that satisfies a threshold from the aggregated network data to generate a reduced set of the aggregated network data. The device may divide the reduced set of the aggregated network data into groups of network factors associated with determining network parameters for the base stations. The device may apply weights to the network factors in the groups of network factors to generate groups of weighted network factors and may generate baselines for the network parameters based on the groups of weighted network factors. The device may compare the network data and the baselines to identify one or more anomalies in the network parameters and may perform one or more actions based on the one or more anomalies.