Terminal, base station device, and control method for improving handover performance using ai / ml

Abstract

To improve communication efficiency by enhancing handover processing.SOLUTION: Receiving, by a terminal, information on an event based on a probability of occurrence of at least one of a radio link failure (RLF) and a handover failure (HOF); Measuring radio quality of a signal transmitted from a base station apparatus, and performing, on a learned model acquired by machine learning in which the radio quality of the signal transmitted from the base station apparatus acquired in a learning phase is used as an input and a value indicating whether or not at least one of an RLF in a case where a handover process has not been performed and an HOF in a case where the handover process has been performed has occurred at one or more timings after a predetermined period from a timing at which the radio quality has been acquired is used as teacher data, An occurrence probability of at least one of an RLF and an HOF acquired by inputting radio quality measured in an inference phase is determined, and when the occurrence probability satisfies an event condition, a predetermined notification is transmitted to a base station device.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to an advanced handover technique in a cellular communication system. [Background technology]

[0002] In wireless communication systems that comply with the cellular communication standards of the Third Generation Partnership Project (3GPP (registered trademark)), handover processing is specified to switch the base station to which a mobile terminal device is connected, so that the mobile terminal device can continue to communicate with high quality. The handover processing is performed, for example, when the wireless quality of a signal transmitted from a base station to which the terminal device is currently connected falls below a predetermined level for a certain period of time, by transmitting a measurement result to the base station, and the base station then instructs the mobile terminal device to perform a handover. Summary of the Invention [Problem to be solved by the invention]

[0003] For example, if the wireless quality of the terminal device further deteriorates during the time between when the wireless quality falls below a predetermined level and when a handover command is transmitted from the base station, the handover process may fail, resulting in a decrease in communication efficiency. Also, since it takes time for the terminal device to detect a more suitable base station to connect to and when a handover command is transmitted from the base station, an opportunity to improve communication efficiency may be missed. [Means for solving the problem]

[0004] The present invention provides a technique for improving communication efficiency.

[0005] A terminal device according to one aspect of the present invention comprises: a measurement means for measuring the wireless quality of a signal transmitted from a base station device to which it is connected; an identification means for inputting the wireless quality acquired for the signal transmitted from the base station device in a learning phase, and for identifying, for a trained model acquired by machine learning using as training data a value indicating whether or not at least one of an RLF when handover processing is not performed and an HOF when handover processing is performed, the RLF and the HOF acquired by inputting the wireless quality measured by the measurement means in an inference phase; and a transmission means for transmitting predetermined information regarding the one or more occurrence probabilities to the base station device.

[0006] A base station device according to one aspect of the present invention has a receiving means for receiving from a terminal device connected to the base station device, predetermined information regarding one or more occurrence probabilities corresponding to each of one or more timings of at least one of the RLF and the HOF, which is acquired by inputting the wireless quality acquired for a signal transmitted from the base station device in a learning phase and using as training data a value indicating whether or not at least one of an RLF when a handover process is not performed at one or more timings after the timing at which the wireless quality is acquired, and an HOF when a handover process is performed; and a determining means for determining whether or not to hand over the terminal device from a cell provided by the base station device to another cell based on the predetermined information. [Effects of the Invention]

[0007] According to the present invention, the handover process can be enhanced to improve communication efficiency. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of a wireless communication system. [Figure 2] FIG. 2 is a diagram illustrating an example of the hardware configuration of a base station device and a terminal device. [Figure 3] FIG. 2 is a diagram illustrating an example of a functional configuration of a terminal device. [Figure 4] FIG. 2 is a diagram illustrating an example of a functional configuration of a base station device. [Figure 5] FIG. 1 is a diagram illustrating an example of a flow of processing executed in a wireless communication system. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, the embodiments will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the scope of the invention as claimed, and not all combinations of features described in the embodiments are necessarily essential to the invention. Two or more of the features described in the embodiments may be combined in any desired manner. Furthermore, the same reference numerals are used to designate identical or similar components, and redundant descriptions will be omitted.

[0010] (System Configuration) FIG. 1 shows an example of the configuration of a communication system according to this embodiment. This communication system is a wireless communication system that complies with, for example, the Long Term Evolution (LTE) or fifth generation (5G) cellular communication standards of the Third Generation Partnership Project (3GPP (registered trademark)) or their successor standards. This wireless communication system includes a base station device 101 and a terminal device 111. The terminal device 111 establishes a connection with the base station device 101 and performs wireless communication in a cell 121 provided by the base station device 101. The terminal device 111 is assumed to be, for example, portable by a user and to be mobile. If the terminal device 111 moves beyond the range of the cell 121, it will no longer be able to maintain communication in that cell 121. For this reason, for example, the terminal device 111 executes handover to another cell (cell 122 or cell 123) upon the occurrence of a predetermined event, such as a deterioration in communication quality in the currently connected cell 121. Note that the cell 122 and the cell 123 may be provided by a base station device other than the base station device 101, or may be provided by the base station device 101. In other words, the base station device 101 may provide multiple cells.

