Electronic device and method for providing tracking area list for non-terrestrial network

Abstract

This mobility management device may comprise: a memory comprising one or more storage media storing instructions; and at least one processor comprising a processing circuit. The instructions may instruct the mobility management device to: in response to a request message for a registration area received from a user equipment (UE), and on the basis of a prediction model trained on the basis of a plurality of layers for classifying a plurality of base stations, mobility information of the UE, and mobility information of at least one base station among the plurality of base stations, obtain a partial registration area, for indicating a base station where the UE is expected to be located, in each of the layers, wherein the plurality of layers include a first layer associated with a terrestrial base station for providing a terrestrial network, and a second layer associated with a satellite base station for providing a non-terrestrial network; generate a tracking area list corresponding to registration areas including the partial registration areas of the respective layers; and, as a response to the request message, transmit an accept message containing the tracking area list to the UE.

Description

Electronic device and method for providing a tracking area list for a non-terrestrial network

[0001] The present disclosure relates to an electronic device and method for providing a tracking area list for a non-terrestrial network.

[0002] The service area provided by the mobile communication network is divided into multiple tracking areas. To manage the mobility of the UE (user equipment), if the UE moves out of the registered tracking areas, the UE may use a procedure to update the registered areas.

[0003] The information described above may be provided as related art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing may be applied as prior art related to the present disclosure.

[0004] According to embodiments of the present disclosure, a mobility management device is provided. The mobility management device may include a memory comprising one or more storage media for storing instructions; and at least one processor comprising a processing circuit. The instructions may cause the mobility management device to receive a request message for a registration area from a UE (user equipment) individually or collectively by the at least one processor, and in response to the request message, to obtain a partial registration area for representing a base station where the UE is expected to be located in each layer based on a plurality of layers for classifying a plurality of base stations, mobility information of the UE, and a prediction model learned according to mobility information of at least one of the plurality of base stations, - the plurality of layers include a first layer associated with a ground base station for providing a ground network and a second layer associated with a satellite base station for providing a non-ground network -, generate a tracking area list corresponding to the registration area including the partial registration area of ​​each layer, and in response to the request message, transmit an acceptance message to the UE including the tracking area list.

[0005] According to embodiments of the present disclosure, a method performed by a mobility management device is provided. The method may include receiving a request message for a registration area from a UE (user equipment); in response to the request message, obtaining a partial registration area for representing a base station where the UE is expected to be located in each layer based on a plurality of layers for classifying a plurality of base stations, mobility information of the UE, and mobility information of at least one base station of the plurality of base stations, and a prediction model learned according to the plurality of layers - the plurality of layers include a first layer associated with a ground base station for providing a ground network and a second layer associated with a satellite base station for providing a non-ground network -, generating a tracking area list corresponding to a registration area including a partial registration area of ​​each layer, and transmitting an acceptance message including the tracking area list to the UE in response to the request message.

[0006] According to embodiments of the present disclosure, an apparatus configured to perform the functions of an access and mobility management function (AMF) is provided. The apparatus may include a memory comprising one or more storage media for storing instructions; and at least one processor comprising a processing circuit. The instructions may cause the apparatus to receive, individually or collectively by the at least one processor, a registration request message for initiating a registration procedure or updating a registration area from a user equipment (UE), and to transmit a registration acceptance message to the UE in response to the registration request message, the message comprising a tracking area list including at least one tracking area identifier. The at least one tracking area identifier may include a tracking area identifier corresponding to a base station where the UE is expected to be located in each of a plurality of layers. The plurality of layers may include a first layer associated with a ground base station for providing a ground network and a second layer associated with a satellite base station for providing a non-ground network.

[0007] According to embodiments of the present disclosure, a mobility management entity (MME) device is provided. The MME device may include a memory comprising one or more storage media for storing instructions; and at least one processor comprising a processing circuit. The instructions may cause the device to receive a tracking area update request message from a user equipment (UE) individually or collectively by the at least one processor, and to transmit a tracking area update acceptance message to the UE in response to the tracking area update request message, the tracking area update acceptance message comprising a tracking area list including at least one tracking area identifier. The at least one tracking area identifier may include a tracking area identifier corresponding to a base station where the UE is expected to be located in each of a plurality of layers. The plurality of layers may include a first layer associated with a ground base station for providing a ground network and a second layer associated with a satellite base station for providing a non-ground network.

[0008] Figure 1 shows an example of a wireless communication environment.

[0009] Figure 2 shows an example of multiple base stations located across multiple layers.

[0010] Figures 3a and 3b show examples of signaling for predicting a registration area by a mobility management device.

[0011] Figure 4 shows an example of training for prediction by layer.

[0012] Figure 5 shows an example of performance according to prediction by layer.

[0013] Figure 6 shows the operation flow of a mobility management device for configuring a registration area through layer-by-layer prediction.

[0014] Figure 7 shows examples of components of a mobility management device.

[0015] The terms used in this disclosure are used merely to describe specific embodiments and are not intended to limit the scope of other embodiments. A singular expression may include a plural expression unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as generally understood by those skilled in the art described in this disclosure. Terms used in this disclosure that are defined in a general dictionary may be interpreted as having the same or similar meaning as they have in the context of the relevant technology, and are not to be interpreted in an ideal or overly formal sense unless explicitly defined in this disclosure. In some cases, even terms defined in this disclosure are not to be interpreted to exclude the embodiments of this disclosure.

[0016] In the various embodiments of the present disclosure described below, a hardware-based approach is described as an example. However, since the various embodiments of the present disclosure include techniques using both hardware and software, the various embodiments of the present disclosure do not exclude a software-based approach.

[0017] Terms used in the following description to refer to signals (e.g., signal, information, message, signaling), terms referring to data types (e.g., list, set, subset), terms for operation states (e.g., step, operation, procedure), terms referring to data (e.g., packet, user stream, information, bit, symbol, codeword), terms referring to resources (e.g., symbol, slot, subframe, radio frame, subcarrier, RE (resource element), RB (resource block), BWP (bandwidth part), occasion), terms referring to channels, terms referring to network entities, terms referring to device components, etc., are provided as examples for convenience of explanation. Accordingly, the present disclosure is not limited to the terms described below, and other terms having equivalent technical meanings may be used.

[0018] Terms used in the following description to refer to signals (e.g., signal, information, message, signaling), terms referring to data types (e.g., list, set, subset), terms for operation states (e.g., step, operation, procedure), terms referring to data (e.g., packet, user stream, information, bit, symbol, codeword), terms referring to resources (e.g., symbol, slot, subframe, radio frame, subcarrier, RE (resource element), RB (resource block), BWP (bandwidth part), occasion)), terms referring to channels, terms referring to network entities (e.g., entity, function, node, object, object), terms referring to device components, etc., are provided as examples for convenience of explanation. Accordingly, the present disclosure is not limited to the terms described below, and other terms having equivalent technical meanings may be used. Additionally, terms such as '...part', '...device', '...piece', '...body' used below may refer to at least one shape structure or a unit that processes a function.

[0019] Additionally, in this disclosure, expressions of "greater than" or "less than" may be used to determine whether a specific condition is satisfied or fulfilled; however, this is merely for the purpose of expressing an example and does not exclude descriptions of "greater than" or "less than." Conditions described as "greater than" may be replaced with "greater than," conditions described as "less than" may be replaced with "less than," and conditions described as "greater than and less than" may be replaced with "greater than and less than." Furthermore, "A" to "B" below refer to at least one of elements from A (including A) to B (including B). Below, "C" and / or "D" refers to including at least one of "C" or "D," i.e., {"C", "D", "C" and "D"}.

