View source for US Patent Application 18351201. METHOD OF PERFORMING COMMUNICATION LOAD BALANCING WITH MULTI-TEACHER REINFORCEMENT LEARNING, AND AN APPARATUS FOR THE SAME simplified abstract

=METHOD OF PERFORMING COMMUNICATION LOAD BALANCING WITH MULTI-TEACHER REINFORCEMENT LEARNING, AND AN APPARATUS FOR THE SAME=


==Organization Name==

[[Samsung Electronics Co., Ltd.]]


==Inventor(s)==

[[Jikun Kang of Montreal (CA)]]


[[Xi Chen of Montreal (CA)]]


[[Chengming Hu of Montreal (CA)]]


[[Ju Wang of Brossard (CA)]]


[[Gregory Lewis Dudek of Westmount (CA)]]


[[Xue Liu of Montreal (CA)]]


==METHOD OF PERFORMING COMMUNICATION LOAD BALANCING WITH MULTI-TEACHER REINFORCEMENT LEARNING, AND AN APPARATUS FOR THE SAME - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18351201 titled 'METHOD OF PERFORMING COMMUNICATION LOAD BALANCING WITH MULTI-TEACHER REINFORCEMENT LEARNING, AND AN APPARATUS FOR THE SAME

==Simplified Explanation==

The abstract describes a system for load balancing in a communication system using artificial intelligence (AI) models.
* The system includes a server that obtains AI models for multiple base stations.
* The server collects traffic data from the base stations to create teacher models.
* The server then uses knowledge distillation to create student models from the teacher models.
* The student models are combined to create an ensemble student model.
* The server interacts with the ensemble student model to create a policy model.
* The policy model is provided to each base station for evaluation.
* If a base station sends a training continue signal, the ensemble student model and policy model are updated using the policy rehearsal process.


==Original Abstract Submitted==

A server may be provided to obtain a load balancing artificial intelligence (AI) model for a plurality of base stations in a communication system. The server may obtain teacher models based on traffic data sets collected from the base stations, respectively; perform a policy rehearsal process including obtaining student models based on knowledge distillation from the teacher models, obtaining an ensemble student model by ensembling the student models, and obtaining a policy model by interacting with the ensemble student mode; provide the policy model to each of the base stations for a policy evaluation of the policy model; and based on a training continue signal being received from at least one of the base stations as a result of the policy evaluation, update the ensemble student model and the policy model by performing the policy rehearsal process on the student models.