[0011] Conventionally, the terminal device 111 measures the wireless quality of signals transmitted in the currently connected cell 121 and other adjacent cells (cells 122 and 123), and determines whether an event for initiating handover processing has occurred based on whether the wireless quality satisfies a predetermined condition. Events are defined in cellular communication standards. Examples of events include event A2, which is determined to have occurred when the wireless quality of the currently connected cell falls below a predetermined threshold; event A3, which is determined to have occurred when the wireless quality of the adjacent cell becomes higher (by a predetermined offset) than the wireless quality of the currently connected cell; event A4, which is determined to have occurred when the wireless quality of the adjacent cell exceeds a predetermined threshold; and event A5, which is determined to have occurred when the wireless quality of the currently connected cell falls below a first predetermined threshold and the wireless quality of the adjacent cell exceeds a second predetermined threshold. When the terminal device 111 determines that any of the events set by the base station device 101 has occurred, it transmits a predetermined report to the base station device 101. Then, in response to receiving the predetermined report, the base station device 101 executes processing for handing over the terminal device 111 from the currently connected cell 121 to another cell (for example, cell 122 or cell 123). Note that the handover of the terminal device 111 can be performed, for example, by transmitting setting information for communication in a neighboring cell from the base station device 101 to the terminal device 111 before the occurrence of an event, and then transmitting a command (Handover command) instructing the terminal device 111 to perform the handover. Also, after the occurrence of an event in the terminal device 111, the handover processing can be started by transmitting setting information for communication in a neighboring cell of the handover from the base station device 101 to the terminal device 111 by an RRC Reconfiguration message. Note that RRC is an abbreviation for Radio Resource Control.

[0012] Conventionally, handover processing is initiated in response to the wireless quality measured at that time in the terminal device 111 satisfying each of the above-mentioned events. In such a procedure, for example, the wireless quality of the connected cell (serving cell) deteriorates in event A2, event A3, etc., and a predetermined report is transmitted to the base station device 101 after the event occurs, and then a handover instruction is transmitted from the base station device 101. Therefore, a certain period of time is required from the deterioration of wireless quality to the execution of handover. Then, during the period from when the occurrence of the event is detected in the terminal device 111 to when the handover instruction is transmitted from the base station device 101, the wireless quality may further deteriorate, and the handover instruction may not be received by the terminal device 111.

[0013] The terminal device 111 is supposed to start timer T310 when it is deemed that downlink synchronization with the base station device 101 cannot be established. More specifically, the terminal device 111 starts timer T310 when it receives N310 consecutive out-of-sync indications from the physical layer in the RRC layer. The terminal device 111 receives information on the number of N310 and the expiration time of T310 in advance as configuration information from the connected base station device 101. When timer T310 expires after being started, the terminal device 111 determines that a radio link failure (RLF) has occurred. Timer T310 is stopped when a handover command is received. However, as described above, if wireless quality deteriorates after the occurrence of an event and the terminal device 111 is unable to receive the command, the terminal device 111 is unable to stop timer T310. As a result, the terminal device 111 is unable to perform handover processing, resulting in a handover failure (HOF). Furthermore, the terminal device 111 cannot start a connection re-establishment process (RRC Connection Re-establishment process) unless an RLF occurs after waiting until the timer T310 expires. For this reason, in order to improve the efficiency of communication in a wireless communication system, it is important to reduce the probability of occurrence of HOF and RLF.