[0020] This disclosure describes various embodiments using terms used in some communication standards (e.g., 3GPP (3rd Generation Partnership Project), ETSI (European Telecommunications Standards Institute), xRAN (extensible radio access network), O-RAN (open-radio access network), but these are merely illustrative examples. Various embodiments of this disclosure can be easily modified and applied to other communication systems.

[0021] Figure 1 shows an example of a wireless communication environment.

[0022] Referring to FIG. 1, FIG. 1 illustrates a base station (110) (e.g., a first base station (110-1), a second base station (110-2), a third base station (110-3), and / or a fourth base station (110-4)) and a terminal (120) as a part of nodes using a wireless channel in a wireless communication system.

[0023] A base station (110) is a network infrastructure that provides wireless access to a terminal (120). The base station (110) has coverage defined based on the distance over which it can transmit signals. In addition to being a base station, the base station (110) may be referred to as a 'RAN (radio access network) node', 'access point (AP)', 'eNodeB (eNB)', '5G node (5th generation node)', 'next generation nodeB (gNB)', 'wireless point', 'transmission / reception point (TRP)', 'communication node', 'communication device', 'electronic device', 'wireless communication device', 'wireless communication equipment', 'network node', 'network entity', or other terms having an equivalent technical meaning.

[0024] A terminal (120) is a device used by a user and communicates with a base station (110) via a wireless channel. The link from the base station (110) to the terminal (120) is referred to as a downlink (DL), and the link from the terminal (120) to the base station (110) is referred to as an uplink (UL). Additionally, although not shown in FIG. 1, the terminal (120) and another terminal can communicate with each other via a wireless channel. In this case, the link between the terminal (120) and another terminal (device-to-device link, D2D) is referred to as a sidelink, and the sidelink may be used interchangeably with the PC5 interface. In some other embodiments, the terminal (120) may be operated without user involvement. According to one embodiment, the terminal (120) is a device that performs machine type communication (MTC) and may not be carried by the user. Additionally, according to one embodiment, the terminal (120) may be a narrowband (NB)-Internet of Things (IoT) device. The terminal (120) may be referred to as 'user equipment (UE)', 'customer premises equipment (CPE)', 'mobile station', 'subscriber station', 'remote terminal', 'wireless terminal', 'communication device', 'electronic device', or 'user device' or other terms having an equivalent technical meaning.

[0025] The network between the base station (110) and the terminal (120) may be referred to as an access network or a radio access network (RAN). A set of network entities connected to the base station (110) may be referred to as a core network. The core network may include a mobility management device (150) for managing the access and / or mobility of the terminal (120) in the control plane. The core network may include a data gateway (160) for managing the data transmission of the terminal (120) in the data plane. For example, the core network may be a 5G core (5GC). The mobility management device (150) may include an access and mobility management function (AMF), and the gateway (160) may include a user plane function (UPF). For example, the core network may be an evolved packet core (EPC). The mobility management device (150) includes a mobility management entity (MME), and the gateway (160) may include a serving gateway (S-GW).

[0026] A base station (110) may provide one or more cells. A cell represents a unit of service specified according to a specific frequency range. Each cell may have a center frequency (which may be referred to as a carrier frequency) and a bandwidth. A base station (110) may be implemented as a single node or as a plurality of nodes (e.g., a combination of at least two of a central unit (CU), a distributed unit (DU), and a radio unit (RU)) through a separated arrangement. For example, a first base station (110-1) may provide cells #11, #12, #13, and #14. For example, a second base station (110-2) may provide cells #21, #22, #23, and #24. For example, a third base station (110-3) may provide cells #32, #33, #34, and #343. For example, the fourth base station (110-4) can provide cell #71, cell #72, cell #73, and cell 74.

[0027] A tracking area representing a geographical area may be used to manage the mobility of the terminal (120). For example, one or more cells to cover a specific geographical area may correspond to a tracking area. For example, a first tracking area may include cell #11, cell #12, cell #13, cell 14, cell #21, cell #22, cell #23, and cell 24. For example, a second tracking area may include cell #32, cell #33, cell #34, cell #343, cell #71, cell #72, cell #73, and cell 74. A tracking area may be identified through a tracking area identity (TAI). The tracking area identity may be constructed from a public land mobile network (PLMN) identifier and a tracking area code (TAC). A tracking area identifier may be broadcast on the cell(s) of the tracking area. For example, information regarding the identifier of the first tracking area may be broadcast on each of cell #11, cell #12, cell #13, cell 14, cell #21, cell #22, cell #23, and cell 24. For example, information regarding the identifier of the second tracking area may be broadcast on each of cell #32, cell #33, cell #34, cell #343, cell #71, cell #72, cell #73, and cell 74.

[0028] The mobility management device (150) may set a registration area for the terminal (120). The registration area represents one or more tracking areas registered with the mobility management device (150). When the terminal (120) detects that the terminal (120) has moved out of the registration area, the terminal (120) may initiate a procedure to request an update to the registration area. For example, in 5GS, the terminal (120) may send a registration request message to the mobility management device (150) (e.g., AMF). The terminal (120) may receive a registration acceptance message from the mobility management device (150) (e.g., AMF). The registration acceptance message may include a tracking area identifier list (TAI list). The tracking area identifier list may include one or more tracking areas. For example, in EPS, the terminal (120) may send a tracking area update (TAU) request message to the mobility management device (150) (e.g., MME). The terminal (120) may receive a TAU acceptance message from a mobility management device (150) (e.g., MME). The TAU acceptance message may include a tracking area identifier list (TAI list). The tracking area identifier list may include one or more tracking areas. The following description is based on a registration procedure with 5GS, but the operations according to the embodiments of the present disclosure may also be applied to a tracking area update (TAU) procedure with EPS.

[0029] A registration area can be used for location management of a terminal (120). The terminal (120) can perform a registration procedure when it leaves the registration area in idle mode or at periodic timing. Through the registration procedure, the terminal (120) can notify the mobility management device (150) of the changed location information. For example, if the terminal (120) does not have a current TAI included in the TAI list of the registration area, it can send a registration request message to the AMF of the core network. The registration request message may include the identifier of a TA recently visited by the terminal (120). The mobility management device (150) can search for the terminal (150) in idle mode by sending a paging message on each cell within the registration area. A registration procedure using a registration request message and a registration acceptance message, and / or a paging procedure using a paging message, can cause a traffic load in the core network. To reduce this traffic load, techniques are being used to optimize the registration area through a location prediction model of the terminal (120).

[0030] Meanwhile, unlike base stations deployed at fixed locations on the ground, base stations utilizing satellites or High Altitude Platform Stations (HAPS) that provide a non-terrestrial network (NTN) are being discussed. Satellites or HAPS continuously move from locations separated from the ground. When a satellite functioning as a base station (hereinafter, a satellite base station (e.g., a regenerative payload)) is included in a registration area, the terminal (120) may move out of the registration area (i.e., registered tracking areas) or the registration area may change due to the movement of the satellite, even if the terminal (120) does not move. As a result, the number of signalings in the registration process may increase. Furthermore, since the terminal (120) is understood to be located outside the registration area due to the movement of the satellite, the number of paging messages to search for the terminal (120) may also increase.

[0031] To reduce the aforementioned problems, embodiments of the present disclosure describe a technique for predicting a registration area by distinguishing between a satellite base station providing a non-terrestrial network and a base station located on the ground (hereinafter, a terrestrial base station). A prediction model may be used that distinguishes between a terrestrial base station providing a terrestrial network (TN) and a base station having mobility (e.g., speed, orbit, direction), and utilizes the mobility of the base station as well as the mobility of the terminal.

[0032] FIG. 2 illustrates an example of multiple base stations located across multiple layers. The present disclosure describes techniques for effectively configuring a registration area for a terminal (120) using a non-terrestrial network environment. Layers for classifying multiple base stations may be used to distinguish between a base station providing a terrestrial network and a base station providing a non-terrestrial network, such as a satellite.

