Patent Publication Number: US-2023134583-A1

Title: Base station load balancing method and apparatus

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority to and the benefit of Korean Patent Application No. 10-2021-0147009 filed in the Korean Intellectual Property Office on Oct. 29, 2021, the entire contents of which are incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     (a) Field of the Invention 
     The present invention relates to a base station load balancing method and apparatus, and more particularly, to a base station load balancing method and apparatus capable of effectively monitoring an overload state of a base station and distributing a load. 
     (b) Description of the Related Art 
     Recently, with the advent of the 5G mobile communication era, the demand for services such as remote robot control, vehicle autonomous driving, drone control, unmanned aerial vehicle (UAV), and augmented reality (AR)/virtual reality (VR) requiring large data is increasing. 
     With the development of various terminals and the exponential increase in services based on large-capacity content, the traffic that base stations have to process is greatly increasing. 
     Mobile communication base stations using high frequency are developing into structures that can provide high-capacity data services, but in order to satisfy all user needs, it is necessary to construct high-density base stations, and it is becoming increasingly difficult for an operator to manage the high-density base stations due to cost and complexity. 
     SUMMARY OF THE INVENTION 
     The present invention has been made in an effort to a base station load balancing method and apparatus capable of effectively controlling the overload of the base station while minimizing intervention of the operator. 
     According to an exemplary embodiment, a base station load balancing method in a base station is provided. The base station load balancing method includes: updating a traffic amount of a current time by reflecting a predicted traffic amount of a future time in the traffic amount of the current time; and determining parameters necessary for load balancing of the base station by comparing the updated traffic amount of the current time with a predetermined threshold. 
     The updating may include predicting the traffic amount of the future time from the traffic amount of the current time using a prediction model, and the weight value of the prediction model may be determined through federated learning between a service and operation management system and a plurality of base stations. 
     The base station load balancing method may further include: learning a machine learning model using learning data; updating the machine learning model; and repeating the learning of the machine learning model and updating of the machine learning model to use the machine learning model as the predictive model. 
     The updating of the machine learning model may include: transmitting a weight value and learning accuracy according to the learning result of the machine learning model to the service and operation management system; receiving a global weight value determined by the service and operation management system based on the weight values and learning accuracy received from the plurality of base stations; and updating a weight value of the machine learning model with the global weight value. 
     The global weight value may be an average value of part of the weight values received from the plurality of base stations, and the part of the weight values may be randomly selected according to a standard deviation for learning accuracy provided by the plurality of base stations, or are selected in the order of high learning accuracy. 
     The updating may include calculating the traffic amount at the current time by applying a first weight value and a second weight value to downlink physical resource block (PRB) usage and uplink PRB usage, respectively, and the first weight value and the second weight value may be determined according to a ratio of PRBs allocated to downlink and uplink in the entire PRB. 
     The updating may include applying a first weight value to the traffic amount of the current time and applying a second weight value to the predicted traffic amount of the future time, and the first weight value may be set to be greater than the second weight value. 
     The determining may include adjusting handover-related parameters so that the terminals at the edge of the base station move to another base station earlier if the updated traffic amount at the current time is greater than the threshold value. 
     According to another embodiment, a base station load balancing method for balancing load of a plurality of base stations in a base station load control apparatus is provided. The base station load balancing method includes: generating and managing policies necessary for the load balancing operation; modifying the policies using a load balancing result of an overloaded base station; and determining a weight value of a machine learning model used for predicting traffic of a future time in the plurality of base stations by performing federated learning with the plurality of base stations. 
     The determining may include: receiving a weight value and learning accuracy according to a learning result of the machine learning model from the plurality of base stations, respectively; calculating a global weight value based on the weight values and learning accuracy received from the plurality of base stations; and transmitting the global weight value to the plurality of base stations so that the plurality of base stations update the weight values of the machine learning model with the global weight value. 
     The calculating may include: selecting weight values of part of the weight values received from the plurality of base stations according to the standard deviation for the learning accuracy provided by the plurality of base stations; and calculating an average of the selected weight values of the part as the global weight value. 
     The selecting may include: randomly selecting the weight values of the part if a random value between 0 and 1 is less than a value corresponding to the standard deviation; and selecting the weight values of the part in order of high learning accuracy if the random value is equal to or greater than a value calculated based on the standard deviation. 
     According to yet another embodiment, a base station load balancing apparatus for load balancing by interworking with a service and operation management system in a base station is provided. The base station load balancing apparatus includes: a local traffic predictor that predicts a traffic amount in a future time using a prediction model; and a load balancing processor that updates a traffic amount of a current time by reflecting the predicted traffic amount of the future time in the traffic amount of the current time, and determines parameters necessary for load balancing of the base station by comparing the updated traffic amount of the current time with a predetermined threshold. 
     The local traffic predictor may include: a model updater for updating a machine learning model with a global weight value determined by the service and operation management system through federated learning between a plurality of base stations and the service and operation management system; and a model learner for learning the updated machine learning model, and the prediction mode may be finally generated through repetition of the learning the machine learning model and updating the machine learning model. 
     The model learner may transmit a weight value and learning accuracy according to a learning result of the machine learning model to the service and operation management system, and the global weight value may be determined by the service and operation management system based on the weight values and learning accuracy received from the plurality of base stations. 