[0014] In recent years, the application of artificial intelligence (AI) / machine learning (ML) to cellular communication systems has been discussed. The use of AI / ML can predict whether RLF will occur in the future, for example, based on the wireless quality of the currently connected cell. For example, a trained model can be acquired by machine learning using the wireless quality of the cell 121 acquired in the learning phase as input and whether RLF will occur in the absence of handover at a predetermined time after the wireless quality is acquired as training data. Once such a trained model is acquired, the terminal device 111 can obtain an estimated value of the probability of RLF occurring at a predetermined time after the wireless quality is acquired by inputting the results of measuring the wireless quality of a signal transmitted from the cell 121 in the inference phase. Furthermore, the terminal device 111 provides the base station device 101 with the output of the trained model, enabling the base station device 101 to make the determination. Furthermore, for example, a trained model can be acquired by machine learning using the wireless quality of the cell 121 acquired in the learning phase as input and whether or not HOF occurs when handover processing is performed at a timing a predetermined period after the timing at which the wireless quality is acquired as training data. When such a trained model is acquired, the terminal device 111 can obtain an estimated value of the probability of HOF occurring at a timing a predetermined period after the timing at which the wireless quality is acquired by inputting the results of measuring the wireless quality of a signal transmitted from the cell 121 in the inference phase. Furthermore, the terminal device 111 provides the base station device 101 with the output of the trained model, thereby enabling the base station device 101 to make this determination. Here, the predetermined period is set arbitrarily, for example, to a period of several seconds. Note that this setting information can be notified in advance from the base station device 101 to the terminal device 111.

[0015] As described above, the terminal device 111 can obtain the probability of RLF or HOF occurring at a predetermined timing in the future (a timing a predetermined period after the measurement value is obtained) by using AI / ML, but conventional standards do not assume that such probabilities will be obtained, and therefore cannot perform handover processing using these probabilities. In view of such circumstances, this embodiment provides a technique for performing handover processing based on predicted values ​​of RLF and HOF at appropriate timing.

[0016] In this embodiment, the terminal device 111 identifies one or more occurrence probabilities corresponding to one or more timings of at least one of RLF and HOF, and transmits predetermined information related to the identified one or more occurrence probabilities to the base station device 101. The terminal device 111 uses a trained model acquired by machine learning to identify the one or more occurrence probabilities. More specifically, for the cell 121 to which the terminal device 111 is connected in the learning phase, one or more wireless qualities measured in the cell 121 are input, and the training data may be acquired by machine learning using whether or not at least one of an RLF when a handover process is not performed at one or more timings after a predetermined period from the timing at which a predetermined one of the one or more wireless qualities is acquired and an HOF when a handover process is performed has occurred. Note that the predetermined one of the one or more wireless qualities may be, for example, the first acquired wireless quality (the wireless quality acquired earliest among the input wireless qualities) or the last acquired wireless quality (the wireless quality acquired latest among the input wireless qualities). Furthermore, the predetermined one of the one or more wireless qualities may be determined according to another rule. Then, the terminal device 111 acquires, for example, one or more wireless qualities corresponding to one or more timings for the connected cell 121 (and, as necessary, other cells such as neighboring cells (cell 122, cell 123, etc.)), and inputs the values ​​of the one or more wireless qualities into the trained model. This allows an estimated value of the occurrence probability of at least one of RLF and HOF at one or more timings a predetermined period after (in the future) the timing corresponding to a predetermined one of the one or more wireless qualities identified according to the rule used in the learning phase.

[0017] The terminal device 111 may transmit, to the base station device 101, information indicating each of the one or more occurrence probabilities of at least one of RLF and HOF as predetermined information regarding the occurrence probabilities of the one or more acquired future timings. For example, if the occurrence probabilities of at least one of RLF and HOF at four consecutive timings are values ​​such as 10%, 20%, 40%, and 30%, respectively, information indicating time fluctuations in the occurrence probabilities, such as 10, 20, 40, and 30, may be transmitted to the base station device 101 as predetermined information. Note that if the probability is expressed in units of 10%, information such as 1, 2, 4, and 3 may be transmitted to the base station device 101 as predetermined information. Furthermore, a difference value from the occurrence probability at the immediately preceding timing may be transmitted as predetermined information. In this case, information such as 10, 10, 20, and -10 may be transmitted to the base station device 101 as predetermined information indicating values ​​such as 10%, 20%, 40%, and 30%. Furthermore, each value may be expressed by, for example, a reference value and a difference value. For example, by setting the reference value to 20%, information such as -10, 0, 20, and 10 may be transmitted to the base station device 101 as predetermined information indicating values ​​such as 10%, 20%, 40%, and 30%. Note that the timing may be expressed in units of time slots, for example. In this case, if the first timing is the xth time slot, four consecutive timings may correspond to the xth to x+3th time slots, respectively. Furthermore, the timing may be expressed in units of the transmission period of a synchronization signal (SS) / physical broadcast channel (PBCH) block (SSB). That is, a value indicating one occurrence probability may be transmitted to the base station device 101 for each SSB transmission period. Alternatively, only information indicating an occurrence probability exceeding a predetermined threshold may be transmitted to the base station device 101. For example, if the predetermined threshold is 30%, only information such as 40 and 30 may be transmitted to the base station device 101 out of 10%, 20%, 40%, and 30%. In this case, information indicating the timing at which the occurrence probability is due may also be transmitted to the base station device 101. For example, information such as (40, x+2) and (30, x+3) may be transmitted to the base station device 101.This allows the base station device 101 to identify the timing when RLF or HOF is likely to occur in the terminal device 111, and to perform processing such as handing over the terminal device 111 to another cell before such timing arrives.