[0033] Referring to FIG. 2, a plurality of base stations in a wireless communication environment may be classified into a plurality of layers. The plurality of base stations may include various types of base stations. For example, the plurality of base stations may include a satellite for providing NTN. For example, the satellite may be a regenerative payload for functioning as an independent base station. For example, the satellite may be a transparent payload for functioning as a relay satellite for a ground base station. For example, the plurality of base stations may include an aircraft (e.g., unmanned aerial vehicle, drone, balloon) moving in a high-altitude environment. According to embodiments of the present disclosure, the plurality of base stations may be classified into a plurality of layers according to the characteristics of each base station. Each of the plurality of base stations may be associated with one of the plurality of layers. To train a prediction model for each layer and to configure a registration area for each layer, the plurality of base stations may be classified according to the plurality of layers.

[0034] According to one embodiment, the plurality of base stations may be pre-classified according to a predefined method (e.g., operator settings or policies). For example, the plurality of base stations may be pre-classified according to radio access technology (RAT), coverage, type of satellite (e.g., transparent payload or regenerative payload), type of access network, characteristics of the base station, deployment of the base station, channel capacity of the base station, performance of the base station, altitude, and / or other characteristics of the base station. For example, among the plurality of base stations, a base station located in a first altitude range and a base station located in a second altitude range may be associated with different layers. For example, among the plurality of base stations, a satellite base station corresponding to low earth orbit (LEO) and a satellite base station corresponding to geostationary earth orbit (GEO) may be associated with different layers. For example, a base station providing an LTE network (i.e., EUTRAN (Evolved Universal Terrestrial Radio Access Network)) (e.g., a satellite base station for IoT NTN) and a base station providing an NR network (e.g., a satellite base station providing NR NTN) may be associated with different layers. For example, among the plurality of base stations, a base station moving at a first speed (e.g., a UAV) and a base station moving at a second speed (e.g., a satellite base station) may be associated with different layers. For example, among the plurality of base stations, a base station moving along a first orbit and a base station moving along a second orbit different from the first orbit may be associated with different layers. For example, among the plurality of base stations, a base station configured to service a first coverage and a base station configured to service a second coverage different from the first coverage may be associated with different layers.For example, among the plurality of base stations, a base station having a channel capacity of a first range and a base station having a channel capacity of a second range different from the first range may be associated with different layers. For example, among the plurality of base stations, a base station having a channel capacity of a first range and a base station having a channel capacity of a second range different from the first range may be associated with different layers. For example, among the plurality of base stations, a base station implemented as a single network node and a base station implemented as a distributed deployment (e.g., a combination of CU and DU) may be associated with different layers.

[0035] According to one embodiment, the plurality of base stations may be classified into a plurality of layers through a clustering technique (e.g., K-Means technique). The clustering technique is an unsupervised learning method, and by grouping base stations of similar types, a layer corresponding to each of the plurality of base stations may be established. The clustering technique may be performed based on mobility information of the base stations over a certain period of time. For example, the mobility information may include at least one of information regarding whether the base station is moving, information regarding the speed of the base station, information regarding the direction of movement of the base station, information regarding the performance of the base station (e.g., maximum number of cells, maximum number of connected terminals, movement speed, coverage area), information regarding the capacity of the base station (e.g., buffer capacity, channel capacity, number of cells), and / or RAT type information. By grouping base stations with similar characteristics through the clustering technique, base stations with similar characteristics may be associated with a single layer. Through the clustering technique, a high-resolution registration area may be configured in response to dynamic changes in the base stations.

[0036] When the above base stations are classified into multiple layers, an area corresponding to each layer (hereinafter referred to as a partial registration area) (in addition to the partial registration area, it may be referred to as a sub-registration area, detailed registration area, layer-specific registration area, altitude-specific registration area, semi-registration area, partial tracking area, sub-tracking area, detailed tracking area, layer-specific tracking area, altitude-specific tracking area, semi-tracking area, and / or equivalent technical terms) may be configured as a subset of the registration area of ​​the core network. Through a registration procedure, the registration area may be configured for the terminal (120). Depending on the location of the terminal (120), multiple partial areas corresponding to multiple layers may be configured for the terminal (120). For example, depending on the altitude, multiple base stations may be classified into multiple layers. The plurality of layers may include a first layer (210) for representing a base station located in a first altitude range (e.g., ground), a second layer (220) for representing a base station located in a second altitude range (e.g., an altitude range of about 18 km or more and less than about 25 km), and a third layer (230) for representing a base station located in a third altitude range (e.g., an altitude range of about 100 km or more and less than about 200 km). A registration area configured for a first terminal (120-1) (e.g., vehicle) may include a first partial registration area (211) for the first layer (210), a second partial registration area (221) for the second layer (220), and a third partial registration area (231) for the third layer (230). A registration area configured for a second terminal (120-2) (e.g., smartphone) may include a first partial registration area (212) for the first layer (210). A registration area configured for a third terminal (120-3) (e.g., UAV) may include a first partial registration area (213) for a first layer (210), a second partial registration area (223) for a second layer (220), and a third partial registration area (233) for a third layer (230).

[0037] Based on mobility information of the terminal (120) (e.g., location of the terminal (120), TAI of the terminal (120)) as well as mobility information of the base station (110) (e.g., direction of movement, speed of movement, whether it is moving, RAT type), a layer-by-layer partial registration area can be configured for the terminal (120). To more accurately identify the layer-by-layer partial registration area, a prediction model may be used to learn both the mobility information of the terminal (120) and the mobility information of the base station (110). The prediction model predicts base stations where the terminal (120) is likely to be located through deep learning, and the registration area can be configured based on the result of the prediction. By setting the base station(s) where the terminal (120) is expected to be located as the partial registration area on a per-layer basis, the possibility of the registration area changing due to the movement of the base station can be reduced. Through this, the signaling burden in the core network can be reduced. For example, the number of messages in the registration process and / or messages in the paging process may be reduced.

[0038] FIGS. 3a and 3b illustrate examples of signaling for predicting a registration area by a mobility management device (e.g., mobility management device (150)). The registration area may be assigned by the mobility management device (150) through a registration procedure (or a TAU procedure in LTE). The registration area assigned by the mobility management device (150) may be provided to the terminal (120) through an acceptance message. The same reference number may be used for the same description.

[0039] Referring to FIG. 3a, in operation (305), the analysis device (390) can collect and learn mobility information of the terminal (120) and mobility information of the base station (110) layer by layer. According to one embodiment, the analysis device (390) may represent a network data analytics function (NWDAF) used to collect and / or analyze data in a 5G core network and provide intelligence to other network functions (e.g., AMF).

[0040] The analysis device (390) may use a prediction model for a registration area. For example, the analysis device (390) may include a prediction model for a registration area or connect to a server having said prediction model. said prediction model may be used to track the base stations that the terminal (120) connects to during a recent period of time (e.g., n hours) for each terminal, and to learn the results of the tracking. The analysis device (390) may collect mobility information of the terminal (120). The mobility information of the terminal (120) may indicate the base stations that the terminal (120) connects to according to the movement of the terminal (120). The mobility information of the terminal (120) may be classified by layer. Here, a layer may represent a unit grouped according to the characteristics of the base station (e.g., altitude, placement, location, performance, capacity, speed, movement status). The analysis device (390) can collect mobility information of base stations (110) (e.g., a first base station (110-1), a second base station (110-2), a third base station (110-3), a fourth base station (110-4), and base stations within a wireless communication environment). The mobility information of the base station (110) may include characteristics related to the movement of the base station (e.g., orbit, speed, direction, amount of movement, movement period).

