Cellular system

1. A system, comprising:

one or more sensors coupled to equipment to monitor operation and to predict a problem within a predetermined 5G latency of 1 ms or less;

one or more 5G antennas;

one or more 5G transceivers coupled to the one or more 5G antennas; and

a processor coupled to the one or more 5G transceivers to control the equipment in real-time within the predetermined 5G latency in response to the problem detected by the one or more sensors.

2. The system of claim 1 , comprising a remote processor to perform predictive maintenance based on sensor output.

3. The system of claim 1 , comprising a remote processor to track equipment performance over time to predict a potential maintenance issue before they occur.

4. The system of claim 1 , wherein the transceiver connects sensors, machines or robots.

5. The system of claim 1 , wherein one or more sensors are at the edge to communicate with equipment.

6. The system of claim 1 , comprising maintaining equipment based on statistics for wear rates or sensed equipment operation over time.

7. The system of claim 1 , comprising a neural network or an artificial intelligence (AI) to perform predictive maintenance.

8. The system of claim 1 , comprising an Internet of Things (loT) device wirelessly coupled to the processor.

9. The system of claim 1 , comprising a remote processor to compare real-time data from sensors on connected equipment to an equipment history.

10. The system of claim 1 , comprising a remote processor to proactively trigger maintenance activities based on sensor outputs indicating a potential breakdown.

11. The system of claim 1 , comprising a remote processor and AI predictive analytics to request predictive maintenance.

12. The system of claim 1 , comprising one or more cameras and sensors to capture security information.

13. The system of claim 1 , wherein the processor analyzes walking gaits and facial features for identity identification.

14. The system of claim 1 , wherein the processor analyzes sound captured using a microphone to determine events in progress.

15. The system of claim 1 , comprising an edge processor to provide local edge processing for Internet-of-Things (IOT) sensors.

16. The system of claim 1 , comprising an edge learning machine that uses pre-trained models and modifies the pre-trained models for a selected task.

17. The system of claim 1 , comprising a cloud trained neural network whose network parameters are reduced before transferring to an edge neural network.

18. A system, comprising:

one or more 5G antennas;

one or more 5G transceivers coupled to the one or more 5G antennas; and

a processor with a learning machine or neural network to control the one or more 5G transceivers, the processor automatically changing antenna parameters based on learned parameters and determining a handover based on a signal strength of an anchor connection, a second connection, a latency;

equipment speed, equipment direction, traffic level, or quality of service.

19. A system, comprising:

one or more 5G antennas;

one or more 5G transceivers coupled to the one or more 5G antennas; and

a processor to control the one or more 5G transceivers with the one or more 5G antennas, the processor running code to:

collect performance data including Spatial and Modulation Symbols, signal strength, channel state information, attributes on channel matrix, and error vector magnitude;

extract features and train a learning machine to optimize spectral efficiency and energy efficiency;

during live communication, extract features from live 5G data and use the 5G data to select antenna parameters based on a client device, resources available, and tower network property.

20. The systern of claim 19 , comprising an edge learning machine in communication with the processor, wherein the edge learning machine uses pre-trained models and modifies the pre-trained models during run-time for a selected task.