Electronic device, operation method thereof, and storage medium

Abstract

An electronic device is disclosed. The electronic device comprises: at least one processor including a processing circuit; a communication circuit; and a memory storing instructions and including one or more storage media, wherein the instructions, when individually or collectively executed by the at least one processor, cause the electronic device to: identify the current temperature of the electronic device; input first context information related to the electronic device into a trained artificial intelligence model on the basis of identifying that the current temperature of the electronic device is equal to or higher than a first temperature; identify, via the trained artificial intelligence model, a first temperature change rate of the electronic device corresponding to a first temperature range of the electronic device related to the first context information; on the basis of the identified first temperature change rate, determine a first operation cycle related to a random access operation for establishing a first communication connection; and perform the random access operation via the communication circuit on the basis of the determined first operation cycle.

Description

Electronic devices and their operating methods and storage media

[0001] The present disclosure relates to an electronic device for performing a random access operation, an operating method thereof, and a storage medium.

[0002] To meet the growing demand for wireless data traffic following the commercialization of 4G communication systems, 5G communication systems are being developed. To achieve high data rates, 5G communication systems are being designed to utilize not only existing communication bands, such as 3G or LTE, but also new bands, such as ultra-high frequency bands (e.g., FR2 bands).

[0003] 5G communication technology can transmit relatively large amounts of data and consume more power, potentially increasing the temperature of electronic devices. For example, the recent commercialization of smartphones has expanded their use to include work-related tasks, and the frequency of demand for high uplink throughput and high power output in high-heat environments, such as real-time transmission of high-definition video conferencing, is increasing. As described above, electronic devices inevitably consume more current due to the use of higher frequency bands and increased data throughput. This increased heat generation can lead to overheating. Furthermore, electronic devices can install and utilize various applications that include data transmission and reception capabilities via 5G communication. When electronic devices execute applications that transmit and receive excessive amounts of data via 5G communication, the use of higher frequency bands and increased data throughput can further increase heat generation.

[0004] The above information may be provided as background art to aid in understanding the present disclosure. No claim or determination is made as to whether any of the above-described matters constitute prior art related to the present disclosure.

[0005] An electronic device according to one embodiment of the present disclosure may include at least one processor including a processing circuit, a communication circuit, and a memory storing instructions and including one or more storage media. According to one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to determine a current temperature of the electronic device.

[0006] In one embodiment, the instructions may cause the electronic device to input first context information related to the electronic device into a learned artificial intelligence model based on determining that a current temperature of the electronic device is greater than or equal to a first temperature.

[0007] In one embodiment, the instructions may cause the electronic device to determine, through the learned artificial intelligence model, a first temperature change rate of the electronic device corresponding to a first temperature range of the electronic device associated with the first context information.

[0008] In one embodiment, the instructions may cause the electronic device to determine a first operation cycle associated with a random access operation for establishing a first communication connection based on the identified first temperature change rate.

[0009] In one embodiment, the instructions may cause the electronic device to perform the random access operation through the communication circuit based on the determined first operation cycle.

[0010] A method of operating an electronic device according to one embodiment of the present disclosure may include an operation of checking a current temperature of the electronic device.

[0011] An operating method according to one embodiment may include an operation of inputting first context information related to the electronic device into a learned artificial intelligence model based on determining that the current temperature of the electronic device is equal to or higher than a first temperature.

[0012] An operating method according to one embodiment may include an operation of checking a first temperature change rate of the electronic device corresponding to a first temperature range of the electronic device related to the first context information through the learned artificial intelligence model.

[0013] An operating method according to one embodiment may include an operation of determining a first operation cycle related to a random access operation for establishing a first communication connection based on the first temperature change rate identified above.

[0014] An operating method according to one embodiment may include an operation of performing the random access operation through a communication circuit based on the determined first operation cycle.

[0015] In a storage medium storing computer-readable instructions according to one embodiment of the present disclosure, the instructions, when executed by at least one processor of an electronic device, can cause the electronic device to check a current temperature of the electronic device.

[0016] In one embodiment, the instructions may cause the electronic device to input first context information related to the electronic device into a learned artificial intelligence model based on determining that a current temperature of the electronic device is greater than or equal to a first temperature.

[0017] In one embodiment, the instructions may cause the electronic device to determine, through the learned artificial intelligence model, a first temperature change rate of the electronic device corresponding to a first temperature range of the electronic device associated with the first context information.

[0018] In one embodiment, the instructions may cause the electronic device to determine a first operating cycle associated with a random access procedure for establishing a first communication connection based on the identified first temperature change rate.

[0019] In one embodiment, the instructions may cause the electronic device to perform the random access operation based on the determined first operation cycle.

[0020] In connection with the description of the drawings, the same or similar reference numerals may be used for the same or similar components.

[0021] FIG. 1 is a block diagram of an electronic device within a network environment, according to one embodiment.

[0022] FIG. 2 is a block diagram of configurations of an electronic device according to one embodiment.

[0023] FIG. 3 is a flowchart illustrating an operating method of an electronic device according to one embodiment.

[0024] FIG. 4 is a flowchart illustrating a method for performing a random access operation according to one embodiment.

[0025] Figure 5 is a flowchart illustrating a method for checking a temperature change rate according to one embodiment.

[0026] FIG. 6 is a flowchart illustrating a method for performing a random access operation according to one embodiment.

[0027] Figure 7 is a flowchart illustrating a method for performing a random access operation.

[0028] FIG. 8 is a flowchart illustrating a method for performing a random access operation according to one embodiment.

[0029] Figure 9 is a flowchart illustrating a method for performing learning on an artificial intelligence model according to one embodiment.

[0030] Fig. 10 is a flowchart illustrating a method for checking a temperature change rate according to one embodiment.

[0031] Figure 11 is a flowchart illustrating a method for performing learning on an artificial intelligence model according to one embodiment.

[0032] Figures 12a, 12b and 12c are drawings for explaining an operation cycle according to one embodiment.

[0033] FIG. 13 is a diagram for explaining a throttling policy according to one embodiment.

[0034] Fig. 14 is a drawing for explaining the current consumption by operation of an electronic device as a comparative example according to one embodiment.

[0035] Fig. 15 is a diagram for explaining the temperature change rate per operation cycle according to one embodiment.

[0036] FIG. 16 is a drawing for comparing an electronic device as a comparative example according to one embodiment and an electronic device of the present invention.

[0037] FIG. 17 is a drawing for comparing an electronic device as a comparative example according to one embodiment and an electronic device of the present invention.

[0038] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Furthermore, in the drawings and related descriptions, descriptions of well-known functions and configurations may be omitted for clarity and conciseness.

[0039] FIG. 1 is a block diagram of an electronic device (101) within a network environment (100), according to one embodiment. Referring to FIG. 1 , in the network environment (100), the electronic device (101) may communicate with the electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of the electronic device (104) or the server (108) via a second network (199) (e.g., a long-range wireless communication network). In one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0040] The processor (120) may, for example, execute software (e.g., a program (140)) to control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) and perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operations, the processor (120) may store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the commands or data stored in the volatile memory (132), and store result data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or an auxiliary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together with the main processor (121). For example, when the electronic device (101) includes the main processor (121) and the auxiliary processor (123), the auxiliary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a given function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0041] The auxiliary processor (123) may control at least a portion of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0042] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0043] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0044] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0045] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0046] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0047] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0048] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0049] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0050] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0051] The haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. According to one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0052] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0053] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0054] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0055] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0056] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) can support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0057] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). In one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). In one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas, for example, by the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device via the at least one selected antenna. In some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0058] In one embodiment, the antenna module (197) may form a mmWave antenna module. In one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0059] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0060] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0061] In the detailed description below, reference numerals in the drawings may be used interchangeably or omitted for components that can be easily understood through the preceding embodiments, and their detailed descriptions may also be omitted. An electronic device according to an embodiment disclosed in this document may be implemented by selectively combining components of different embodiments, and components of one embodiment may be replaced by components of another embodiment. For example, it should be noted that the present invention is not limited to specific drawings or embodiments.

