Radio Access Network Service Mediated Enhanced Session Records for Artificial Intelligence or Machine Learning

1. A computer-implemented method of monitoring a improving radio access network (RAN) performance, the computer-implemented method comprising: receiving access stratum (AS) data that is a function of cell trace records (CTRs) associated with wireless communication transported to or from one or more cells of the RAN a radio access network (RAN), wherein the CTRs are obtained at a granularity sufficient to detect one or more radio resource control (RRC) events, the events including one or more events that define a segment and one or more events that occur during a segment, wherein a segment is defined by the beginning, end, or any handovers of a call included in the wireless communication; detecting in the access stratum (AS) data one or more state transitions as indicated by the one or more RRC events, wherein the one or more state transitions comprise a state transition of a call from a first state to a second state comprising an RRC idle state, an RRC connected state, an RRC connection re-establishment state, or a handover state; and outputting an enhanced session record (ESR) including information processed from the access stratum AS data associated with the respective one or more detected state transitions; inputting the ESR into a machine learning model to identify one or more call gaps associated with the one or more detected state transitions; and adjusting, based on the one or more identified call gaps, one or more parameters of the one or more cells of the RAN to improve performance of the RAN.

2. The computer-implemented method of claim 1, wherein the access stratum AS data includes geolocation data, the computer-implemented method further comprising correlating a the geolocation data from the access stratum AS data to the respective one or more detected state transitions, and the ESR further includes the geolocation data correlated to the respective one or more detected state transitions.

3. The computer-implemented method of claim 1, wherein the ESR includes data from at least two of: control or user plane messages between a user equipment of one or more user equipment and any of the base stations of the one or more cells of the RAN, wherein each of the one or more user equipment is using the RAN for communication with another of the one or more user equipment; control messages between any of the base stations and one or more core components of an evolved packet core station of a data packet communication network; communication between any two of the base stations; control messages between a user equipment and the one or more core components; identification information identifying one or more of the user equipment; and identification information identifying one or more subscribers using the one or more user equipment.

4. The computer-implemented method of claim 1, wherein the access stratum AS data includes information about radio frequency conditions internal to a cell of the one or more cells and associated with the one or more detected state transitions, wherein detecting in the access stratum AS data the one or more state transitions further includes detecting the information about the radio frequency conditions internal to the cell correlated to the one or more detected state transitions, and wherein the ESR further includes the information about the radio frequency conditions internal to the cell correlated to the one or more detected state transitions.

5. The computer-implemented method of claim 1, wherein the access stratum AS data includes high level radio bearer level user plane data including bandwidth and throughput from the CTRs and indicators of identification of the one or more cells, wherein the computer-implemented method further includes: detecting conditions, over time of the high level radio bearer level user plane data about the bandwidth and throughput user plane conditions over time, internal to an identified cell of the one or more cells and correlated to the one or more detected state transitions, and wherein the ESR further includes the conditions over time of the detected high level radio bearer level user plane data about the bandwidth and throughput user plane conditions over time, internal to an identified cell of the one or more cells and correlated to the one or more detected state transitions.

6. The computer-implemented method of claim 1, wherein the one or more detected state transitions include at least one of: receipt of measurement reports received from the user equipment, and the enhanced ESR further includes selected information from the received measurement reports; receipt of signaling information on the an addition of one or more secondary carriers between the user equipment and one or more secondary cells, and the ESR further includes selected information related to the added one or more secondary carriers; and receipt of signaling information on the a release of one or more secondary carriers between the user equipment and one or more secondary cells, and the ESR further includes selected information related to the released one or more secondary carriers.

7. The computer-implemented method of claim 1, further comprising: receiving non-access stratum (NAS) user plane data derived from NAS user plane signaling from a core component of a data packet communication network, wherein the NAS user plane data correlates to the wireless communication; and correlating the NAS user plane data with the AS data, wherein the ESR further includes results of the correlation between the NAS user plane data and the AS data.

8. The computer-implemented method of claim 7, wherein the NAS user plane data includes quality of service information and correlating the NAS user plane data with the AS data includes correlating the quality of service information with the one or more state transitions detected in the AS data.

9. The computer-implemented method of claim 7, further comprising generating a visual display of the correlations between the NAS user plane data and the AS data.

10. The computer-implemented method of claim 1, further comprising: receiving subscriber correlation data that correlates a non-international mobile subscriber identity (IMSI non-IMSI) subscriber temporary ID included in the ESR to an IMSI; and correlating the non-IMSI subscriber temporary ID to its the IMSI, wherein the ESR further includes representation of the correlation of the non-IMSI subscriber temporary ID to its the IMSI.

11. The computer-implemented method of claim 1 10, further comprising: inputting results of the correlation to a machine learning algorithm.

