Systems and methods for a proxy model of network quality
A machine learning-based proxy model may be generated to determine quality of user experience in a vehicle-based communication network in the absence of direct feedback from the users regarding the user experience. The machine learning-based proxy model may include supervised and/or unsupervised machine learning models, including for example a k-means clustering algorithm and/or an artificial neural network. Once generated, the proxy model may be applied to observed operational parameters of the vehicle-based communication network to quantify the user experience for any user for any duration of the vehicle-based communication network.
1 . A computer-implemented method implemented via one or more processors, the computer-implemented method comprising:
training, based upon training data, a proxy model to analyze values of a plurality of operational parameters associated with providing a vehicle-based communication network of a vehicle to generate measurements of quality of user experience in the vehicle-based communication network, the training data comprising training sets of operational parameter values associated with uses of the vehicle-based communication network by users;
obtaining a first observed set of operational parameter values corresponding to use of the vehicle-based communication network by a first user during a first transit of the vehicle; and
obtaining, based at least in part on the first observed set of operational parameter values, an output of the trained proxy model, wherein the output comprises a first measurement of quality of user experience for the first user of the vehicle-based communication network during the first transit of the vehicle.
2 . The computer-implemented method of claim 1 , wherein the vehicle is an aircraft.
3 . The computer-implemented method of claim 1 , wherein the proxy model includes a k-means clustering model, wherein training the proxy model comprises training the k-means clustering model to categorize respective sets of operational parameters for respective users into one of a plurality of clusters associated with different levels of quality of user experience in the vehicle-based communication network.
4 . The computer-implemented method of claim 1 , wherein the proxy model includes one or more artificial neural networks, wherein the training data comprises labeled data comprising a respective labeled user experience measurement for each of the training sets of operational parameter values, and wherein training the proxy model comprises training the one or more artificial neural networks to generate correct measurements of user experience based upon the training data.
5 . The computer-implemented method of claim 1 , wherein the training of the proxy model comprises training one or more ground-based processing elements external to the vehicle.
6 . The computer-implemented method of claim 1 , wherein obtaining the output of the trained proxy model comprises analyzing the first observed set of operational parameter values via the one or more processors disposed within the vehicle.
7 . The computer-implemented method of claim 1 , wherein obtaining the output of the trained proxy model comprises analyzing the first observed set of operational parameter values via one or more second processors of one or ground-based computing devices external to the vehicle.
8 . The computer-implemented method of claim 1 , wherein the plurality of operational parameters comprises one or more parameters indicative of one or more hardware components of the vehicle-based communication network or a configuration of the one or more hardware components of the vehicle-based communication network.
9 . The computer-implemented method of claim 1 , wherein the plurality of operational parameters comprises one or more parameters indicative of a status, availability, or capability of one or more elements of the vehicle-based communication network.
10 . The computer-implemented method of claim 1 , wherein the plurality of operational parameters comprises one or more parameters indicative of usage behavior of the users of the vehicle-based communication network.
11 . The computer-implemented method of claim 1 , wherein the plurality of operational parameters comprises one or more parameters indicative of subscription of a user to one or more service terms for the vehicle-based communication network.
12 . One or more non-transitory computer readable media comprising instructions that, when executed via one or more processors, cause one or more computing devices to:
train, based upon training data, a proxy model to analyze values of a plurality of operational parameters associated with providing a vehicle-based communication network of a vehicle to generate measurements of quality of user experience in the vehicle-based communication network, the training data comprising training sets of operational parameter values associated with uses of the vehicle-based communication network by users;
obtain a first observed set of operational parameter values corresponding to use of the vehicle-based communication network by a first user during a first transit of the vehicle; and
obtain, based at least in part on the first observed set of operational parameter values, an output of the trained proxy model, wherein the output comprises a first measurement of quality of user experience for the first user of the vehicle-based communication network during the first transit of the vehicle.
13 . The one or more non-transitory computer readable media of claim 12 , wherein the vehicle is an aircraft.
14 . The one or more non-transitory computer readable media of claim 12 , wherein the proxy model includes a k-means clustering model, wherein the instructions to train the proxy model comprise instructions to train the k-means clustering model to categorize respective sets of operational parameters for respective users into one of a plurality of clusters associated with different levels of quality of user experience in the vehicle-based communication network.
15 . The one or more non-transitory computer readable media of claim 12 , wherein the proxy model includes one or more artificial neural networks, wherein the training data comprises labeled data comprising a respective labeled user experience measurement for each of the training sets of operational parameter values, and wherein the instructions to train the proxy model comprise instructions to train the one or more artificial neural networks to generate correct measurements of user experience based upon the training data.
16 . The one or more non-transitory computer readable media of claim 12 , wherein the instructions to train the proxy model comprise instructions to train one or more ground-based processing elements external to the vehicle.
17 . The one or more non-transitory computer readable media of claim 12 , wherein the instructions to obtain the output of the trained proxy model comprise instructions to analyze the first observed set of operational parameter values via the one or more processors disposed within the vehicle.
18 . The one or more non-transitory computer readable media of claim 12 , wherein the instructions to obtain the output of the trained proxy model comprise instructions to analyze the first observed set of operational parameter values via one or more second processors of one or ground-based computing devices external to the vehicle.
19 . The one or more non-transitory computer readable media of claim 12 , wherein the plurality of operational parameters comprises one or more parameters indicative of one or more hardware components of the vehicle-based communication network or a configuration of the one or more hardware components of the vehicle-based communication network.
20 . The one or more non-transitory computer readable media of claim 12 , wherein the plurality of operational parameters comprises one or more parameters indicative of a status, availability, or capability of one or more elements of the vehicle-based communication network.
21 . The one or more non-transitory computer readable media of claim 12 , wherein the plurality of operational parameters comprises one or more parameters indicative of usage behavior of the users of the vehicle-based communication network.
22 . The one or more non-transitory computer readable media of claim 12 , wherein the plurality of operational parameters comprises one or more parameters indicative of subscription of a user to one or more service terms for the vehicle-based communication network.
23 . A computing system comprising:
one or more processors; and
one or more memories storing non-transitory instructions that, when executed via the one or more processors, cause the computing system to:
train, based upon training data, a proxy model to analyze values of a plurality of operational parameters associated with providing a vehicle-based communication network of a vehicle to generate measurements of quality of user experience in the vehicle-based communication network, the training data comprising training sets of operational parameter values associated with uses of the vehicle-based communication network by users;
obtain a first observed set of operational parameter values corresponding to use of the vehicle-based communication network by a first user during a first transit of the vehicle; and
obtain, based at least in part on the first observed set of operational parameter values, an output of the trained proxy model, wherein the output comprises a first measurement of quality of user experience for the first user of the vehicle-based communication network during the first transit of the vehicle.