[0041] The analysis device (390) can perform preprocessing by layer. Since the prediction model according to the embodiments of the present disclosure is configured to provide partial registration areas by layer, the analysis device (390) can classify the collected data (e.g., mobility information of the terminal (120) and mobility information of the base station (110)) by layer. The analysis device (390) can perform preprocessing (e.g., normalization) on the classified data. The prediction model can be trained based on the preprocessed data. The analysis device (390) can train the prediction model based on the mobility information of the terminal (120), the mobility information of the base station (110), and the layers. To train the prediction model, each of the layer IDs (identifiers) corresponding to the layers can be used as input to the prediction model. As an example, a recurrent neural network (RNN), a temporal convolutional network (TCN), or a transformer for sequence processing may be used as the prediction model.

[0042] In operation (311), the terminal (120) can detect movement outside the registered area of ​​the terminal (120). For example, the terminal (120) can identify that the current TAI of the terminal (120) is not included in the list of tracking areas previously registered in the mobility management device (150). The tracking areas registered in the mobility management device (150) may correspond to the registered area. Based on the identification, the terminal (120) can detect that the terminal (120) is located outside the registered area.

[0043] In operation (313), the terminal (120) may transmit a request message to a mobility management device (150). For example, the request message may be a registration request message for a registration procedure in 5GS. The mobility management device (150) may be an AMF. As another example, the request message may be a TAU request message for a TAU procedure in EPS. The mobility management device (150) may be an MME. The request message may include identification information of the terminal (120) (e.g., IMSI (International Mobile Subscriber Identity), TMSI (Temporary Mobile Subscriber Identity), or 5G-GUTI (Globally Unique Temporary Identity)). As an example, but not limited to, the request message may include location information of the terminal (120) (e.g., the last visited TAI of the terminal (120), hereinafter last visited TAI).

[0044] In operation (315), the mobility management device (150) may transmit a prediction request message to the analysis device (390). The mobility management device (150) may transmit the prediction request message to the analysis device (390), which has a prediction model for a registration area or has a prediction model available for the registration area, in order to request a prediction of the registration area. According to one embodiment, the prediction request message may include identification information of the terminal (120). According to one embodiment, the prediction request message may include identification information of the terminal (120) and / or location information of the terminal (120) (e.g., last visited TAI). According to one embodiment, the prediction request message may include identification information of the terminal (120), identification information of a base station associated with the terminal (120) (e.g., a base station connected for the registration procedure), and / or location information of the terminal (120) (e.g., last visited TAI).

[0045] In operation (320), the analysis device (390) can perform a prediction of a partial registration area by layer. The analysis device (390) can perform inference based on a prediction model learned through the learning procedure of operation (305). The analysis device (390) can use the path previously traveled by the terminal (120) (e.g., a list of base stations) as a layer-by-layer input value for the prediction model. The prediction model may be configured to output information about one or more base stations on a layer-by-layer basis in response to the input value. Through the prediction model, the analysis device (390) can provide probability information for each base station of the corresponding layer on a layer-by-layer basis. The probability information may represent the probability that the terminal (120) is located in an area associated with the corresponding base station (e.g., a tracking area). A detailed explanation of the prediction of the partial registration area by layer is described in detail through FIG. 4. The analysis device (390) can determine the registration area by layer through the results of the prediction model. For example, the analysis device (390) can determine, in each layer, at least one base station having a probability greater than or equal to a threshold as a registration area (i.e., a partial registration area) in that layer. The analysis device (390) can determine a registration area including the partial registration area of ​​each layer.

[0046] In operation (321), the analysis device (390) may transmit a prediction response message to the mobility management device (150). The prediction response message may include information about a registration area determined by the analysis device (390). The analysis device (390) may transmit the prediction response message to the mobility management device (150) in response to a prediction request message of operation (315). Through the prediction response message, the mobility management device (150) may obtain one or more identifiers of one or more tracking areas corresponding to the determined registration area. The one or more identifiers may be included in a TAI list.

[0047] In operation (323), the mobility management device (150) may transmit an acceptance message to the terminal (120). The mobility management device (150) may transmit the acceptance message to the terminal (120) via a base station (e.g., base station (110), gNB, eNB). The acceptance message may be a response to a request message of operation (313). For example, the acceptance message may be a registration acceptance message of a registration procedure in 5GS. The mobility management device (150) may be an AMF. As another example, the request message may be a TAU acceptance message of a TAU procedure in EPS. The mobility management device (150) may be an MME. The acceptance message may include a list of tracking area identifier(s) (which may be referred to as a TAI list, and hereinafter referred to as a tracking area list). The tracking area list may represent one or more tracking areas. Each tracking area may represent one or more base stations. Base stations belonging to the tracking area list may include satellite base stations providing NTN and ground base stations providing ground networks. If a registration area is configured that includes both a tracking area corresponding to a ground base station and a tracking area corresponding to a satellite base station for a terminal (120) placed at a fixed location, it may be understood as an embodiment of the present disclosure.

[0048] In FIG. 3a, an example is described in which an analysis device (390) determines a registration area including a layer-by-layer partial registration area and transmits the determined registration area to a mobility management device (150), but the embodiments of the present disclosure are not limited thereto. The analysis device (390) transmits data according to a prediction model to the mobility management device (150), and the mobility management device (150) may determine a layer-by-layer partial registration area based on the data.

[0049] In FIG. 3a, an example is described in which a prediction model for determining a partial registration area by layer is placed in a separate entity (e.g., an analysis device (390)) from the mobility management device (150), but the embodiments of the present disclosure are not limited thereto. The prediction model according to the embodiments of the present disclosure may be included in the mobility management device (150) or in a server connected to the mobility management device (150). The mobility management device (150) may determine a partial registration area by layer by training the prediction model and through inference using the prediction model. A procedure for determining a partial registration area by layer by the mobility management device (150) is described in detail through FIG. 3b.

[0050] Referring to FIG. 3b, in operation (355), the mobility management device (150) can collect and learn mobility information of the terminal (120) and mobility information of the base station (110) by layer. The mobility management device (150) can use a prediction model for a registration area. For example, the mobility management device (150) may include a prediction model for a registration area or connect to a server having said prediction model. said prediction model can be used to track the base stations that the terminal (120) connects to during a recent period of time (e.g., n hours) for each terminal, and to learn the results of the tracking. The mobility management device (150) can collect mobility information of the terminal (120). The mobility information of the terminal (120) may indicate the base stations that the terminal (120) connects to according to the movement of the terminal (120). The mobility information of the terminal (120) can be classified by layer. Here, a layer may represent a unit grouped according to the characteristics of a base station (e.g., altitude, placement, location, performance, capacity, speed, movement status). A mobility management device (150) may collect mobility information of base stations (110) (e.g., a first base station (110-1), a second base station (110-2), a third base station (110-3), a fourth base station (110-4), and base stations within a wireless communication environment). The mobility information of a base station (110) may include characteristics related to the movement of the base station (e.g., orbit, speed, direction, amount of movement, movement period).

[0051] The mobility management device (150) can perform preprocessing by layer. Since the prediction model according to the embodiments of the present disclosure is configured to provide a partial registration area by layer, the mobility management device (150) can classify the collected data (e.g., mobility information of the terminal (120) and mobility information of the base station (110)) by layer. The mobility management device (150) can perform preprocessing (e.g., normalization) on the classified data. The prediction model can be trained based on the preprocessed data. The mobility management device (150) can train the prediction model based on the mobility information of the terminal (120), the mobility information of the base station (110), and the layers. To train the prediction model, each of the layer IDs corresponding to the layers can be used as an input to the prediction model. As an example, an RNN, TCN, or Transformer for sequence processing may be used as the prediction model.

