Abstract: A machine learning system automatically diagnoses and resolves issues in a telecommunications network. When a customer reports a network issue using a mobile application, the device performs a diagnostic test, such as a speed test. In addition, network logs or performance metrics during occurrence of the network issue are collected. The results of the diagnostic are used as inputs to a machine learning model in combination with the network logs or metrics to predict the cause of the network issue or to perform a corrective action.

Abstract: A machine learning system automatically diagnoses and resolves issues in a telecommunications network. When a customer reports a network issue using a mobile application, the device performs a diagnostic test, such as a speed test. In addition, network logs or performance metrics during occurrence of the network issue are collected. The results of the diagnostic are used as inputs to a machine learning model in combination with the network logs or metrics to predict the cause of the network issue or to perform a corrective action.

Abstract: A machine learning system automatically diagnoses and resolves issues in a telecommunications network. When a customer reports a network issue using a mobile application, the device performs a diagnostic test, such as a speed test. In addition, network logs or performance metrics during occurrence of the network issue are collected. The results of the diagnostic are used as inputs to a machine learning model in combination with the network logs or metrics to predict the cause of the network issue or to perform a corrective action.

Abstract: A machine learning system automatically diagnoses and resolves issues in a telecommunications network. When a customer reports a network issue using a mobile application, the device performs a diagnostic test, such as a speed test. In addition, network logs or performance metrics during occurrence of the network issue are collected. The results of the diagnostic are used as inputs to a machine learning model in combination with the network logs or metrics to predict the cause of the network issue or to perform a corrective action.

Abstract: A first time that a Domain Name System (DNS) response message is sent by the gateway server of a network to a user device may be determined, the DNS response message being generated by a Local DNS server of the network in response to a DNS request message from the user device for routing via the gateway server to the user device. A second time that a client handshake message for an external server identified by a network address in the DNS response is received at the gateway server may be determined, in which the client handshake message is sent by the user device. A roundtrip time for the network that is a time difference between the first time and the second time is calculated. The roundtrip trip time is then halved to obtain a network latency time measurement for the network.

Abstract: A geolocation of a mobile device determined during an event allows a communications network provider to associate geolocation data with QoS issues. The geolocation data may be ascertained by mapping call log data with a temporary subscriber identifier assigned by the communications network provider. Geolocation data may also be obtained from URL data. The geolocation data obtained from URL data may be verified by comparing the URL data with a geo-distance from a connected cell tower. The QoS issue can be associated with geolocation data and subsequently used to improve the network or strengthen the coverage at the geolocation.

Abstract: A geolocation of a mobile device determined during an event allows a communications network provider to associate geolocation data with QoS issues. The geolocation data may be ascertained by mapping call log data with a temporary subscriber identifier assigned by the communications network provider. Geolocation data may also be obtained from URL data. The geolocation data obtained from URL data may be verified by comparing the URL data with a geo-distance from a connected cell tower. The QoS issue can be associated with geolocation data and subsequently used to improve the network or strengthen the coverage at the geolocation.

Abstract: A geolocation of a mobile device determined during an event allows a communications network provider to associate geolocation data with QoS issues. The geolocation data may be ascertained by mapping call log data with a temporary subscriber identifier assigned by the communications network provider. Geolocation data may also be obtained from URL data. The geolocation data obtained from URL data may be verified by comparing the URL data with a geo-distance from a connected cell tower. The QoS issue can be associated with geolocation data and subsequently used to improve the network or strengthen the coverage at the geolocation.