[0018] Furthermore, the terminal device 111 may transmit, as predetermined information, to the base station device 101, a statistic of the occurrence probability of at least one of RLF and HOF for each of one or more acquired future timings. The statistic may be, for example, a maximum value. For example, if the occurrence probabilities for at least one of RLF and HOF at four consecutive timings are 10%, 20%, 40%, and 30%, respectively, the value "40%" may be transmitted to the base station device 101. At this time, information on the timing at which the maximum value is obtained may also be transmitted to the base station device 101. For example, information such as (40, x+2) may be transmitted to the base station device 101. The statistic may be, for example, a minimum value. For example, if the occurrence probabilities for at least one of RLF and HOF at four consecutive timings are 10%, 20%, 40%, and 30%, respectively, the value "10%" may be transmitted to the base station device 101. At this time, information about the timing at which the minimum value was obtained may also be transmitted to the base station device 101. For example, information such as (10, x) may be transmitted to the base station device 101. The statistic may also be an average value. For example, if the occurrence probabilities at four consecutive timings for at least one of RLF and HOF are 10%, 20%, 40%, and 30%, respectively, information such as "25%" may be transmitted to the base station device 101.

[0019] Furthermore, the probability of occurrence of HOF can be specified for each of multiple candidate handover destination cells. That is, the probability of occurrence of HOF when a handover process is performed from cell 121 to cell 122 and the probability of occurrence of HOF when a handover process is performed from cell 122 to cell 123 can be estimated. In one example, a separate trained model can be prepared for each candidate handover destination cell. The terminal device 111 can obtain an estimated value of the probability of occurrence of HOF for each of multiple candidate handover destination cells by inputting measured wireless quality values ​​into each of multiple trained models. In this case, the terminal device 111 may, for example, notify the base station device 101 of all estimated values ​​of the probability of occurrence of HOF obtained for each of multiple candidate handover destination cells, or may notify the base station device 101 of statistics of the probability of occurrence of HOF (maximum, minimum, average, etc.). That is, the predetermined information as described above may be transmitted for each of multiple candidate handover destination cells. In addition, the probability of occurrence of HOF may be output for one or more candidate cells as the handover destination. That is, the trained model may be designed so that the probability of occurrence of HOF is output from the trained model regardless of which cell is selected as the handover destination.

[0020] Incidentally, the above-mentioned predetermined information can be included in, for example, the Measurement Report of Layer 3 and notified from the terminal device 111 to the base station device 101. Also, the above-mentioned predetermined information may be included in the CSI-report of Layer 1 and notified from the terminal device 111 to the base station device 101. Here, the terminal device 111 may transmit, together with the predetermined information, information on the radio quality of the measured cell 121 (and other cells such as cell 122 and cell 123 if necessary) to the base station device 101. That is, the terminal device 111 can be configured to add the above-mentioned predetermined information to, for example, a conventional Measurement Report or CSI report and then transmit it. For example, the RSRP for each cell and the predetermined information regarding the occurrence probabilities of RLF and HOF can be transmitted from the terminal device 111 to the base station device 101 by one message. Also, the predetermined information may be transmitted by a message separate from the conventional Measurement Report or CSI report. In that case, the separate message used for transmitting the predetermined information may be an existing message or a newly defined message.

[0021] Also, all of the radio quality values output from the learned model may be considered, or only a part of those radio quality values may be considered. For example, when n values indicating radio quality corresponding to n consecutive timings are output from the learned model, m values (m < n) indicating radio quality among them may be considered to generate the predetermined information to be transmitted to the base station device 101. In this case, the setting information indicating the number m of the values to be considered can be notified in advance from the base station device 101 to the terminal device 111.

[0022] Furthermore, machine learning may be performed separately for the probability of RLF occurrence and the probability of HOF occurrence, generating separate trained models. Alternatively, these may be combined and machine-learned to generate a single trained model. That is, machine learning may be performed using, as training data, measurement values ​​of the wireless quality of cell 121 (or other cells such as cell 122 or cell 123 as necessary) and whether or not an RLF has occurred and whether or not an HOF has occurred a predetermined time after the measurement values ​​are obtained. The terminal device 111 may then acquire, as output of the trained model, one or more future RLF occurrence probabilities and one or more future HOF occurrence probabilities (at a predetermined time after the wireless quality is obtained).

