# US 2019/0246299 A1 · Method and test system for mobile network testing as well as a network testing system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0246299 A1 - Document ID: US20190246299A1 - Status: Published application - Kind: A1 - Application number: 16/242,878 - 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/US20190246299A1 ## CPC - H04W 24/08: Testing, {supervising or monitoring} using real traffic - G06N 3/045: Combinations of networks - G06N 3/0475: Generative networks - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning - G06N 3/094: Adversarial learning - H04L 41/16: using machine learning or artificial intelligence - H04L 43/50: Testing arrangements - H04W 24/06: Testing, {supervising or monitoring} using simulated traffic - H04W 24/10: Scheduling measurement reports {; Arrangements for measurement reports} - H04L 43/0852: Delays - H04L 43/0888: Throughput - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## 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. ## Claims ### Claim 1 A method for mobile network testing by using at least one testing device configured to be connected to the mobile network, comprising: running a predefined set of test procedures on the at least one testing device for generating at least one heat map assigned to the mobile network; evaluating the test results of the test procedures via a machine learning model; and training the machine learning model to complete missing information on an already present heat map 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 inputting valid and complete heat map information obtained from the test results. ### Claim 5 (depends on 4) The method according to claim 4, wherein the valid and complete heat map information comprises network characteristics. ### Claim 6 (depends on 5) The method according to claim 5, wherein the network characteristics correspond to coverage information, information regarding covered networks, throughput network information, bandwidth information, signal strength information, interference information and/or latency information. ### Claim 7 (depends on 1) The method according to claim 1, wherein the at least one testing device records network characteristics obtained from different network measurements. ### Claim 8 (depends on 1) The method according to claim 1, wherein the training of the machine learning model takes geolocation information and/or time information into account. ### Claim 9 (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 10 (depends on 1) The method according to claim 1, wherein the machine learning model is provided by generative adversarial networks and/or a derivative of generative adversarial networks. ### Claim 11 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 complete missing information on an already present heat map based on the test results obtained. ### Claim 12 (depends on 11) The test system according to claim 11, wherein the test system is configured to: run a predefined set of test procedures on the at least one testing device for generating at least one heat map assigned to the mobile network; evaluate the test results of the test procedures via a machine learning model; and train the machine learning model to complete missing information on an already present heat map based on the test results obtained. ### Claim 13 (depends on 11) The test system according to claim 11, wherein the machine learning model is provided by generative adversarial networks and/or a derivative of generative adversarial networks. ### Claim 14 A method for mobile network testing by using at least one testing device configured to be connected to a mobile network, comprising: providing a network testing system configured to complete missing information on an already present heat map by running a machine learning model trained; performing a single test by using the at least one testing device; evaluating the test result of the single test, wherein the test result corresponds to a heat map with missing information; and obtaining a valid and complete heat map from the network testing system, wherein the missing information was completed by the machine learning model trained. ### Claim 15 (depends on 14) The method according to claim 14, wherein the machine learning model is provided by generative adversarial networks and/or a derivative of generative adversarial networks. ### Claim 16 (depends on 1) A network testing system for outputting a valid and complete heat map, 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 network testing system is configured to: perform a single test by using the at least one testing device; evaluate the test result of the single test, wherein the test result corresponds to a heat map with missing information; and obtain a valid and complete heat map from the network testing system, wherein the missing information was completed by the machine learning model trained. ### Claim 17 (depends on 16) The network testing system according to claim 16, wherein the processing unit comprises generative adversarial networks and/or a derivative of generative adversarial networks.