     The global weight value may be an average value of part of the weight values received from the plurality of base stations, and the part of the weight values may be randomly selected according to a standard deviation for learning accuracy provided by the plurality of base stations, or are selected in the order of high learning accuracy. 
     The load balancing processor may include an overload determiner for calculating the updated traffic amount of the current time by applying a first weight value to the traffic amount of the current time and applying a second weight to the predicted traffic amount of the future time, and the first weight value may be set to be greater than the second weight value. 
     The load balancing processor may include a mobility load balancing (MLB) controller for adjusting handover-related parameters so that the terminals at the edge of the base station move to another base station earlier if the updated traffic amount at the current time is greater than the threshold value. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a diagram for explaining load balancing of base stations according to an embodiment of the present invention. 
         FIG.  2    is a diagram illustrating the MLB manager shown in  FIG.  1   . 
         FIG.  3    is a diagram for explaining a method of optimizing a weight value of a machine learning model by interworking between the global traffic predictor and the local traffic predictor of each base station shown in  FIG.  1   . 
         FIG.  4    is a diagram for explaining a method of determining a weight value in the global traffic predictor shown in  FIG.  3   . 
         FIG.  5    is a diagram illustrating a detailed configuration of a base station load balancing apparatus in the base station shown in  FIG.  1   . 
         FIG.  6    is a diagram for explaining a method of optimizing parameters in the load balancing processor shown in  FIG.  5   . 
         FIG.  7    is a diagram illustrating a base station load balancing apparatus according to another embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     Hereinafter, embodiments of the disclosure will be described in detail with reference to the attached drawings so that a person of ordinary skill in the art may easily implement the disclosure. As those skilled in the art would realize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the disclosure. The drawings and description are to be regarded as illustrative in nature and not restrictive. Like reference numerals designate like elements throughout the specification. 
     Throughout the specification and claims, when a part is referred to “include” a certain element, it means that it may further include other elements rather than exclude other elements, unless specifically indicated otherwise. 
     Furthermore, in this specification, each of the phrases such as “A or B”, “at least one of A and B”, “at least one of A or B”, “A, B, or C”, “at least one of A, B, and C”, and “at least one of A, B, or C” may include any one of the items listed together in the corresponding one of the phrases, or all possible combinations thereof. 
     Now, a base station load balancing method and apparatus according to an embodiment will be described in detail with reference to the drawings. 
       FIG.  1    is a diagram for explaining load balancing of base stations according to an embodiment of the present invention. 
     Referring to  FIG.  1   , the service and operation management system  10  includes a base station load balancing apparatus  100  that monitors a plurality of base stations  20   1  to  20   N  managed by an operator and predicts load states of the base station  20   1  to  20   N . 
     The base station load balancing apparatus  100  includes a mobility load balancing (MLB) manager  110  and a global traffic predictor  120 . 
     The MLB manager  110  monitors the traffic state (overload state) for all the base stations  20   1  to  20   N  operated by the network operator, and generates and manages policies necessary for the MLB optimization operation. In addition, the MLB manager  110  evaluates the MLB execution result executed by the policies, and supplements the policies through the MLB evaluation. 
     The global traffic predictor  120  optimizes the traffic prediction in the local traffic predictor  210  of each base station  20   1  to  20   N  while exchanging and updating information necessary for machine learning by interworking with the local traffic predictor  210  of each base station  20   1  to  20   N . 
     Each of the base stations  20   1  to  20   N  includes a base station load balancing apparatus  200  that performs traffic prediction and load balancing. 
     The base station load balancing apparatus  200  includes a local traffic predictor  210  and a load balancing processor  220 . 
     The local traffic predictor  210  calculates new traffic reflecting the traffic of the future time from the current time. The local traffic predictor  210  collects traffic at the current time, and predicts traffic at a future time using a machine learning model from the traffic at the current time. 
     The load balancing processor  220  calculates a new traffic amount reflecting the traffic of the future time at the current time using the traffic usage of the current time and the traffic information of the future time predicted by the local traffic predictor  210 , and determines the parameters necessary for load balancing of the base station based on the calculated new traffic amount. 
     The determined parameters are transferred to the relevant base station functions. 
     Related base station functions update base station configuration information by reflecting the parameters determined by the load balancing processor  220 , and transmit measurement configuration information to the terminal based on the updated base station configuration information. The terminal performs measurement based on the measurement configuration information. 
       FIG.  2    is a diagram illustrating the MLB manager shown in  FIG.  1   . 
     Referring to  FIG.  2   , the MLB manager  110  may include a policy generator and manager  112 , a data collector  114 , a traffic monitor  116 , and an MLB monitor  118 . 
     The policy generator and manager  112  generates and manages policies necessary for MLB optimization operation. The policy generator and manager  112  may generate load policies that set criteria for determining overload of the traffic monitor  116 . For example, the load policies may include a threshold for determining whether or not an overload is present. The policy generator and manager  112  may generate a data collection policy indicating a performance measurement (PM) data type and a data collection period. 
     The policy generator and manager  112  may supplement the policies using the MLB performance result evaluated by the MLB monitor  118 . The policy generator and manager  112  may delete, modify, and generate the policies based on evaluation result information obtained by evaluating the load balancing performance result of the overloaded base station. 
     The data collector  114  collects PM data collected from each base station based on the data collection policy transmitted from the policy generator and manager  112 . The PM data collected by each base station may be stored in an operation administration maintenance (OAM) DB. Furthermore, the data collector  114  transmits data for traffic monitoring and data for MLB policy performance evaluation among the collected PM data to the traffic monitor  116  and the MLB monitor  118 , respectively. 
     Table 1 shows data for traffic monitoring and MLB policy performance evaluation. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                   
                 Optional 
               