[0062] FIG. 2 is a block diagram of electronic device configurations according to one embodiment.

[0063] According to FIG. 2, according to one embodiment, an electronic device (200) (e.g., electronic device (101) of FIG. 1) may include a memory (210) (e.g., memory (130) of FIG. 1) for storing instructions, a communication circuit (220) (e.g., communication module (190) of FIG. 1), and at least one processor (230, or processor).

[0064] According to one embodiment, the memory (210) may have at least a portion of the same or similar configuration as the memory (130) of FIG. 1. For example, the memory (210) may be configured to temporarily or permanently store digital data and may include at least a portion of the configuration and / or functions of the memory (130) of FIG. 1.

[0065] The memory (210) according to one embodiment can store various instructions that can be executed by at least one processor (230). In addition, the memory (210) can store at least a portion of the program (140) of FIG. 1. Such instructions can include control commands such as logical operations and data input / output that can be recognized and executed by the processor (230). There is no limitation on the type and / or amount of data that the memory (210) can store, but this document will describe the configuration and function of the memory related to the operation of the processor (230) that performs the method and the method of confirming a user command according to various embodiments. The memory (210) can store various information, and the various information stored by the memory (210) will be described in detail below.

[0066] A communication circuit (220) according to an embodiment may support establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (200) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and performance of communication through the established communication channel, and may include at least some of the configurations and / or functions of the communication module (190) of FIG. 1.

[0067] According to one embodiment, at least one processor (230) (hereinafter, “processor”) may have at least a portion of the same or similar configuration as the processor (120) of FIG. 1. According to one embodiment, the processor (230) may include one or more processors.

[0068] According to one embodiment, the processor (230) may perform various operations by executing instructions stored in the memory (220).

[0069] According to one embodiment, the processor (230) may check the temperature of the electronic device (200) (or the current temperature of the electronic device (200). According to one example, the processor (230) may check the temperature of the electronic device (200) using a specified algorithm. For example, the electronic device (200) may include a temperature sensor (e.g., the sensor module (176) of FIG. 1), and the processor (230) may check the temperature of the electronic device (200) using sensing data acquired through the temperature sensor and a specified algorithm (e.g., a linear regression process).

[0070] In one example, the processor (230) may determine whether the temperature of the electronic device (200) is equal to or greater than a first temperature. In one example, the specified first temperature may be 37°C, but may also be a different temperature.

[0071] According to one embodiment, the processor (230) may input first context information related to the electronic device (200) into the learned artificial intelligence model based on determining that the temperature of the electronic device (200) is equal to or higher than a first temperature. According to one embodiment, the processor (230) may input first context information related to the electronic device (200) into the learned artificial intelligence model to determine a first temperature change rate corresponding to a first temperature range.

[0072] For example, the context information may be information about factors that may affect the temperature of the electronic device (200). For example, the context information may include at least one of the type of application running on the electronic device (200) (e.g., messenger, game, or video playback), the type of a background application corresponding to the electronic device (200), and status information of the electronic device (200). For example, the status information of the electronic device (200) may include at least one of information about whether the electronic device (200) is playing a video, whether it is charging, or the volume size. For example, the electronic device (200) may use an application identifier (AID) to check the context information of the electronic device (200). The context information will be described in detail with reference to FIG. 5.

[0073] In one example, the designated temperature range may be at least one temperature range. For example, the designated temperature range may include at least one of a temperature range less than 37 degrees (°C) (hereinafter referred to as degrees Celsius), a temperature range greater than or equal to 37 degrees and less than 38 degrees, a temperature range greater than or equal to 38 degrees and less than 39 degrees, a temperature range greater than or equal to 39 degrees and less than 40 degrees, a temperature range greater than or equal to 40 degrees and less than 41 degrees, and a temperature range greater than or equal to 41 degrees. Alternatively, in one example, the size of the temperature range may be different from the examples described above.

[0074] For example, the artificial intelligence model may be a model trained to output a temperature change rate for each temperature section corresponding to the input context information when context information related to the electronic device (200) is input. For example, the temperature change rate may be a temperature corresponding to a specified temperature change amount (e.g., 1 degree). For example, the trained artificial intelligence model may output a temperature change rate for each specified temperature section when first context information related to the electronic device (200) is input. For example, the trained artificial intelligence model may output a first temperature change rate corresponding to a first temperature section among a plurality of temperature sections, and may output a second temperature change rate corresponding to a second temperature section among a plurality of temperature sections, respectively.

[0075] For example, the temperature change rate of the electronic device (200) may be the time it takes for the temperature to reach a second temperature from a first temperature corresponding to a designated temperature range. For example, the temperature change rate corresponding to a temperature range greater than or equal to 38 degrees (°C, hereinafter referred to as degrees Celsius) and less than or equal to 39 degrees may be the time it takes from the time the temperature of the electronic device (200) reaches 38 degrees to the time the temperature of the electronic device (200) reaches 39 degrees. For example, the processor (230) may also perform learning on an artificial intelligence model, which will be described later.

[0076] According to one embodiment, the processor (230) may identify (or determine) a first operation cycle associated with at least one operation (e.g., a random access operation) for establishing a first communication connection based on the identified first temperature change rate.

[0077] In one example, the first communication connection may refer to a communication connection to a fifth-generation (5G) new radio (NR) network. In another example, the operation cycle associated with a random access operation may refer to periodically performing an operation for performing random access for a communication connection to a 5G network. In another example, the processor (230) may perform a random access operation for establishing the first communication connection at a specified time cycle.

[0078] For example, when a first communication connection is established, the electronic device (200) may transmit and receive data based on the first communication connection during a first designated time interval, and during a second designated time interval after the first time interval, release NR radio resource control (RRC) and operate in a long term evolution (LTE) fallback state. For example, after the second time interval, the processor (230) may perform a random access operation for establishing the first communication connection.

[0079] For example, the first operation cycle may be an operation cycle in which an operation for transmitting and receiving data based on a first communication connection is performed for 5 ms (milliseconds), an LTE fallback state is operated for 5 ms after the 5 ms in which a random access operation is performed, and a random access operation for establishing a first communication connection is performed again after 5 ms of the LTE fallback state operation. In one example, in the first operation cycle, the processor (230) may perform a random access operation for establishing a first communication connection every 10 ms. In one example, the size of the above-described cycle may vary depending on the execution time of the random access operation.

[0080] In one example, the electronic device (200) may operate in an NR idle state while operating in an LTE (long term evolution) fallback state. In one example, in the NR idle state, the electronic device (200) may be registered with a network. In one example, the electronic device (200) may be implemented as an electronic device supporting ENDC (E-UTRAN new radio dual connectivity). In one example, when a first communication connection is established based on a random access operation, the electronic device (200) may transmit and receive data based on the first communication connection. The operation cycle related to the random access operation will be described in detail with reference to FIGS. 12A to 12C.

[0081] For example, the processor (230) may verify an operation cycle corresponding to a temperature change rate. For example, it may be assumed that first context information is input into a learned artificial intelligence model. When the first context information is input and a first temperature change rate corresponding to a first temperature section is verified, the processor (230) may verify a first operation cycle corresponding to the first temperature change rate. Alternatively, for example, when the second temperature change rate corresponding to a second temperature section is verified when second context information is input, the processor (230) may verify a second operation cycle corresponding to the second temperature change rate.

[0082] According to one embodiment, the processor (230) may perform a random access operation through the communication circuit (220) based on the identified first operation cycle.

[0083] For example, the processor (230) may check the temperature (or current temperature) of the electronic device (200) using a specified algorithm. For example, the processor (230) may perform a random access operation with a first operation cycle corresponding to the first temperature range based on the determination that the checked temperature of the electronic device (200) falls within a first temperature range.

[0084] Alternatively, according to an example, the processor (230) may, based on the determination that the temperature of the identified electronic device (200) falls within a second temperature range, determine a second temperature change rate corresponding to the second temperature range, and perform a random access operation with a second operation cycle corresponding to the determined second temperature change rate. In this case, the first operation cycle and the second operation cycle may be different.

