# US 2023/0055079 A1 · Method of load forecasting via attentive knowledge transfer, and an apparatus for the same

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- Number: US 2023/0055079 A1
- Document ID: US20230055079A1
- Status: Published application
- Kind: A1
- Application number: 17/874,925
- Filed: 2022-07-27
- Published: 2023-02-23
- Earliest claimed: 2021-07-30
- Assignees: Samsung Electronics Co., Ltd.
- Applicants: Samsung Electronics Co., Ltd.
- Inventors: Di WU; Michael Jenkin; Yi Tian Xu; Xue Liu; Xi Chen; Gregory Lewis Dudek
- Web page: https://patentgrep.com/patent/US20230055079A1

## CPC

- H04L 41/147: for predicting network behaviour
- H04L 47/822: {Collecting or measuring resource availability data}
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/045: Combinations of networks
- G06N 3/0499: Feedforward networks
- G06N 3/096: Transfer learning
- H04L 41/16: using machine learning or artificial intelligence
- H04L 47/824: {Applicable to portable or mobile terminals}
- H04L 47/83: based on usage prediction
- H04W 28/08: Load balancing or load distribution (transferring a connection for handling the traffic H04W36/22; wireless traffic scheduling H04W72/12)
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

A method of forecasting a future load may include: obtaining source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively; among a plurality of source machine learning models, selecting at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model through a negative transfer analysis; obtaining model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models; obtaining a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights; and predicting a future communication traffic load of the target base station based on the load forecasting model.

## Claims

### Claim 1

A method of forecasting a future load by one or more processors, the method comprising:
obtaining source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively;
among a plurality of source machine learning models configured to predict a communication traffic load of the plurality of source base stations, selecting at least one machine learning source model that has a traffic load prediction performance higher than that of a target machine learning model configured to predict a communication traffic load of the target base station through a negative transfer analysis on the plurality of source base stations;
obtaining model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models;
obtaining a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights; and
predicting a future communication traffic load of the target base station based on the load forecasting model.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
transmitting information of the future communication traffic load to the target base station to enable the target base station to assign a plurality of user equipment (UEs) to a plurality of cells of the target base station according to the information of the future communication traffic load.

### Claim 3 (depends on 2)

The method of claim 2, wherein the information of the future communication traffic load comprises any one or any combination of a number of active UEs in each of the plurality of cells, a cell load ratio, an internet protocol (IP) throughput per cell, and a cell physical resource block (PRB) usage ratio.

### Claim 4 (depends on 1)

The method of claim 1, wherein at least one of the target machine learning model, the plurality of source machine learning model, and the load forecasting model is implemented as a Long Short-Term Memory (LSTM) model that comprises an input layer, a plurality of LSTM layers, a dense layer, and an output layer.

### Claim 5 (depends on 1)

The method of claim 1, wherein the attention neural network is implemented using a multi-layer perceptron (MLP).

### Claim 6 (depends on 1)

The method of claim 1, wherein the traffic load prediction performance is measured by calculating a mean average percentage error (MAPE) of each of the target machine learning model and the selected at least one source machine learning model.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
determining whether a target domain from which the target data set is collected, and source domains from which the source data sets are collected, have a same feature space; and
prior to obtaining the model weights, performing iterative feature matching on the target data set and the source data sets in response to determining that the target domain and the source domains do not have the same feature space.

### Claim 8 (depends on 1)

The method of claim 1, further comprising:
inputting the predicted future communication traffic load and a ground truth communication traffic load that is observation from the target base station, to a loss function configured to calculate a mean average percentage error of the load forecasting model;
determining whether a performance degradation has occurred in the load forecasting model based on the mean average percentage error of the load forecasting model; and
retraining the load forecasting model in response to determining that the performance degradation has occurred in the load forecasting model.

### Claim 9

An electronic device comprising:
at least one memory storing computer-readable instructions; and
at least one processor configured to execute the computer-readable instructions to:
obtain source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively;
among a plurality of source machine learning models configured to predict a communication traffic load of the plurality of source base stations, select at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model configured to predict a communication traffic load of the target base station through a negative transfer analysis on the plurality of source base stations;
obtain model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models;
obtain a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights; and
predict a future communication traffic load of the target base station based on the load forecasting model.