               
                   
                 Mandatory 
                 (Enrichment) 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 Traffic 
                 Downlink (DL)/Uplink (UP) PRB 
                 RRC connection 
               
               
                 monitoring 
                 (Physical resource block) Usage 
                 number related 
               
               
                   
                 DL/UL PRB used for data traffic 
                 Information 
               
               
                   
                 DL/UL total available PRB 
                 Number of Active 
               
               
                   
                   
                 UEs related 
               
               
                   
                   
                 Information 
               
               
                 MLB policy 
                 Failed Handover related 
                 Ping-Pong related 
               
               
                 performance 
                 Information 
                 Information 
               
               
                 evaluation 
                 Measurement related Information 
                 UE throughput 
               
               
                   
                 to MRO (Too Early, Too Late, 
               
               
                   
                 To Wrong) 
               
               
                 Learning 
                 DL/UL PRB Usage 
                 PM data generated 
               
               
                 data 
                 Number of Active UEs 
                 by the base station 
               
               
                   
               
            
           
         
       
     
     As defined in Table 1, in addition to mandatory data, optional data may be transmitted to the traffic monitor  116  and the MLB monitor  118  for more effective monitoring and evaluation. In addition, the data shown in Table 1 is an example, and other information not mentioned in Table 1 may be used for traffic monitoring and MLB policy performance evaluation. 
     The traffic monitor  116  recognizes an overload state for all base stations  20   1  to  20   N  operated by a network operator based on the load policies provided by the policy generator and manager  112  and performs a function of alarming the overload state. 
     A determination criterion that satisfies the alarm condition in the traffic monitor  116  may be defined as Equation 1. The traffic monitor  116  calculates the amount of traffic at the current time of the corresponding base station based on the downlink PRB usage and the uplink PRB usage of the base station corresponding to the cell identifier Cellio as shown in Equation 1, determines that the corresponding base station is in an overload state if the amount of traffic at the current time is greater than the threshold for the load policy, and may transmit an alarm to the base station in the overload state. That is, the traffic monitor  116  calculates the traffic of the base station based on Equation 1 and informs an overload state to the base station in the overload state. 
       (α·PRBU[cell_ID] DL   T  +β·PRBU[cell ID ] UL   T )&gt;Thredshold α+β=1   (Equation 1)
 
     Here, PRBU[cell_ID] DL   T  denotes downlink PRB usage, and PRBU[cell_ID] DL   T  denotes uplink PRB usage. α and β are variables that determine the weights of the downlink PRB usage and the uplink PRB usage, and may be determined according to the PRB ratio allocated to the downlink and the uplink in the entire PRB. Threshold indicates a threshold value for the load policy generated by the policy generator and manager  112 , and the unit is %. 
     The MLB monitor  118  evaluates the result performed by the overloaded base station for load balancing using the MLB algorithm and transmits the evaluation result to the policy generator and manager  112 , and the policy generator and manager  112  may modify the policies in case of a wrong result. 
     For MLB policy performance evaluation, the MLB monitor  118  periodically collects data for MLB policy performance evaluation shown in Table 1 and performs MLB policy performance evaluation. The performance evaluation method compares the statistics before and after MLB operation, including the number of handover attempts that have failed, mobility robust optimization (MRO) related statistical information, the number of ping-pongs, and other additional information. Next, if the comparison result is out of a certain ratio defined by the operator, the MLB monitor  118  may instruct to the policy generator and manager  112  to modify the policies related to MLB operation. 
