Abstract: A computer-implemented method for predicting received signal strength in a telecommunication network includes receiving, by one or more processors that execute a convolutional neural network, geographic data representing geographic information of a geographic area and antenna and transmit power information of a base station in the geographic area; inputting the geographic data and the antenna and transmit power information into the convolutional neural network; predicting received signal strength using the convolutional neural network that includes a number of convolution layers based on the received geographic data and the antenna and transmit power information, the received signal strength representing signal strength of wireless signals received at different locations in the geographic area; and outputting the predicted received signal strength.

Abstract: Techniques are provided for monitoring the performance of a user device in a communication network. The techniques include detecting an anomaly in a performance measurement such as a key quality indicator (KQI) of the user device. The techniques include obtaining historical measurements of the KQI for user devices. The historical measurements are assigned to states to reflect whether the performance is good or bad, or somewhere in between. The states can be defined differently for different hours in the day so that the states represent the relative performance for that time of day. For each user device, a Markov model is provided indicating probabilities of transitions between the states. Additional measurements are obtained of the KQI for a selected user device, and the Markov model of the selected user device is used to detect an anomaly in the additional measurements.

Abstract: A processor implemented method of identifying a root cause of degraded network quality in a wireless network. The method includes accessing historical network performance data, the performance data including a time sequenced measure of performance indicators for the network. The method evaluates the historical performance data to determine regularly occurring associations between indicators to define a set of rules characterizing the associations of the wireless network, and stores the set of rules in a data structure. The wireless network is monitored by accessing analysis data reporting time sequenced performance indicator data. Next, anomalies are detected in a performance indicator in the analysis data and matched to at least one rule in the set of rules. The method outputs an indication of a cause of degradation in the wireless network resulting from the anomaly in the performance indicator.

Abstract: The disclosure relates to technology for processing data sets to generate data rules for the data sets in a communications network. A first set of data including key quality indicators (KQIs) indicative of a quality of service and a second set of data including key performance indicators (KPIs) indicative of a performance level are received. The first data set and the second data set are categorized using a first value into a plurality of KQI groups and a second value into a plurality of KPI groups, respectively. Each of the KQI and KPI groups are identified with a label. Each of the KQI and KPI groups identified with a same label are processed by application of association rule learning to generate the data rules. The data rules model a relationship between the KQIs and the KPIs by calculating association frequencies.

Abstract: A computer-implemented method for predicting received signal strength in a telecommunication network includes receiving, by one or more processors that execute a convolutional neural network, geographic data representing geographic information of a geographic area and antenna and trasnmit power information of a base station in the geographic area; inputing the geographic data and the antenna and trasnmit power information into the convolutional neural network; predicting received signal strength using the convolutional neural network that includes a number of convolution layers based on the received geographic data and the antenna and trasnmit power information, the received signal strength representing signal strength of wireless signals received at different locations in the geographic area; and outputting the predicted received signal strength.

Abstract: Techniques are provided for monitoring the performance of a user device in a communication network. The techniques include detecting an anomaly in a performance measurement such as a key quality indicator (KQI) of the user device. The techniques include obtaining historical measurements of the KQI for user devices. The historical measurements are assigned to states to reflect whether the performance is good or bad, or somewhere in between. The states can be defined differently for different hours in the day so that the states represent the relative performance for that time of day. For each user device, a Markov model is provided indicating probabilities of transitions between the states. Additional measurements are obtained of the KQI for a selected user device, and the Markov model of the selected user device is used to detect an anomaly in the additional measurements.

Abstract: A system and method for detecting anomalies in a communication network includes detecting first outliers in a first set of quality indicators for a cellular group, detecting second outliers in a second set of performance indicators for the cellular group, correlating the first outliers and the second outliers to produce an anomaly candidate, determining a confidence threshold for the anomaly candidate, and indicating a network anomaly in response to the confidence threshold exceeding a predetermined threshold.

Abstract: The disclosure relates to technology for processing data sets to generate data rules for the data sets in a communications network. A first set of data including key quality indicators (KQIs) indicative of a quality of service and a second set of data including key performance indicators (KPIs) indicative of a performance level are received. The first data set and the second data set are categorized using a first value into a plurality of KQI groups and a second value into a plurality of KPI groups, respectively. Each of the KQI and KPI groups are identified with a label. Each of the KQI and KPI groups identified with a same label are processed by application of association rule learning to generate the data rules. The data rules model a relationship between the KQIs and the KPIs by calculating association frequencies.

Abstract: A processor implemented method of identifying a root cause of degraded network quality in a wireless network. The method includes accessing historical network performance data, the performance data including a time sequenced measure of performance indicators for the network. The method evaluates the historical performance data to determine regularly occurring associations between indicators to define a set of rules characterizing the associations of the wireless network, and stores the set of rules in a data structure. The wireless network is monitored by accessing analysis data reporting time sequenced performance indicator data. Next, anomalies are detected in a performance indicator in the analysis data and matched to at least one rule in the set of rules. The method outputs an indication of a cause of degradation in the wireless network resulting from the anomaly in the performance indicator.

Abstract: A system and method for detecting anomalies in a communication network includes detecting first outliers in a first set of quality indicators for a cellular group, detecting second outliers in a second set of performance indicators for the cellular group, correlating the first outliers and the second outliers to produce an anomaly candidate, determining a confidence threshold for the anomaly candidate, and indicating a network anomaly in response to the confidence threshold exceeding a predetermined threshold.

Abstract: In embodiments of adaptive idle timeout for storage devices, a computing device includes a storage device that stores data for read and write access on a rotating media. An operating system of the computing device maintains a device cycle number as an accounting of each time the storage device is powered on-off. The computing device implements a storage device driver that is implemented to obtain the device cycle number of the storage device from the operating system, and determine a projected cycle number over a duration of operational time of the storage device based on the device cycle number. The storage device driver can then determine whether the projected cycle number exceeds a maximum of power on-off cycles within a warranty period of the storage device, and control a frequency of the storage device being powered-off if the projected cycle number exceeds the maximum of power on-off cycles.

Abstract: In embodiments of adaptive idle timeout for storage devices, a computing device includes a storage device that stores data for read and write access on a rotating media. An operating system of the computing device maintains a device cycle number as an accounting of each time the storage device is powered on-off. The computing device implements a storage device driver that is implemented to obtain the device cycle number of the storage device from the operating system, and determine a projected cycle number over a duration of operational time of the storage device based on the device cycle number. The storage device driver can then determine whether the projected cycle number exceeds a maximum of power on-off cycles within a warranty period of the storage device, and control a frequency of the storage device being powered-off if the projected cycle number exceeds the maximum of power on-off cycles.