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Event driven grant allocation

  • US 8,780,823 B1
  • Filed: 10/08/2010
  • Issued: 07/15/2014
  • Est. Priority Date: 10/08/2009
  • Status: Active Grant
First Claim
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1. A method for event-driven predictive scheduling of a return link in a communication system comprising:

  • detecting, at a provider side of the communications system, an event predictor corresponding to a download packet associated with an HTTP object destined for a user terminal over a forward link of the communications system, the event predictor indicating a future event to occur in association with receipt of the download packet at the user terminal;

    selecting a prediction model according to the detected event predictor from a plurality of prediction models maintained at the provider side of the communications system, wherein each prediction model of the plurality of prediction models estimates a size or timing of a future communications event associated with the HTTP object;

    generating a resource allocation according to the selected prediction model at the provider side of the communications system, the resource allocation indicating a magnitude by which to adjust resources of the return link of the communications system associated with the user terminal, and the resource allocation indicating a timing offset at which to adjust the resources by the magnitude, the timing offset corresponding to an amount of time after occurrence of the event;

    scheduling the resources of the return link of the communications system associated with the user terminal at least according to the resource allocation; and

    communicating data corresponding to the data packet and data corresponding to the resource allocation to the user terminal over the forward link of the communications system;

    wherein selecting the prediction model according to the detected event predictor comprises;

    scanning the download packet to extract metadata, the metadata indicating at least one HTTP object likely to be requested subsequent to receiving the download packet at the user terminal; and

    selecting the prediction model at least according to the metadata.

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