	US 12,238,535 B2
	Methods, systems, and computer program products for optimizing a predictive model for mobile network communications based on historical context information
Joseph Farkas, Merrimack, NH (US); Brandon Hombs, Merrimack, NH (US); and Barry West, Temple, NH (US)
Assigned to Signal Decode, Inc., Peterborough, NH (US)
Filed by Signal Decode, Inc., Peterborough, NH (US)
Filed on Aug. 16, 2021, as Appl. No. 17/403,144.
Application 17/403,144 is a continuation of application No. 15/967,379, filed on Apr. 30, 2018, granted, now 11,096,063.
Application 15/967,379 is a continuation of application No. 14/448,435, filed on Jul. 31, 2014, granted, now 9,961,560, issued on May 1, 2018.
Prior Publication US 2021/0377745 A1, Dec. 2, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. H04W 16/22 (2009.01); G06N 5/04 (2023.01); G06N 5/048 (2023.01); H04W 24/08 (2009.01)

CPC H04W 16/22 (2013.01) [G06N 5/048 (2013.01); H04W 24/08 (2013.01)]

20 Claims

1. A method for optimizing mobile network communications using network learning, the method comprising:

collecting historical context data representing performance of a plurality of mobile devices operating in a plurality of cells of a mobile network,

wherein the historical context data is collected in the plurality of cells of the mobile network, and

wherein the historical context data includes at least one of communication environment, communication parameter estimates, mobile device statistics, mobile device transmit settings, base station receiver settings, past network statistics and settings, and adjacent network node information statistics and settings;

building a predictive multi-cell model to optimize scheduling of network traffic in one or more cells of the mobile network based on the historical context data for the plurality of cells;

determining a first predictive cell-level model for a first cell based at least in part on the predictive multi-cell model;

determining a second predictive cell-level model for a second cell based at least in part on the predictive multi-cell model;

scheduling network traffic for a mobile device in the first cell using the first predictive cell-level model for the first cell;

scheduling network traffic for a mobile device in the second cell using the second predictive cell-level model for the second cell;

adjusting the first predictive cell-level model to further optimize scheduling of network traffic for the first cell based at least in part on additional information from the first cell;

adjusting the second predictive cell-level model to further optimize scheduling of network traffic for the second cell based at least in part on additional information from the second cell.