[0023] Furthermore, as the predetermined information, configuration information indicating which of the above-described information (a list of one or more occurrence probabilities of RLF / HOF corresponding to each timing, statistics of the occurrence probabilities, etc.) should be transmitted may be notified from the base station device 101 to the terminal device 111, for example, via a radio resource control (RRC) message. In accordance with the notification, the terminal device 111 may generate information on the occurrence probability of RLF / HOF obtained by estimation and transmit it to the base station device 101 as the predetermined information. Furthermore, information specifying which message (such as a Measurement Report of layer 3 or a CSI-report of layer 1) should be used to transmit the predetermined information may be notified from the base station device 101 to the terminal device 111. Note that the content of the predetermined information and the message to be used may be determined in advance (for example, by a standard), and the terminal device 111 may generate the predetermined information based on the predetermined configuration and transmit it to the base station device 101 without receiving any particular configuration information from the base station device 101.

[0024] Based on the reception of predetermined information, the base station device 101 can determine, for example, whether to execute handover of the terminal device 111. That is, if there is a high probability that RLF or HOF will occur in the near future, the base station device 101 can decide to handover the terminal device 111 to another cell regardless of the current wireless quality. The base station device 101 starts handover processing in response to that decision.

[0025] Furthermore, the terminal devices present in the cell 121 may include terminal devices that are not capable of estimating at least one of the probability of occurrence of RLF and the probability of occurrence of HOF using a trained model. The terminal devices may notify the base station device 101 in advance (for example, when establishing a connection) capability information indicating whether they are capable of estimating at least one of the probability of occurrence of RLF and HOF using a trained model, and, if they are capable of estimating, whether they are capable of estimating the RLF or the HOF.

[0026] Note that the performance evaluation of the trained model may be performed periodically, for example. For example, the base station device 101 may collect information on the occurrence probability of at least one of RLF and HOF estimated in some terminal devices, and observe whether RLF or HOF actually occurs at a predetermined timing corresponding to the occurrence probability without transmitting a handover command to the terminal device. Then, the base station device 101 compares the actual measurement value of the occurrence probability of RLF or HOF with the notified estimated value of the occurrence probability of RLF or HOF, and calculates the difference (error) between the actual measurement value and the estimated value. If the error is equal to or greater than a predetermined value, the base station device 101 may evaluate that the trained model is not suitable for the real environment and decide to perform re-learning (additional learning). The base station device 101 may perform re-learning for a certain period of time, for example, and notify the terminal device 111 of the trained model after re-learning. The base station device 101 then receives from the terminal device 111 a notification of the estimated value of the probability of occurrence of RLF or HOF obtained by the trained model, and compares the estimated value with the actual measured value of the probability of occurrence of RLF or HOF again. The base station device 101 may terminate the re-learning when the error between the estimated value of the output of the trained model and the actual measured value becomes equal to or less than a predetermined level. Note that, if the terminal device 111 has generated a trained model itself, it may notify the base station device 101 that it will perform this re-learning. Alternatively, the terminal device 111 may notify the base station device 101 that re-learning is necessary, and perform the re-learning in response to receiving a predetermined instruction from the base station device 101. This allows the trained model to be maintained in an appropriate state.

[0027] (Device configuration) FIG. 2 shows an example of the hardware configuration of the base station device 101 and the terminal device 111 according to this embodiment. In one example, the base station device 101 and the terminal device 111 are configured to include a processor 201, a ROM 202, a RAM 203, a storage device 204, and a communication circuit 205. The processor 201 is a computer configured to include one or more processing circuits, such as a general-purpose CPU (Central Processing Unit) or an ASIC (Application Specific Integrated Circuit), and executes the overall processing of the device and each of the above-mentioned processes by reading and executing programs stored in the ROM 202 or the storage device 204. The ROM 202 is a read-only memory that stores information such as programs and various parameters related to the processing executed by the base station device 101 and the terminal device 111. The RAM 203 functions as a workspace when the processor 201 executes a program and is a random access memory that stores temporary information. The storage device 204 is configured, for example, by a removable external storage device. The communication circuit 205 is configured, for example, by a circuit for wireless communication of 5G or its successor standards. Although FIG. 2 illustrates one communication circuit 205, the base station device 101 and the terminal device 111 may have multiple communication circuits. For example, the base station device 101 and the terminal device 111 may have wireless communication circuits for 5G and its successor standard, and a common antenna for these circuits. The base station device 101 and the terminal device 111 may have separate antennas suitable for each standard. The base station device 101 may also have a wired communication circuit used when communicating with other base station devices or nodes in the core network. The terminal device 111 may also have a communication circuit compliant with a wireless communication standard other than the cellular communication standard, such as a wireless local area network (LAN) or Bluetooth (registered trademark). The base station device 101 and the terminal device 111 may have separate communication circuits 205 for each of multiple available frequency bands, or may have a common communication circuit 205 for at least some of these frequency bands.

