Methods and Apparatus for Network Coverage Configuration

A computer-implemented method for configuring network coverage using a machine learning (ML) agent hosting a ML model, the method comprising:

• obtaining first training data and second training data relating to an operating environment, wherein the operating environment comprises a volume of space that comprises one or more static objects;
• training the ML model in a first training stage using the first training data, wherein the first training stage comprises modelling a static deployment of transceivers in the volume of space;
• when the ML model satisfies a first performance criterion, ending the first training stage;
• training the ML model in a second training stage using the second training data, wherein the second training stage comprises modelling a dynamic deployment of transceivers in the volume of space;
• when the ML model satisfies a second performance criterion, ending the second training stage;
• and
• using the ML model to generate a deployment configuration for configuring the spatial deployment of a plurality of transceivers to provide network connection capability to the operating environment.

A machine learning (ML) agent configured to host a ML model, the ML agent comprising processing circuitry and a memory containing instructions executable by the processing circuitry, whereby the ML agent is operable to:

• obtain first training data and second training data relating to an operating environment, wherein the operating environment comprises a volume of space that comprises one or more static objects;
• train the ML model in a first training stage using the first training data, in the first training stage to model a static deployment of transceivers in the volume of space;
• end the first training stage when the ML model satisfies a first performance criterion;
• train the ML model in a second training stage using the second training data, in the second training stage to model a dynamic deployment of transceivers in the volume of space;
• end the second training stage when the ML model satisfies a second performance criterion;
• and
• generate a deployment configuration for configuring the spatial deployment of a plurality of transceivers to provide network connection capability to the operating environment using the ML model.