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: A device may receive uplink physical resource block (PRB) interference data from a base station, and may process the uplink PRB interference data, with a first machine learning model, to generate same sector similarity score data for the base station. The device may process the same sector similarity score data, with a second machine learning model, to identify and classify at least one antenna issue of the base station, and may create sector-carrier pair data, from the same sector similarity score data, based on the at least one antenna issue. The device may process the sector-carrier pair data, with a third machine learning model, to identify issues that span sector carriers of the base station, and may calculate an issue status and alignment score based on the issues and the same sector similarity score data. The device may perform actions based on the issue status and alignment score.

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: A system described herein may provide a technique for the analysis of antennas in a wireless network, and the ensuing detection of misaligned antennas. Images reflecting measures of interference associated with different antennas may be analyzed based on structural similarity index (“SSIM”) or other similarity analysis techniques, and antennas may be paired according to this analysis. The paired antennas may be compared to a known configuration. Mismatches may indicate misalignment or other malfunctions, based on which automated remedial measures (e.g., antenna realignment) may be performed.

Abstract: An interference detection system in a network determines that an unknown radio frequency (RF) interference source that causes RF interference experienced by a first wireless station is a persistent RF interference source over a plurality of time intervals in a selected time period. A predicted interference source location is identified for each time interval in the selected time period. An aggregated predicted interference source location is calculated based on the identified one or more predicted interference source locations.

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 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 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 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: An interference detection system in a network determines that an unknown radio frequency (RF) interference source that causes RF interference experienced by a first wireless station is a persistent RF interference source over a plurality of time intervals in a selected time period. A predicted interference source location is identified for each time interval in the selected time period. An aggregated predicted interference source location is calculated based on the identified one or more predicted interference source locations.

Abstract: In some implementations, a network monitoring system may measure a traffic metric for a first component of a network device. The network monitoring system may determine, based on a trained component monitoring model, that an obtained measurement of the traffic metric for the first component satisfies a criteria associated with the traffic metric pattern. The network monitoring system may compare, based on determining that the obtained measurement satisfies the criteria, the obtained measurement to a corresponding measurement of the traffic metric for a second component of the network device. The network monitoring system may determine that a measurement difference between the obtained measurement for the first component and the corresponding measurement for the second component satisfies a difference threshold. The network monitoring system may perform, based on the measurement difference satisfying the difference threshold, an action associated with the first component.

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: An interference detection system in a network identifies a first wireless station that has experienced radio frequency (RF) interference from an unknown source and identifies one or more second wireless stations that have experienced similar interference. A plurality of estimated interference source locations are scored based on a comparison of estimated interference to observed interference at the one or more second wireless stations. A predicted interference source location is identified based on the scored plurality of estimated interference source locations. It is determined whether the unknown interference source is a persistent interference source over a selected time period, wherein the predicted interference source location is identified for each interval in the selected time period.

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: In some implementations, a network monitoring system may measure a traffic metric for a first component of a network device. The network monitoring system may determine, based on a trained component monitoring model, that an obtained measurement of the traffic metric for the first component satisfies a criteria associated with the traffic metric pattern. The network monitoring system may compare, based on determining that the obtained measurement satisfies the criteria, the obtained measurement to a corresponding measurement of the traffic metric for a second component of the network device. The network monitoring system may determine that a measurement difference between the obtained measurement for the first component and the corresponding measurement for the second component satisfies a difference threshold. The network monitoring system may perform, based on the measurement difference satisfying the difference threshold, an action associated with the first component.

Abstract: In some implementations, a network monitoring system may measure a traffic metric for a first component of a network device. The network monitoring system may determine, based on a trained component monitoring model, that an obtained measurement of the traffic metric for the first component satisfies a criteria associated with the traffic metric pattern. The network monitoring system may compare, based on determining that the obtained measurement satisfies the criteria, the obtained measurement to a corresponding measurement of the traffic metric for a second component of the network device. The network monitoring system may determine that a measurement difference between the obtained measurement for the first component and the corresponding measurement for the second component satisfies a difference threshold. The network monitoring system may perform, based on the measurement difference satisfying the difference threshold, an action associated with the first component.

Abstract: In some implementations, a network monitoring system may measure a traffic metric for a first component of a network device. The network monitoring system may determine, based on a trained component monitoring model, that an obtained measurement of the traffic metric for the first component satisfies a criteria associated with the traffic metric pattern. The network monitoring system may compare, based on determining that the obtained measurement satisfies the criteria, the obtained measurement to a corresponding measurement of the traffic metric for a second component of the network device. The network monitoring system may determine that a measurement difference between the obtained measurement for the first component and the corresponding measurement for the second component satisfies a difference threshold. The network monitoring system may perform, based on the measurement difference satisfying the difference threshold, an action associated with the first component.

Abstract: An interference detection system in a network identifies a first wireless station that has experienced radio frequency (RF) interference from an unknown source and identifies one or more second wireless stations that have experienced similar interference. A plurality of estimated interference source locations are scored based on a comparison of estimated interference to observed interference at the one or more second wireless stations. A predicted interference source location is identified based on the scored plurality of estimated interference source locations. It is determined whether the unknown interference source is a persistent interference source over a selected time period, wherein the predicted interference source location is identified for each interval in the selected time period.

Abstract: A system described herein may provide a technique for the analysis of antennas in a wireless network, and the ensuing detection of misaligned antennas. Images reflecting measures of interference associated with different antennas may be analyzed based on structural similarity index (“SSIM”) or other similarity analysis techniques, and antennas may be paired according to this analysis. The paired antennas may be compared to a known configuration. Mismatches may indicate misalignment or other malfunctions, based on which automated remedial measures (e.g., antenna realignment) may be performed.

Abstract: A system described herein may provide a technique for the analysis of antennas in a wireless network, and the ensuing detection of misaligned antennas. Images reflecting measures of interference associated with different antennas may be analyzed based on structural similarity index (“SSIM”) or other similarity analysis techniques, and antennas may be paired according to this analysis. The paired antennas may be compared to a known configuration. Mismatches may indicate misalignment or other malfunctions, based on which automated remedial measures (e.g., antenna realignment) may be performed.