[0028] FIG. 3 shows an example of the functional configuration of the terminal device 111. The terminal device 111 includes, for example, a capability information notifying unit 301, a setting information receiving unit 302, a quality measuring unit 303, an occurrence probability estimating unit 304, and a reporting unit 305. The terminal device 111 may also include a learning unit 306 as an option. Note that FIG. 3 only shows functions particularly related to this embodiment, and does not illustrate various other functions that the terminal device 111 may have. For example, the terminal device 111 naturally has other functions that terminal devices conforming to LTE, 5G, or subsequent standards generally have. The functional blocks in FIG. 3 are shown schematically, and the respective functional blocks may be realized as an integrated unit or may be further subdivided. The functions in FIG. 3 may be realized, for example, by the processor 201 executing a program stored in the ROM 202 or the storage device 204, or by a processor within the communication circuit 205 executing predetermined software. The details of the processes executed by each functional unit have been described above, so only the general functions of the terminal device 111 will be outlined here.

[0029] The capability information notifying unit 301 notifies the base station device 101 of capability information possessed by the terminal device 111 as UE Capability. The capability information notifying unit 301 notifies the base station device 101, for example, of information indicating whether the terminal device 111 has the capability to estimate at least one of the probability of RLF occurrence and the probability of HOF occurrence from the measurement value of the wireless quality of the connected cell 121 (and other cells such as neighboring cells (e.g., cell 122 and cell 123) as necessary) using AI / ML. Note that the terminal device 111 may have the capability to estimate only the probability of RLF occurrence, or may have the capability to estimate only the probability of HOF occurrence. The setting information receiving unit 302 receives setting information related to communication from the base station device 101. The setting information may include, for example, information indicating whether the terminal device 111 should estimate and report the probability of RLF or HOF occurrence using a trained model. Furthermore, as described above, the setting information may include setting information regarding reporting on the probability of RLF / HOF occurrence, such as the format of predetermined information to be transmitted to the base station device 101. Note that both an instruction to estimate and report the occurrence probability of RLF or HOF using the trained model and configuration information regarding the report on the occurrence probability of RLF / HOF may be notified to the terminal device 111 as configuration information, or only one of them may be notified to the terminal device 111. Furthermore, configuration information for reporting the measurement results of the wireless quality of each cell may be notified to the terminal device 111 along with the report on the occurrence probability of RLF / HOF. The quality measurement unit 303 measures the wireless quality of the currently connected cell and, if necessary, other cells such as neighboring cells. The measured wireless quality may be information such as reference signal received power (RSRP), reference signal received quality (RSRQ), and signal-to-interference and noise ratio (SINR). The occurrence probability estimation unit 304 inputs the wireless quality measured by the quality measurement unit 303 into the trained model to estimate the occurrence probability of at least one of RLF and HOF at one or more future timings. The reporting unit 305 transmits predetermined information regarding the occurrence probability estimated by the occurrence probability estimation unit 304 to the base station device 101.The reporting unit 305 generates predetermined information based on the setting information received by the setting information receiving unit 302 and transmits the information to the base station device 101. When a trained model should be generated in the terminal device 111, the learning unit 306 generates the trained model by machine learning. Furthermore, the learning unit 306 can re-train the trained model in response to an instruction from the base station device 101, for example.

[0030] FIG. 4 shows an example of the functional configuration of the base station device 101. The base station device 101 includes a capability information receiving unit 401, a setting information notifying unit 402, a report receiving unit 403, and a handover control unit 404. The base station device 101 may also include a learning unit 405 as an option. Note that FIG. 4 only shows functions particularly related to this embodiment, and does not illustrate various other functions that the base station device 101 may have. For example, the base station device 101 naturally has other functions that are generally possessed by base station devices conforming to LTE, 5G, or subsequent standards. The functional blocks in FIG. 4 are shown schematically, and the respective functional blocks may be realized as an integrated unit or may be further subdivided. The functions in FIG. 4 may be realized, for example, by the processor 201 executing a program stored in the ROM 202 or the storage device 204, or by a processor within the communication circuit 205 executing predetermined software. The details of the processes executed by each functional unit are as described above, so only the general functions of the base station device 101 will be outlined here.

