System and method for generating communication network performance alarms

• US 20030064720A1
• Filed: 10/03/2001
• Published: 04/03/2003
• Est. Priority Date: 10/03/2001
• Status: Abandoned Application

• Current Assignee: Telefonaktiebolaget LM Ericsson
• Original Assignee: Telefonaktiebolaget LM Ericsson
• Inventors: Valins, Daniel, Ruth, Todd, Cochran, Philip
• Application Number:
  US09/971,305
• Publication Number:
  US 20030064720A1
• Time in Patent Office: Days
• US Class Current: 455/423
• CPC Class Codes: H04W 24/00 Supervisory, monitoring or testing arrangements

Abstract

Statistical processing of event outcomes, such as call attempts or handoff attempts, allows reliable generation of performance alarms within a communication network without requiring analysis of historic performance data. Base station controllers might implement such statistical processing so that the controllers themselves rather than other, further removed network management entities generate performance alarms. Attendant advantages include but are not limited to relatively fast and reliable alarm generation using relatively small sample sets. These and other advantages permit detecting and reporting performance alarm conditions more quickly without sacrificing alarm generation reliability.

Claims

1. A method of alarm generation in a wireless communication network, the method comprising:

accumulating a data set of binomial outcomes for a plurality of events of a desired network event type;

inferring an outcome probability for said desired network event type from said data set using inferential statistical testing; and

generating an alarm if said outcome probability satisfies an alarm condition defined for said event type.

2. The method of claim 1 wherein accumulating the data set of binomial outcomes comprises accumulating binomial outcomes for a plurality of events of the desired network type at least until it is valid to approximate the binomial distribution of the data set as a normal distribution.

3. The method of claim 2 further comprising testing the data set to determine whether the normal distribution approximation is valid.

4. The method of claim 3 further comprising accumulating a new data set if the normal distribution approximation is not valid.

5. The method of claim 3 further comprising accumulating one or more additional binomial outcomes until testing of the data set determines that the normal distribution approximation is valid.

6. The method of claim 1 wherein accumulating a data set of binomial outcomes for a plurality of events of a desired network event type comprises recording the binomial outcome for each one of said plurality of communication network events as one of a successful outcome or a failed outcome.

7. The method of claim 1 wherein inferring an outcome probability from said data set using inferential statistical testing comprises inferring a probability of failure for said event type based on an observed incidence of failure in said data set.

8. The method of claim 7 wherein inferring a probability of failure for said event type based on an observed incidence of failure in said data set comprises performing a t-test or a z-score test.

9. The method of claim 1 wherein said alarm condition is a probability threshold, and wherein generating an alarm if said outcome probability satisfies an alarm condition defined for said event type comprises generating said alarm if said outcome probability exceeds said probability threshold.

10. The method of claim 1 wherein inferring an outcome probability for said desired network event type from said data set using inferential statistical testing comprises:

determining a desired confidence interval for performing a t-test using said data set; and

performing said t-test based on said confidence interval to determine said outcome probability.

11. The method of claim 10 wherein determining a desired confidence interval for said t-test comprises accessing a pre-determined confidence interval value.

12. The method of claim 10 further comprising calculating an estimated standard deviation for said data set in the performance of said t-test.

13. The method of claim 10 further comprising determining a t statistic for said t-test by table look-up.

14. The method of claim 10 further comprising determining a t statistic for said t-test by calculating a statistical formula.

15. The method of claim 1 further comprising performing the steps of claim 1 in a base station controller comprising a portion of said wireless communication network.

16. The method of claim 15 further comprising storing alarm information bearing on generating said alarm at said base station controller.

17. The method of claim 15 further comprising receiving said alarm information from a remote network entity.

18. The method of claim 15 further comprising reporting said alarm condition to a remote network entity.

19. The method of claim 18 wherein reporting said alarm condition comprises reporting said alarm to a network manager.

20. The method of claim 1 further comprising performing the steps of claim 1 in a radio base station comprising a portion of said wireless communication network.

21. The method of claim 1 further comprising performing the steps of claim 1 in a network manager comprising a portion of said wireless communication network.

22. The method of claim 1 further comprising generating alarms for a plurality of different types of communication network events based on inferentially determining outcome probabilities for said plurality of different types of communication network events.

23. The method of claim 22 further comprising storing an alarm threshold as said alarm condition for each of said plurality of different types of communication network events, such that the alarm condition for each event type is evaluated based on the corresponding alarm threshold.

24. The method of claim 1 further comprising defining said alarm condition as a configurable alarm threshold.

25. A method of generating performance alarms in a wireless communication network, the method comprising:

accumulating a sample set as a plurality of event outcomes for a communication event type, said event outcomes being recorded as one of an event failure and an event success;

determining a first failure rate for said plurality of event outcomes based on the number of event failures and event successes;

inferring by statistical analysis a general failure rate for said communication event type based on said first failure rate; and

determining whether an alarm condition exists based on comparing said general failure rate to an alarm threshold failure rate.

26. The method of claim 25 further comprising indicating an alarm condition if said general failure rate exceeds said alarm threshold failure rate.