[0052] In operation (350), the terminal (120) can detect movement outside the registered area of ​​the terminal (120). For example, the terminal (120) can identify that the current TAI of the terminal (120) is not included in the list of tracking areas previously registered in the mobility management device (150). The tracking areas registered in the mobility management device (150) may correspond to the registered area. Based on the identification, the terminal (120) can detect that the terminal (120) is located outside the registered area.

[0053] In operation (361), the terminal (120) may transmit a request message (313) to a mobility management device (150). For example, the request message may be a registration request message for a registration procedure in 5GS. The mobility management device (150) may be an AMF. As another example, the request message may be a TAU request message for a TAU procedure in EPS. The mobility management device (150) may be an MME. The request message may include identification information of the terminal (120) (e.g., IMSI, TMSI, or 5G-GUTI). As an example, but not limited to, the request message may include location information of the terminal (120) (e.g., last visited TAI).

[0054] In operation (370), the mobility management device (150) can perform prediction of partial registration areas by layer. The mobility management device (150) can perform prediction of partial registration areas by layer. The mobility management device (150) can perform inference based on a prediction model learned through the learning procedure of operation (355). The mobility management device (150) can use the path previously traveled by the terminal (120) (e.g., a list of base stations) as a layer-by-layer input value for the prediction model. The prediction model may be configured to output information about one or more base stations on a layer-by-layer basis in response to the input value. Through the prediction model, the mobility management device (150) can provide probability information for each base station of the corresponding layer on a layer-by-layer basis. The probability information may represent the probability that the terminal (120) is located in an area associated with the corresponding base station (e.g., a tracking area). A detailed description of the prediction of partial registration areas by layer is described in detail through FIG. 4. The mobility management device (150) can determine a layer-specific registration area through the results of the prediction model. For example, the mobility management device (150) can determine, in each layer, at least one base station having a probability greater than or equal to a threshold as a registration area (i.e., a partial registration area) in that layer. The mobility management device (150) can determine a registration area including the partial registration area of ​​each layer.

[0055] In operation (363), the mobility management device (150) may transmit an acceptance message to the terminal (120). The mobility management device (150) may transmit the acceptance message to the terminal (120) via a base station (e.g., base station (110), gNB, eNB). The acceptance message may be a response to a request message of operation (313). For example, the acceptance message may be a registration acceptance message of a registration procedure in 5GS. The mobility management device (150) may be an AMF. As another example, the request message may be a TAU acceptance message of a TAU procedure in EPS. The mobility management device (150) may be an MME. The acceptance message may include a list of tracking area identifier(s) (which may be referred to as a TAI list, and hereinafter referred to as a tracking area list). The tracking area list may represent one or more tracking areas. Each tracking area may represent one or more base stations. Base stations belonging to the tracking area list may include satellite base stations providing NTN and ground base stations providing ground networks. If a registration area is configured that includes both a tracking area corresponding to a ground base station and a tracking area corresponding to a satellite base station for a terminal (120) placed at a fixed location, it may be understood as an embodiment of the present disclosure.

[0056] In FIGS. 3a and 3b, it is illustrated that a registration procedure (or TAU procedure) is initiated when the terminal (120) moves out of the registration area, but embodiments of the present disclosure are not limited thereto. For example, the terminal (120) may periodically send a request message for an update upon the expiration of a timer. For example, the terminal (120) may send a request message for an update of the registration area upon a change in the terminal's settings. For example, the terminal (120) may send a request message for an update of the registration area upon a change in the connected system.

[0057] Figure 4 shows an example of training for layer-by-layer prediction. The same reference numbers may be used for the same description.

[0058] Referring to FIG. 4, according to embodiments of the present disclosure, a prediction model may be used to determine a partial registration area by layer. Input data (410) may include mobility information of a terminal (120) and mobility information of a base station (110). Data (Sn) may be collected for time n. The data may include one or more base stations in relation to the movement of the terminal (120). The data may include characteristics of each base station (e.g., whether it is moving, altitude, orbit, speed, direction, amount of movement, movement period, location, performance, capacity) in the one or more base stations. The history of actual movement of the terminal (120) (e.g., a list of base stations) may be used as an output vector (420) for mean squared error (MSE) (470) and back propagation.

[0059] Multiple base stations can be classified into multiple layers. Each of the multiple base stations can be associated with one of the multiple layers through classification. Each base station can be mapped to a layer ID of one layer. The classification of the multiple base stations can be performed according to a predefined method (e.g., operator policy or manual setting) or a clustering technique. Input data (410) can be classified by layer and then input into a prediction model. A layer ID (430) can be used for learning and inference on a layer-by-layer basis. The layer ID can be input together with the input data (410) and used for learning and inference. The prediction model can output a first result (440). The first result (440) can represent probability information for each base station. The probability information can represent the probability that a terminal (120) is expected to be located. The prediction model can sort the probability information for each base station in descending order (450). Based on the result of the above alignment, a second result (460) may be output. The mobility management device (150) and / or the analysis device (390) may output at least one base station having a probability greater than a threshold value in each layer based on the second result (460) through the prediction model. As an example that is not limited to the threshold value, N base station(s) having a probability of the top N (N is an integer) may be determined as partial registration areas. The mobility management device (150) and / or the analysis device (390) may output tracking areas corresponding to the top N base stations in each layer as partial registration areas based on the second result (460) through the prediction model.

[0060] Figure 5 shows an example of performance according to prediction by layer.

[0061] Referring to FIG. 5, the graph (500) represents the signaling amount for each environment. As the registration area becomes larger, the frequency of changes to the registration area decreases, so the number of signaling events caused by the registration procedure may decrease. On the other hand, as the registration area becomes larger, the paging range increases, so paging signaling may increase. In the graph (500), the horizontal axis represents the size of the registration area, and the vertical axis represents the amount of signaling in the registration procedure and the paging procedure. The signaling amount and registration area in the TN environment can be used as a standard. The signaling amount in other environments can be expressed as a multiple (unit: %) of the signaling amount in the TN environment. The size of the registration area in other environments can be expressed as a multiple (unit: %) of the size of the registration area in the TN environment. Line (501) represents the signaling amount in the NTN environment without a separate prediction model. Line (502) represents the signaling amount in the TN environment without a separate prediction model. Line (503) represents the signaling amount when determining the registration area using a prediction model corresponding to the TCN. Line (504) indicates the amount of signaling when determining the registration area using a prediction model corresponding to LSTM (Long Short-Term Memory). Line (505) indicates the amount of signaling when determining the registration area using a prediction model corresponding to GRU (Gated Recurrent Unit). Line (506) indicates the amount of signaling when determining the registration area using a prediction model corresponding to BiLSTM (Bidirectional Long Short-Term Memory). Line (507) indicates the amount of signaling when determining the registration area using a prediction model corresponding to BiGRU (Bidirectional Gated Recurrent Unit).

[0062] By referring to lines (501), (502), (503), (504), (505), (506), and (507), it can be confirmed that the signaling burden is reduced through the prediction model. It can also be confirmed that the signaling burden is reduced to a level similar to that of the TN environment in the NTN environment. As a non-limiting example, by referring to lines (501), (502), (503), (504), (505), (506), and (507), it can be identified that the performance of the prediction model is best in TCN.

[0063] FIG. 6 illustrates the flow of operation of a mobility management device (e.g., mobility management device (150)) for configuring a registration area through layer-by-layer prediction. The same reference numbers may be used for the same description.

[0064] Referring to FIG. 6, in operation (601), the mobility management device (150) may receive a request message for a registration area. For example, the request message may be a registration request message for a registration procedure. The request message may include identification information of the terminal (120) (e.g., IMSI, TMSI, or 5G-GUTI). As an example, but not limited to, the request message may include location information of the terminal (120) (e.g., last visited TAI).

