Abstract: A method for mobile network testing is described wherein at least one testing device is used that is configured to be connected to a mobile network. A predefined set of test procedures is run on the at least one testing device in order to obtain test results assigned to at least one test parameter. The test results of the test procedures are evaluated via a machine learning model. The machine learning model is trained to predict output parameters assigned to the quality of a service based on the test results obtained. Further, a test system, a method for mobile network testing as well as a prediction system are described.

Abstract: A method for mobile network testing is described wherein at least one testing device is used that is configured to be connected to a mobile network. A predefined set of test procedures is run on the at least one testing device in order to obtain binary test results assigned to at least one test parameter. The binary test results of the test procedures are evaluated via a machine learning model. The machine learning model is trained to predict the probability of at least one binary test result based on the binary test results obtained. Further, a test system, a method for mobile network testing as well as a prediction system are described.

Abstract: A method for mobile network testing uses at least one testing device that is configured to be connected to the mobile network. A predefined set of test procedures is run on the at least one testing device for generating at least one heat map assigned to the mobile network. The test results of the test procedures are evaluated via a machine learning model. The machine learning model is trained to complete missing information on an already present heat map based on the test results obtained.

Abstract: A method for mobile network testing is described wherein at least one testing device is used that is configured to be connected to a mobile network. A predefined set of test procedures is run on the at least one testing device in order to obtain test results assigned to at least one test parameter. The test results of the test procedures are evaluated via a machine learning model. The machine learning model is trained to predict output parameters assigned to the quality of a service based on the test results obtained. Further, a test system, a method for mobile network testing as well as a prediction system are described.

Abstract: A method for mobile network testing is described, wherein at least one testing device is used that is configured to be connected to the mobile network. A predefined set of test procedures is run on the at least one testing device for generating at least one heat map assigned to the mobile network. The test results of the test procedures are evaluated via a machine learning model. The machine learning model is trained to complete missing information on an already present heat map based on the test results obtained. Further, a test system, a method for mobile network testing and a network testing system are described.

Abstract: A method for mobile network testing is described wherein at least one testing device is used that is configured to be connected to a mobile network. A predefined set of test procedures is run on the at least one testing device in order to obtain binary test results assigned to at least one test parameter. The binary test results of the test procedures are evaluated via a machine learning model. The machine learning model is trained to predict the probability of at least one binary test result based on the binary test results obtained. Further, a test system, a method for mobile network testing as well as a prediction system are described.