Self-configuring long term evolution radio access network on unmanned autonomous vehicles

1. A computer-implemented method for positioning an unmanned autonomous vehicle (UAV) in a long term evolution (LTE) radio access network (RAN), the method comprising:

acquiring, by a processor-device, a position of the UAV with a global position system (GPS);

determining, by the processor-device, physical distances from the UAV to each of a plurality of user equipment (UE) responsive to a time-of-flight (ToF) from the UAV to each of the plurality of UE;

generating, by the processor-device, radio environment maps (REMs) for each of the plurality of UE with signal-to-noise ratios (SNR) from each of the plurality of UEs to the UAV;

selecting, by the processor-device, a determined position for the UAV as a position with a minimum SNR in the REMs;

computing a flight trajectory of the UAV in an operation area of the UAV by resolving a gradient map of differences between SNR in adjacent cells of an aggregate map; and

commanding the UAV to move to the determined position.

2. The computer-implemented method as recited in claim 1 , wherein determining includes generating the ToF from the UAV to each of the plurality of UE utilizing the position of the UAV and a synchronization reference signal (SRS) from each of the plurality of UE.

3. The computer-implemented method as recited in claim 2 , wherein utilizing includes assigning averages of a plurality of SRSs to a GPS report to generate a stream of GPS-ToF tuples.

4. The computer-implemented method as recited in claim 3 , wherein assigning includes resolving a UE position with a multilateration algorithm employing the stream of GPS-ToF tuples.

5. The computer-implemented method as recited in claim 1 , wherein generating includes assigning an optimal altitude for the UAV by selecting an altitude that has a lowest signal path loss between the UAV and the plurality of UE for a predefined altitude range.

6. The computer-implemented method as recited in claim 1 , wherein computing includes aggregating the REMs into the aggregate REM.

7. The computer-implemented method as recited in claim 1 , wherein computing includes clustering grid cells in a gradient map into high gradient clusters and low gradient clusters.

8. The computer-implemented method as recited in claim 1 , wherein computing includes calculating an information gain for each of the plurality of UE along a new trajectory.

9. The computer-implemented method as recited in claim 1 , wherein computing includes calculating cost-to-information ratios for each of the plurality of UE along a trajectory.

10. A computer program product for positioning an unmanned autonomous vehicle (UAV) in a long term evolution (LTE) radio access network (RAN), the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

acquiring, by a processor-device, a position of the UAV with a global position system (GPS);

determining, by the processor-device, physical distances from the UAV to each of a plurality of user equipment (UE) responsive to a time-of-flight from the UAV to each of the plurality of UE;

generating, by the processor-device, radio environment maps (REMs) for each of the plurality of UE with signal-to-noise ratios (SNR) from each of the plurality of UEs to the UAV;

selecting, by the processor-device, a determined position for the UAV as a position with a minimum SNR in the REMs;

aggregating, by the processor-device, the REMs into an aggregate REM and resolve a gradient map of differences between SNR in adjacent cells of the aggregate REM; and

commanding the UAV to move to the determined position.

11. An unmanned autonomous vehicle (UAV) in a long term evolution (LTE) radio access network (RAN), the UAV comprising:

a processing system including a processor and memory coupled to the processor, the processing system programmed to:

acquire a position of the UAV with a global position system (GPS);

determine physical distances from the UAV to each of a plurality of user equipment (UE) responsive to a time-of-flight (ToF) from the UAV to each of the plurality of UE;

generate radio environment maps (REMs) for each of the plurality of UE with signal-to-noise ratios (SNR) from each of the plurality of UEs to the UAV;

select a determined position for the UAV as a position with a minimum SNR in the REMs; and

aggregate the REMs into an aggregate REM and resolve a gradient map of differences between SNR in adjacent cells of the aggregate REM; and

move the UAV to the determined position.

12. The UAV as recited in claim 11 , wherein the processing system is further programmed to generate the ToF from the UAV to each of the plurality of UE utilizing the position of the UAV and a synchronization reference signal (SRS) from each of the plurality of UE.

13. The UAV as recited in claim 12 , wherein the processing system is further programmed to assign averages of a plurality of SRS s to a GPS report to generate a stream of GPS-ToF tuples.

14. The UAV as recited in claim 13 , wherein the processing system is further programmed to resolve a UE position with a multilateration algorithm employing the stream of GPS-ToF tuples.

15. The UAV as recited in claim 11 , wherein the processing system is further programmed to assign an optimal altitude for the UAV by selecting an altitude that has a lowest signal path loss between the UAV and the plurality of UE for a predefined altitude range.

16. The UAV as recited in claim 11 , wherein the processing system is further programmed to compute a flight trajectory of the UAV in an operation area of the UAV.

17. The UAV as recited in claim 11 , wherein the processing system is further programmed to calculate an information gain and cost-to-information ratios for each of the plurality of UE along a new trajectory.