# US 2019/0246298 A1 · Method and test system for mobile network testing as well as prediction system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0246298 A1 - Document ID: US20190246298A1 - Status: Published application - Kind: A1 - Application number: 16/242,859 - Filed: 2019-01-08 - Published: 2019-08-08 - Earliest claimed: 2018-02-07 - Assignees: Rohde & Schwarz GmbH & Co. KG - Applicants: Rohde & Schwarz GmbH & Co. KG - Inventors: Miguel Angel Roman; David Gonzalez - Web page: https://patentgrep.com/patent/US20190246298A1 ## CPC - H04W 24/08: Testing, {supervising or monitoring} using real traffic - H04W 24/00: Supervisory, monitoring or testing arrangements - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## 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. ## Claims ### Claim 1 A method for mobile network testing by using at least one testing device configured to be connected to a mobile network, comprising: running a predefined set of test procedures on the at least one testing device in order to obtain test results assigned to at least one test parameter; evaluating the test results of the test procedures via a machine learning model; and training the machine learning model to predict output parameters assigned to the quality of a service based on the test results obtained. ### Claim 2 (depends on 1) The method according to claim 1, wherein the test procedures running on the at least one testing device simulate the behavior of participants of the mobile network. ### Claim 3 (depends on 1) The method according to claim 1, wherein calls, web browsing, video playing and/or downloads are simulated. ### Claim 4 (depends on 1) The method according to claim 1, wherein the training of the machine learning model is based on the at least one test parameter together with the resulting output parameters assigned to the quality of a respective service. ### Claim 5 (depends on 1) The method according to claim 1, wherein the training of the machine learning model is based on a series of test parameters together with the resulting output parameters assigned to the quality of a respective service. ### Claim 6 (depends on 1) The method according to claim 1, wherein the machine learning model is trained to predict the quality of several services based on the at least one test parameter. ### Claim 7 (depends on 1) The method according to claim 1, wherein the machine learning model is trained to predict the quality of at least one mobile network application. ### Claim 8 (depends on 1) The method according to claim 1, wherein the test parameters are investigated over time and/or wherein geographical information is taken into account. ### Claim 9 (depends on 1) The method according to claim 1, wherein the at least one test parameter corresponds to at least one network key performance indicator. ### Claim 10 (depends on 1) The method according to claim 1, wherein the at least one test parameter corresponds to network key performance indicators assigned to availability, health and/or performance. ### Claim 11 (depends on 1) The method according to claim 1, wherein the at least one test parameter corresponds to a hypertext transfer protocol, a video test and/or a file transfer protocol, and/or wherein the at least one network key performance indicator corresponds to latency, round trip time, jitter, bandwidth, throughput, failure rate, signal level and/or rebuffering. ### Claim 12 (depends on 1) The method according to claim 1, wherein the test results are evaluated on the testing device itself and/or wherein the test results are forwarded to a separate evaluation unit that evaluates the test results. ### Claim 13 (depends on 1) The method according to claim 1, wherein the machine learning model is provided by a neural network. ### Claim 14 (depends on 1) The method according to claim 1, wherein the machine learning model is provided by a recurrent neural network. ### Claim 15 A test system for mobile network testing, comprising at least one testing device and a processing unit configured to run a machine learning model to be trained, wherein the processing unit is configured to receive test results of a set of test procedures run on the at least one testing device and to evaluate the test results via the machine learning model, and wherein the processing unit is configured to train the machine learning model to predict output parameters assigned to the quality of a service based on the test results obtained. ### Claim 16 (depends on 15) The test system according to claim 15, wherein the test system is configured to: run a predefined set of test procedures on the at least one testing device in order to obtain test results assigned to at least one test parameter; evaluate the test results of the test procedures via a machine learning model; and train the machine learning model to predict output parameters assigned to the quality of a service based on the test results obtained. ### Claim 17 A method for mobile network testing by using at least one testing device configured to be connected to a mobile network, comprising: providing a prediction system configured to predict output parameters assigned to the quality of a service by running a machine learning model trained; performing a single test on the at least one testing device; forwarding the test result of the single test and the at least one test parameter assigned to the test results to the prediction system for evaluating the test result; and obtaining a prediction regarding the quality of at least one service from the prediction system. ### Claim 18 (depends on 17) The method according to claim 17, wherein predictions regarding the quality of at least one service or several services are obtained from the prediction system. ### Claim 19 (depends on 17) The method according to claim 17, wherein the prediction system outputs a prediction regarding the quality of at least one mobile application. ### Claim 20 (depends on 1) A prediction system for predicting output parameters assigned to the quality of at least one service, comprising a processing unit configured to run a machine learning model, wherein the machine learning model is trained by a method according to claim 1 or wherein the prediction system is configured to: perform a single test on the at least one testing device; forward the test result of the single test and the at least one test parameter assigned to the test results to the prediction system for evaluating the test result; and obtain a prediction regarding the quality of at least one service from the prediction system.