[0031] The capability information receiving unit 401 receives capability information (UE Capability) from the terminal device 111, including information indicating whether or not the terminal device 111 has the capability to estimate the occurrence probability of at least one of RLF and HOF by AI / ML. The setting information notifying unit 402 can transmit information instructing the terminal device 111, which has the capability to estimate the occurrence probability of at least one of RLF and HOF by AI / ML, whether or not to perform the estimation. The setting information notifying unit 402 also notifies the terminal device 111, which has the capability to estimate the occurrence probability of at least one of RLF and HOF by AI / ML, of setting information for reporting predetermined information regarding the estimation result. The report receiving unit 403 receives predetermined information from the terminal device 111, including values ​​indicating the occurrence probability itself of at least one of RLF and HOF, and values ​​such as statistics of the occurrence probability. In response to receiving the predetermined information, the handover control unit 404 determines whether or not to hand over the terminal device 111 to another cell. Then, when it is determined that the terminal device 111 should be handed over to another cell, the handover control unit 404 starts processing for handing over the terminal device 111. This processing can be executed in accordance with existing regulations.

[0032] The learning unit 405 performs machine learning to generate a trained model for estimating the occurrence probability of at least one of RLF and HOF. Note that it is sufficient for either the learning unit 306 of the terminal device 111 or the learning unit 405 of the base station device 101 to perform machine learning, and it is not necessary for both to perform machine learning. Also, machine learning may be performed in a network node different from either the base station device 101 or the terminal device 111. Either the learning unit 306 of the terminal device 111 or the learning unit 405 of the base station device 101 performs re-learning of the trained model when the difference between the estimated value and the actual measured value of the occurrence probability of at least one of RLF and HOF exceeds a predetermined value. Note that when machine learning is performed in a network node different from either the base station device 101 or the terminal device 111, the base station device 101 may notify the network node that re-learning should be performed.

[0033] (Processing flow) An example of the processing flow according to this embodiment is shown in Fig. 5. Note that, since the details of the processing executed in the wireless communication system are as described above, only an overview of the processing flow will be given here, and the details will not be repeated.

[0034] First, the terminal device 111 notifies the base station device 101 of its own device's capability information (S501). Here, the capability information including information indicating that the terminal device 111 is capable of estimating the probability of occurrence of at least one of RLF and HOF by AI / ML is notified from the terminal device 111 to the base station device 101. In response to determining from the capability information that the terminal device 111 is capable of estimating the probability of occurrence of at least one of RLF and HOF by AI / ML, the base station device 101 notifies the terminal device 111 of configuration information for reporting an estimated value of the occurrence probability, for example, by using an RRC Reconfiguration message. Note that the base station device 101 can instruct the terminal device 111, in the configuration information, to estimate the probability of occurrence of RLF / HOF by AI / ML and the format of reporting the estimated value (such as information indicating each of one or more estimated values, or statistics of one or more estimated values). Thereafter, the terminal device 111 measures the reference signal transmitted from the base station device 101 (S503, S504) and inputs the measurement value into the trained model to obtain an estimated value of the occurrence probability of at least one of RLF and HOF (S505). Then, the terminal device 111 transmits predetermined information (a report of the inference result) indicating the estimated value to the base station device 101 (S506). Then, the base station device 101 determines, for example, based on the predetermined information, whether to hand over the terminal device 111 from the currently connected cell to another cell (S507). Thereafter, in response to the determination to hand over the terminal device 111, the base station device 101 executes a predetermined handover process (not shown).

[0035] As described above, according to the present embodiment, it is possible to determine whether to hand over the terminal device 111 based on an estimated value of the probability of occurrence of a future radio link failure (RLF) or handover failure (HOF). This makes it possible to determine whether to hand over at the present time according to the probability of a future RLF / HOF, rather than the current wireless quality of the terminal device 111, for example, and therefore to more reliably maintain the connection of the terminal device 111. This makes it possible to contribute to Goal 9 of the Sustainable Development Goals (SDGs) led by the United Nations, which is to "build resilient infrastructure, promote sustainable industrialization and foster innovation."

[0036] The invention is not limited to the above-described embodiment, and various modifications and variations are possible within the scope of the gist of the invention.