       FIG.  3    is a diagram for explaining a method of optimizing a weight value of a machine learning model by interworking between the global traffic predictor and the local traffic predictor of each base station shown in  FIG.  1   . 
     Referring to  FIG.  3   , by extending a federated learning technique of machine learning, the global traffic predictor  120  and the local traffic predictor  210  of each base station  20   1  to  20   N  share weight values of the machine learning model with each other, and update weight values of the machine learning model by interacting with each other. 
     The global traffic predictor  120  designs an initial machine learning model. Then, the unlearned initial machine learning model is transferred to the local traffic predictor  210  of each base station  20   1  to  20   N . 
     The local traffic predictor  210  of each base station  20   1  to  20   N  performs learning on the machine learning model based on the PM data collected by each base station  20   1  to  20   N . After learning the machine learning model for one predetermined learning cycle, the local traffic predictor  210  of each base station  20   1  to  20   N  transmits weights W 1  to W N  and learning accuracy (acc) according to the learning result to the global traffic predictor  120 . Here, the unit of learning accuracy varies depending on the machine learning model, and in the case of a classification model, it may be a percentage. In the case of a regression model, learning accuracy means an error value due to a loss function such as mean squared error (MSE), mean of absolute scaled errors (MASE), and root mean squared error (RMSE). 
     The global traffic predictor  120  performs sample determination using the weights W 1  to W N  and the learning accuracy acc received from the local traffic predictor  210  of each base station  20   1  to  20   N . The global traffic predictor  120  determines a weight value Δw of the machine learning model using a specific number of weights selected through sample determination, and transmits the weight value Δw to the local traffic predictor  210  of each base station  20   1  to  20   N . The local traffic predictor  210  of each base station  20   1  to  20   N  updates the weight value W 1  to W N  of the machine learning model as the weight value Δw, respectively. 
     A method of determining the weight value Δw of the machine learning model in the global traffic predictor  120  will be described with reference to  FIG.  4   . 
       FIG.  4    is a diagram for explaining a method of determining a weight value in the global traffic predictor shown in  FIG.  3   . 
     Referring to  FIG.  4   , the global traffic predictor  120  selects a specific number of weights from among the weights W 1  to W N  received from the local traffic predictor  210  of each base station  20   1  to  20   N , and may determine an average of the selected weights as the weight value Δw of the machine learning model to be updated. 
     The global traffic predictor  120  first sorts the weights W 1  to W N  received from the local traffic predictor  210  of each base station  20   1  to  20   N  in order of accuracy. In the regression model, a smaller learning accuracy value means higher accuracy. It is difficult to reflect the characteristics of the base stations  20   1  to  20   N  and the convergence of learning may be delayed if the average is calculated average with all weights W 1  to W N  of the base stations  20   1  to  20   N . Accordingly, the global traffic predictor  120  samples S weights from among the N weights W 1  to W N  and calculates an average of the S weights. Here, since the effect on learning is different depending on the sampling method, an effective sampling method of the global traffic predictor  120  is proposed. 
     The global traffic predictor  120  may randomly sample and select S weights by sampling, but if only the weights that do not learn well are selected, the learning effect may be reduced. Therefore, the global traffic predictor  120  uses a method of randomly selecting S weights at the beginning of learning, exploring the weights of all base stations, and selecting only the weights of base stations with high accuracy whenever the number of times of learning increases. The global traffic predictor  120  randomly selects S weights from among N weights W 1  to W N  if the random value between 0 and 1 is less than the value E of Equation 2, and selects S weights from among the N weights W 1  to W N  in the order of learning accuracy if the random value between 0 and 1 is equal to or greater than the value E of Equation 2, using a characteristic that the standard deviation of the weights W 1  to W N  provided by each base station  20   1  to  20   N  becomes smaller as the number of times of learning increases. 
         E =min(1, δ×Std), 0≤δ&lt;1   (Equation 2)
 
