The disclosed technology is directed towards automatically detecting failure states and the cause of the failure. For a network, the technology collects status messages from equipment and customers into batches as they occur. The technology groups and aggregates messages, then transforms the aggregations to the frequency domain. Anomalies induce detectable changes in the particle distribution of a trained particle filter, from which an anomalous spectrogram is generated. The status messages of each device are iteratively removed from the larger set of messages, resulting in reduced subsets that are each aggregated, transformed into a modified spectrogram and compared against the anomalous spectrogram to obtain a distance score. The distance score for each device is used to rank the devices with respect to being the cause of the failure.

The disclosed technology is directed towards automatically detecting failure states and the cause of the failure. For a network, the technology collects status messages from equipment and customers into batches as they occur. The technology groups and aggregates messages, then transforms the aggregations to the frequency domain. Anomalies induce detectable changes in the particle distribution of a trained particle filter, from which an anomalous spectrogram is generated. The status messages of each device are iteratively removed from the larger set of messages, resulting in reduced subsets that are each aggregated, transformed into a modified spectrogram and compared against the anomalous spectrogram to obtain a distance score. The distance score for each device is used to rank the devices with respect to being the cause of the failure.

Methods, systems, and computer-readable media are disclosed herein that monitor and improve network performance and reliability of a plurality of devices and nodes. In aspects, alert types are categorized based on the role, model, and operating system of a device or node within the network for which the alert was generated. A command set that is responsive to the alert and that is specially configured for the role, model, and operating system of the device or node is automatically selected to address the alert. The command set can be executed against the device or node (or neighboring device/node) in order to investigate the cause or source of the alert. Based on the results returned by the command set's execution, remediation actions can be selected and implemented to improve the technological performance (e.g., memory, CPU, connectivity) of the device or node in the network.

Methods, systems, and computer-readable media are disclosed herein that monitor and improve network performance and reliability of a plurality of devices and nodes. In aspects, alert types are categorized based on the role, model, and operating system of a device or node within the network for which the alert was generated. A command set that is responsive to the alert and that is specially configured for the role, model, and operating system of the device or node is automatically selected to address the alert. The command set can be executed against the device or node (or neighboring device/node) in order to investigate the cause or source of the alert. Based on the results returned by the command set's execution, remediation actions can be selected and implemented to improve the technological performance (e.g., memory, CPU, connectivity) of the device or node in the network.

During operation, an electronic device receives, from a second electronic device in a network, a request for testing. In response, the electronic device set ups a video call with a video-call service. Then, the electronic device provides, to the second electronic device, an invitation for the video call. When the electronic device receives a notification (e.g., from the video-call service) that the video call has started, the electronic device provides content via the video-call service for the second electronic device. Next, the electronic device obtains communication-performance metrics associated with communication via the network during the video call and video-service performance metrics associated with the video call. Furthermore, the electronic device diagnoses a type of problem experienced at the second electronic device during the video call based at least in part on the communication-performance metrics, the video-service performance metrics and a pretrained machine-learning model.

During operation, an electronic device receives, from a second electronic device in a network, a request for testing. In response, the electronic device set ups a video call with a video-call service. Then, the electronic device provides, to the second electronic device, an invitation for the video call. When the electronic device receives a notification (e.g., from the video-call service) that the video call has started, the electronic device provides content via the video-call service for the second electronic device. Next, the electronic device obtains communication-performance metrics associated with communication via the network during the video call and video-service performance metrics associated with the video call. Furthermore, the electronic device diagnoses a type of problem experienced at the second electronic device during the video call based at least in part on the communication-performance metrics, the video-service performance metrics and a pretrained machine-learning model.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, using a beam, a reference signal associated with a channel for wireless communication. The UE may generate a non-binary beam failure indicator based at least in part on a measurement associated with the beam, wherein the non-binary beam failure indicator comprises at least one non-binary beam failure indicator value. Numerous other aspects are described.

A device may provide, to a user device, a first message instructing a technician to move fiber cables and may receive a first signal based on the technician moving the fiber cables and a rest signal based on the technician stopping movement of the fiber cables. The device may calculate a distance, an average peak signal, and a baseline signal based on the first signal and the rest signal and may calculate a data collection window based on the distance, the average peak signal, and the baseline signal. The device may provide, to the user device, a second message instructing the technician to move one fiber cable at a time and may receive second signals based on the technician moving one fiber cable at a time. The device may provide, for display to the user device, the data collection window and indications of the second signals.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, using a beam, a plurality of reference signals associated with a channel for wireless communication. The UE may report, based at least in part on a plurality of measurements associated with the beam, a plurality of beam failure indicators according to a periodic beam failure indicator reporting configuration. Numerous other aspects are described.

Various techniques include detecting noise resulting from data network impairments and analyzing the noise to determine a likely source and location of the data network impairments. The analysis is used to generate noise reports that instruct network technicians how to check network devices for network impairments. The instructions can be provided on portable electronic devices that are further configured to receive data characterizing any impairments identified at the network devices. The data generated by the network technicians can be used to improve the ability of the techniques to correctly identify the source of data network impairments.