Iterative initialization of machine-learning agent parameters in wireless communication network

A network node in a wireless communication network, said network node comprising:

• at least one processor;
• and
• at least one memory including computer program code;
• wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the network node to:
• receive, from a machine-learning (ML) orchestrator entity, a first indication to iteratively run an ML agent installed on the network node in a training mode, each run of the ML agent in the training mode yielding a local set of parameters for the ML agent;
• and
• after each run of the ML agent in the training mode:
• transmit the local set of parameters to the ML orchestrator entity;
• and
• receive, from the ML orchestrator entity,
• a second indication to:
• (i) update the local set of parameters based on a common set of parameters, and (ii) use the updated local set of parameters in a subsequent run of the ML agent in the training mode;
• or
• a third indication to:
• (i) terminate the training mode for the ML agent, and (ii) run the ML agent in an inference mode by using the common set of parameters,
• wherein each of the second indication and the third indication comprises the common set of parameters, and the common set of parameters is obtained based on the local set of parameters of the network node and at least one other local set of parameters of at least one other network node.

A method for operating a machine-learning (ML) orchestrator entity in a wireless communication network, said method comprising:

• grouping a set of network nodes in the wireless communication network into at least one node cluster based on at least one radio condition of a set of cells served by the set of network nodes, each network node from the set of network nodes having an ML agent installed thereon, the ML agent being configured to run based on a radio measurement in a training mode and in an inference mode;
• and
• for each node cluster from the at least one node cluster:
• transmitting, to each network node of the node cluster, a first indication to iteratively run the ML agent in the training mode, each run of the ML agent in the training mode yielding a local set of parameters for the ML agent;
• and
• after each run of the ML agents in the training mode:
• receiving at least two local sets of parameters from at least two network nodes of the node cluster;
• based on the at least two local sets of parameters, obtaining a common set of parameters;
• based on the common set of parameters, checking whether a termination criterion for the training mode is met;
• and
• if the termination criterion for the training mode is not met, transmitting, to each network node of the node cluster, a second indication to:
• (i) update the local set of parameters based on the common set of parameters, and (ii) use the updated local set of parameters in a subsequent run of the ML agent in the training mode, the second indication comprising the common set of parameters.

A method for operating a network node in a wireless communication network, said method comprising:

• receiving, from a machine-learning (ML) orchestrator entity, a first indication to iteratively run an ML agent installed on the network node in a training mode, each run of the ML agent in the training mode yielding a local set of parameters for the ML agent;
• and
• after each run of the ML agent in the training mode:
• transmitting the local set of parameters to the ML orchestrator entity;
• and
• receiving, from the ML orchestrator entity,
• a second indication to:
• (i) update the local set of parameters based on a common set of parameters, and (ii) use the updated local set of parameters in a subsequent run of the ML agent in the training mode;
• or
• a third indication to:
• (i) terminate the training mode for the ML agent, and (ii) run the ML agent in an inference mode by using the common set of parameters,
• wherein each of the second indication and the third indication comprises the common set of parameters, and the common set of parameters is obtained based on the local set of parameters of the network node and at least one other local set of parameters of at least one other network node.