     Here, δ can be defined by the operator and has a value between 0 and 1, and Std represents the standard deviation of the learning accuracy acc provided by each base station  20   1  to  20   N . 
     After selecting the S weights, the global traffic predictor  120  calculates an average weight of the S weights as shown in Equation 3. 
     
       
         
           
             
               
                 
                   
                     Average 
                     ⁢ 
                         
                     Weight 
                   
                   = 
                   
                     
                       1 
                       S 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         S 
                       
                       
                         ( 
                         
                           w 
                           i 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                     ⁢ 
                         
                     3 
                   
                   ) 
                 
               
             
           
         
       
     
     The global traffic predictor  120  determines the calculated average weight as a weight value of the machine learning model to be updated, and transmits the average weight to the local traffic predictor  210  of each base station  20   1  to  20   N . 
     The local traffic predictor  210  of each base station  20   1  to  20   N  updates the weight value of the machine learning model to the received average weight, and performs learning on the machine learning model based on the collected PM data. 
       FIG.  5    is a diagram illustrating a detailed configuration of a base station load balancing apparatus in the base station shown in  FIG.  1   . 
     Referring to  FIG.  5   , the local traffic predictor  210  includes a model updater  212 , a model learner  214 , and a traffic predictor  216 . 
     The model updater  212  updates the machine learning model. The model updater  212  may receive the global weight from the global traffic predictor  120  and update the weight value of the machine learning model to the global weight. The global weight means an average weight calculated by the global traffic predictor  120 . 
     The model learner  214  learns the updated machine learning model. The model learner  214  may learn the machine learning model by using the learning data stored in the DB. When the learning of the machine learning model is completed, the model learner  214  stores the weight w and the learning accuracy acc of the machine learning model, and transmits the weight w and the learning accuracy acc to the global traffic predictor  120 . 
     The machine learning model update of the model updater  212  and the machine learning model of the model learner  214  are repeated until the learning is finally completed, and the finally learned machine learning model is used as a prediction model to predict traffic in the traffic predictor  216 . 
     The traffic predictor  216  predicts the amount of traffic in the future time after a predetermined time from data input in real time by using the prediction model, and transmits the predicted amount of traffic of the future time to the load balancing processor  220 . 
     The load balancing processor  220  includes an overload determiner  222  and an MLB controller  224 . 
     The overload determiner  222  receives policies required for MLB optimization operation from the MLB manager  110  of the service and operation management system  100 . The overload determiner  222  receives the predicted traffic amount from the local traffic predictor  210 , and generates a new traffic amount New PRBU reflecting the traffic amount PRBU Predicted  of the future time from the current time, using the predicted traffic amount PRBU Predicted  and the traffic usage PRBU Current  of the current time as in Equation 4. 
     
