Abstract: A network performance degradation detection system is provided. The system receives Key Performance Indicator (KPI) values for devices of different device types running different software versions. The system determines baseline values of a first device type by averaging the KPI values of the different software versions running on devices of the first device type. The system compares KPI values for a first software version running on devices of the first device type with the determined baseline values to produce a set of comparison results. The system applies the set of comparison results to a classification model to determine whether the first software version running on devices of the first device type causes network performance degradation.

Abstract: This disclosure describes techniques for determining technological capability of devices whose technological capability are unknown. The devices can be users of a wireless telecommunication network. A device, such as a cell phone, has a type allocation code (TAC) which can indicate a make and model of the device. Once the make and model of the device are known, the technical capability of the device is known. Certain TAC numbers, however, do not indicate the make and model of the device, and thus a device's technical capability is unknown. To determine the technological capability of the devices, a machine learning model is trained using usage data of the devices with known technological capability. After training, the machine learning model can be deployed to predict the technological capability of devices with unrecognizable TAC numbers by providing usage data associated with the devices with unrecognizable TAC numbers.

Abstract: A network performance degradation detection system is provided. The system receives Key Performance Indicator (KPI) values for devices of different device types running different software versions. The system determines baseline values of a first device type by averaging the KPI values of the different software versions running on devices of the first device type. The system compares KPI values for a first software version running on devices of the first device type with the determined baseline values to produce a set of comparison results. The system applies the set of comparison results to a classification model to determine whether the first software version running on devices of the first device type causes network performance degradation.

Abstract: This disclosure describes techniques for determining technological capability of devices whose technological capability are unknown. The devices can be users of a wireless telecommunication network. A device, such as a cell phone, has a type allocation code (TAC) which can indicate a make and model of the device. Once the make and model of the device are known, the technical capability of the device is known. Certain TAC numbers, however, do not indicate the make and model of the device, and thus a device's technical capability is unknown. To determine the technological capability of the devices, a machine learning model is trained using usage data of the devices with known technological capability. After training, the machine learning model can be deployed to predict the technological capability of devices with unrecognizable TAC numbers by providing usage data associated with the devices with unrecognizable TAC numbers.

Abstract: This disclosure describes techniques for determining technological capability of devices whose technological capability are unknown. The devices can be users of a wireless telecommunication network. A device, such as a cell phone, has a type allocation code (TAC) which can indicate a make and model of the device. Once the make and model of the device are known, the technical capability of the device is known. Certain TAC numbers, however, do not indicate the make and model of the device, and thus a device's technical capability is unknown. To determine the technological capability of the devices, a machine learning model is trained using usage data of the devices with known technological capability. After training, the machine learning model can be deployed to predict the technological capability of devices with unrecognizable TAC numbers by providing usage data associated with the devices with unrecognizable TAC numbers.