Primary signal detection using distributed machine learning in multi-area environment
Methods and systems for primary signal detection using distributed machine learning in a multi-area environment are disclosed. In an example method, it is determined that a first user equipment (UE) device moved to a first predefined area from a second predefined area. A controller sends, to the first UE device, a first machine learning model configured to detect an anomaly in an RF environment associated with the first area. The first machine learning model may have been determined by a second UE device associated with the first area. The controller receives, from the first UE device, anomaly data indicative of an anomaly detected by the first UE device via the first machine learning model. The controller may optionally determine that a primary signal is present in an RF environment associated with the first area based on the anomaly data from the first UE device.
1. A method comprising:
determining, by a controller, that a first user equipment (UE) device moved to a first predefined area from a second predefined area;
sending, by the controller and to the first UE device, a first machine learning model configured to detect an anomaly in an RF environment associated with the first predefined area; and
receiving, by the controller and from the first UE device, anomaly data indicative of an anomaly detected by the first UE device via the first machine learning model.
2. The method of claim 1 , further comprising:
determining, by the controller and based on the anomaly data from the first UE device, that a primary signal is present in an RF environment associated with the first predefined area.
3. The method of claim 1 , wherein the first machine learning model was determined by a second UE device associated with the first predefined area.
4. The method of claim 3 , further comprising:
receiving, by the controller and from the second UE device, second anomaly data indicative of an anomaly detected by the second UE device via the first machine learning model; and
determining, by the controller and based on the anomaly data from the first UE device and the second anomaly data from the second UE device, that a primary signal is present in an RF environment associated with the first predefined area.
5. The method of claim 1 , further comprising:
causing the first UE device to replace a second machine learning model, previously determined by the first UE device and configured to determine an anomaly in an RF environment associated with the second predefined area, with the first machine learning model associated with the first predefined area.
6. The method of claim 1 , further comprising:
causing the first UE device to update a second machine learning model, previously determined by the first UE device, with the first machine learning model to detect the anomaly via the first machine learning model.
7. The method of claim 1 , further comprising:
causing the first UE device to further train the first machine learning model based on RF data collected by the first UE device while in the first predefined area.
8. The method of claim 1 , wherein the controller is integrated with a base station configured to wirelessly communicate with the first UE device.
9. A method comprising:
moving, by a first user equipment (UE) device, to a first predefined area from a second predefined area;
receiving, by the first UE device, a first machine learning model configured to detect an anomaly in an RF environment associated with the first predefined area;
determining, by the first UE device, anomaly data indicative of an anomaly detected by the first UE device via the first machine learning model; and
sending, by the first UE device, the anomaly data to a controller configured to determine that a primary signal is present in an RF environment based on anomaly data sent from one or more UE devices.
10. The method of claim 9 , wherein the first machine learning model was determined by a second UE device associated with the first predefined area.
11. The method of claim 9 , further comprising:
switching, by the first UE device, its operating frequency spectrum to a frequency spectrum that will not cause interference with a primary signal.
12. The method of claim 9 , wherein the anomaly is detected by the first UE device via a combination of the first machine learning model with a second machine learning model previously determined by the first UE device.
13. The method of claim 9 , further comprising:
updating, by the first UE device, a second machine learning model, previously determined by the first UE device, with the first machine learning model to detect the anomaly via the first machine learning model.
14. The method of claim 13 , wherein the updating the second machine learning model with the first machine learning model comprises updating the second machine learning model with one or more machine learning parameters of the first machine learning model.
15. The method of claim 9 , further comprising:
causing the first UE device to further train the first machine learning model based on RF data collected by the first UE device while in the first predefined area.
16. A method comprising:
determining that a first user equipment (UE) device moved to a first predefined area from a second predefined area;
sending, by a controller and to the first UE device, one or more machine learning parameters of a first machine learning model configured to detect an anomaly in an RF environment associated with the first predefined area;
causing the first UE device to update a second machine learning model with the one or more machine learning parameters of the first machine learning model, wherein the second machine learning model was previously determined by the first UE device and configured to detect an anomaly in an RF environment associated with the second predefined area; and
receiving, by the controller and from the first UE device, anomaly data indicative of an anomaly detected by the first UE device via the second machine learning model configured, at least in part, with the one or more machine learning parameters of the first machine learning model.
17. The method of claim 16 , further comprising:
determining, by the controller and based on the anomaly data from the first UE device, that a primary signal is present in an RF environment associated with the first predefined area.
18. The method of claim 17 , further comprising:
causing the first UE device to switch its operating frequency spectrum to a frequency spectrum that will not cause interference with the primary signal.
19. The method of claim 16 , wherein the first machine learning model was determined by a second UE device associated with the first predefined area.
20. The method of claim 16 , further comprising:
causing the first UE device to further train the first machine learning model based on RF data collected by the first UE device while in the first predefined area.