Adaptive cross-layer cross-node optimization

• US 20070266134A1
• Filed: 05/11/2006
• Published: 11/15/2007
• Est. Priority Date: 05/11/2006
• Status: Active Grant

• Current Assignee: Mitre Corporation
• Original Assignee: Mitre Corporation
• Inventors: Szelc, Dawn, Dunyak, James, Shyy, Dong-Jye
• Granted Patent: US 7,975,036 B2
• Time in Patent Office: Days
• US Class Current: 709/223
• CPC Class Codes: H04W 24/00 Supervisory, monitoring or testing arrangements
• CPC Class Codes: H04W 28/06 Optimizing the usage of the radio link, e.g. header compression, information sizing , discarding information system modifying transmission characteristic according to link quality by modifying frame length H04L1/0007; dynamic adaptation of the packet size for flow control or congestion control H04L47/365
• CPC Class Codes: H04W 99/00 Subject matter not provided for in other groups of this subclass

Abstract

Methods and systems for adaptive cross-layer cross-node optimization in wireless communication systems are provided. Adaptive cross-layer cross-node optimization allows for conventional cross-layer optimization coupled with the ability to adaptively optimize cross-layer interactions across node boundaries. In one aspect, adaptive cross-layer cross-node optimization includes adaptively and dynamically shifting functions/layers among nodes in a network, so that a global network objective is achieved. In another aspect, adaptive cross-layer cross-node optimization includes adaptively and dynamically distributing functions/layers across a network, according to changes and/or events in the network. In a further aspect, adaptive cross-layer cross-node optimization includes dynamically defining or changing individual node functions within a network, so that a global network functionality may emerge.

Claims

1. A method for cross-layer cross-node optimization in a wireless network having a plurality of nodes, the method comprising:

a) sensing network conditions and/or events at one or more of the plurality of nodes;

b) determining, using a set of network objectives, an optimal distribution of functions among the plurality of nodes based on the sensed network conditions and/or events; and

c) distributing functions among the plurality of nodes according to the determined optimal distribution of functions, thereby optimizing the network for said set of network objectives.

2. The method of claim 1, wherein said sensing step is performed using one or more intelligent engines at said one or more plurality of nodes.

3. The method of claim 2, wherein said intelligent engines perform cross-layer optimization within respective ones of said one or more plurality of nodes, according to a set of node objectives associated with each of the plurality of nodes.

4. The method of claim 1, wherein said network conditions include one or more of network traffic conditions, network load conditions, network connectivity conditions, and network capacity conditions.

5. The method of claim 1, wherein said network events include one or more of network attacks, mission defining events, and node failure/addition events.

6. The method of claim 1, wherein steps (a)-(c) are repeatedly performed over the lifetime of the network, thereby providing dynamic cross-layer cross-node optimization.

7. The method of claim 1, wherein said determining step comprises:

i) communicating sensed network conditions and/or events between said one or more plurality of nodes;

ii) performing a joint optimization of functions between said one or more plurality of nodes based on said sensed network conditions and/or events, using said set of network objectives.

8. The method of claim 7, wherein said joint optimization of functions is distributedly performed by said one or more plurality of nodes of the network.

9. The method of claim 7, wherein said joint optimization of functions is centrally performed at a central node of the network.

10. The method of claim 1, wherein said set of network objectives includes one or more of security objectives, performance objectives, and mission constraints.

11. The method of claim 1, wherein said determining step comprises determining an optimal split of network model layers between said one or more plurality of nodes.

12. The method of claim 1, wherein said distributing step includes performing over-the-air software download of functions, according to the optimal distribution of functions, to said one or more plurality of nodes.

13. The method of claim 12, wherein said one or more plurality of nodes are configurable platform devices.

14. The method of claim 13, wherein said one or more plurality of nodes include software defined radio platforms.

15. The method of claim 13, wherein said one or more plurality of nodes include field programmable gated array platforms.

16. The method of claim 13, wherein said one or more plurality of nodes include tiled processors.

17. The method of claim 1, wherein said distributing step comprises shifting network model layers between an access controller and access points in a Wireless Local Area Network.

18. The method of claim 1, wherein said distributing step comprises distributing a Multiple Input Multiple Output layer to an optimal number of nodes to jointly form a virtual Multiple Input Multiple Output system.

19. The method of claim 1, wherein said distributing step comprises shifting functions between a base station and mobile stations in a cellular network.

20. The method of claim 1, wherein said distributing step comprises shifting function between routers and joint tactical radio system devices in a tactical network.