[0085] FIG. 3 is a flowchart illustrating an operating method of an electronic device according to one embodiment.

[0086] Hereinafter, an operating method of an electronic device (e.g., an electronic device (200) of FIG. 2) according to various embodiments will be described in detail. According to various embodiments, operations performed by the electronic device described below may be executed by a processor (e.g., at least one processor (230) of FIG. 2) including at least one processing circuitry of the electronic device. According to one embodiment, the operations performed by the electronic device may be stored in a memory (e.g., a memory (210) of FIG. 2) and, when executed, may be executed by instructions that cause the processor (230) to operate. In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. Depending on the implementation, certain operations may be omitted.

[0087] Referring to FIG. 3, according to an embodiment, in operation 301, the operating method may include an operation of checking the current temperature of the electronic device (e.g., the current temperature of the electronic device of FIG. 2). According to an example, the electronic device may check the temperature of the electronic device using a specified algorithm. According to an example, the electronic device may check whether the temperature of the electronic device is equal to or higher than a first temperature (e.g., the first temperature of FIG. 2). According to an example, the specified first temperature may be 37°C, but may also be a different temperature.

[0088] According to one embodiment, in operation 303, the method may include inputting first context information (e.g., context information of FIG. 2) related to the electronic device into a learned artificial intelligence model (e.g., the artificial intelligence model of FIG. 2) based on determining that a current temperature of the electronic device is equal to or greater than a first temperature.

[0089] For example, an electronic device may identify first context information of the electronic device using an application identifier (AID). For example, the electronic device may identify at least one of the following: the type of application running on the electronic device (e.g., a messenger, a game, or a video player), the type of a background application corresponding to the electronic device, and status information of the electronic device.

[0090] In one example, the electronic device may input the identified first context information into a learned artificial intelligence model. In one example, the learned artificial intelligence model may be stored in memory or may be stored in an external electronic device (e.g., server (108) of FIG. 1).

[0091] In one embodiment, in operation 305, the method may include an operation of determining, through a learned artificial intelligence model, a first temperature change rate (e.g., the first temperature change rate of FIG. 2 ) of the electronic device corresponding to a first temperature range (e.g., the first temperature range of FIG. 2 ) of the electronic device related to first context information. In one example, the electronic device may input first context information related to the electronic device into the learned artificial intelligence model to determine the first temperature change rate corresponding to the first temperature range.

[0092] For example, the electronic device may determine, from the learned artificial intelligence model, a temperature change rate for at least one temperature range corresponding to the first context information. For example, the electronic device may determine a first temperature change rate corresponding to a first temperature range among the at least one temperature range.

[0093] According to one embodiment, in operation 307, the method of operation may include an operation of identifying (or determining) a first operation cycle (e.g., the operation cycle of FIG. 2) associated with a random access operation for establishing a first communication connection (e.g., the first communication connection of FIG. 2) based on the identified first temperature change rate.

[0094] For example, it may be assumed that first context information is input into a learned artificial intelligence model. When the first context information is input and a first temperature change rate corresponding to a first temperature range is confirmed, the electronic device can confirm a first operating cycle corresponding to the first temperature change rate.

[0095] According to one embodiment, in operation 309, the method of operation may include performing a random access operation through the communication circuit (220) (e.g., the communication circuit (220) of FIG. 2 ) based on the verified (or determined) first operation cycle. According to one example, the electronic device may perform the random access operation in the first operation cycle corresponding to the first temperature range based on the verified temperature (or current temperature) of the electronic device being included in the first temperature range.

[0096] FIG. 4 is a flowchart illustrating a method for performing a random access operation (e.g., the random access operation of FIG. 2) according to one embodiment.

[0097] Referring to FIG. 4, according to one embodiment, in operation 401, the operating method may include an operation of inputting first context information (e.g., context information of FIG. 2) into a learned artificial intelligence model (e.g., artificial intelligence model of FIG. 2) to check a first temperature change rate corresponding to a first temperature section (e.g., temperature change rate of FIG. 2) and a second temperature change rate corresponding to a second temperature section different from the first temperature section, respectively.

[0098] In one example, an electronic device (e.g., the electronic device (200) of FIG. 2) may identify first context information corresponding to the electronic device. In one example, the electronic device may input the identified first context information into a learned first artificial intelligence model. In one example, as the first context information is input, the learned first artificial intelligence model may identify a temperature change rate corresponding to the first context information for each designated temperature range (e.g., the designated temperature range of FIG. 2).

[0099] For example, when first context information is input, the learned first artificial intelligence model can output a temperature change rate corresponding to each of a temperature range of 38 degrees or more and less than 39 degrees, a temperature range of 39 degrees or more and less than 40 degrees, and a temperature range of 40 degrees or more and less than 41 degrees.

[0100] In one embodiment, at operation 403, the method may include performing a random access operation with a first operation cycle corresponding to a first temperature change rate based on the temperature of the electronic device being within a first temperature range.

[0101] In one example, an electronic device may determine the temperature of the electronic device using a designated algorithm (e.g., the designated algorithm of FIG. 2). In one example, the electronic device may determine a first temperature range that includes the temperature of the electronic device. The electronic device may determine a first operation cycle (e.g., the operation cycle of FIG. 2) corresponding to the first temperature range, and perform a random access operation using the determined first operation cycle.

[0102] For example, it can be assumed that the first temperature range is a temperature range greater than or equal to 38 degrees Celsius and less than or equal to 39 degrees Celsius. When the electronic device determines that the temperature of the electronic device is 38.5 degrees Celsius, the electronic device can determine a first operation cycle corresponding to the first temperature range and perform a random access operation for establishing a first communication connection using the determined first operation cycle.

[0103] In one embodiment, at operation 405, the method may include performing a random access operation with a second operation cycle corresponding to a second temperature change rate based on the temperature of the electronic device being within a second temperature range.

[0104] For example, it may be assumed that an electronic device is performing a random access operation in a confirmed first operation cycle. For example, the electronic device may determine that, as the temperature of the electronic device increases, the temperature of the electronic device falls within a second temperature range (e.g., a temperature range of 39 degrees or more and less than 40 degrees Celsius) that is a range after the existing first temperature range. The electronic device may determine a second operation cycle corresponding to a second temperature change rate corresponding to the second temperature range, and perform a random access operation in the confirmed second operation cycle.

[0105] According to one embodiment, the electronic device can determine a declaration value based on the temperature and temperature change rate of the electronic device, and determine an operation cycle based on the determined declaration value. According to one example, the electronic device can determine a declaration value corresponding to context information using Tables 1 and 2 below.

[0106] NoTemperature(℃)First declared value1Temperature<37"10"237≤Temperature<38"20"338≤Temperature<39"30"439≤Temperature<41"40"5Temperature≥41"50"

[0107] NoTemperature change rateSecond declared value1Temperature change rate≥2min"1"21min≤Temperature change rate<2min"2"30.5min≤Temperature change rate<1min"3"4Temperature change rate<0.5min"4"

[0108] For example, the electronic device may determine a third declaration value related to the temperature of the electronic device in the first context based on the first declaration value for each temperature section shown in Table 1 and the second declaration value for each temperature change rate shown in Table 2.

[0109] For example, in the first context, the electronic device may determine "10" as the first declaration value if the temperature of the electronic device is below 37 degrees. If the first declaration value is determined to be "10," the electronic device may perform data transmission and reception operations according to the basic operation cycle. The basic operation cycle will be described later.

[0110] Alternatively, for example, the electronic device may determine "20" as the first declaration value when the temperature of the electronic device rises to 37 degrees. The electronic device may input the first context information into the learned artificial intelligence model, and determine the second declaration value as "1" when the temperature change rate corresponding to the temperature range of 37 degrees or more and less than 38 degrees is determined to be 2.0 min. The electronic device may add the determined first declaration value "20" and the second declaration value "1" to determine the third declaration value as "21."

