# US 11,444,844 B2 · Simulating a dynamic hybrid network > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,444,844 B2 - Document ID: US11444844B2 - Status: Granted - Kind: B2 - Application number: 17/069,582 - Filed: 2020-10-13 - Published: 2022-04-14 - Granted: 2022-09-13 - Earliest claimed: 2020-10-13 - Assignees: SoftBank Corp. - Applicants: SoftBank Corp. - Inventors: Jin Zhang - Web page: https://patentgrep.com/patent/US11444844B2 ## CPC - H04L 41/145: {involving simulating, designing, planning or modelling of a network} - H04B 7/18504: {Aircraft used as relay or high altitude atmospheric platform} - H04B 7/18508: {with satellite system used as relay, i.e. aeronautical mobile satellite service} - H04L 41/0823: characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability (for optimising operational conditions of wireless networks H04W24/02) - H04L 41/0895: Configuration of virtualised networks or elements, e.g. virtualised network function or OpenFlow elements - H04L 41/40: using virtualisation of network functions or resources, e.g. SDN or NFV entities - H04L 43/0888: Throughput - H04L 43/20: the monitoring system or the monitored elements being virtualised, abstracted or software-defined entities, e.g. SDN or NFV - H04W 16/22: Traffic simulation tools or models - H04W 24/02: Arrangements for optimising operational condition - H04W 24/06: Testing, {supervising or monitoring} using simulated traffic - H04W 40/22: using selective relaying for reaching a BTS [Base Transceiver Station] or an access point - H04W 40/32: for defining a routing cluster membership - H04L 41/0816: the condition being an adaptation, e.g. in response to network events - H04L 41/0896: Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities (flow or congestion control using dynamic resource allocation, e.g. in-call renegotiation, H04L47/76) - H04L 43/045: for graphical visualisation of monitoring data - H04L 43/0876: Network utilisation, e.g. volume of load or congestion level - H04W 84/06: Airborne or Satellite Networks (space-based or airborne stations H04B7/185) - H04W 84/18: Self-organising networks, e.g. ad-hoc networks or sensor networks - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - H04B: TRANSMISSION - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract The technology relates to simulating a dynamic hybrid network. A method for simulating a dynamic hybrid network includes modeling a transmission supply of a mesh network of moving stations on aerial vehicles in flight at each time step in a time series, modeling capacity demand as a function of user distribution data and user behavior data, and computing an aggregate network metric for the dynamic hybrid network including throughput. A method for optimizing throughput of a dynamic hybrid network includes modeling a dynamic hybrid network, including a transmission supply of a mesh network of moving stations, modeling capacity demand as a function of user distribution data and user behavior data, and computing an aggregate network metric; then providing feedback to a controller and causing a change to a station in the network to increase throughput. ## Claims ### Claim 1 A method for simulating a dynamic hybrid network, the method comprising: modeling, at each time step in a time series, a transmission supply of a mesh network of a plurality of moving stations on a plurality of aerial vehicles in flight, including modeling point-to-point radio wave transmission capacity; modeling capacity demand as a function of user distribution data and user behavior data, wherein modeling the capacity demand results in a cyclical representation or a static representation of a capacity demand per handset; and computing, at each time step, an aggregate network metric for the dynamic hybrid network comprising at least a throughput, wherein the aggregate network metric is refreshed at each time step in the time series based on the transmission supply refreshed based on (1) at least one of an updated current location or most recent location and (2) antenna characteristics of the plurality of moving stations in the mesh network. ### Claim 2 (depends on 1) The method of claim 1, wherein the dynamic hybrid network comprises the mesh network and one or more fixed stations. ### Claim 3 (depends on 2) The method of claim 2, further comprising modeling the transmission supply of the mesh network relative to the transmission supply of the one or more fixed stations. ### Claim 4 (depends on 1) The method of claim 1, wherein the plurality of aerial vehicles comprises more than one type of aerial vehicle. ### Claim 5 (depends on 1) The method of claim 1, wherein the mesh network comprises a homogeneous network of LTE stations. ### Claim 6 (depends on 1) The method of claim 1, wherein the mesh network comprises a heterogeneous network of two or more types of stations. ### Claim 7 (depends on 1) The method of claim 1, further comprising modeling multi-station interference among the mesh network of the plurality of moving stations. ### Claim 8 (depends on 1) The method of claim 1, further comprising modeling multi-station interference between the mesh network and one or more fixed stations. ### Claim 9 (depends on 1) The method of claim 1, wherein the aggregate network metric is based on a signal quality per handset output from modeling the transmission supply and a capacity demand per handset output from modeling the capacity demand. ### Claim 10 (depends on 1) The method of claim 1, further comprising outputting a simulated dynamic hybrid network using the aggregate network metric. ### Claim 11 (depends on 10) The method of claim 10, wherein the simulated dynamic hybrid network is represented using a heatmap. ### Claim 12 A method for optimizing throughput of a dynamic hybrid network, the method comprising: modeling a dynamic hybrid network, including modeling a transmission supply of a mesh network of moving stations at each time step in a time series, modeling capacity demand as a function of user distribution data and user behavior data, and computing an aggregate network metric comprising at least a throughput; providing feedback to a controller, the feedback comprising at least the throughput; and causing, by the controller, a change to one, or a combination of, a location, direction, and transmission strength of a station in the network, the change configured to increase the throughput, wherein the station is part of a self-organizing network (SON) and is configured to adjust an antenna characteristic in response to information received from another station, and the information includes the modeled transmission supply and the modeled capacity demand of the another station. ### Claim 13 (depends on 12) The method of claim 12, wherein modeling the dynamic hybrid network comprises performing a regression analysis over a series of locations from an actual vehicle trajectory. ### Claim 14 (depends on 12) The method of claim 12, wherein modeling the dynamic hybrid network comprises performing a regression analysis over a series of locations from a simulated vehicle trajectory. ### Claim 15 (depends on 12) The method of claim 12, wherein modeling the transmission supply comprises modeling point-to-point transmission between a plurality of stations in the dynamic hybrid network and a handset density. ### Claim 16 (depends on 12) The method of claim 12, wherein the station is located on an aerial vehicle. ### Claim 17 (depends on 16) The method of claim 16, wherein the aerial vehicle is part of a fleet of aerial vehicles carrying the mesh network of moving stations.