[0065] In operation (603), the mobility management device (150) can determine a partial registration area by layer through a prediction model learned based on multiple layers classifying multiple base stations, mobility information of the terminal (120), and mobility information of at least one base station. The wireless communication environment managed by the mobility management device (150) may include multiple base stations. The multiple base stations may be classified according to RAT, coverage, type of satellite (e.g., transparent payload or regenerative payload), type of access network, characteristics of the base station, deployment of the base station, channel capacity of the base station, performance of the base station, altitude, and / or other characteristics of the base station. Each of the multiple base stations may be associated with one of the multiple layers through classification. Each base station may be mapped to a layer ID of one layer. The classification of the multiple base stations may be performed according to a predefined method (e.g., operator policy or manual setting) or a clustering technique.

[0066] The mobility management device (150) can collect mobility information of the terminal (120) and mobility information of at least one base station. The mobility management device (150) can classify the collected data by layer and learn a prediction model on a layer-by-layer basis. The mobility management device (150) can determine partial registration areas by layer through the learned prediction model. The prediction model can output probability information for each base station of the corresponding layer at each layer. The probability information may represent the probability that the terminal (120) is expected to be located. For example, the mobility management device (150) can identify at least one base station having a probability greater than or equal to a threshold value at each layer. As another example, the mobility management device (150) can identify N base stations having the top N probabilities (N is an integer) at each layer. For each layer, the mobility management device (150) can determine tracking areas corresponding to the identified base stations as partial registration areas.

[0067] In FIG. 6, an example is described in which the mobility management device (150) determines a partial registration by layer, but the embodiments of the present disclosure are not limited thereto. Even if the mobility management device (150) ultimately determines the registration area as illustrated in FIG. 3a, the learning of the prediction model and the inference of the partial registration area by layer may be performed by another device (e.g., NWDAF, analysis device (390)). Subsequently, the prediction result of the analysis device (390) or information regarding the partial registration area by layer may be provided to the mobility management device (150).

[0068] In operation (605), the mobility management device (150) can generate a list of tracking areas corresponding to the registration areas. The mobility management device (150) can determine the registration areas for all layers by combining all partial registration areas for each layer.

[0069] In operation (607), the mobility management device (150) may transmit a response message containing the tracking area list. For example, the response message may be a registration acceptance message of a registration procedure in 5GS. The mobility management device (150) may be an AMF. As another example, the request message may be a TAU acceptance message of a TAU procedure in EPS. The response message may include a list of tracking area identifier(s) (hereinafter, tracking area list). The tracking area list may represent one or more tracking areas. Each tracking area may represent one or more base stations. Base stations belonging to the tracking area list may include satellite base stations providing NTN and ground base stations providing ground networks.

[0070] FIG. 7 illustrates examples of components of a mobility management device (e.g., mobility management device (150)). For example, the mobility management device may be configured to perform the functions of an AMF. For example, the mobility management device may be configured to perform the functions of an MME. Terms such as '...part', '...unit', '...circuit', '...module' used below refer to a unit that processes at least one function or operation, which may be implemented in hardware or a combination of hardware and software.

[0071] Referring to FIG. 7, the mobility management device (150) may include a transceiver (1110), a memory (1120), and a processor (1130). The transceiver (1110) may perform functions for transmitting and receiving signals in a wired communication environment. The transceiver (1110) may include a wired interface for controlling a direct connection between devices through a transmission medium (e.g., copper wire, optical fiber). For example, the transceiver (1110) may transmit an electrical signal to another device through a copper wire or perform conversion between an electrical signal and an optical signal. According to one embodiment, the transceiver (1110) of the mobility management device (150) may communicate with an electronic device (e.g., a terminal (120)) through a network node (e.g., a base station (110)). The transceiver (1110) may also perform functions for transmitting and receiving signals in a wireless communication environment. For example, the transceiver (1110) can perform a conversion function between a baseband signal and a bit sequence according to the physical layer specifications of the system. For example, when transmitting data, the transceiver (1110) generates complex symbols by encoding and modulating the transmitted bit sequence. Also, when receiving data, the transceiver (1110) restores the received bit sequence by demodulating and decoding the baseband signal. Additionally, the transceiver (1110) may include a plurality of transmission and reception paths. The transceiver (1110) transmits and receives signals as described above. Accordingly, all or part of the transceiver (1110) may be referred to as a 'communication unit', 'transmitter unit', 'receiver unit', 'transmitter and receiver unit', or 'transceiver'.

[0072] Memory (1120) can store data such as basic programs, applications, and configuration information for the operation of the mobility management device (150). Memory (1120) may be referred to as a storage unit. Memory (1120) may be composed of volatile memory, non-volatile memory, or a combination of volatile and non-volatile memory. Also, memory (1120) may provide stored data upon request from the processor (1130). Memory (1120) may represent a storage space as a functional component. For example, memory (1120) may be understood as representing a memory (e.g., hard disk, flash memory, RAM) placed as a component within the mobility management device (150), as well as a space for storing instructions and / or programs.

[0073] The processor (1130) controls the overall operations of the mobility management device (150). The processor (1130) may include at least one processor. The processor (1130) may be referred to as a control unit. For example, the processor (1130) transmits and receives signals through the transceiver (1110). Additionally, the processor (1130) writes and reads data to and from memory (1120). Furthermore, the processor (1130) can perform functions of a protocol stack required by the communication standard (e.g., functions of AMF or MME).

[0074] According to one embodiment, the processor (1130) may perform a prediction procedure for a registration area in response to a registration request message from the terminal (120). According to one embodiment, the processor (1130) may obtain a partial registration area by layer. According to one embodiment, the processor (1130) may obtain a partial registration area by layer. According to one embodiment, the processor (1130) may learn a prediction model by layer for the prediction procedure for a registration area. According to one embodiment, the processor (1130) may determine a registration area including a partial registration area by layer. According to one embodiment, the processor (1130) may transmit a registration acceptance message including a list of tracking area identifiers corresponding to the registration area to the terminal (120) through the transceiver (110). According to one embodiment, the processor (1130) may learn a prediction model by layer for the prediction procedure for a registration area. According to one embodiment, the processor (1130) may provide data to an analysis device (e.g., NWDAF) for a prediction procedure of a registration area and obtain a prediction result from the analysis device.

[0075] The components of the mobility management device (150) illustrated in FIG. 7 are merely examples, and examples of components of the mobility management device (150) for performing embodiments of the present disclosure are not limited to the configuration illustrated in FIG. 7. In some embodiments, some components may be added, deleted, or changed.

[0076] Regenerative NTN environments are set to become one of the major communication methods of the future. However, current RA management methods and RA prediction models primarily consider TN environments centered on terrestrial base stations, making them unsuitable for NTN environments characterized by base station mobility. In particular, in Regenerative NTN, base station movement often causes devices to exit the RA even when the UE remains stationary, leading to frequent unnecessary Registration Update procedures and a significant increase in signaling load. To address these issues, specialized learning and prediction models are required that consider base station movement as well as UE movement.

[0077] In order to satisfy this need, the present invention separates layers by UE and configures sub-registration areas (sub-RA) that reflect the mobility of base stations for each layer. In addition, a deep learning model capable of learning both the movement patterns of base stations and the movement patterns of UEs is introduced to predict base stations where UEs are likely to be located in the future, and configures RAs based on this.

[0078] Verification results confirmed that using the proposed method can reduce signaling to a level nearly similar to that of a TN environment, even in an NTN environment. This implies that efficient network management is possible even in a Regenerative NTN environment. Therefore, the present invention presents a new method for managing the location of UEs in an NTN environment, thereby enabling more stable and efficient network operation.

[0079] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs from the description below.