       
         
           
             
               
                 
                   
                     New 
                     ⁢ 
                         
                     PRBU 
                   
                   = 
                   
                     
                       
                         
                           w 
                           1 
                         
                         ⨯ 
                         
                           PRBU 
                           Current 
                         
                       
                       , 
                       
                         + 
                         
                           
                             w 
                             12 
                           
                           ⨯ 
                           
                             PRBU 
                             Predicted 
                           
                         
                       
                     
                     
                       
                         w 
                         1 
                       
                       + 
                       
                         w 
                         2 
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                     ⁢ 
                         
                     4 
                   
                   ) 
                 
               
             
           
         
       
       
         
           
             
               w 
               
                 1 
                   
               
             
             &gt; 
             
               w 
               2 
             
           
         
       
     
     Here, W 1  and W 2  are weight values r applied to the traffic amount of the current time and the traffic amount of the future time, respectively, and W 1  has a greater value than W 2 . PRBU Current  indicates the downlink and uplink PRB usage at the current time as shown in Equation 1. PRBU Predicted  indicates the amount of traffic in the future time predicted by the traffic predictor  216 . 
     The overload determiner  222  determines the overload by using the threshold determined by the policy and the new traffic amount New PRBU. 
     MLB hands over the terminals at the edge to distribute the load of the overloaded base station. In all communication technologies (4G, LTE, and 5G), handover is determined by the base station based on the measurement report of the terminal. 
     3GPP proposes a set of measurement reporting mechanisms to be performed by the terminal in order to minimize unnecessary handover, and this is called an event. The event type to be reported by the terminal is indicated by the RRC signaling message transmitted from the base station. 3GPP TS 38.331 defines eight event types in 5G NR. As shown in Table 2, all events have an offset called hysteresis. 
     
       
         
           
               
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Event 
                 Parameter 
                 Range 
                 Value 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 A1, A2, 
                 RSRP Threshold 
                 0 
                 127 
                 −156 
                 dBm 
                 −31 
                 dBm 
               
               
                 A4, A5, 
                 RSRQ Threshold 
                 0 
                 127 
                 −40 
                 dB 
                 20 
                 dB 
               
               
                 B1 
                 SINR Threshold 
                 0 
                 127 
                 −23 
                 dB 
                 40 
                 dB 
               
               
                 All 
                 Hysteresis 
                 0 
                 30 
                 0 
                 dB 
                 15 
                 dB 
               
               
                 A3, A6 
                 Offset 
                 −30 
                 30 
                 −15 
                 dB 
                 +15 
                 dB 
               
               
                 A3, A4, 
                 Cell Specific 
                   
                   
                 −24 
                 dB 
                 +24 
                 dB 
               
               
                 A5, A6, 
                 Offset (CIO) 
               
               
                 B1, B2 
               
               
                 B1, B2 
                 LTE RSRP 
                 0 
                 97 
                 −140 
                 dBm 
                 −44 
                 dBm 
               
               
                   
                 LTE RSRQ 
                 0 
                 34 
                 −19.5 
                 dB 
                 −3 
                 dBm 
               
               
                   
                 LTE SINR 
                 23 
                 40 
                 −23 
                 dB 
                 40 
                 dB 
               
               
                   
               
            
           
         
       
     