27. The method of claim 25 wherein said alarm threshold failure rate comprises a pre-defined failure probability value, and wherein inferring by statistical analysis a general failure rate for said communication event type comprises determining a calculated failure probability value.

28. The method of claim 27 wherein determining whether an alarm condition exists based on comparing said general failure rate to said alarm threshold failure rate comprises comparing said calculated failure probability value to said pre-defined failure probability value.

29. The method of claim 25 wherein inferring by statistical analysis a general failure rate for said communication event type comprises performing a t-test using said sample set.

30. The method of claim 29 further comprising performing said t-test on a desired confidence interval.

31. The method of claim 29 further comprising performing said t-test as a one tail t-test.

32. The method of claim 29 further comprising performing said t-test as a two tailed t-test.

33. The method of claim 29 further comprising estimating a standard deviation for said sample set for use in performing said t-test.

34. The method of claim 25 wherein inferring by statistical analysis a general failure rate for said communication event type comprises performing one of a t-test or a z-score test using said sample set.

35. The method of claim 34 further comprising determining whether to perform a t-test or a z-score test based on one or more characteristics of said sample set.

36. The method of claim 25 wherein accumulating a sample set as a plurality of event outcomes for a communication event type comprises recording event outcomes at least until said sample set is suitable for use in inferential statistical analysis to infer said general failure rate.

37. The method of claim 36 further comprising determining whether said sample set is suitable for inferential statistical testing by evaluating one or more values determined as the product of the number of samples in said sample set and an observed incidence of failure for said sample set.

38. The method of claim 37 further comprising determining whether to use a statistical t-test or a statistical z-score test based on the number of samples in said sample set.

39. The method of claim 25 wherein accumulating a sample set as a plurality of event outcomes for a communication event type comprises accumulating event outcomes at least until a binomial distribution of said event outcomes may be approximated as a normal distribution.

40. The method of claim 25 wherein accumulating a sample set as a plurality of event outcomes for a communication event type comprises:

accumulating event outcomes for a defined accumulation interval; and

testing said sample set to determine if the binomial distribution of said event outcomes may be approximated as a normal distribution.

41. The method of claim 40 further comprising:

discarding said sample set if the normal distribution approximation cannot be used;

beginning a new accumulation interval over which a new sample set will be accumulated; and

repeating the test for normal distribution approximation after said new sample set is accumulated.

42. The method of claim 25 further comprising assuming a Student'"'"'s t-distribution for said sample set, and wherein inferring by statistical analysis a general failure rate for said communication event type comprises performing a t-test on said plurality of event outcomes.

43. The method of claim 25 further comprising defining said alarm threshold failure rate as a configurable alarm threshold failure rate.

44. A network entity for use in a wireless communication network, said network entity comprising a processor to provide performance alarm generation for at least one type of communication event by:

recording a plurality of event outcomes for said communication event type, each said event outcome recorded as one of an event failure and an event success;

determining a first failure rate for said plurality of event outcomes based on the number of event failures and event successes;

inferring by statistical analysis a general failure rate for said communication event type based on said first failure rate; and

determining whether an alarm condition exists based on comparing said general failure rate to an alarm threshold failure rate.

45. The network entity of claim 44 wherein said processor infers by statistical analysis said general failure rate by performing inferential statistical testing using said plurality of event outcomes recorded.

46. The network entity of claim 45 wherein said processor performs said inferential statistical testing as a Student'"'"'s t-test.

47. The network entity of claim 45 wherein said processor performs said inferential statistical testing as a z-score test.

48. The network entity of claim 44 wherein said processor accumulates event outcomes until a binomial distribution of said plurality of event outcomes may be approximated as a normal distribution.

49. The network entity of claim 44 wherein said processor accumulates event outcomes in each of one or more defined accumulation intervals until a binomial distribution of said plurality of event outcomes accumulated may be approximated as a normal distribution.

50. The network entity of claim 44 further comprising memory, and wherein said alarm threshold failure rate is stored in said memory.

51. The network entity of claim 50 wherein a plurality of said alarm threshold failure rates for a plurality of different communication event types is stored in said memory.

52. The network entity of claim 51 wherein said processor generates performance alarms for said plurality of different communication network event types based on said plurality of alarm threshold failure rates stored in said memory.

53. The network entity of claim 50 wherein said processor accumulates said event outcomes in said memory.

54. The network entity of claim 44 wherein said network entity receives said alarm threshold failure rate from a remote entity.

55. The network entity of claim 44 wherein said processor uses a confidence level used in said statistical testing, such that determination of whether said alarm condition exists is calculated with a desired confidence level.

56. The network entity of claim 44 wherein said processor generates a performance alarm for said type of communication network event if said alarm condition exists.

57. The network entity of claim 56 wherein said network entity notifies a remote entity of said alarm condition.

58. The network entity of claim 44 wherein said network entity generates performance alarms for a plurality of different types of communication events.

59. The network entity of claim 44 wherein said network entity comprises a base station controller.

60. The network entity of claim 44 wherein said network entity comprises a radio base station.

61. The network entity of claim 44 wherein said network entity comprises a network manager.

62. The network entity of claim 44 where said processor uses a configurable alarm threshold failure rate.