[0111] Alternatively, for example, if the temperature of the electronic device rises to 38 degrees, the electronic device may determine "30" as the first declaration value. If the electronic device inputs the first context information into the learned artificial intelligence model and determines that the temperature change rate corresponding to the temperature range of 38 degrees or more and less than 39 degrees is 1.5 min, the electronic device may determine the second declaration value as "2." The electronic device may add the determined first declaration value "30" and the second declaration value "2" to determine the third declaration value as "32."

[0112] Alternatively, for example, if the temperature of the electronic device rises to 39 degrees in the first context, the electronic device can confirm "40" as the first declaration value. The electronic device can input the first context information into the learned artificial intelligence model, and if the temperature change rate corresponding to the temperature range of 39 degrees or more and less than 41 degrees is confirmed to be 0.9 min, the electronic device can confirm the second declaration value as "3". The electronic device can add the confirmed first declaration value "40" and the second declaration value "3" and confirm the third declaration value as "43".

[0113] Alternatively, for example, if the temperature of the electronic device in the first context rises to 41 degrees, the electronic device may identify "50" as the first declaration value. If the first declaration value is identified as "50," the electronic device may suspend the random access operation for establishing the first communication connection and perform LTE (long term evolution) fallback operation (or, an operation for transmitting and receiving data in the LTE fallback state). This will be described later.

[0114] In one example, the electronic device can determine the operation cycle based on the verified third declaration value. In one example, the electronic device can perform a random access operation with a basic operation cycle when the third declaration value is "1x" or "x1". As an example, "x" can mean any number greater than or equal to 1 and less than or equal to 5. In one embodiment, "51" can be excluded. In one example, the electronic device can perform a random access operation with a first operation cycle when the third declaration value is "22", "32", or "42". In one example, the electronic device can perform a random access operation with a second operation cycle when the third declaration value is "23", "33", or "43". In one example, the electronic device can perform a random access operation with a third operation cycle when the third declaration value is "24", "34", or "44". In one example, the electronic device may suspend the random access operation for establishing the first communication connection and perform a long term evolution (LTE) fallback operation when the third declaration value is "5x". In one example, the electronic device may check the third declaration value when the temperature of the electronic device reaches a threshold temperature (e.g., at least one of 37 degrees, 38 degrees, 39 degrees, or 41 degrees).

[0115] According to various embodiments, the electronic device may determine the operation cycle based on the verified second declaration value. For example, if the second declaration value is "2", the electronic device may perform the random access operation in the first operation cycle. According to one example, if the second declaration value is "3", the electronic device may perform the random access operation in the second operation cycle. According to one example, if the second declaration value is "4", the electronic device may perform the random access operation in the third operation cycle.

[0116] According to the above-described example, when the electronic device reaches a critical temperature, the electronic device can use the learned artificial intelligence model to check the third declaration value, and perform a random access operation according to the operation cycle for each temperature section based on the checked third declaration value. In one example, the operation cycle for each temperature section may be different, but may also be the same, and the basic operation cycle, the first operation cycle, the second operation cycle, and the third operation cycle will be described in detail with reference to FIGS. 12A to 12C.

[0117] According to one embodiment, when learning about an artificial intelligence model is insufficient (or when the amount of learning data is insufficient), the electronic device may determine a designated operating cycle based on a temperature range that includes the temperature of the electronic device, and perform a random access operation based on the determined operating cycle. Alternatively, according to one embodiment, the electronic device may determine the operating cycle based on learning data used to learn the artificial intelligence model. This will be described in detail with reference to FIGS. 6 and 7.

[0118] In one example, the electronic device may provide a user interface (UI) via a display (e.g., the display module (160) of FIG. 1) for selecting whether to perform a random access operation with different operation cycles. In one example, the electronic device may include a display. In one example, the electronic device may provide a UI via the display for selecting whether to perform a random access operation with different operation cycles depending on the temperature of the electronic device, or to perform an operation for transmitting and receiving data with a basic operation cycle regardless of the temperature of the electronic device.

[0119] FIG. 5 is a flowchart illustrating a method for checking a temperature change rate (e.g., the temperature change rate of FIG. 2) according to one embodiment.

[0120] Referring to FIG. 5, according to one embodiment, in operation 501, the operating method can identify a first learning data set corresponding to first context information (e.g., context information of FIG. 2) based on determining that the temperature of an electronic device (e.g., electronic device (200) of FIG. 2) is equal to or greater than a first temperature.

[0121] For example, an electronic device may classify context information according to multiple types of information as shown in Table 3 below. For example, in the status information of Table 3 below, 'video' may mean 'a video is being played', 'charging' may mean 'a state in which the electronic device is being wired and charged', 'charging (wireless)' may mean 'a state in which the electronic device is being wirelessly charged', and 'volume maximum' may mean 'a state in which the output volume of the electronic device is at its maximum'. Alternatively, the status information may include information on a camera operation status.

[0122] No. Running application Background application Status information 1 1st application - video 2 2nd application - video 3 3rd application - charging, video 4 4th application 2nd application charging, video 5 5th application 2nd application charging, video 6 6th application 3rd application video 7 6th application 7th application video 8 7th application - charging, video 9 8th application - charging, video 10 9th application - charging (wireless) 11 10th application 2nd application Volume level maximum

[0123] In one example, the electronic device may identify learning data corresponding to each piece of context information. In one example, the learning data may refer to data used for learning an artificial intelligence model (e.g., the artificial intelligence model of FIG. 2) (or data used for previous learning). In one example, the learning data may include context information and a temperature change rate corresponding to a temperature range in the context (e.g., at least one temperature range of FIG. 2). In one example, a learning data set corresponding to context information may include at least one piece of learning data.

[0124] For example, the learning data corresponding to the first context information may include a temperature change rate corresponding to each of at least one temperature range of the electronic device in the first context. For example, the first learning data corresponding to the first context information may include a temperature change rate corresponding to the first temperature range in the first context, a temperature change rate corresponding to the second temperature range, and a temperature change rate corresponding to the third temperature range, respectively.

[0125] In one example, the first learning data set corresponding to the first context information may include at least one learning data corresponding to the first context information. In one example, the electronic device may acquire learning data corresponding to each piece of context information under specified conditions.

[0126] For example, an electronic device can identify the temperature change rate for at least one temperature range for each context corresponding to the electronic device, acquire this as learning data, and use it to train an artificial intelligence model. For example, the acquired learning data can be added to a learning data set corresponding to each contextual piece of information. A detailed description of the method for acquiring the learning data is provided with reference to FIG. 6.

[0127] In one example, the electronic device may identify a first learning data set corresponding to the first context information based on determining that the temperature of the electronic device is equal to or higher than a first temperature (e.g., the first temperature of FIG. 2). For example, it may be assumed that the electronic device is in a first context state. If the temperature of the electronic device is determined to be equal to or higher than 37 degrees, the electronic device may identify a first learning data set corresponding to the first context information. In one example, the first learning data set may be stored in a memory (e.g., the memory (130) of FIG. 1), or the first learning data set may be stored in an external electronic device (e.g., the server (108) of FIG. 1).

[0128] According to one embodiment, in operation 503, the operating method may determine whether the number of learning data included in the identified first learning data set is greater than or equal to a first value (e.g., 100). Based on the determination that the number of learning data is greater than or equal to the first value (or if it is determined that the number of learning data is greater than or equal to the first value), the electronic device may input first context information into the learned artificial intelligence model to determine a first temperature change rate (e.g., the first temperature change rate of FIG. 2 ).

[0129] FIG. 6 is a flowchart illustrating a method for performing a random access operation according to one embodiment.

[0130] Referring to FIG. 6, according to one embodiment, the operating method may, in operation 601, determine a third temperature range (e.g., temperature range of FIG. 2) and a third temperature change rate (e.g., temperature change rate of FIG. 2) included in the last added first learning data among the first learning data sets based on the number of learning data (e.g., learning data of FIG. 5) included in the confirmed first learning data set (e.g., the first learning data set of FIG. 5) being less than a first value.