     When the overload is determined by the overload determiner  222 , the MLB controller  224  optimizes handover-related measurement configuration parameters so that terminals connected to its own base station can be moved to another base station. 
     The MLB controller  224  adjusts the terminal to handover earlier or the terminal to stay in its base station for a longer period according to the load state of the base station by optimizing the hysteresis values. 
     The MLB controller  224  transmits the optimized measurement configuration parameters to the related base station function. The MLB controller  224  may delete or cancel the measurement configuration parameters according to the policies received from the MLB manager of the service and operation management system  100 . 
       FIG.  6    is a diagram for explaining a method of optimizing parameters in the load balancing processor shown in  FIG.  5   . 
     Referring to  FIG.  6   , the overload determiner  222  calculates the amount of traffic at the current time by collecting the traffic (uplink and downlink PRB usage) of the base station at the current time. The amount of traffic at the current time may be calculated based on the downlink PRB usage and the uplink PRB usage of the base station as shown in Equation 1. 
     The overload determiner  222  receives a threshold value and a hysteresis adjustment range for the A 3  event among the policies generated by the MLB manager of the service and operation management system  100  (S 610 ). The initial hysteresis value and the threshold value are determined by the policies, and the values are transmitted to the base station as an initial set value when operating the base station. In the embodiment, it is shown that the middle value (7 dB) of the hysteresis range is used as the initial hysteresis value for effective operation, as shown in Table 2. 
     The overload determiner  222  receives the predicted traffic amount of the future time from the local traffic predictor. 
     The overload determiner  222  calculates a new traffic amount New PRBU reflecting the traffic amount of the future time at the current time by using the predicted traffic amount of the future time and the calculated traffic amount of the current time (S 620 ). 
     The overload determiner  222  determines the overload by comparing the new traffic amount New PRBU of the current time with the threshold (S 630 ). 
     When the new traffic amount New PRBU is greater than the threshold value, the MLB controller  224  reduces a hysteresis value by a predetermined dB (S 640 ), so that the terminals can move to another base station earlier. 
     On the other hand, when the new traffic amount New PRBU is less than or equal to the threshold value, the MLB controller  224  increases the hysteresis value by a predetermined dB (S 650 ), so that the terminals can access the currently connected base station for a longer period of time. 
     By applying the method for optimizing the parameters described in the embodiment, offsets of eight events can be optimized. 
       FIG.  7    is a diagram illustrating a base station load balancing apparatus according to another embodiment. 
     Referring to  FIG.  7   , the base station load balancing apparatus  700  may represent a computing device in which the method for load balancing of a base station is implemented. 
     The base station load balancing apparatus  700  may be implemented in the base station. Alternatively, the base station load balancing apparatus  700  may be implemented in a service and operation management system  100 . 
     The base station load balancing apparatus  700  may include at least one of a processor  710 , a memory  720 , an input interface device  730 , an output interface device  740 , and a storage device  750 . Each of the components may be connected by a common bus  760  to communicate with each other. In addition, each of the components may be connected through an individual interface or a separate bus centering on the processor  710  instead of the common bus  760 . 
     The processor  710  may be implemented as various types such as an application processor (AP), a central processing unit (CPU), a graphics processing unit (GPU), etc., and may be any semiconductor device that executes a command stored in the memory  720  or the storage device  750 . The processor  710  may execute program commands stored in at least one of the memory  720  and the storage device  750 . 
     In the case of the base station load balancing apparatus  700  implemented in the base station, the processor  710  stores program commands for implementing at least some functions of the local traffic predictor  210  and the load balancing processor  220  in the memory  720 , and may control to perform the operation described with reference to  FIGS.  1  to  6   . 
     In addition, in the case of the base station load balancing apparatus  700  implemented in the service and operation management system  100 , the processor  710  stores program commands for implementing at least some functions of the MLB manager  110  and the global traffic predictor  120  in the memory  720 , and may control to perform the operation described with reference to  FIGS.  1  to  6   . 
     The memory  720  and the storage device  750  may include various types of volatile or non-volatile storage media. For example, the memory  720  may include a read-only memory (ROM)  721  and a random access memory (RAM)  722 . The memory  720  may be located inside or outside the processor  710 , and the memory  720  may be connected to the processor  710  through various known means. 
     The input interface device  730  is configured to provide data to the processor  710 . 
     The output interface device  740  is configured to output data from the processor  710 . 
     At least some of the method for load balancing of a base station according to an embodiment may be implemented as a program or software executed in a computing device, and the program or software may be stored in a computer-readable medium. 
     In addition, at least some of the method for load balancing of a base station according to an embodiment may be implemented as hardware that can be electrically connected to the computing device. 
