System and method to facilitate troubleshooting and predicting application performance in wireless networks

1. A method comprising:

gathering current wireless local area network (WLAN) data for a WLAN, wherein the WLAN data comprises network data, Radio Frequency (RF) data, and transmission data for a plurality of user equipment (UE) operating within the WLAN;

generating a plurality of color maps, wherein each color map displays color information associated with operational characteristics of the WLAN;

merging the plurality of color maps to generate a combined color map; and

calculating a predicted application score for at least one UE operating within the WLAN based, at least in part, on application of the combined color map to a trained statistical model that represents linking relationships between the WLAN data gathered for the WLAN and a plurality of possible application scores for the plurality of UE.

2. The method of claim 1 , wherein the plurality of color maps comprise:

an RF color map associated with RF data gathered for the WLAN;

a transmission color map associated with transmission data gathered for the WLAN; and

a Quality of Service (QoS) color map associated with QoS packet marking data gathered for the plurality of UE.

3. The method of claim 2 , wherein the RF color map illustrates brightness information indicating one or more RF signal deviations from averages.

4. The method of claim 1 , further comprising:

gathering historical WLAN data over a plurality of time intervals;

gathering historical application score data for each respective UE of the plurality of UE over the plurality of time intervals, wherein the historical application score data gathered for each respective UE is associated with application performance of a respective application operating via each respective UE over the plurality of time intervals;

generating, for each time interval, a plurality of color maps;

generating, for each time interval, a combined color map based on the plurality of color maps; and

training the statistical model for each time interval using the combined color map and at least one historical application score for at least one UE.

5. The method of claim 4 , wherein the training is performed to train the statistical model to converge towards a minimized error criterion.

6. The method of claim 4 , wherein the statistical model is trained for each of a plurality of application score classes.

7. The method of claim 1 , further comprising:

gathering a current application score for a particular UE operating at a particular location;

comparing the current application score to a plurality of application score classes;

identifying a particular application score class to which the current application score belongs; and

identifying one or more operational characteristics of the WLAN that caused the current application score based on application of WLAN data for the particular UE at the particular location to the trained statistical model.

8. The method of claim 7 , further comprising displaying at least one of:

the predicted application score for the particular UE at the particular location on at least one color map of the plurality of color maps or on the combined color map; and

the predicted application score and the current application score for the particular UE at the particular location on at least one color map of the plurality of color maps or on the combined color map.

9. The method of claim 7 , further comprising:

calculating an error between the current application score and the predicted application score;

comparing the calculated error to an error threshold; and

based on a determination that the calculated error does not satisfy the error threshold, re-training the statistical model using the current application score for the particular UE.

10. One or more non-transitory tangible media encoding logic that includes instructions for execution by a processor, wherein the execution causes the processor to perform operations, comprising:

gathering current wireless local area network (WLAN) data for a WLAN, wherein the WLAN data comprises network data, Radio Frequency (RF) data, and transmission data for a plurality of user equipment (UE) operating within the WLAN;

generating a plurality of color maps, wherein each color map displays color information associated with operational characteristics of the WLAN;

merging the plurality of color maps to generate a combined color map; and

calculating a predicted application score for at least one UE operating within the WLAN based, at least in part, on application of the combined color map to a trained statistical model that represents linking relationships between the WLAN data gathered for the WLAN and a plurality of possible application scores for the plurality of UE.

11. The media of claim 10 , wherein the plurality of color maps comprise:

an RF color map associated with RF data gathered for the WLAN;

a transmission color map associated with transmission data gathered for the WLAN; and

a Quality of Service (QoS) color map associated with QoS packet marking data gathered for the plurality of UE.

12. The media of claim 10 , wherein the execution causes the processor to perform further operations, comprising:

gathering historical WLAN data over a plurality of time intervals;

gathering historical application score data for each respective UE of the plurality of UE over the plurality of time intervals, wherein the historical application score data gathered for each respective UE is associated with application performance of a respective application operating via each respective UE over the plurality of time intervals;

generating, for each time interval, a plurality of color maps;

generating, for each time interval, a combined color map based on the plurality of color maps; and

training the statistical model for each time interval using the combined color map and at least one historical application score for at least one UE.

13. The media of claim 12 , wherein the statistical model is trained for each of a plurality of application score classes.

14. The media of claim 10 , wherein the execution causes the processor to perform further operations, comprising:

gathering a current application score for a particular UE operating at a particular location;

comparing the current application score to a plurality of application score classes;

identifying a particular application score class to which the current application score belongs; and

identifying one or more operational characteristics of the WLAN that caused the current application score based on application of WLAN data for the particular UE at the particular location to the trained statistical model.

15. The media of claim 14 , wherein the execution causes the processor to perform further operations, comprising displaying at least one of:

the predicted application score for the particular UE at the particular location on at least one color map of the plurality of color maps or on the combined color map; and

the predicted application score and the current application score for the particular UE at the particular location on at least one color map of the plurality of color maps or on the combined color map.

16. The media of claim 14 , wherein the execution causes the processor to perform further operations, comprising:

calculating an error between the current application score and the predicted application score;

comparing the calculated error to an error threshold; and

based on a determination that the calculated error does not satisfy the error threshold, re-training the statistical model using the current application score for the particular UE.

17. A wireless network management system, comprising:

at least one memory element for storing data; and

at least one processor for executing instructions associated with the data, wherein the executing causes the wireless network management system to perform operations, comprising:

gathering current wireless local area network (WLAN) data for a WLAN, wherein the WLAN data comprises network data, Radio Frequency (RF) data, and transmission data for a plurality of user equipment (UE) operating within the WLAN;

generating a plurality of color maps, wherein each color map displays color information associated with operational characteristics of the WLAN;

merging the plurality of color maps to generate a combined color map; and

calculating a predicted application score for at least one UE operating within the WLAN based, at least in part, on application of the combined color map to a trained statistical model that represents linking relationships between the WLAN data gathered for the WLAN and a plurality of possible application scores for the plurality of UE.

18. The wireless network management system of claim 17 , wherein the plurality of color maps comprise:

an RF color map associated with RF data gathered for the WLAN;

a transmission color map associated with transmission data gathered for the WLAN; and

a Quality of Service (QoS) color map associated with QoS packet marking data gathered for the plurality of UE.

19. The wireless network management system of claim 17 , wherein the executing causes the wireless network management system to perform further operations, comprising:

gathering historical WLAN data over a plurality of time intervals;

gathering historical application score data for each respective UE of the plurality of UE over the plurality of time intervals, wherein the historical application score data gathered for each respective UE is associated with application performance of a respective application operating via each respective UE over the plurality of time intervals;

generating, for each time interval, a plurality of color maps;

generating, for each time interval, a combined color map based on the plurality of color maps; and

training the statistical model for each time interval using the combined color map and at least one historical application score for at least one UE.

20. The wireless network management system of claim 17 , wherein the executing causes the wireless network management system to perform further operations, comprising:

gathering a current application score for a particular UE operating at a particular location;

comparing the current application score to a plurality of application score classes;

identifying a particular application score class to which the current application score belongs; and

identifying one or more operational characteristics of the WLAN that caused the current application score based on application of WLAN data for the particular UE at the particular location to the trained statistical model.