Distributed antenna networks for wireless communication by wireless devices

1. A method, comprising:

detecting an issue in providing wireless communication by one or more holographic beamforming antennas;

employing one or more machine learning engines to infer a type of locomotion at a location that is associated with a user of each of a plurality of wireless devices;

determining one or more new configurations for the one or more holographic beamforming antennas to provide a remediation to compensate for the wireless communications issue, wherein the one or more new configurations include one or more changes to a waveform provided to a selected one of the one or more holographic beamforming antennas to mitigate the wireless communications issue based on a current interference on the one or more holographic beamforming antennas in the wireless communication for the plurality of wireless devices and one or more locomotion types associated with the location of the user of each of the plurality of wireless devices; and

downloading the one or more new configurations to the one or more holographic beamforming antennas.

2. The method of claim 1 , wherein the detected issue further comprises a load balancing issue.

3. The method of claim 2 , wherein the one or more new configurations include a configuration that moves an overload of wireless devices serviced by an overloaded holographic beamforming antenna to the one or more holographic beamforming antennas that are not overloaded.

4. The method of claim 2 , wherein the one or more configurations further comprise a configuration proportionally balancing a load between two or more of the holographic beamforming antennas according to their associated load capabilities.

5. The method of claim 4 , wherein the proportionally balancing includes considering amounts of data communicated by each wireless device serviced by the one or more holographic beamforming antennas.

6. The method of claim 1 , wherein the one or more new configurations include a configuration providing a null or zero in the waveform provided for the selected holographic beamforming antenna to avoid a physical location of a wireless signal interference source.

7. The method of claim 1 , wherein the wireless communications issue is an intermittent, temporary, or permanent interference issue.

8. The method of claim 1 , wherein the detected issue is a presence of one or more physical obstructions that block or degrade a quality of wireless communication.

9. The method of claim 8 , wherein the one or more physical obstructions include a building or tree.

10. The method of claim 8 , wherein the one or more physical obstructions include bird defecations on one or more surfaces of the one or more holographic beamforming antennas.

11. The method of claim 1 , further comprising:

reporting the detecting of the issue and the remediation thereof.

12. The method of claim 1 , further comprising:

creating a recommendation to correct the issue so that the remediation is no longer needed.

13. The method of claim 1 , wherein the determining is based on a machine learning model.

14. A network computer system, comprising:

a memory for storing instructions; and

one or more processors configured to execute the instructions to perform actions, including:

detecting an issue in providing wireless communication by one or more holographic beamforming antennas;

employing one or more machine learning engines to infer a type of locomotion at a location that is associated with a user of each of a plurality of wireless devices;

determining one or more new configurations for the one or more holographic beamforming antennas to provide a remediation to compensate for the wireless communications issue, wherein the one or more new configurations include one or more changes to a waveform provided to a selected one of the one or more holographic beamforming antennas to mitigate the wireless communications issue based on a current interference on the one or more holographic beamforming antennas in the wireless communication for the plurality of wireless devices and one or more locomotion types associated with the location of the user of each of the plurality of wireless devices; and

downloading the one or more new configurations to the one or more holographic beamforming antennas.

15. The system of claim 14 , wherein the detected issue further comprises a load balancing issue or a presence of one or more physical obstructions that block or degrade the quality of wireless communication.

16. The system of claim 14 , wherein the determining is based on a machine learning model.

17. The method of claim 14 , wherein the network computer system is a cloud computing system.

18. A computer-readable non-transitory media that includes instructions for a network computer system to perform actions, including:

detecting an issue in providing wireless communication by one or more holographic beamforming antennas;

employing one or more machine learning engines to infer a type of locomotion at a location that is associated with a user of each of a plurality of wireless devices;

determining one or more new configurations for the one or more holographic beamforming antennas to provide a remediation to compensate for the wireless communications issue, wherein the one or more new configurations include one or more changes to a waveform provided to a selected one of the one or more holographic beamforming antennas to mitigate the wireless communications issue based on a current interference on the one or more holographic beamforming antennas in the wireless communication for the plurality of wireless devices and one or more locomotion types associated with the location of the user of each of the plurality of wireless devices; and

downloading the one or more new configurations to the one or more holographic beamforming antennas.

19. The computer-readable non-transitory media of claim 18 , wherein the network computer system is a cloud computing system.