### Claim 10 (depends on 9)

The electronic device of claim 9, further comprising a transceiver,
wherein the at least one processor is further configured to execute the computer-readable instructions to:
control the transceiver to transmit information of the future communication traffic load to the target base station to enable the target base station to assign a plurality of user equipment (UEs) to a plurality of cells of the target base station according to the information of the future communication traffic load.

### Claim 11 (depends on 10)

The electronic device of claim 10, wherein the information of the future communication traffic load comprises any one or any combination of a number of active UEs in each of the plurality of cells, a cell load ratio, an internet protocol (IP) throughput per cell, and a cell physical resource block (PRB) usage ratio.

### Claim 12 (depends on 9)

The electronic device of claim 9, wherein at least one of the target machine learning model, the plurality of source machine learning model, and the load forecasting model is implemented as a Long Short-Term Memory (LSTM) model that comprises an input layer, a plurality of LSTM layers, a dense layer, and an output layer.

### Claim 13 (depends on 9)

The electronic device of claim 9, wherein the attention neural network is implemented using a multi-layer perceptron (MLP).

### Claim 14 (depends on 9)

The electronic device of claim 9, wherein the at least one processor is further configured to execute the computer-readable instructions to:
measure the traffic load prediction performance by calculating a mean average percentage error (MAPE) of each of the target machine learning model and the selected at least one source machine learning model.

### Claim 15 (depends on 9)

The electronic device of claim 9, wherein the at least one processor is further configured to execute the computer-readable instructions to:
determine whether a target domain from which the target data set is collected, and source domains from which the source data sets are collected, have a same feature space; and
prior to obtaining the model weights, perform iterative feature matching on the target data set and the source data sets in response to determining that the target domain and the source domains do not have the same feature space.

### Claim 16 (depends on 9)

The electronic device of claim 9, wherein the at least one processor is further configured to execute the computer-readable instructions to:
input the predicted future communication traffic load and a ground truth communication traffic load that is observation from the target base station, to a loss function configured to calculate a mean average percentage error of the load forecasting model;
determine whether a performance degradation has occurred in the load forecasting model based on the mean average percentage error of the load forecasting model; and
retrain the load forecasting model in response to determining that the performance degradation has occurred in the load forecasting model.

### Claim 17

A non-transitory computer-readable storage medium having recorded thereon instructions to be executed by at least one processor to perform a method of forecasting a future load, the method comprising:
obtaining source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively;
among a plurality of source machine learning models configured to predict a communication traffic load of the plurality of source base stations, selecting at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model configured to predict a communication traffic load of the target base station through a negative transfer analysis on the plurality of source base stations;
obtaining model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models;
obtaining a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights; and
predicting a future communication traffic load of the target base station based on the load forecasting model.

### Claim 18 (depends on 17)

The non-transitory computer-readable storage medium of claim 17, wherein the method further comprises:
transmitting information of the future communication traffic load to the target base station to enable the target base station to assign a plurality of user equipment (UEs) to a plurality of cells of the target base station according to the information of the future communication traffic load,
wherein the information of the future communication traffic load comprises any one or any combination of a number of active UEs in each of the plurality of cells, a cell load ratio, an internet protocol (IP) throughput per cell, and a cell physical resource block (PRB) usage ratio.

### Claim 19 (depends on 17)

The non-transitory computer-readable storage medium of claim 17, wherein the method further comprises:
determining whether a target domain from which the target data set is collected, and source domains from which the source data sets are collected, have a same feature space; and
prior to obtaining the model weights, performing iterative feature matching on the target data set and the source data sets in response to determining that the target domain and the source domains do not have the same feature space.

### Claim 20 (depends on 17)

The non-transitory computer-readable storage medium of claim 17, wherein the method further comprises:
inputting the predicted future communication traffic load and a ground truth communication traffic load that is observed from the target base station, to a loss function configured to calculate a mean average percentage error of the load forecasting model;
determining whether a performance degradation has occurred in the load forecasting model based on the mean average percentage error of the load forecasting model; and
retraining the load forecasting model in response to determining that the performance degradation has occurred in the load forecasting model.