[0131] In one example, the learning data may include a temperature change rate for at least one temperature interval corresponding to the context of an electronic device (e.g., electronic device (200) of FIG. 2). Alternatively, in one example, the learning data may include only a temperature change rate corresponding to at least a portion of at least one temperature interval corresponding to the context of the electronic device.

[0132] According to an example, when first context information (e.g., the first context information of FIG. 5) is identified as context information corresponding to the electronic device, the electronic device may identify a first learning data set corresponding to the identified first context information. According to an example, the electronic device may identify whether the number of learning data included in the identified first learning data set is less than a first value (e.g., 100). According to an example, when the number of learning data included in the first learning data set is less than the first value, the electronic device may identify the first learning data added to the first learning data set last among the learning data included in the first learning data set.

[0133] For example, the electronic device can verify the temperature change rate for each temperature range included in the verified first learning data. For example, the electronic device can verify the temperature change rate corresponding to the first temperature range, the temperature change rate corresponding to the second temperature range, and the temperature change rate corresponding to the third temperature range, respectively, through the first learning data.

[0134] Alternatively, according to an example, the electronic device may identify the last added learning data for each temperature range. For example, if the first learning data only includes the temperature change rate corresponding to the first temperature range, the electronic device may identify learning data among the first learning data set that includes the temperature change rate corresponding to the second temperature range, and identify the second learning data that was added most recently among them.

[0135] For example, an electronic device may, for each context corresponding to the electronic device, identify a temperature change rate for at least one temperature range, acquire this as learning data, and use this to train an artificial intelligence model. For example, if the number of learning data corresponding to the first context information is less than a first value, the electronic device may perform an operation for acquiring learning data. For example, if the temperature of the electronic device reaches a second temperature (e.g., 38 degrees Celsius), the electronic device may identify a temperature change rate corresponding to a second temperature range (e.g., a temperature range that is 37 degrees Celsius or higher and less than 38 degrees Celsius). Alternatively, for example, if the temperature of the electronic device reaches a third temperature (e.g., 39 degrees Celsius), the electronic device may identify a temperature change rate corresponding to a third temperature range (e.g., a temperature range that is 38 degrees Celsius or higher and less than 39 degrees Celsius). Alternatively, for example, if the temperature of the electronic device reaches a fourth temperature (e.g., 41 degrees Celsius), the electronic device may identify a temperature change rate corresponding to a fourth temperature range (e.g., a temperature range that is 39 degrees Celsius or higher and less than 41 degrees Celsius). Through the above-described process, the electronic device can obtain learning data corresponding to the first context information.

[0136] According to one embodiment, the operating method may perform a random access operation (e.g., the random access operation of FIG. 2) in a third operation cycle corresponding to a third temperature change rate, based on the temperature of the electronic device being included in a third temperature range, at operation 603.

[0137] In one example, the electronic device may determine the temperature of the electronic device (e.g., the temperature of the electronic device (200) of FIG. 2). In one example, the electronic device may determine a third temperature change rate corresponding to a third temperature range based on the first learning data. In one example, the electronic device may perform a random access operation with a third operation cycle corresponding to the third temperature change rate.

[0138] FIG. 7 is a flowchart illustrating a method for performing a random access operation (e.g., the random access operation of FIG. 2).

[0139] Referring to FIG. 7, according to one embodiment, the operating method may, in operation 701, determine a designated operating cycle (e.g., operating cycle of FIG. 2) for at least one temperature section (e.g., at least one temperature section of FIG. 2) based on the number of learning data (e.g., learning data of FIG. 5) included in a first learning data set (e.g., the first learning data set of FIG. 5) corresponding to first context information (e.g., context information of FIG. 2) being less than a second value. According to one embodiment, the second value may be set to be the same as or different from the first value.

[0140] In one example, an electronic device (e.g., electronic device (200) of FIG. 2) may check a first learning data set corresponding to first context information when it is determined that the temperature of the electronic device is higher than or equal to a first temperature (e.g., the first temperature of FIG. 5). In one example, the electronic device may check a designated operation cycle for at least one temperature range when it is determined that there is no first learning data set corresponding to the first context information.

[0141] For example, the electronic device can identify a first operation cycle corresponding to a first temperature range (e.g., a temperature range of 37 degrees or more and less than 38 degrees). Alternatively, for example, the electronic device can identify a second operation cycle corresponding to a second temperature range (e.g., a temperature range of 38 degrees or more and less than 39 degrees). Alternatively, for example, the electronic device can identify a third operation cycle corresponding to a third temperature range (e.g., a temperature range of 39 degrees or more and less than 41 degrees). In one example, each of the first to third operation cycles may be different operation cycles, and the first to third operation cycles will be described in detail with reference to FIGS. 12A to 12C.

[0142] According to one embodiment, the operating method may perform a random access operation at operation 703 based on a temperature of the electronic device and a designated operation cycle for each of at least one identified temperature range. According to one example, the electronic device may perform the random access operation in a first operation cycle when the temperature of the electronic device falls within a first temperature range. According to one example, the electronic device may perform the random access operation in a second operation cycle when the temperature of the electronic device falls within a second temperature range. According to one example, the electronic device may perform the random access operation in a third operation cycle when the temperature of the electronic device falls within a third temperature range.

[0143] FIG. 8 is a flowchart illustrating a method for performing a random access operation (e.g., the random access operation of FIG. 2) according to one embodiment.

[0144] Referring to FIG. 8, according to one embodiment, the operating method may perform a random access operation in operation 801 based on the temperature of an electronic device (e.g., the electronic device (200) of FIG. 2) being included in a fourth temperature section among at least one temperature section (e.g., the at least one temperature section of FIG. 2), with an operating cycle corresponding to the fourth temperature section (e.g., the operating cycle of FIG. 2).

[0145] For example, if the temperature of the electronic device falls within a fourth temperature range (e.g., a temperature range of 37 degrees Celsius or higher and less than 38 degrees Celsius), the electronic device may identify an operation cycle corresponding to the fourth temperature range. The electronic device may perform a random access operation with an operation cycle corresponding to the identified fourth temperature range.

[0146] According to one embodiment, the operating method may perform a random access operation with an operation cycle corresponding to the fifth temperature range, based on the temperature of the electronic device being included in a fifth temperature range different from a fourth temperature range among at least one temperature range, in operation 803.

[0147] For example, it may be assumed that an electronic device is performing a random access operation with an operation cycle corresponding to a fourth temperature range. If the electronic device determines that the temperature of the electronic device is included in a fifth temperature range (e.g., a temperature range of 38 degrees Celsius or higher and less than 39 degrees Celsius) as the temperature of the electronic device increases, the electronic device may determine an operation cycle corresponding to the fifth temperature range. For example, the electronic device may perform a random access operation with an operation cycle different from the operation cycle corresponding to the fourth temperature range as the temperature of the electronic device increases.

[0148] FIG. 9 is a flowchart illustrating a method for performing learning on an artificial intelligence model (e.g., the artificial intelligence model of FIG. 2) according to one embodiment.

[0149] Referring to FIG. 9, according to one embodiment, the operating method may determine, in operation 901, whether the number of learning data included in a first learning data set (e.g., the first learning data set of FIG. 7) is less than a first value based on confirmation of second context information (e.g., context information of FIG. 2) that is identical to first context information (e.g., the first context information of FIG. 7).

[0150] For example, when the temperature of an electronic device (e.g., the electronic device (200) of FIG. 2) reaches a specified temperature (e.g., 37 degrees), the context corresponding to the electronic device may be checked to determine second context information. For example, the electronic device may determine a first learning data set corresponding to the first context information as the same context as the second context information. For example, the electronic device may determine whether the number of learning data included in the first learning data set is less than 100.

[0151] According to one embodiment, the operating method may, in operation 903, determine a temperature change rate for each temperature section of an electronic device corresponding to second context information based on determining that the number of learning data is less than the first value.

