Systems and methods for performing simulations at a base station router

Abstract

The present disclosure at least describes a computer-implemented method. The method includes a step of predicting, via a trained machine learning model trained on any one or more of simulated wireless data or actual wireless data, a coverage change and/or a capacity change in a network. The method also includes a step of transmitting, to a node in the network, information regarding the predicted change in capacity and/or coverage in the network. The information facilitates the node in determining satisfaction of performance criteria.

What is claimed is: 
     
         1 . A computer-implemented method, comprising:
 predicting, via a trained machine learning model trained on any one or more of simulated wireless data or actual wireless data, a coverage change and/or a capacity change in a network; and   transmitting, to a node in the network, information regarding the predicted change in capacity and/or coverage in the network for determining satisfaction of performance criteria.   
     
     
         2 . The method of  claim 1 , wherein the simulated or actual wireless data includes any one or more of cellular traffic or satellite traffic. 
     
     
         3 . The method of  claim 2 , wherein the prediction further predicts any one or more of a fraudulent user equipment (UE), satellite or base station in the network. 
     
     
         4 . The method of  claim 2 , wherein the prediction further predicts an anomaly in the network. 
     
     
         5 . The method of  claim 1 , further comprising:
 causing the node to switch between the simulated wireless data and the actual wireless data.   
     
     
         6 . The method of  claim 5 , further comprising:
 learning, via the machine learning model, a behavior or a pattern based on the actual wireless data.   
     
     
         7 . The method of  claim 6 , further comprising:
 transmitting the learned behavior or pattern to the node; and   causing the node to update one or more configuration parameters based on the learned behavior or pattern.   
     
     
         8 . The method of  claim 1 , wherein the trained machine learning model implements any one or more of a clustering analysis, support vector machine (SVM), linear discriminant analysis, time frequency pattern analysis, singular value decomposition (SVD), artificial neural network, deep neural network (DNN), recurrent neural network (RNN), convolutional neural network (CNN), hidden Markov model (HMM), or Bayesian network (BN). 
     
     
         9 . An apparatus comprising:
 a non-transitory memory including stored instructions; and   a processor operably coupled to non-transitory memory, and configured to execute the instructions of:   predicting, via a trained machine learning model trained on any one or more of simulated wireless data or actual wireless data, a coverage change and/or a capacity change in a network; and   transmitting, to a node in the network, information regarding the predicted change in capacity and/or coverage in the network.   
     
     
         10 . The apparatus of  claim 9 , wherein the simulated or actual wireless data includes any one or more of cellular traffic or satellite traffic. 
     
     
         11 . The apparatus of  claim 9 , wherein
 the prediction further predicts any one or more of a fraudulent user equipment (UE), satellite or base station in the network,   the prediction further predicts an anomaly in the network.   
     
     
         12 . The apparatus of  claim 9 , wherein the processor is further configured to execute the instructions of:
 causing the node to switch between the simulated wireless data and the actual wireless data.   
     
     
         13 . The apparatus of  claim 12 , wherein the processor is further configured to execute the instructions of:
 learning, via the machine learning model, a behavior or a pattern based on the actual wireless data.   
     
     
         14 . The apparatus of  claim 13 , wherein the processor is further configured to execute the instructions of:
 transmitting the learned behavior or pattern to the node; and   causing the node to update one or more configuration parameters based on the learned behavior or pattern.   
     
     
         15 . The apparatus of  claim 9 , wherein the trained machine learning model implements any one or more of a clustering analysis, support vector machine (SVM), linear discriminant analysis, time frequency pattern analysis, singular value decomposition (SVD), artificial neural network, deep neural network (DNN), recurrent neural network (RNN), convolutional neural network (CNN), hidden Markov model (HMM), or Bayesian network (BN). 
     
     
         16 . A computer readable medium including program instructions that, when executed by a processor, effectuate:
 predicting, via a trained machine learning model trained on any one or more of simulated wireless data or actual wireless data, a coverage change and/or a capacity change in a network; and   transmitting, to a node in the network, information regarding the predicted change in capacity and/or coverage in the network.   
     
     
         17 . The computer readable medium of  claim 16 , wherein
 the prediction further predicts any one or more of a fraudulent user equipment (UE), satellite or base station in the network, or   the prediction further predicts an anomaly in the network.   
     
     
         18 . The computer readable medium of  claim 16 , wherein the program instructions, when executed by the processor, further effectuate:
 causing the node to switch between the simulated wireless data and the actual wireless data.   
     
     
         19 . The computer readable medium of  claim 16 , wherein the program instructions, when executed by the processor, further effectuate:
 learning, via the machine learning model, a behavior or a pattern based on the actual wireless data.   
     
     
         20 . The computer readable medium of  claim 19 , wherein the program instructions, when executed by the processor, further effectuate:, further comprising:
 transmitting the learned behavior or pattern to the node; and   causing the node to update one or more configuration parameters based on the learned behavior or pattern.