Dynamic capacity management of a wireless network

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

What is claimed: 
     
         1 . A method comprising:
 identifying, by a processing system including a processor, that a first predicted volume for a first base station of a plurality of base stations in a network 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 user equipment via the second network device.   
     
     
         2 . The method of  claim 1 , wherein one or more artificial intelligence models analyze past traffic of the network to generate the first predicted volume. 
     
     
         3 . The method of  claim 2 , wherein the one or more artificial intelligence models include one or more machine learning models. 
     
     
         4 . The method of  claim 3 , further comprising:
 predicting, by the processing system, based on analyzing 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.   
     
     
         5 . The method of  claim 4 , wherein the second unmanned vehicle is dispatched from the first base station. 
     
     
         6 . The method of  claim 1 , wherein the first unmanned vehicle is equipped with cell capabilities. 
     
     
         7 . 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. 
     
     
         8 . The method of  claim 7 , wherein the platform is capable of electrically charging one or more batteries of the first unmanned vehicle. 
     
     
         9 . The method of  claim 1 , wherein the first unmanned vehicle is dispatched from an unmanned vehicle garage. 
     
     
         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:   identifying that a first predicted volume for a first base station of a plurality of base stations in a network 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 user equipment via the second network device.   
     
     
         11 . The system of  claim 10 , wherein one or more artificial intelligence models analyze past traffic of the network to determine the first predicted volume. 
     
     
         12 . The system of  claim 11 , wherein the one or more artificial intelligence models include one or more machine learning models. 
     
     
         13 . The system of  claim 10 , wherein the first unmanned vehicle is equipped with cell capabilities. 
     
     
         14 . 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. 
     
     
         15 . The system of  claim 14 , wherein the platform is capable of electrically charging one or more batteries of the first unmanned vehicle. 
     
     
         16 . The system of  claim 10 , wherein the first unmanned vehicle is dispatched from an unmanned vehicle garage. 
     
     
         17 . The system of  claim 10 , wherein the instructions are further configured to cause the system to:
 predicting, based on 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.   
     
     
         18 . The system of  claim 17 , wherein the second unmanned vehicle is dispatched from the first base station. 
     
     
         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:   identifying that a predicted volume for a base station of a plurality of base stations in a network 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 user equipment via the second network device.   
     
     
         20 . The non-transitory computer program product of  claim 19 , wherein the operations further comprise one or more artificial intelligence models analyzing of past traffic of the network to determine the predicted volume.