[0152] For example, if the number of learning data included in the first learning data set is confirmed to be less than 100, the electronic device may check the temperature change rate (e.g., the temperature change rate of FIG. 2) for at least one temperature section (e.g., at least one temperature section of FIG. 2) of the electronic device. For example, the electronic device may check the first temperature change rate corresponding to the first temperature section (e.g., the temperature section of 37 degrees or more and less than 38 degrees) of the electronic device in the second context condition (or the second context situation). Alternatively, for example, the electronic device may check the second temperature change rate corresponding to the second temperature section (e.g., the temperature section of 38 degrees or more and less than 39 degrees) of the electronic device in the second context condition. Alternatively, for example, the electronic device may check the third temperature change rate corresponding to the third temperature section (e.g., the temperature section of 39 degrees or more and less than 41 degrees) of the electronic device in the second context condition.

[0153] According to one embodiment, the operating method may perform learning on an artificial intelligence model (e.g., the artificial intelligence model of FIG. 2) based on a temperature change rate per temperature section of an electronic device corresponding to the second context information, in operation 905.

[0154] For example, when the electronic device acquires at least one temperature change rate per temperature section of the electronic device corresponding to the second context information, the electronic device can add it to the first learning data set and input the added first learning data set into the artificial intelligence model, thereby performing learning on the artificial intelligence model.

[0155] FIG. 10 is a flowchart illustrating a method for checking a temperature change rate (e.g., the temperature change rate of FIG. 2) according to one embodiment.

[0156] Referring to FIG. 10, according to one embodiment, the operating method may, in operation 1001, check the similarity between learning data (e.g., learning data of FIG. 5) included in a first learning data set (e.g., the first learning data set of FIG. 5).

[0157] For example, each of the learning data included in the first learning data set may include a temperature change rate for at least one temperature range (e.g., at least one temperature range of FIG. 2). An electronic device (e.g., the electronic device (200) of FIG. 2) may, for each of the learning data included in the first learning data set, check the similarity of the temperature change rate corresponding to the first temperature range.

[0158] For example, the similarity between learning data may be a matching rate of temperature change rates for each specified temperature interval included in the learning data. For example, the matching rate may be a proportion of learning data, among learning data included in the first learning data set, that have an error less than a specified rate from the mean value for the temperature change rate in the first temperature interval. Alternatively, the matching rate may be a proportion of learning data, among learning data included in the first learning data set, that have an error less than a specified rate from the median value for the temperature change rate in the first temperature interval. For example, the electronic device may check at least one similarity (or matching rate) for each temperature interval for the first learning data set.

[0159] According to one embodiment, the operating method can determine a first temperature change rate corresponding to a first temperature section based on the determined similarity being greater than or equal to a third value in operation 1003.

[0160] For example, if the similarity corresponding to the first learning data set is confirmed to be a third value (e.g., 90%) or higher, the electronic device may identify the first temperature change rate corresponding to the first temperature section. For example, the electronic device may identify the similarity corresponding to the first temperature section with respect to the first learning data set, and if the identified similarity is confirmed to be 90% or higher, input the first context information (e.g., the context information of FIG. 2) into the learned artificial intelligence model (e.g., the learned artificial intelligence model of FIG. 2) to identify the first temperature change rate corresponding to the first temperature section.

[0161] Alternatively, for example, the electronic device may check the similarity corresponding to each of at least one temperature range with respect to the first learning data set, and input the first context information into the learned artificial intelligence model only when each of the checked similarities is 90% or higher, thereby checking the first temperature change rate corresponding to the first temperature range.

[0162] For example, if the verified similarity is determined to be equal to or greater than the third value, the electronic device may not perform a learning data acquisition operation related to the first context information (e.g., an operation for acquiring learning data of FIG. 6). For example, it may be assumed that the similarity corresponding to the first learning data set is equal to or greater than 90%. Even if the context of the electronic device is the same as the first context information corresponding to the first learning data set, the electronic device may not perform the operation for acquiring learning data because the similarity corresponding to the first learning data set is equal to or greater than 90%. For example, if the similarity corresponding to any one of at least one temperature range among the first learning data set is less than 90%, the electronic device may perform the learning data acquisition operation related to the first context information.

[0163] According to the above example, the electronic device can determine the temperature change rate through the AI ​​model only if the training data set for AI model training has a high match rate (or similarity). Since the AI ​​model trained with highly reliable training data can be used to determine the temperature change rate for each temperature range, random access operations can be performed more efficiently.

[0164] FIG. 11 is a flowchart illustrating a method for performing learning on an artificial intelligence model (e.g., the artificial intelligence model of FIG. 2) according to one embodiment.

[0165] Referring to FIG. 11, according to one embodiment, the operating method may determine, in operation 1101, whether the confirmed similarity (e.g., the similarity of FIG. 10) is less than a third value based on the confirmation of second context information (e.g., the second context information of FIG. 7) that is the same as the first context information (e.g., the first context information of FIG. 7).

[0166] In one example, when the temperature of an electronic device (e.g., the electronic device (200) of FIG. 2) reaches a specified temperature (e.g., 37 degrees), the electronic device may check a context corresponding to the electronic device to check second context information. In one example, the electronic device may check a first learning data set corresponding to the first context information as the same context as the second context information. In one example, the electronic device (e.g., the electronic device (200) of FIG. 2) may check the similarity of the temperature change rate for each of the learning data included in the first learning data set for at least one temperature section (e.g., at least one temperature section of FIG. 2). In one example, the electronic device may check whether the checked similarity is less than 90%.

[0167] According to one embodiment, the operating method may, in operation 1103, determine a temperature change rate for each temperature section of an electronic device corresponding to the second context information based on the determination that the similarity is less than a third value.

[0168] For example, if the verified similarity is confirmed to be less than 90%, the electronic device can check the temperature change rate of the electronic device in each temperature section in the context corresponding to the second context information.

[0169] According to one embodiment, the operating method may perform learning on an artificial intelligence model based on a temperature change rate per temperature section of an electronic device corresponding to second context information in operation 1105.

[0170] For example, the electronic device may acquire learning data based on the temperature change rate of each temperature section of the electronic device in a context corresponding to the second context information, and perform learning on an artificial intelligence model using the acquired learning data.

[0171] According to the above-described example, the electronic device can perform learning by adding learning data when the similarity of learning data for learning an artificial intelligence model is low.

[0172] FIGS. 12A to 12C are drawings for explaining an operation cycle (e.g., the operation cycle of FIG. 2) according to one embodiment.

[0173] Referring to FIGS. 12A to 12C , according to one embodiment, an electronic device (e.g., the electronic device (200) of FIG. 2 ) may perform a random access operation (e.g., the random access operation of FIG. 2 ) related to establishing a first communication connection (e.g., the first communication connection of FIG. 2 ) at a specified operation cycle. According to one embodiment, the first communication connection may be a communication connection to a 5G (fifth-generation) NR (new radio) network.

[0174] For example, the basic cycle (or basic operation cycle) may be an operation cycle in which an uplink section and a downlink section are periodically arranged based on a first communication connection. For example, in the basic operation cycle, the electronic device may transmit and receive data according to a specified schedule after a first communication connection is established through a random access operation related to the first communication connection. For example, in a time division duplex (TDD) domain and an n77 frequency band, the first block (1200) may include 10 slots. For example, when the subcarrier spacing (SCS) is 30 kHz (kilohertz), the size of the first block (1200) may be 5 milliseconds (ms). For example, within the first block (1200), an uplink operation may be performed based on the first communication connection in a specified number (e.g., 2) of slots. For example, the first slot (1201) and the second slot (1202) within the first block (1200) may be uplink sections, and the remaining eight slots excluding the first slot (1201) and the second slot (1202) within the first block (1200) may be downlink sections. In one example, in the basic operation cycle, the first block (1200) may be repeatedly arranged. In one example, in the basic operation cycle, after the first communication connection is established according to the initial random access operation, data transmission and reception may be performed based on the first communication connection.

