Samsung Electronics Co., Ltd.

Jikun Kang

Xue Liu

Xi Chen

Gregory Lewis Dudek

Di Wu

Yi Tian Xu

Intaik Park

Taeseop Lee

Ju Wang

Chengming Hu

Seowoo Jang

Michael Jenkin

Hang Li

Dmitriy Rivkin

Amal Feriani

Jimmy Li

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.

Method of load forecasting via knowledge distillation, and an apparatus for the same

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.

Method of performing communication load balancing with multi-teacher reinforcement learning, and an apparatus for the same

Rapid and data-efficient training of an artificial intelligence (AI) algorithm are disclosed. Ground truth data are not available and a policy must be learned based on limited interactions with a system. A policy bank is used to explore different policies on a target system with shallow probing. A target policy is chosen by comparing a good policy from the shallow probing with a base target policy which has evolved over other learning experiences. The target policy then interacts with the target system and a replay buffer is built up. The base target policy is then updated using gradients found with respect to the transition experience stored in the replay buffer. The base target policy is quickly learned and is robust for application to new, unseen, systems.

Load balancing using data-efficient learning

A method is provided. The method includes receiving a first dimension set, extracting a first latent feature set from the first dimension set, training a first base predictor based on the first feature set, generating a second dimension set based on the first dimension set, the second dimension set having fewer dimensions than the first dimension set, extracting a second latent feature set from the second dimension set, training a second base predictor based on the second feature set, and generating a traffic prediction based on the first base predictor and the second base predictor.

Communication load forecasting accuracy with adaptive feature boosting

Hybrid use of dual policies is provided to improve a communication system. In a multiple access scenario, when an inactive user equipment (UE) transitions to an active state, it may be become a burden to a radio cell on which it was previously camping. In some embodiments, hybrid load balancing is provided using a hierarchical machine learning paradigm based on reinforcement learning in which an LSTM generates a goal for one policy influencing cell reselection so that another policy influencing handover over active UEs can be assisted. The communication system as influenced by the policies is modeled as a Markov decision process (MDP). The policies controlling the active UEs and inactive UEs are coupled, and measureable system characteristics are improved. In some embodiments, policy actions depend at least in part on energy saving.

Hierarchical policy learning for hybrid communication load balancing

An apparatus distributing communication load over a plurality of communication cells may select action centers from random cell reselection values, based on a standard deviation of an internet protocol (IP) throughout over the plurality of communication cells; input a first vector indicating a communication state of a communication system and a second vector indicating the standard deviation of the IP throughout of the plurality of communication cells, to a neural network to output a sum of the action centers and offsets as cell reselection parameters; and transmit the cell reselection parameters to the communication system to enable a base station of the communication system to perform a cell reselection based on the cell reselection parameters.

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Montreal, Canada

Jikun Kang