Cognitive adaptive network management areas

• US 7,843,822 B1
• Filed: 05/24/2007
• Issued: 11/30/2010
• Est. Priority Date: 05/24/2007
• Status: Active Grant

• Current Assignee: Rockwell Collins, Inc. (Rtx Corporation)
• Original Assignee: Rockwell Collins, Inc. (Rtx Corporation)
• Inventors: Kloster, Bruce S., Paul, Lizy
• Primary Examiner(s): Yao; Kwang B
• Assistant Examiner(s): Ngo; Nguyen
• Application Number:
  US11/805,666
• Time in Patent Office: 1,286 Days
• Field of Search: 370/229, 370/230, 370/230.1, 370/235, 370/249, 370/254, 370/310.2, 370/311, 370/328, 455/404.2, 455/414.2, 455/423, 455/456.1, 455/67.11, 455/69, 455/115.1, 455/126
• US Class Current: 370/230
• CPC Class Codes: H04L 41/0816 the condition being an adaptation, e.g. in response to network events
• CPC Class Codes: H04L 41/12 Discovery or management of network topologies
• CPC Class Codes: H04W 16/18 Network planning tools

Abstract

A radio network management system, having at least one centralized node is described. The at least one centralized node includes a radio transceiver having more than one adjustable parameter. The centralized node also includes at least one adaptive tuning engine configured to make changes to the at least one adjustable parameter. A weighted analysis function is configured to provide a weighted analysis based on the output of the at least one adaptive tuning engine. Further, a cognitive learning function is configured to provide feedback to make optimally directed adjustments to the at least one adaptive tuning engine.

Claims

1. A radio network management system, having at least one centralized node, the at least one centralized node comprising:

a radio transceiver having a plurality of adjustable parameters;

a plurality of adaptive tuning engines configured to make changes to the adjustable parameters, the plurality of adaptive tuning engines including a terrain-aware topology tuning engine;

a weighted analysis engine configured to provide a weighted analysis based on the outputs of the adaptive tuning engines, the weighted analysis prioritizing the adaptive tuning engines based on relative importance of the adaptive tuning engines; and

a cognitive learning engine configured to provide feedback to make optimally directed adjustments to the plurality of adaptive tuning engines, wherein the cognitive learning engine is configured for providing feedback to the terrain-aware topology tuning engine based on terrain information for allowing the system to predict link disruptions based on said terrain information, the cognitive learning engine including a situation aware cognitive application model, the situation aware cognitive application model being configured for initiating a bandwidth shaping application for filtering unconnected traffic to the node from using specified resources in a specified geographic location.

2. The radio network management system of claim 1, wherein the cognitive learning engine comprises a game theory based cognitive traffic shaping model.

3. The radio network management system of claim 1, wherein the cognitive learning engine comprises a queue and buffer analysis of network radio nodes within a geographical location.

4. The radio network management system of claim 1, wherein the cognitive learning engine comprises a battery state analysis of network radio nodes within a geographical location.

5. The radio network management system of claim 1, wherein the cognitive learning engine comprises a radio resource analysis of network radio nodes within a geographical location.

6. The radio network management system of claim 1, wherein the cognitive learning engine comprises a flow localization analysis of network radio nodes within a geographical location.

7. A radio transceiver having a plurality of adjustable parameters, the radio transceiver being part of a network of radio nodes, comprising:

a processing circuit; and

a program running on the processing circuit, the program including;

a plurality of adaptive tuning engines configured to make changes to the adjustable parameters, the plurality of adaptive tuning engines including a terrain-aware topology tuning engine;

a weighted analysis engine configured to provide a weighted analysis based on the outputs of the adaptive tuning engines, the weighted analysis prioritizing the adaptive tuning engines based on relative importance of the adaptive tuning engines; and

a cognitive learning engine configured to provide feedback to make optimally directed adjustments to the adaptive tuning engines, wherein the cognitive learning engine is configured for providing feedback to the terrain-aware topology tuning engine based on terrain information for allowing the system to predict link disruptions based on said terrain information, the cognitive learning engine including a situation aware cognitive application model, the situation aware cognitive application model being configured for initiating a bandwidth shaping application for filtering unconnected traffic to the radio transceiver from using specified resources in a specified geographic location.

8. The radio transceiver of claim 7, wherein the cognitive learning engine comprises a game theory based cognitive traffic shaping model.

9. The radio transceiver of claim 7, wherein the cognitive learning engine comprises a queue and buffer analysis of network radio nodes within a geographical location.

10. The radio transceiver of claim 7, wherein the cognitive learning engine comprises a battery state analysis of network radio nodes within a geographical location.

11. The radio transceiver of claim 7, wherein the cognitive learning engine comprises a radio resource analysis of network radio nodes within a geographical location.

12. The radio transceiver of claim 7, wherein the cognitive learning engine comprises a flow localization analysis of network radio nodes within a geographical location.