Abstract: A server may obtain teacher artificial intelligence (AI) models from source base stations; obtain target traffic data from a target base station; obtain an integrated teacher prediction based on the target traffic data by integrating teacher prediction results of the teacher AI models based on teacher importance weights; obtain a student AI model that is trained to converge a student loss on the target traffic data; update the teacher importance weights to converge a teacher loss between a student prediction of the student AI model on the target traffic data, and the integrated teacher prediction of the teacher AI models on the target traffic data; update the student AI model based on the updated teacher importance weights being applied to the teacher prediction results of the teacher AI models; and predict a communication traffic load of the target base station using the updated student AI model.

Abstract: Parameters for load balancing in a cellular communication system are determined. The cellular communication system performance is measured by key performance indicators (KPIs). A policy (artificial intelligence model) is obtained to optimize the cellular communication system performance with respect to the KPIs. The policy for determining parameters used for load balancing the cellular communication system is obtained using meta multi-objective reinforcement learning (meta MORL). A distilled policy may be obtained to initialize the meta MORL determination. Various loss functions may be used to obtain the distilled policy.