[0080] According to embodiments of the present disclosure, a mobility management device (150) is provided. The mobility management device (150) may include a memory comprising one or more storage media for storing instructions; and at least one processor comprising a processing circuit. The above instructions may cause the mobility management device (150), individually or collectively by the at least one processor, to receive a request message for a registration area from a UE (user equipment), and in response to the request message, to obtain a partial registration area for representing a base station where the UE is expected to be located in each layer based on a prediction model learned according to a plurality of layers for classifying a plurality of base stations, mobility information of the UE, and mobility information of at least one of the plurality of base stations, - the plurality of layers include a first layer associated with a ground base station for providing a ground network and a second layer associated with a satellite base station for providing a non-ground network -, to generate a tracking area list corresponding to the registration area including the partial registration area of ​​each layer, and to transmit an acceptance message including the tracking area list to the UE in response to the request message.

[0081] For example, each of the plurality of base stations may be associated with the layer ID (identifier) ​​of the corresponding layer among the plurality of layers. The mobility information of the UE may be classified into at least one layer among the plurality of layers. The prediction model may be configured to provide information on at least one base station corresponding to the partial registration area on a layer-by-layer basis, based on the result of the classification and the layer ID.

[0082] For example, the mobility information of the UE may include location data for base stations providing the cells to which the UE connects. The mobility information of the at least one base station may include satellite data indicating the orbit and speed of the satellite base station.

[0083] For example, the above instructions may cause the mobility management device (150) to collect mobility information of the UE and mobility information of at least one of the plurality of base stations, individually or collectively by the at least one processor, classify the mobility information of the UE according to each layer of the plurality of layers, and perform learning of the prediction model based on the mobility information of at least one of the plurality of base stations, the result of the classification, and the layer ID (identifier) ​​of each layer.

[0084] For example, among the plurality of base stations mentioned above, base stations having different altitude ranges may be associated with different layers.

[0085] For example, a ground base station providing a ground network via a satellite corresponding to the transparent payload and a satellite base station providing a non-ground network corresponding to the regenerative payload may be associated with different layers.

[0086] For example, each of the plurality of base stations may be classified into one of a plurality of layers based on the mobility information of the base station. The mobility information of the base station may include at least one of information indicating whether the base station is moving, information regarding the speed of movement of the base station, information regarding the direction of movement of the base station, or orbit information of the base station.

[0087] For example, the request message may include identification information of the UE and information about the tracking area that the UE last visited. The identification information of the UE and the information about the tracking area may be used to determine the layer-specific partial registration area based on the prediction model.

[0088] For example, the instructions may cause the mobility management device (150), individually or collectively by the at least one processor to respond to the request message, to transmit a prediction request message to a Network Data Analytics Function (NWDAF) utilizing the prediction model in response to the request message, and to receive a prediction response message from the NWDAF in response to the prediction request message. The NWDAF may be configured to learn the prediction model according to the plurality of layers, the mobility information of the UE, and the mobility information of at least one base station of the plurality of base stations. The prediction response message may include information regarding the partial registration area of ​​each layer.

[0089] For example, the above mobility management device (150) can correspond to the access and mobility management function (AMF) of the 5GS (5th generation system) or the mobility management entity (MME) of the EPS (evolved packet system).

[0090] According to embodiments of the present disclosure, a method performed by a mobility management device (150) is provided. The method may include receiving a request message for a registration area from a UE (user equipment); in response to the request message, obtaining a partial registration area for representing a base station where the UE is expected to be located in each layer based on a plurality of layers for classifying a plurality of base stations, mobility information of the UE, and mobility information of at least one base station of the plurality of base stations, and a prediction model learned according to the plurality of layers - the plurality of layers include a first layer associated with a ground base station for providing a ground network and a second layer associated with a satellite base station for providing a non-ground network -, generating a tracking area list corresponding to a registration area including a partial registration area of ​​each layer, and transmitting an acceptance message including the tracking area list to the UE in response to the request message.

[0091] For example, each of the plurality of base stations may be associated with the layer ID (identifier) ​​of the corresponding layer among the plurality of layers. The mobility information of the UE may be classified into at least one layer among the plurality of layers. The prediction model may be configured to provide information on at least one base station corresponding to the partial registration area on a layer-by-layer basis, based on the result of the classification and the layer ID.

[0092] For example, the mobility information of the UE may include location data for base stations providing the cells to which the UE connects. The mobility information of the at least one base station may include satellite data indicating the orbit and speed of the satellite base station.

[0093] For example, the method may include collecting mobility information of the UE and mobility information of at least one of the plurality of base stations, classifying the mobility information of the UE according to each layer of the plurality of layers, and performing training of the prediction model based on the mobility information of at least one of the plurality of base stations, the result of the classification, and the layer ID (identifier) ​​of each layer.

[0094] For example, among the plurality of base stations mentioned above, base stations having different altitude ranges may be associated with different layers.

[0095] For example, among multiple base stations, a ground base station providing a ground network via a satellite corresponding to a transparent payload and a satellite base station providing a non-ground network corresponding to a regenerative payload may be associated with different layers.

[0096] For example, each of the plurality of base stations may be classified into one of a plurality of layers based on the mobility information of the base station. The mobility information of the base station may include at least one of information indicating whether the base station is moving, information regarding the speed of movement of the base station, information regarding the direction of movement of the base station, or orbit information of the base station.

[0097] For example, the request message may include identification information of the UE and information about the tracking area that the UE last visited. The identification information of the UE and the information about the tracking area may be used to determine the layer-specific partial registration area based on the prediction model.

[0098] For example, acquiring the partial registration area may include transmitting a prediction request message to a Network Data Analytics Function (NWDAF) utilizing the prediction model in response to the request message, and receiving a prediction response message from the NWDAF as a response to the prediction request message. The NWDAF may be configured to learn the prediction model according to the plurality of layers, the mobility information of the UE, and the mobility information of at least one base station among the plurality of base stations. The prediction response message may include information regarding the partial registration area of ​​each layer.

[0099] According to embodiments of the present disclosure, an apparatus configured to perform the functions of an access and mobility management function (AMF) is provided. The apparatus may include a memory comprising one or more storage media for storing instructions; and at least one processor comprising a processing circuit. The instructions may cause the apparatus to receive, individually or collectively by the at least one processor, a registration request message for initiating a registration procedure or updating a registration area from a user equipment (UE), and to transmit a registration acceptance message to the UE in response to the registration request message, the message comprising a tracking area list including at least one tracking area identifier. The at least one tracking area identifier may include a tracking area identifier corresponding to a base station where the UE is expected to be located in each of a plurality of layers. The plurality of layers may include a first layer associated with a ground base station for providing a ground network and a second layer associated with a satellite base station for providing a non-ground network.

[0100] According to embodiments of the present disclosure, a mobility management entity (MME) device is provided. The MME device may include a memory comprising one or more storage media for storing instructions; and at least one processor comprising a processing circuit. The instructions may cause the device to receive a tracking area update request message from a user equipment (UE) individually or collectively by the at least one processor, and to transmit a tracking area update acceptance message to the UE in response to the tracking area update request message, the tracking area update acceptance message comprising a tracking area list including at least one tracking area identifier. The at least one tracking area identifier may include a tracking area identifier corresponding to a base station where the UE is expected to be located in each of a plurality of layers. The plurality of layers may include a first layer associated with a ground base station for providing a ground network and a second layer associated with a satellite base station for providing a non-ground network.

[0101] For one or more embodiments, at least one of the components described in one or more of the prior art drawings may be configured to perform one or more operations, techniques, processes and / or methods as described in the present disclosure. For example, a processor (e.g., a baseband processor) described in the present disclosure in relation to one or more of the prior art drawings may be configured to operate according to one or more examples described in the present disclosure. As another example, circuits associated with user equipment (UE), a base station, a network element, etc., as described above in relation to one or more of the prior art drawings may be configured to operate according to one or more examples described herein.

