Machine learning-based analytics at network layers

A method for training machine learning models to be executed by a network device, the method comprising:

• training a first machine learning model using first information available at a first network layer as a model input and a first ground truth output for the first information in a first analytic task;
• extracting, using the trained first machine learning model, a first latent representation of the first information;
• training a second machine learning model using second information available at a second network layer, the first latent representation, and a second ground truth output for the second information in a second analytic task;
• and
• providing the trained first machine learning model and the trained second machine learning model for execution by the network device,
• wherein the first ground truth output indicates whether at least one serving cell is to be scheduled for a terminal device, and the second ground truth output indicates whether the at least one serving cell is to be selected as a potential serving cell for the terminal device, or
• wherein the first ground truth output indicates resource allocation to at least one terminal device located in a network slice, and the second ground truth output indicates potential resource allocation to a traffic flow in the network slice.

A network device comprising:

• at least one processor;
• and
• at least one memory coupled to the at least one processor, the at least one memory comprising instructions that when executed by the at least one processor implement a method comprising:
• receiving, from a first network layer, a first latent representation of first information available at the first network layer, the first latent representation being extracted by a trained first machine learning model executed at the first network layer, the first machine learning model being configured to generate a first predicted output for a first analytic task based on the first information;
• generating, at a second network layer and using a trained second machine learning model, a second predicted output for a second analytic task based on the first latent representation and second information available at the second network layer, the second network layer being different from the first network layer;
• and
• determining, at the second network layer, a target output for the second analytic task based on the second predicted output,
• wherein the first predicted output indicates whether at least one serving cell is to be scheduled for a terminal device, the second predicted output indicates whether the at least one serving cell is to be selected as a potential serving cell for the terminal device, and the target output indicates a subset of serving cells selected from a set of available serving cells for the terminal device, or
• wherein the first predicted output indicates resource allocation to at least one terminal device located in a network slice, the second predicted output indicates potential resource allocation to a traffic flow in the network slice, and the target output indicates target resource allocation to the traffic flow in the network slice.

A computing system comprising:

• at least one processor;
• and
• at least one memory coupled to the at least one processor, the at least one memory comprising instructions that when executed by the at least one processor implement a method for training machine learning models to be executed by a network device, the method comprising:
• training a first machine learning model using first information available at a first network layer as a model input and a first ground truth output for the first information in a first analytic task;
• extracting, using the trained first machine learning model, a first latent representation of the first information;
• training a second machine learning model using second information available at a second network layer, the first latent representation, and a second ground truth output for the second information in a second analytic task;
• and
• providing the trained first machine learning model and the trained second machine learning model for execution by the network device,
• wherein the first ground truth output indicates whether at least one serving cell is to be scheduled for a terminal device, and the second ground truth output indicates whether the at least one serving cell is to be selected as a potential serving cell for the terminal device, or
• wherein the first ground truth output indicates resource allocation to at least one terminal device located in a network slice, and the second ground truth output indicates potential resource allocation to a traffic flow in the network slice.