[0175] For example, the first period (or, first operation period) may be a period in which blocks corresponding to data transmission and reception operations based on the first communication connection are arranged in a designated period. For example, the second block (1210) may include 10 slots in a TDD (time division duplex) domain and an n77 frequency band. For example, when the SCS is 30 kHz, the size of the second block (1210) may be 5 ms. For example, within the second block (1210), an uplink operation and a downlink operation may be performed based on the first communication connection in a designated number of slots (e.g., 2). For example, when the duty value is 20%, there may be 2 slots corresponding to the uplink section and 8 slots corresponding to the downlink section within the second block (1210).

[0176] For example, the third block (1211) following the second block (1210) may be a block that suspends the establishment of the first communication connection and performs a long term evolution (LTE) fallback operation. For example, in the third block (1211), the NR is in an idle state, and only LTE network-related operations may be performed. For example, the size of the third block (1211) may be 5 ms. For example, when the third block (1211) only performs the LTE fallback operation, a random access operation for establishing the first communication connection may be performed after the third block (1211). After the random access operation is performed and the first communication connection is established, in the block following the third block (1211), an uplink operation may be performed based on the first communication connection in a specified number of slots (for example, 2).

[0177] For example, the first operation cycle may be a cycle in which a random access operation for establishing a first communication connection is performed again 5 ms after the LTE fallback operation is performed. For example, if the fourth block (1212) only performs the LTE fallback operation, a random access operation for establishing a first communication connection may be performed after the fourth block (1212). After the random access operation is performed and the first communication connection is established, in the fifth block (1213), an uplink operation may be performed based on the first communication connection in a designated number of slots (e.g., 2). For example, in the fourth block (1212), the electronic device (e.g., the electronic device (200) of FIG. 2) is connected (registrated) to a network related to the first communication connection, and in order to perform a data transmission and reception operation in the fifth block (1213), the electronic device may perform a random access operation related to the establishment of the first communication connection and may not perform a separate cell search operation. For example, in the first cycle, the random access operation may be performed every 10 ms. Alternatively, depending on the execution time of the random access, the random access operation may be performed at a cycle different from 10 ms.

[0178] For example, referring to FIG. 12c, when the duty value is 20%, in the second block (1210), among the 10 slots included in the second block (1210), the 9th slot and the 10th slot may be slots corresponding to uplink. For example, in the third block (1211), since the NR is in an idle state and only LTE network-related operations are performed, in the case of the third block (1211), even if a specified number of slots corresponding to uplink are included, similar to the second block (1210), a separate uplink operation may not be performed.

[0179] For example, the second cycle (or second operation cycle) may be a cycle in which blocks corresponding to data transmission and reception operations based on the first communication connection are arranged in a designated cycle. For example, the sixth block (1220) may, like the first block (1200), include 10 slots in a TDD (time division duplex) domain and an n77 frequency band. For example, when the SCS is 30 kHz, the size of the sixth block (1220) may be 5 ms. For example, within the sixth block (1220), an uplink operation may be performed based on the first communication connection in a designated number of slots (e.g., 2). For example, when the duty value is 20%, the number of slots corresponding to the uplink section and the number of slots corresponding to the downlink section in the sixth block (1220) may be 2 and 8, respectively.

[0180] For example, the seventh block (1221) following the sixth block (1220) may be a block that suspends the establishment of the first communication connection and performs a long term evolution (LTE) fallback operation. For example, in the seventh block (1221), the NR is in an idle state, and only LTE network-related operations may be performed. For example, the size of the seventh block (1221) may be 5 ms. For example, the second operation cycle may be a cycle in which an operation for establishing the first communication connection is performed again 10 ms after the LTE fallback operation is performed. For example, after the LTE fallback operation is performed in a section corresponding to two blocks (1221, 1222) including the seventh block (1221), a random access operation for establishing the first communication connection may be performed. Once the first communication connection is established, data transmission and reception operations based on the first communication connection may be performed in the 8th block (1223). For example, in the second cycle, random access operations may be performed every 15 ms. Alternatively, depending on the random access execution time, random access operations may be performed at a cycle different from 15 ms.

[0181] For example, the third cycle (or third operation cycle) may be a cycle in which blocks corresponding to data transmission and reception operations based on the first communication connection are arranged in a designated cycle. For example, the ninth block (1230), like the first block (1200), may include 10 slots in the TDD (time division duplex) domain and the n77 frequency band. For example, when the SCS is 30 kHz, the size of the ninth block (1230) may be 5 ms.

[0182] For example, the tenth block (1231) following the ninth block (1230) may be a block that suspends the establishment of the first communication connection and performs a long term evolution (LTE) fallback operation. For example, in the tenth block (1231), the NR is in an idle state, and only LTE network-related operations may be performed. For example, the size of the tenth block (1231) may be 5 ms. For example, the third operation cycle may be a cycle in which the operation for establishing the first communication connection is performed again 15 ms after the LTE fallback operation is performed. For example, after the LTE fallback operation is performed in a section corresponding to three blocks (1231, 1232, 1233) including the tenth block (1231), a random access operation for establishing the first communication connection may be performed. Once the first communication connection is established, data transmission and reception operations based on the first communication connection may be performed in block 11 (1234). For example, in the second cycle, random access operations may be performed every 20 ms. Alternatively, depending on the random access execution time, random access operations may be performed at a cycle different from 20 ms.

[0183] In one embodiment, the electronic device may abort establishment of the first communication connection and perform a long term evolution (LTE) fallback operation if the temperature of the electronic device is determined to be above a second temperature (e.g., 41 degrees).

[0184] FIG. 13 is a diagram for explaining a throttling policy according to one embodiment.

[0185] Referring to FIG. 13, according to one embodiment, an electronic device (e.g., the electronic device 200 of FIG. 2) may perform an operation based on a throttling policy when a specified temperature (or threshold temperature) is reached. In one example, the throttling policy may refer to a policy that reduces heat generation by forcibly lowering the clock or voltage of at least some components within the electronic device or forcibly turning off the power to prevent damage to the electronic device when the electronic device is excessively overheated. The throttling policy may be a policy that forces only LTE uplink operation (uplink only) for thermal mitigation. Accordingly, as illustrated in FIG. 13, the electronic device may stop an operation to establish a first communication connection (e.g., the first communication connection of FIG. 2) when the electronic device reaches a threshold temperature. In one example, the threshold temperature may be, but is not limited to, 41 degrees.

[0186] FIG. 14 is a diagram for explaining the current consumption by operation of an electronic device as a comparative example according to one embodiment.

[0187] Referring to Fig. 14, the electronic device as a comparative example consumes relatively more current in an ENDC (E-UTRAN new radio dual connectivity) connected state than in a standby mode. Furthermore, the electronic device as a comparative example consumes relatively more current when playing a video in an ENDC connected state or an LTE connected state than in a standby mode. This increase in current consumption may lead to heat generation in the electronic device, and the electronic device as a comparative example may perform the following operations to delay the heat generation rate.

[0188] In one embodiment, the electronic device as a comparative example may be an electronic device supporting ENDC. In one embodiment, when the temperature of the electronic device as a comparative example reaches a specified temperature (e.g., 41 degrees Celsius), the electronic device as a comparative example may stop establishing a first communication connection (e.g., the first communication connection of FIG. 2 ) and perform a long term evolution (LTE) fallback operation. Accordingly, the electronic device as a comparative example may perform a data transmission and reception operation based on the first communication connection in a basic operation cycle (e.g., the basic cycle of FIG. 12a ) when the temperature is below the specified temperature (e.g., 41 degrees Celsius), and may only perform an LTE fallback operation when the temperature is above the specified temperature.

[0189] For example, an electronic device (e.g., electronic device (200) of FIG. 2) may divide an operation cycle based on a temperature change rate for at least one temperature section (e.g., at least one temperature section of FIG. 2), and perform an operation for establishing a first communication connection for each temperature section based on the divided operation cycle.

[0190] According to the above-described example, the electronic device can delay the heating rate of the electronic device by distinguishing an operating cycle based on a temperature change rate identified for at least one temperature range and performing an operation for establishing a first communication connection based on the distinguished operating cycle. The electronic device may delay the time it takes to reach a temperature at which a throttling policy is implemented, and thus performance degradation, such as a deterioration in communication quality and resolution, may be delayed.

