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

Abstract

A machine-learning (ML) orchestrator entity provides distributed, flexible, and efficient parameter initialization and updating for ML agents can be installed on network nodes operating under similar radio conditions. The ML orchestrator entity instructs each of such network nodes to iteratively run the ML agent in a training mode. Each run yields a local set of parameters for the ML agent. After each run, the ML orchestrator entity collects and uses the local sets of parameters from two or more network nodes to derive a common set of parameters for the network nodes. The ML orchestrator further instructs each of the network nodes to update its own local set of parameters based on the common set of parameters and use the updated local set of parameters in a subsequent run. The ML orchestrator entity repeats these steps until a termination criterion for the training mode is met.