System and method for root cause analysis of call failures in a communication network

1. A system for root cause analysis of cellular network failures comprising:

an aggregator which receives data on network anomalies from at least one self-organizing network (SON) node;

a correlator which receives data on network issues from a group comprising:

an aggregator; and

an anomaly detector;

a machine learning processor which receives network status data from a group comprising:

the correlator;

a configuration management watchdog; and

a network watchdog;

wherein the machine learning processor is physically configured to analyze the network status data to determine a possible root cause and most likely causes or solutions from a plurality of possible solutions based on past problems and previous solutions; and

a root cause analysis operator in communication with the machine learning processor comprising a root cause processor physically configured to analyze the possible root cause, determine a ranking of possible solutions according to a ranking algorithm, wherein the ranking represents radio frequency analysis of the possible solutions, and create a response selected from at least one of the following:

report;

actions;

alerts; and

optimization.

2. The system of claim 1 further comprising a cost optimizer which reviews the response from the root cause analysis operator and determines at least one of response to be selected.

3. The system of claim 1 , wherein the SON nodes comprise root cause analysis elements in communication with the cellular network, which track their status and report their status to an aggregator.

4. The system of claim 1 , wherein the aggregator receives data from the SON nodes wherein the data comprises at least one from the group comprising:

parameter changes;

neighbor changes;

tilt changes;

current parameters;

current neighbors; and

changes on neighbors.

5. The system of claim 1 , wherein proprietary databases of previous data and solutions are used to further train a machine learning system.

6. The system of claim 5 , wherein the machine learning system selects a correction for a problem and validates the correction occurred.

7. The system of claim 6 , wherein if the system cannot validate the correction has occurred, the system communicates alerts to supervisors.

8. The system of claim 1 , further comprising a mechanical device which is adapted to implement solutions to implement the action identified as the response.

9. A method for root cause analysis of cellular network failures comprising:

collecting network data at self-organizing network (SON) nodes;

communicating the network data to an aggregator where the network data is aggregated into aggregate data;

communicating the aggregate data to a correlator;

communicating anomaly data to the correlator;

analyzing the aggregate data and the anomaly data to determine root cause data;

receiving at a machine learning processor network status data wherein network status data comprises:

the root cause data;

configuration data from a configuration management watchdog; and

network watchdog data;

analyzing the network status data to determine a possible root cause and most likely solutions from a plurality of solutions based on past problems and previous solutions;

analyzing in a root cause analysis operator possible solutions from the mostly likely solutions;

determining a ranking of the possible solutions according to a ranking algorithm wherein the ranking represents radio frequency analysis of the possible solutions;

creating a response to the possible root cause by following the ranking of possible solutions;

communicating the response to the network; and

monitoring if the response has a desired result;

if the response does not have the desired result, selecting a subsequent response; and

if the subsequent response does not have the desired result, communicating an alert to a supervisor.

10. The method of claim 9 , further comprising

determining a ranking of responses according to impact on the network; and

implementing the responses in order of the determined ranking.

11. The method of claim 10 , wherein the responses are communicated to a task manager.

12. The method of claim 10 , wherein responses are selected from a group comprising:

a report;

actions;

alerts; and

optimization steps.

13. The method of claim 9 , further comprising:

using a machine learning system to analyze past problems and solutions; and

improving problem response and solutions based on the machine learning system.

14. The method of claim 9 , further comprising implementing a mechanical device which is adapted to implement solutions to implement the action identified as the response.

15. A tangible computer readable medium comprising computer executable instructions for executing a root cause analysis of cellular network failures comprising instructions for:

collecting network data at self-organizing network (SON) nodes;

communicating the network data to an aggregator where the network data is aggregated into aggregate data;

communicating the aggregate data to a correlator;

communicating anomaly data to the correlator;

analyzing the aggregate data and anomaly data to determine root cause data;

receiving at a machine learning processor network status data wherein network status data comprises:

the root cause data;

configuration data from a configuration management watchdog; and

a network watchdog;

analyzing the network status data to determine and select a most likely solution from a plurality of solutions based on past problems and previous solutions;

analyzing in a root cause analysis operator possible solutions;

determining a ranking of the possible solutions according to a ranking algorithm wherein the ranking represents radio frequency analysis of the possible solutions;

creating a response to the possible root cause by following the ranking of possible solutions;

communicating the response to the network; and

monitoring if the response has a desired result;

if the response does not have the desired result, selecting a subsequent response; and

if the subsequent response does not have the desired result, communicating an alert to a supervisor.

16. The tangible computer readable medium of claim 14 , further comprising:

determining a ranking of a response according to a ranking algorithm, wherein the ranking represents a monetary cost, an amount of time for the response to occur and an amount of labor to implement the response; and

implementing the responses in order of the determined ranking.

17. The tangible computer readable medium of claim 15 , wherein the responses are communicated to a task manager.

18. The tangible computer readable medium of claim 15 , wherein responses are selected from a group comprising:

a report;

actions;

alerts; and

optimization steps.

19. The tangible computer readable medium of claim 15 , further comprising:

using an machine learning system to analyze past problems and solutions; and

improving problem response and solutions based on the machine learning analysis.

20. The tangible computer readable medium of claim 15 , further comprising instructing a mechanical device which is adapted to implement solutions to implement the action identified as the response.