Claims

1. A terminal device, a measuring means for measuring the wireless quality of a signal transmitted from a connected base station; an identification means for inputting the wireless quality acquired for a signal transmitted from the base station device in a learning phase, and for identifying, as training data, one or more occurrence probabilities of at least one of the RLF and the HOF acquired by inputting the wireless quality measured by the measurement means in an inference phase, the occurrence probabilities corresponding to one or more timings of at least one of the RLF and the HOF acquired by inputting the wireless quality acquired for the signal transmitted from the base station device in a learning phase, and the HOF acquired by inputting the wireless quality acquired by the measurement means in an inference phase, to a trained model acquired by machine learning, and using as training data values ​​indicating whether or not at least one of the RLF when a handover process is not performed and the HOF when a handover process is performed has occurred. a transmitting means for transmitting predetermined information relating to the one or more occurrence probabilities to the base station device; A terminal device comprising:

2. 2. The terminal device according to claim 1, wherein the predetermined information includes information indicating each of the one or more occurrence probabilities.

3. 2. The terminal device according to claim 1, wherein the predetermined information includes at least one of a maximum value, a minimum value, and an average value of the one or more occurrence probabilities.

4. The trained model is prepared for each of a plurality of other cells that are candidates for handover destinations of the terminal device from a cell provided by the base station device, and the trained model is used to identify the one or more occurrence probabilities of HOF for each of the plurality of other cells; 2. The terminal device according to claim 1, wherein the predetermined information includes information indicating each of the one or more occurrence probabilities of HOF for each of the plurality of other cells, or at least one of a maximum value, a minimum value, and an average value of the one or more occurrence probabilities of HOF for each of the plurality of other cells.

5. 2. The terminal device according to claim 1, wherein the predetermined information is included in a measurement report of layer 3 or a CSI report of layer 1 and transmitted to the base station device.

6. A base station device, a receiving means for receiving, from a terminal device connected to the base station device in an inference phase, predetermined information regarding one or more occurrence probabilities of at least one of the RLF and the HOF, which is acquired by inputting a wireless quality measured by the terminal device connected to the base station device in the learning phase, to the trained model acquired by machine learning using as input a wireless quality acquired for a signal transmitted from the base station device in the learning phase and a value indicating whether or not at least one of an RLF when a handover process is not performed and an HOF when a handover process is performed, has occurred, as training data; and a determination means for determining whether to hand over the terminal device from the cell provided by the base station device to another cell based on the predetermined information; A base station device comprising:

7. 7. The base station device according to claim 6, wherein the predetermined information includes information indicating each of the one or more occurrence probabilities.

8. 7. The base station device according to claim 6, wherein the predetermined information includes at least one of a maximum value, a minimum value, and an average value of the one or more occurrence probabilities.

9. The trained model is prepared for each of a plurality of other cells that are candidates for handover destinations of the terminal device from a cell provided by the base station device, and the trained model is used to identify the one or more occurrence probabilities of HOF for each of the plurality of other cells; 7. The base station device according to claim 6, wherein the predetermined information includes information indicating each of the one or more occurrence probabilities of HOF for each of the plurality of other cells, or at least one of a maximum value, a minimum value, and an average value of the one or more occurrence probabilities of HOF for each of the plurality of other cells.

10. 7. The base station apparatus according to claim 6, wherein the receiving means receives the predetermined information via a Measurement Report of Layer 3 or a CSI report of Layer 1.

11. A control method executed by a terminal device, comprising: measuring wireless quality of a signal transmitted from a connected base station; In a learning phase, a wireless quality acquired for a signal transmitted from the base station device is input, and a trained model is acquired by machine learning using, as training data, a value indicating whether or not at least one of an RLF when a handover process is not performed at one or more timings after a predetermined period from the timing at which the wireless quality is acquired and an HOF when a handover process is performed has occurred, and specifying, in an inference phase, one or more occurrence probabilities corresponding to each of the one or more timings of at least one of the RLF and the HOF acquired by inputting the measured wireless quality; transmitting predetermined information regarding the one or more occurrence probabilities to the base station device; A control method comprising:

12. A control method executed by a base station device, receiving, from a terminal device connected to the base station device in an inference phase, predetermined information regarding one or more occurrence probabilities corresponding to one or more timings of at least one of the RLF and the HOF, which are acquired by inputting wireless quality measured by the terminal device connected to the base station device in the learning phase, to a trained model acquired by machine learning using as input wireless quality acquired for a signal transmitted from the base station device and using as training data values ​​indicating whether or not at least one of an RLF when a handover process is not performed and an HOF when a handover process is performed has occurred at one or more timings after a predetermined period from the timing at which the wireless quality was acquired; determining whether to hand over the terminal device from the cell provided by the base station device to another cell based on the predetermined information; A control method comprising:

13. A program for causing a computer provided in a terminal device to execute the control method according to claim 11.

14. A program for causing a computer provided in a base station device to execute the control method according to claim 12.

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