     According to an embodiment, when constructing a high-density base station, overload of base stations can be automatically solved without operator intervention, and more accurate load balancing can be performed at the present time by predicting traffic of the near future time. 
     In addition, by updating the machine learning model using advanced federated learning technology to obtain fast and effective learning results, more accurate traffic prediction becomes possible. 
     The components described in the example embodiments may be implemented by hardware components including, for example, at least one digital signal processor (DSP), a processor, a controller, an application-specific integrated circuit (ASIC), a programmable logic element such as an FPGA, other electronic devices, or combinations thereof. At least some of the functions or the processes described in the example embodiments may be implemented by software, and the software may be recorded on a recording medium. The components, functions, and processes described in the example embodiments may be implemented by a combination of hardware and software. The method according to embodiments may be embodied as a program that is executable by a computer, and may be implemented as various recording media such as a magnetic storage medium, an optical reading medium, and a digital storage medium. Various techniques described herein may be implemented as digital electronic circuitry, or as computer hardware, firmware, software, or combinations thereof. The techniques may be implemented as a computer program product, i.e., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable storage device (for example, a computer-readable medium) or in a propagated signal for processing, or to control an operation of a data processing apparatus, e.g., by a programmable processor, a computer, or multiple computers. A computer program(s) may be written in any form of programming language, including compiled or interpreted languages, and may be deployed in any form including a stand-alone program or a module, a component, a subroutine, or other units suitable for use in a computing environment. A computer program may be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network. Processors suitable for execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. Elements of a computer may include at least one processor to execute instructions and one or more memory devices to store instructions and data. Generally, a computer will also include or be coupled to receive data from, transfer data to, or perform both on one or more mass storage devices to store data, e.g., magnetic or magneto-optical disks, or optical disks. Examples of information carriers suitable for embodying computer program instructions and data include semiconductor memory devices, for example, magnetic media such as a hard disk, a floppy disk, and a magnetic tape, optical media such as a compact disk read-only memory (CD-ROM), a digital video disk (DVD), etc., and magneto-optical media such as a floptical disk and a read-only memory (ROM), a random access memory (RAM), a flash memory, an erasable programmable ROM (EPROM), and an electrically erasable programmable ROM (EEPROM), and any other known computer readable media. A processor and a memory may be supplemented by, or integrated into, a special purpose logic circuit. The processor may run an operating system (OS) and one or more software applications that run on the OS. The processor device may also access, store, manipulate, process, and create data in response to execution of the software. For the purpose of simplicity, the description of a processor device is used as singular; however, one skilled in the art will appreciate that a processor device may include multiple processing elements and/or multiple types of processing elements. For example, a processor device may include multiple processors or a processor and a controller. In addition, different processing configurations are possible, such as parallel processors. Also, non-transitory computer-readable media may be any available media that may be accessed by a computer, and may include both computer storage media and transmission media. The present specification includes details of a number of specific implementations, but it should be understood that the details do not limit any disclosure or what is claimable in the specification but rather describe features of the specific example embodiment. Features described in the specification in the context of individual example embodiments may be implemented as a combination in a single example embodiment. In contrast, various features described in the specification in the context of a single example embodiment may be implemented in multiple example embodiments individually or in an appropriate sub-combination. Furthermore, the features may operate in a specific combination and may be initially described as claimed in the combination, but one or more features may be excluded from the claimed combination in some cases, and the claimed combination may be changed into a sub-combination or a modification of a sub-combination. Similarly, even though operations are described in a specific order in the drawings, it should not be understood as the operations needing to be performed in the specific order or in sequence to obtain desired results or as all the operations needing to be performed. In a specific case, multitasking and parallel processing may be advantageous. In addition, it should not be understood as requiring separation of various apparatus components in the above-described example embodiments in all example embodiments, and it should be understood that the above-described program components and apparatuses may be incorporated into a single software product or may be packaged in multiple software products. It should be understood that the embodiments disclosed herein are merely illustrative and are not intended to limit the scope of the disclosure. It will be apparent to one of ordinary skill in the art that various modifications of the embodiments may be made without departing from the spirit and scope of the claims and their equivalents.