Systems and methods for applying a proxy model of network quality to adjust network hardware or software
A machine learning-based proxy model may generate measurements of quality of user experience in a vehicle-based communication network in the absence of direct feedback from the users regarding the user experience. Once generated, the proxy model may be applied to observed operational parameters of the on-board network to quantify the user experience for any user in any given instance. User experience measurements (e.g., trends identified therein) may be utilized to identify and implement adjustments to hardware, firmware, software, and/or service procedures associated with implementation of the on-board network. These adjustments may be implemented between transits of the vehicle, or in some cases, during transit of the vehicle to improve the user experience over the duration of use of the vehicle-based communication network.
1 . A computer-implemented method implemented via one or more processors, the computer-implemented method comprising:
obtaining a first observed set of operational parameter values corresponding to use of a vehicle-based communication network of a vehicle by a first user during a first transit of the vehicle;
obtaining, based at least in part on the first observed set of operational parameter values, a first output of a trained proxy model, wherein the first 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; and
causing, based at least in part on the first measurement of quality of user experience, one or more adjustments to the vehicle-based communication network, wherein a second measurement of quality of user experience for the first user or one or more second users of the vehicle-based communication network exceeds the first measurement of quality of user experience based at least in part on the one or more adjustments.
2 . The computer-implemented method of claim 1 , wherein the vehicle is an aircraft.
3 . The computer-implemented method of claim 1 , wherein the trained proxy model includes a k-means clustering model.
4 . The computer-implemented method of claim 1 , wherein the trained proxy model includes one or more artificial neural networks.
5 . The computer-implemented method of claim 1 , wherein the one or more processors include one or more processors on-board the vehicle.
6 . The computer-implemented method of claim 1 , wherein the one or more processors include one or more processors in a ground-based network external to the vehicle.
7 . The computer-implemented method of claim 1 , wherein the obtaining of the first output of the trained proxy model is performed during a transit of the vehicle, wherein the first observed set of operational parameter values corresponds to a first portion of the transit of the vehicle, and wherein the causing of the one or more adjustments to the vehicle-based communication network is performed during the transit of the vehicle.
8 . The computer-implemented method of claim 7 , further comprising, subsequent to the causing of the one or more adjustments to the vehicle-based communication network:
obtaining a second observed set of operational parameter values corresponding to use of the vehicle-based communication network by the first user during a second portion of the first transit of the vehicle;
obtaining, based at least in part on the second observed set of operational parameter values, a second output of the trained proxy model, wherein the second output comprises the second measurement of quality of user experience for the first user of the vehicle-based communication network during the second portion of the first transit of the vehicle; and
comparing the first and second measurements of the quality of user experience for the first user to determine an efficacy of the one or more adjustments to the vehicle-based communication network.
9 . The computer-implemented method of claim 1 , further comprising, subsequent to the causing of the one or more adjustments to the vehicle-based communication network:
obtaining a further one or more observed sets of operational parameter values corresponding to use of the vehicle-based communication network by the first user or the one or more second users during a second transit of the vehicle;
obtaining, based at least in part on the further one or more observed sets of operational parameter values, one or more second outputs of the trained proxy model, wherein the one or more second outputs comprise the second measurement of quality of user experience for the first user or for the one or more second users of the vehicle-based communication network during the second transit of the vehicle; and
comparing the first measurement to the further one or more measurements to determine an efficacy of the one or more adjustments to the vehicle-based communication network.
10 . The computer-implemented method of claim 1 , wherein the one or more adjustments comprise a replacement, maintenance, or reconfiguration of one or more hardware components in the vehicle-based communication network.
11 . The computer-implemented method of claim 1 , wherein the one or more adjustments comprise a replacement, update, reversion, or reconfiguration of one or more software elements in the vehicle-based communication network.
12 . The computer-implemented method of claim 1 , wherein the one or more adjustments comprise an adjustment to availability of one or more services, applications, or web sites via the vehicle-based communication network.