[0102] Any of the embodiments described above may be combined with any other embodiment (or combination of embodiments) unless otherwise explicitly stated. The foregoing description of one or more embodiments is for illustrative and explanatory purposes only, and is not intended to limit or exhaust the scope of the embodiments in the exact form disclosed. Modifications and variations are possible in light of the foregoing teachings or may be obtained from the practice of various embodiments.

[0103] Methods according to the claims or embodiments described in the specification of the present disclosure may be implemented in the form of hardware, software, or a combination of hardware and software.

[0104] When implemented in software, a computer-readable storage medium (e.g., a non-transient computer-readable storage medium) storing one or more programs (software modules) may be provided. One or more programs stored in the computer-readable storage medium are configured for execution by one or more processors within an electronic device. One or more programs include instructions that cause the electronic device to execute methods according to the claims or embodiments described in the specification of this disclosure. The one or more programs may be provided as a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0105] Such programs (software modules, software) may be stored in random access memory, non-volatile memory including flash memory, read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic disc storage devices, compact disc-ROM (CD-ROM), digital versatile discs (DVDs), or other forms of optical storage devices, magnetic cassettes. Alternatively, they may be stored in memory composed of some or all of these. Additionally, each constituent memory may include multiple units.

[0106] Additionally, the program may be stored on an attachable storage device that can be accessed via a communication network such as the Internet, Intranet, LAN (local area network), WAN (wide area network), or SAN (storage area network), or a combination thereof. Such a storage device may be connected to a device performing an embodiment of the present disclosure through an external port. Additionally, a separate storage device on a communication network may be connected to a device performing an embodiment of the present disclosure.

[0107] In the specific embodiments of the present disclosure described above, the components included in the disclosure are expressed in a singular or plural form according to the specific embodiments presented. However, the singular or plural expression is selected to suit the situation presented for convenience of explanation, and the present disclosure is not limited to singular or plural components; even if a component is expressed in the plural form, it may be composed of a singular form, and even if a component is expressed in the singular form, it may be composed of a plural form.

[0108] According to the embodiments, one or more of the aforementioned components or operations may be omitted, or one or more other components or operations may be added. Generally or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the plurality of components in the same or similar manner as those performed by the corresponding component among the plurality of components prior to the integration. According to the embodiments, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0109] Meanwhile, although specific embodiments have been described in the detailed description of the present disclosure, it is understood that various modifications are possible within the scope of the present disclosure.

Claims

1. In a mobility management device, Memory comprising one or more storage media for storing instructions; and It includes at least one processor including a processing circuit, and The above instructions, individually or collectively by the at least one processor, the mobility management device: Receive a request message for a registration area from UE (user equipment), and In response to the above request message, a partial registration area is obtained to indicate the base station where the UE is expected to be located in each layer based on a prediction model learned according to a plurality of layers for classifying a plurality of base stations, mobility information of the UE, and mobility information of at least one of the plurality of base stations, and - the plurality of layers include a first layer associated with a ground base station for providing a ground network and a second layer associated with a satellite base station for providing a non-ground network -, Generate a tracking area list corresponding to a registration area including a partial registration area of ​​each of the above layers, and Causing the transmission of an acceptance message including the tracking area list to the UE in response to the above request message, Mobility management device.

2. In Claim 1, Each of the plurality of base stations is associated with the layer ID (identifier) ​​of the corresponding layer among the plurality of layers, and The mobility information of the above UE is classified into at least one layer among the plurality of layers, and The above prediction model is configured to provide information on at least one base station corresponding to the partial registration area on a layer-by-layer basis, based on the result of the classification and the layer ID. Mobility management device.

3. In Claim 2, The mobility information of the above UE includes location data for base stations providing the cells to which the UE connects, and The mobility information of the above-mentioned at least one base station includes satellite data indicating the orbit and speed of the satellite base station. Mobility management device.

4. In Claim 2, The above instructions, individually or collectively by the at least one processor, the mobility management device: Collecting mobility information of the above UE and mobility information of at least one base station of the above plurality of base stations, According to each layer of the plurality of layers above, the mobility information of the UE is classified, and Causing to perform learning of the prediction model based on mobility information of at least one base station of the plurality of base stations, the result of the classification, and the layer ID (identifier) ​​of each layer, Mobility management device.

5. In Claim 1, Among the plurality of base stations mentioned above, base stations having different altitude ranges are associated with different layers, Mobility management device.

6. In Claim 1, Among the plurality of base stations mentioned above, the ground base station providing a ground network via a satellite corresponding to a transparent payload and the satellite base station providing a non-ground network corresponding to a regenerative payload are related to different layers. Mobility management device.

7. In Claim 1, Each of the above plurality of base stations is classified into one of a plurality of layers based on the mobility information of the corresponding base station, and The mobility information of the base station above includes at least one of information indicating whether the base station is moving, information regarding the speed of movement of the base station, information regarding the direction of movement of the base station, or orbit information of the base station. Mobility management device.

8. In Claim 1, The above request message includes identification information of the UE and information about the tracking area last visited by the UE, and The identification information of the above UE and the information regarding the tracking area are used to determine the layer-specific partial registration area based on the prediction model, Mobility management device.

9. In Claim 1, The above instructions, individually or collectively by the at least one processor, for the mobility management device to acquire the above partial registration area: In response to the above request message, a prediction request message is sent to the NWDAF (Network Data Analytics Function) using the above prediction model, and Causing to receive a prediction response message from the above NWDAF in response to the prediction request message, and The above NWDAF is configured to learn a prediction model according to the plurality of layers, the mobility information of the UE, and the mobility information of at least one base station of the plurality of base stations, and The above prediction response message includes information regarding the partial registration area of ​​each layer, Mobility management device.

10. In Claim 1, The above mobility management device corresponds to the AMF (access and mobility management function) of the 5GS (5th generation system) or the MME (mobility management entity) of the EPS (evolved packet system), Mobility management device.

11. A method performed by a mobility management device, Receiving a request message for a registration area from UE (user equipment), and In response to the above request message, obtaining a partial registration area for indicating a base station where the UE is expected to be located in each layer based on a prediction model learned according to a plurality of layers for classifying a plurality of base stations, mobility information of the UE, and mobility information of at least one of the plurality of base stations, and - the plurality of layers include a first layer associated with a ground base station for providing a ground network and a second layer associated with a satellite base station for providing a non-ground network -, Generating a tracking area list corresponding to a registration area including a partial registration area of ​​each of the above layers, and In response to the above request message, including sending an acceptance message to the UE that includes the tracking area list, method.

12. In Claim 11, Each of the plurality of base stations is associated with the layer ID (identifier) ​​of the corresponding layer among the plurality of layers, and The mobility information of the above UE is classified into at least one layer among the plurality of layers, and The above prediction model is configured to provide information on at least one base station corresponding to the partial registration area on a layer-by-layer basis, based on the result of the classification and the layer ID. method.

13. In Claim 12, The mobility information of the above UE includes location data for base stations providing the cells to which the UE connects, and The mobility information of the above-mentioned at least one base station includes satellite data indicating the orbit and speed of the satellite base station. method.

14. In Claim 12, Collecting mobility information of the above UE and mobility information of at least one base station of the above plurality of base stations, and Classifying the mobility information of the UE according to each layer of the plurality of layers above, and The method comprises performing training of the prediction model based on mobility information of at least one base station of the plurality of base stations, the result of the classification, and the layer ID (identifier) ​​of each layer. method.

15. In Claim 11, Among the plurality of base stations mentioned above, base stations having different altitude ranges are associated with different layers, method.

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