# US 11,451,983 B2 · Dynamic capacity management of a wireless network > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,451,983 B2 - Document ID: US11451983B2 - Status: Granted - Kind: B2 - Application number: 17/093,958 - Filed: 2020-11-10 - Published: 2022-05-12 - Granted: 2022-09-20 - Earliest claimed: 2020-11-10 - Assignees: AT&T Intellectual Property I, L.P. - Applicants: AT&T Intellectual Property I, L.P. - Inventors: Azhar Khan - Web page: https://patentgrep.com/patent/US11451983B2 ## CPC - H04W 16/24: Cell structures - H04W 24/02: Arrangements for optimising operational condition - G06N 20/00: Machine learning - H04W 16/22: Traffic simulation tools or models - H04W 16/32: Hierarchical cell structures - H04W 24/04: Arrangements for maintaining operational condition - 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 ## Abstract Methods, systems, and apparatuses, among other things, may dynamically manage the capacity of a wireless network using unmanned vehicles equipped with small cell capabilities. Moreover, past traffic data may be analyzed using artificial intelligence models to predict a volume for a base station that exceeds a capacity for the base station and one or more unmanned vehicles may be dispatched to the base station to provide capacity relief. ## Claims ### Claim 1 A method comprising: analyzing, by a processing system including a processor, past traffic of a network, wherein the network includes a plurality of base stations; predicting, by the processing system, based on the analyzing of the past traffic of the network, a first traffic profile for a first base station of the plurality of base stations, wherein the first traffic profile includes a first predicted volume of first communication traffic associated with user equipment carried by the first base station, and wherein the first base station includes a first network device for carrying the first communication traffic; identifying, by the processing system, based on the first traffic profile, that the first predicted volume for the first base station will exceed a first capacity of the first base station at a first future time; and dispatching, by the processing system, a first unmanned vehicle to the first base station to increase the first capacity of the first base station at the first future time, wherein the first unmanned vehicle includes a second network device, and wherein the first unmanned vehicle increases the first capacity of the first base station at the first future time by carrying second communication traffic associated with the user equipment via the second network device. ### Claim 2 (depends on 1) The method of claim 1, wherein the analyzing of the past traffic of the network is completed by one or more artificial intelligence models. ### Claim 3 (depends on 2) The method of claim 2, wherein the one or more artificial intelligence models include one or more machine learning models. ### Claim 4 (depends on 1) The method of claim 1, wherein the first unmanned vehicle is equipped with cell capabilities. ### Claim 5 (depends on 1) The method of claim 1, wherein the first base station is equipped with a platform that allows the first unmanned vehicle to camp on the platform for an extended period of time. ### Claim 6 (depends on 5) The method of claim 5, wherein the platform is capable of electrically charging one or more batteries of the first unmanned vehicle. ### Claim 7 (depends on 1) The method of claim 1, wherein the first unmanned vehicle is dispatched from an unmanned vehicle garage. ### Claim 8 (depends on 1) The method of claim 1, further comprising: predicting, by the processing system, based on the analyzing of the past traffic, a second traffic profile for a second base station of the plurality of base stations; identifying, by the processing system, based on the second traffic profile, that a second predicted volume for the second base station will exceed a second capacity of the second base station at a second future time; and dispatching, by the processing system, a second unmanned vehicle to the second base station to increase the second capacity of the second base station at the second future time, wherein the second unmanned vehicle includes a third network device, and wherein the second unmanned vehicle increases the second capacity of the second base station at the second future time by carrying third communication traffic associated with the user equipment via the third network device. ### Claim 9 (depends on 8) The method of claim 8, wherein the second unmanned vehicle is dispatched from the first base station. ### Claim 10 A system comprising: one or more processors; and memory including instructions that, when executed by the one or more processors, cause the system to perform operations comprising: analyzing past traffic of a network, wherein the network includes a plurality of base stations; predicting, based on the analyzing of the past traffic of the network, a first traffic profile for a first base station of the plurality of base stations, wherein the first traffic profile includes a first predicted volume of first communication traffic associated with user equipment carried by the first base station, and wherein the first base station includes a first network device for carrying the first communication traffic; identifying, based on the first traffic profile, that the first predicted volume for the first base station will exceed a first capacity of the first base station at a first future time; and dispatching a first unmanned vehicle to the first base station to increase the first capacity of the first base station at the first future time, wherein the first unmanned vehicle includes a second network device, and wherein the first unmanned vehicle increases the first capacity of the first base station at the first future time by carrying second communication traffic associated with the user equipment via the second network device. ### Claim 11 (depends on 10) The system of claim 10, wherein the analyzing of the past traffic of the network is completed by one or more artificial intelligence models. ### Claim 12 (depends on 11) The system of claim 11, wherein the one or more artificial intelligence models include one or more machine learning models. ### Claim 13 (depends on 10) The system of claim 10, wherein the first unmanned vehicle is equipped with cell capabilities. ### Claim 14 (depends on 10) The system of claim 10, wherein the first base station is equipped with a platform that allows the first unmanned vehicle to camp on the platform for an extended period of time. ### Claim 15 (depends on 14) The system of claim 14, wherein the platform is capable of electrically charging one or more batteries of the first unmanned vehicle. ### Claim 16 (depends on 10) The system of claim 10, wherein the first unmanned vehicle is dispatched from an unmanned vehicle garage. ### Claim 17 (depends on 10) The system of claim 10, wherein the instructions are further configured to cause the system to: predicting, based on the analyzing of the past traffic, a second traffic profile for a second base station of the plurality of base stations; identifying, based on the second traffic profile, that a second predicted volume for the second base station will exceed a second capacity of the second base station at a second future time; and dispatching a second unmanned vehicle to the second base station to increase the second capacity of the second base station at the second future time, wherein the second unmanned vehicle includes a third network device, and wherein the second unmanned vehicle increases the second capacity of the second base station at the second future time by carrying third communication traffic associated with the user equipment via the third network device. ### Claim 18 (depends on 17) The system of claim 17, wherein the second unmanned vehicle is dispatched from the first base station. ### Claim 19 A non-transitory computer program product comprising: a computer-readable storage medium; and instructions stored on the computer-readable storage medium that, when executed by a processor, causes the processor to perform operations comprising: analyzing past traffic of a network, wherein the network includes a plurality of base stations; predicting, based on the analyzing of the past traffic, a traffic profile for a base station of the plurality of base stations, wherein the traffic profile includes a predicted volume of first communication traffic associated with user equipment carried by the base station, and wherein the base station includes a first network device for carrying the first communication traffic; identifying, based on the traffic profile, that the predicted volume for the base station will exceed a capacity of the base station at a future time; and dispatching an unmanned vehicle to the base station to increase the capacity of the base station at the future time, wherein the unmanned vehicle includes a second network device, and wherein the unmanned vehicle increases the capacity of the base station at the future time by carrying second communication traffic associated with the user equipment via the second network device. ### Claim 20 (depends on 19) The non-transitory computer program product of claim 19, wherein the analyzing of the past traffic of the network is completed by one or more artificial intelligence models.