12. A computer-implemented method of monitoring a improving radio access network (RAN) performance, the computer-implemented method comprising: receiving access stratum (AS) data that is a function of cell trace records (CTRs) associated with wireless communication transported to or from one or more cells of the RAN a radio access network (RAN), wherein the access stratum AS data identifies user equipment and associates timestamps with radio resource control (RRC) events represented in the access stratum AS data; receiving non-access stratum (NAS (NAS) user plane data derived from NAS user plane signaling from a core component of a data packet communication network in association with the wireless communication, wherein the NAS user plane data includes timestamps and identification of user equipment; correlating, using the timestamps and identification of end user devices user equipment, the NAS user plane data and the AS data; and outputting an enhanced session record (ESR) including results of the correlation between the NAS user plane data and the AS data; inputting the ESR into a machine learning model to identify one or more call gaps; and adjusting, based on the one or more identified call gaps, one or more parameters of the one or more cells of the RAN or the core components to improve performance of the RAN.

13. The computer-implemented method of claim 12, wherein the access stratum AS data includes geolocation data and the correlating includes correlating the geolocation data from the access stratum AS data to the NAS user plane data.

14. The computer-implemented method of claim 12, wherein the access stratum AS data includes information about radio frequency conditions internal to a cell of the one or more cells and correlating the NAS user plane data and the AS data includes correlating the radio frequency conditions from the access stratum AS data to the NAS user plane data.

15. The computer-implemented method of claim 12, wherein the access stratum AS data includes high level user plane data including bandwidth and throughput from the CTRs and indicators of identification of the one or more cells, and wherein the computer-implemented method further comprises: detecting conditions, over time, of the high level user plane data; and correlating the NAS user plane data with the AS data, wherein correlating the NAS user plane data and the AS data includes correlating the bandwidth and the throughput included by the high level user plane conditions over time for an identified cell to the NAS user plane data.

16. The computer-implemented method of claim 15, wherein the NAS user plane data includes quality of service information and correlating the NAS user plane data with the AS data includes correlating the quality of service information with one or more state transitions detected in the AS data.

17. The computer-implemented method of claim 12 16, further comprising: inputting results of the correlation information to a machine learning algorithm.

18. A communication network monitoring system for monitoring a radio access network (RAN) is provided, the communication network monitoring system comprising: a non-transitory computer-readable memory configured to store instructions; a processor in communication with the non-transitory computer-readable memory, wherein the processor, upon execution of the instructions, is caused to: receive access stratum (AS) data that is a function of cell trace records (CTRs) associated with wireless communication transported to or from one or more cells of the RAN a radio access network (RAN), wherein the CTRs are obtained at a granularity sufficient to detect one or more radio resource control (RRC) events, the events including one or more events that define a segment and one or more events that occur during a segment, wherein a segment is defined by the beginning, end, or any handovers of a call included in the wireless communication; detect in the access stratum AS data one or more state transitions as indicated by the one or more RRC events, wherein the one or more state transitions comprise a state transition of a call from a first state to a second state comprising an RRC idle state, an RRC connected state, an RRC connection re-establishment state, or a handover state; and output an enhanced session record (ESR) including information processed from the access stratum AS data associated with the respective one or more detected state transitions; input the ESR into a machine learning model to identify one or more call gaps associated with the one or more detected state transitions; and adjust, based on the one or more identified call gaps, one or more parameters of the one or more cells of the RAN to improve performance of the RAN.

19. The communication network monitoring system of claim 18, wherein the processor, upon execution of the instructions, is further caused to: receive non-access stratum (NAS) user plane data derived from NAS user plane signaling from a core component of a data packet communication network, wherein the NAS user plane data correlates to the wireless communication; and correlate the NAS user plane data and the AS data, wherein the ESR includes results of the correlation between the NAS user plane data and the AS data.

20. A communication network monitoring system for monitoring a radio access network (RAN) is provided, the communication network monitoring system comprising: a non-transitory computer-readable memory configured to store instructions; a processor in communication with the non-transitory computer-readable memory, wherein the processor, upon execution of the instructions, is caused to: receive access stratum (AS) data that is a function of cell trace records (CTRs) associated with wireless communication transported to or from one or more cells of the RAN a radio access network (RAN), wherein the access stratum AS data identifies user equipment and associates timestamps with radio resource control (RRC) events represented in the access stratum AS data; receive non-access stratum (NAS) user plane data derived from NAS user plane signaling from a core component of a data packet communication network in association with the wireless communication, wherein the NAS user plane data includes timestamps and identification of user equipment; correlate, using the timestamps and identification of end user devices, the NAS user plane data with the AS data, and output an enhanced session record (ESR) including information processed from the access stratum AS data associated with the respective one or more detected state transitions, wherein the ESR includes results of the correlation between the NAS user plane data and the AS data; input the ESR into a machine learning model to identify one or more call gaps associated with the one or more detected state transitions; and adjust, based on the one or more identified call gaps, one or more parameters of the one or more cells of the RAN or core components to improve performance of the RAN.

21. The communication network monitoring system of claim 20, wherein the NAS user plane data includes quality of service information and correlating the NAS user plane data with the AS data includes correlating the quality of service information with one or more state transitions detected in the AS data.