Telecommunications network node and methods

• US 8,942,753 B2
• Filed: 08/03/2010
• Issued: 01/27/2015
• Est. Priority Date: 09/10/2009
• Status: Active Grant

• Current Assignee: WSOU Investments, LLC (WSOU Holdings, LLC)
• Original Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Inventors: Claussen, Holger, Ho, Lester T. W.
• Primary Examiner(s): Nguyen, Simon
• Application Number:
  US13/394,849
• Publication Number:
  US 20120225628A1
• Time in Patent Office: 1,638 Days
• Field of Search: 455/69, 4551151-1154, 455/127.1, 455/522
• US Class Current: 455/522
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: H04W 24/02 Arrangements for optimising operational condition
• CPC Class Codes: H04W 52/143 Downlink power control
• CPC Class Codes: H04W 52/325 Power control of control or pilot channels
• CPC Class Codes: H04W 52/343 taking into account loading or congestion level
• CPC Class Codes: H04W 84/045 using private Base Stations, e.g. femto Base Stations, home Node B

Abstract

A telecommunications network node and methods are disclosed. The method is for generating, by genetic programming, evolved algorithms for adjusting base station transmission power to control coverage of a cell to assist in providing desired base station operating characteristics. The method comprises the steps of: generating, using predetermined functions and terminals defined in a functions and terminals list, a plurality of evolved algorithms each of which determine whether, for any particular base station operating conditions, to adjust said base station transmission power; determining a fitness level indicative of each evolved algorithm'"'"'s ability to adjust base station transmission power to control coverage to achieve said desired base station operating characteristics under expected operating conditions; and iteratively performing said steps of generating and determining to generate further evolved algorithms using at least one of said evolved algorithms determined to have achieved a particular fitness level. In this way, it can be seen that algorithms can be assembled and then tested to see how suitable they are at controlling base station power to achieve particular operating characteristics under particular operating conditions. Those algorithms which are determined to be the best at achieving those characteristics under those particular operating conditions may then be used to generate further algorithms which, in turn, are also then assessed. Hence, those individual algorithms which are suited to the operating characteristics and the operating conditions can rapidly be generated thereby avoiding the need to manually design new algorithms which are specialized to particular environments.

Claims

1. A method of generating, by genetic programming, evolved algorithms for adjusting base station transmission power to control coverage of a cell to assist in providing desired base station operating characteristics, said method comprising:

generating, using predetermined functions and terminals defined in a functions and terminals list, a plurality of evolved algorithms each of which determine whether, for any particular base station operating conditions, to adjust said base station transmission power;

determining a fitness level indicative of each evolved algorithm'"'"'s ability to adjust base station transmission power to control coverage to achieve said desired base station operating characteristics under expected operating conditions, wherein the determining comprises simulating operation of a base station when using each of said evolved algorithms under said expected operating conditions to generate simulated operating metrics and applying those simulated operating metrics to a fitness function representing said desired base station operating characteristics to determine said fitness level; and

iteratively performing the generating and determining to generate further evolved algorithms using at least one of said evolved algorithms determined to have achieved a particular fitness level.

2. The method of claim 1, wherein said functions comprise at least a conditional statement operable to select from different outcomes based on a relationship between a base station operating condition and its corresponding desired base station operating characteristic.

3. The method of claim 2, wherein said conditional statement comprises an if_else statement operable to select a first outcome when said particular base station operating condition is greater than a predetermined threshold related to said corresponding desired base station operating characteristic and else to select a second outcome.

4. The method according to claim 2, wherein the conditional statement comprises an if_X_large statement that selects a first branch when X is larger than a predetermined threshold X_thr and selects a second branch when X is not larger that the predetermined threshold X_thr.

5. The method of claim 1, wherein said terminals comprise at least one of an increase, a decrease and no change in base station transmission power.

6. The method of claim 1, wherein operators of said fitness function are weighted to assist in adjusting said fitness function to represent adjustments in said desired base station operating characteristics.

7. The method of claim 1, wherein said fitness function comprises:

8. The method of claim 7, wherein C_L is determined in accordance with the following algorithm:

9. The method of claim 1, comprising:

selecting a desired one of said evolved algorithms and creating a decision table representative of said desired one of said evolved algorithms, said decision table being operable to indicate to a base station adjustments to said base station transmission power required for any particular base station operating conditions.

10. The method of claim 9, wherein said decision table contains an entry for each permutation of a relationship between a base station operating condition and an associated threshold, each entry indicating an adjustment to said base station transmission power required for that permutation.

11. The method according to claim 10, wherein the adjustment to the base station transmission power required for each permutation is calculated by applying base station operating conditions representative of the given permutation to the desired one of the evolved algorithms.

12. A computer program product encoded with computer-executable instructions for performing the method claim 1.

13. A method of utilizing an evolved algorithm for adjusting base station transmission power to control coverage of a cell to assist in providing desired base station operating characteristics, said method comprising:

determining base station operating conditions; and

applying said base station operating conditions to said evolved algorithm to determine whether to adjust said base station transmission power, wherein said base station operating conditions comprise at least one of a base station load, a coverage overlap and a coverage gap, and determining comprises a corresponding at least one of;

determining said base station load by measuring a highest load experienced by a base station over a predetermined period;

determining said coverage overlap from received measurement reports indicating transmissions received from other base stations having a power greater than a predetermined threshold; and

determining said coverage gap from information received indicative of at least one of numbers of handovers and numbers of dropped calls due to lack of coverage which have occurred.

14. A network node operable to generate, by genetic programming, evolved algorithms for adjusting base station transmission power to control coverage of a cell to assist in providing desired base station operating characteristics, said network node comprising:

generating logic operable to generate, using predetermined functions and terminals defined in a functions and terminals list, a plurality of evolved algorithms each of which determine whether, for any particular base station operating conditions, to adjust said base station transmission power;

determining logic operable to determine a fitness level indicative of each evolved algorithm'"'"'s ability to adjust base station transmission power to control coverage to achieve said desired base station operating characteristics under expected operating conditions, wherein the determining logic simulates operation of a base station when using each of the evolved algorithms under the expected operating conditions to generate simulated operating metrics and applies the simulated operating metrics to a fitness function representing the desired base station operating characteristics to determine the fitness level; and

control logic operable to control said generating logic and determining logic to iteratively generate a further plurality of evolved algorithms using at least one of said evolved algorithms determined to have achieved a particular fitness level.

15. The network node of claim 14, wherein said network node comprises one of a genetic programming unit and a base station.