13 . 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:
obtain a first observed set of operational parameter values corresponding to use of a vehicle-based communication network of a vehicle by a first user during a first transit of the vehicle;
obtain, based at least in part on the first observed set of operational parameter values, a first output of a trained proxy model, wherein the first 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; and
cause, based at least in part on the first measurement of quality of user experience, one or more adjustments to the vehicle-based communication network, wherein a second measurement of quality of user experience for the first user or one or more second users of the vehicle-based communication network exceeds the first measurement of quality of user experience based at least in part on the one or more adjustments.
14 . The one or more non-transitory computer readable media of claim 13 , wherein the vehicle is an aircraft.
15 . The one or more non-transitory computer readable media of claim 13 , wherein the trained proxy model includes a k-means clustering model.
16 . The one or more non-transitory computer readable media of claim 13 , wherein the trained proxy model includes one or more artificial neural networks.
17 . The one or more non-transitory computer readable media of claim 13 , wherein the one or more processors include one or more processors on-board the vehicle.
18 . The one or more non-transitory computer readable media of claim 13 , wherein the one or more processors include one or more processors in a ground-based network external to the vehicle.
19 . The one or more non-transitory computer readable media of claim 13 , wherein the obtaining of the first output of the trained proxy model is performed during a transit of the vehicle, wherein the first observed set of operational parameter values corresponds to a first portion of the transit of the vehicle, and wherein the causing of the one or more adjustments to the vehicle-based communication network is performed during the transit of the vehicle.
20 . The one or more non-transitory computer readable media of claim 19 , wherein the instructions, when executed via the one or more processors, further cause the one or more computing devices to:
obtain a second observed set of operational parameter values corresponding to use of the vehicle-based communication network by the first user during a second portion of the first transit of the vehicle;
obtain, based at least in part on the second observed set of operational parameter values, a second output of the trained proxy model, wherein the second output comprises the second measurement of quality of user experience for the first user of the vehicle-based communication network during the second portion of the first transit of the vehicle; and
compare the first and second measurements of the quality of user experience for the first user to determine an efficacy of the one or more adjustments to the vehicle-based communication network.
21 . The one or more non-transitory computer readable media of claim 13 , wherein the instructions, when executed via the one or more processors, further cause the one or more computing devices to:
obtain a further one or more observed sets of operational parameter values corresponding to use of the vehicle-based communication network by the first user or the one or more second users during a second transit of the vehicle;
obtain, based at least in part on the further one or more observed sets of operational parameter values, one or more second outputs of the trained proxy model, wherein the one or more second outputs comprise the second measurement of quality of user experience for the first user or for the one or more second users of the vehicle-based communication network during the second transit of the vehicle; and
compare the first measurement to the further one or more measurements to determine an efficacy of the one or more adjustments to the vehicle-based communication network.
22 . The one or more non-transitory computer readable media of claim 13 , wherein the one or more adjustments comprise a replacement, maintenance, or reconfiguration of one or more hardware components in the vehicle-based communication network.
23 . The one or more non-transitory computer readable media of claim 13 , wherein the one or more adjustments comprise a replacement, update, reversion, or reconfiguration of one or more software elements in the vehicle-based communication network.
24 . The one or more non-transitory computer readable media of claim 13 , wherein the one or more adjustments comprise an adjustment to availability of one or more services, applications, or web sites via the vehicle-based communication network.
25 . 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:
obtain a first observed set of operational parameter values corresponding to use of a vehicle-based communication network of a vehicle by a first user during a first transit of the vehicle;
obtain, based at least in part on the first observed set of operational parameter values, a first output of a trained proxy model, wherein the first 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; and
cause, based at least in part on the first measurement of quality of user experience, one or more adjustments to the vehicle-based communication network, wherein a second measurement of quality of user experience for the first user or one or more second users of the vehicle-based communication network exceeds the first measurement of quality of user experience based at least in part on the one or more adjustments.