PREDICTIVE MANAGEMENT OF BANDWIDTH CONSUMPTION AND DYNAMIC SATELLITE CONNECTIVITY MANAGEMENT
A system and method are disclosed to efficiently utilize the satellite bandwidth consumed by an air to ground service application that collects data for support and analysis of an on-wing Line Replacement Unit (LRU). The method statistically analyzes the data rates for air to ground traffic and dynamically modifies established bandwidth caps to minimize the available bandwidth needed for groundside reporting. In addition, the system and method comprise a framework to forecast the health of network connectivity. This system adeptly identifies and predicts faults or events in the avionics communication system during flights. Additionally, it leverages the forecasted network performance to implement actions that enhance the overall user experience.
1 . A method, comprising:
establishing a data link service session of a channel within a communication system, the channel corresponding to one or more configured network bandwidth settings;
measuring a plurality of samples of data traffic on the channel to obtain one or more data bandwidth rates and one or more data burst rates for the data traffic, wherein each sample of the plurality of samples corresponds to a respective start time and a respective stop time within the data link service session of the channel;
comparing, based at least in part on the measuring, the one or more data bandwidth rates and the one or more data burst rates to the one or more configured network bandwidth settings; and
adjusting, based at least in part on the comparing and based at least in part on a target quality of data communication for the channel, the one or more configured network bandwidth settings for the channel to an acceptable lowest bandwidth value.
2 . The method of claim 1 , wherein the one or more configured network bandwidth settings comprise a data rate cap, a burst cap, a never-exceed data rate cap, and a never-exceed burst cap.
3 . The method of claim 1 , further comprising:
calculating, based at least in part on obtaining the one or more data bandwidth rates and the one or more data burst rates for the data traffic, an average data rate, a maximum burst rate, an average burst rate, a standard deviation of burst rates for the channel, or any combination thereof for the channel.
4 . The method of claim 3 , wherein comparing the one or more data bandwidth rates and the one or more data burst rates to the one or more configured network bandwidth settings comprises:
comparing the average data rate with the one or more configured network bandwidth settings, wherein the one or more configured network bandwidth settings comprise a never-exceed data rate cap and a data rate cap.
5 . The method of claim 4 , wherein adjusting the one or more configured network bandwidth settings comprises:
adjusting the data rate cap based at least in part on comparing the average data rate with the data rate cap and the never-exceed data rate cap.
6 . The method of claim 4 , wherein adjusting the one or more configured network bandwidth settings comprises:
setting the data rate cap to the never-exceed data rate cap based at least in part on the average data rate being greater than the never-exceed data rate cap;
adjusting the data rate cap to be a threshold percentage greater than the average data rate based at least in part on the average data rate being greater than or equal to the data rate cap but less than the never-exceed data rate cap;
maintaining the data rate cap based at least in part on the average data rate being within a threshold range of the data rate cap; or
adjusting the data rate cap to be a threshold percentage less than the average data rate based at least in part on the average data rate being more than a threshold less than the data rate cap.
7 . The method of claim 3 , wherein comparing the one or more data bandwidth rates and the one or more data burst rates to the one or more configured network bandwidth settings comprises:
comparing the maximum burst rate with the one or more configured network bandwidth settings, wherein the one or more configured network bandwidth settings comprise a burst rate cap, a never-exceed data rate cap, or both.
8 . The method of claim 7 , wherein adjusting the one or more configured network bandwidth settings comprises:
adjusting the burst rate cap based at least in part on comparing the maximum burst rate with the burst rate cap.
9 . The method of claim 7 , wherein adjusting the one or more configured network bandwidth settings comprises:
setting the burst rate cap to the never-exceed data rate cap based at least in part on the maximum burst rate being greater than the never-exceed data rate cap;
increasing the burst rate cap by the standard deviation of the burst rates based at least in part on the maximum burst rate being greater than the burst rate cap;
maintaining the burst rate cap based at least in part on the average burst rate being within a threshold range of the burst rate cap; or
lowering the burst rate cap by the standard deviation of the burst rates based at least in part on the maximum burst rate being less than the burst rate cap.
10 . A method, comprising:
inputting, to a forecasting model, one or more route plans for one or more candidate routes of a vehicle;
predicting, using the forecasting model and based at least in part on the one or more route plans, one or more network performance scores, each network performance score of the one or more network performance scores indicating a respective predicted level of connectivity in a data network on the vehicle during a respective route plan of the one or more route plans;
outputting, via a user interface, the one or more network performance scores; and
receiving, via the user interface, a selection of an optimal route plan from among the one or more route plans that optimizes in-route network connectivity based at least in part on the one or more network performance scores.
11 . The method of claim 10 , wherein each route plan of the one or more route plans indicates a respective physical path of the vehicle from an origin to a destination.
12 . The method of claim 10 , wherein each network performance score of the one or more network performance scores is based at least in part on predicted radio frequency performance during the respective route plan, one or more predicted blockages during the respective route plan, congestion metrics associated with the respective route plan, dynamic flight metrics associated with the respective route plan, or any combination thereof.
13 . The method of claim 10 , further comprising:
training, using training data, the forecasting model to predict the one or more network performance scores of the data network on the vehicle based at least in part on a route of the vehicle.
14 . The method of claim 10 , wherein the forecasting model comprises a Recurrent Neural Network-based forecasting model.
15 . The method of claim 10 , wherein the forecasting model comprises a K nearest neighbor algorithm-based forecasting model.
16 . The method of claim 10 , wherein the forecasting model comprises a support vector regression-based forecasting model.
17 . A method, comprising:
processing, after a flight is complete, data indicative of one or more events that occurred during the flight;
classifying, in accordance with one or more classification models, the data into one or more categories; and
scoring, based at least in part on classifying the data, a performance of an avionics communication system during the flight.
18 . The method of claim 17 , further comprising:
generating, based at least in part on classifying the data, a quality of service score and a quality of experience score for the avionics communication system during the flight, wherein scoring the performance is based at least in part on a combination of the quality of service score and the quality of experience score.
19 . The method of claim 18 , further comprising:
applying a semi-supervised learning process to generate one or more pseudo-labels for unlabeled flight data;
combining a labeled dataset with a second dataset including the one or more pseudo-labels to create a training dataset; and
training a classification model based at least in part on the training dataset, wherein classifying the data into the one or more categories is based at least in part on the classification model.
20 . The method of claim 19 , wherein the classification model comprises one of a bagging ensemble classifier, a stacking ensemble classifier, or a boosting ensemble classifier.