[0191] FIG. 15 is a diagram for explaining the temperature change rate per operating cycle (e.g., the operating cycle of FIG. 2) according to one embodiment.

[0192] Referring to FIG. 15, the temperature reaching times corresponding to the basic cycle (e.g., the basic operation cycle of FIG. 12a), the first operation cycle (e.g., the first operation cycle of FIG. 12a), the second operation cycle (e.g., the second operation cycle of FIG. 12b), and the third operation cycle (e.g., the third operation cycle of FIG. 12b) may be different, and the temperature reaching time corresponding to the third operation cycle may be relatively the largest. In one embodiment, the temperature reaching time corresponding to the second operation cycle may be relatively larger than the temperature reaching time corresponding to the first operation cycle. In one embodiment, the temperature reaching time corresponding to the first operation cycle may be relatively larger than the temperature reaching time corresponding to the basic cycle.

[0193] FIG. 16 is a drawing for comparing an electronic device as a comparative example and an electronic device according to one embodiment.

[0194] Referring to FIG. 16, an electronic device as a comparative example can perform a random access operation (e.g., a random access operation of FIG. 2) for establishing a first communication connection in a basic cycle (e.g., a basic cycle of FIG. 12a) in a temperature range of 37 degrees or more and less than 41 degrees.

[0195] According to one embodiment, an electronic device (e.g., the electronic device (200) of FIG. 2) may perform a random access operation in a first operation cycle (e.g., the first operation cycle of FIG. 12a) when the temperature of the electronic device is included in a first temperature range (e.g., a temperature range of 37 degrees or more and less than 38 degrees). According to one example, the electronic device may perform a random access operation in a second operation cycle (e.g., the second operation cycle of FIG. 12b) when the temperature of the electronic device is included in a second temperature range (e.g., a temperature range of 38 degrees or more and less than 39 degrees). According to one example, the electronic device may perform a random access operation in a third operation cycle (e.g., the third operation cycle of FIG. 12b) when the temperature of the electronic device is included in a third temperature range (e.g., a temperature range of 39 degrees or more and less than 41 degrees).

[0196] According to the above-described example, the electronic device according to one embodiment may perform a random access operation with a designated operation cycle (e.g., a first operation cycle) when the temperature of the electronic device is equal to or higher than a designated temperature (e.g., 37 degrees). In addition, the electronic device according to one embodiment may perform a random access operation with a different operation cycle for at least one temperature section (e.g., at least one temperature section of FIG. 2). Accordingly, the electronic device according to one embodiment may take a relatively longer time to reach a temperature (e.g., 41 degrees) at which a throttling policy (e.g., the throttling policy of FIG. 13) is performed, compared to the electronic device as a comparative example.

[0197] FIG. 17 is a drawing for comparing an electronic device as a comparative example and an electronic device according to one embodiment.

[0198] Referring to FIG. 17, as a comparative example, an electronic device may reach a temperature at which a throttling policy (e.g., the throttling policy of FIG. 13) is performed at t1, and at t3, a random access operation for a first communication connection (e.g., the first communication connection of FIG. 2) may be stopped and only an LTE uplink operation may be performed. According to an embodiment, an electronic device (e.g., the electronic device (200) of FIG. 2) may reach a temperature at which a throttling policy is performed at t2, and at t4, a random access operation for a first communication connection (e.g., the first communication connection of FIG. 2) may be stopped and only an LTE uplink operation may be performed.

[0199] According to the above-described example, the electronic device according to one embodiment may have a relatively long time to reach a temperature at which a throttling policy is performed, compared to the electronic device as a comparative example, and accordingly, the time at which a random access operation for the first communication connection is interrupted may also be relatively long.

[0200] An electronic device (101; 200) according to one embodiment of the present disclosure may include at least one processor (230) including a processing circuit, a communication circuit (220), and a memory (210) storing instructions and including one or more storage media. According to one embodiment, the instructions, when individually or collectively executed by the at least one processor (230), may cause the electronic device (101; 200) to check a current temperature of the electronic device (101; 200).

[0201] In one embodiment, the instructions may cause the electronic device (101; 200) to input first context information related to the electronic device (101; 200) into a learned artificial intelligence model based on determining that the current temperature of the electronic device (101; 200) is equal to or greater than a first temperature.

[0202] According to one embodiment, the instructions may cause the electronic device (101; 200) to determine, through the learned artificial intelligence model, a first temperature change rate of the electronic device corresponding to a first temperature range of the electronic device related to the first context information.

[0203] In one embodiment, the instructions may cause the electronic device (101; 200) to determine a first operation cycle associated with a random access operation for establishing a first communication connection based on the identified first temperature change rate.

[0204] According to one embodiment, the instructions may cause the electronic device (101; 200) to perform the random access operation through the communication circuit (220) based on the determined first operation cycle.

[0205] According to one embodiment, the instructions may cause the electronic device (101; 200) to input the first context information into the learned artificial intelligence model to determine the first temperature change rate and a second temperature change rate corresponding to a second temperature range different from the first temperature range, respectively.

[0206] In one embodiment, the instructions may cause the electronic device (101; 200) to perform the random access operation in the first operation cycle based on the current temperature of the electronic device (101; 200) being within the first temperature range.

[0207] In one embodiment, the instructions may cause the electronic device (101; 200) to perform the random access operation with a second operation cycle corresponding to the second temperature change rate based on the current temperature of the electronic device (101; 200) being included in the second temperature range.

[0208] In one embodiment, the instructions may cause the electronic device (101; 200) to identify a first learning data set corresponding to the first context information based on determining that the current temperature of the electronic device (101; 200) is equal to or greater than the first temperature.

[0209] In one embodiment, the instructions may cause the electronic device (101; 200) to input the first context information into the learned artificial intelligence model based on the number of learning data included in the identified first learning data set being greater than or equal to a first value.

[0210] In one embodiment, the instructions may cause the electronic device (101; 200) to determine the first temperature change rate through the learned artificial intelligence model.

[0211] According to one embodiment, the learning data may include a temperature range corresponding to the electronic device (101; 200) and a temperature change rate corresponding to the temperature range.

[0212] According to one embodiment, the instructions may cause the electronic device (101; 200) to identify a third temperature range and a third temperature change rate included in the last added first learning data among the first learning data set, based on the number of learning data included in the identified first learning data set being less than a first value.

[0213] In one embodiment, the instructions may cause the electronic device (101; 200) to perform the random access operation with a third operation cycle corresponding to the third temperature change rate based on the current temperature of the electronic device (101; 200) being included in the third temperature range.

[0214] In one embodiment, the instructions may cause the electronic device (101; 200) to determine a designated operating cycle for at least one temperature interval based on a number of learning data included in a first learning data set corresponding to the first context information being less than a second value.

[0215] According to one embodiment, the instructions may cause the electronic device (101; 200) to perform the random access operation based on a current temperature of the electronic device (101; 200) and a designated operation cycle for the determined at least one temperature interval.

[0216] In one embodiment, the instructions may cause the electronic device (101; 200) to perform the random access operation with an operation cycle corresponding to a fourth temperature range based on the current temperature of the electronic device (101; 200) being included in a fourth temperature range of the at least one temperature range.

[0217] In one embodiment, the instructions may cause the electronic device (101; 200) to perform the random access operation with an operation cycle corresponding to the fifth temperature range, based on the current temperature of the electronic device (101; 200) being within a fifth temperature range different from the fourth temperature range among the at least one temperature range.

[0218] According to one embodiment, the first context information may include at least one of a type of an application running on the electronic device (101; 200), a type of a background application corresponding to the electronic device (101; 200), and information about a state of the electronic device (101; 200).

[0219] In one embodiment, the instructions may cause the electronic device (101; 200) to determine whether the number of learning data included in the first learning data set is less than the first value based on the determination of second context information that is identical to the first context information.

[0220] According to one embodiment, the instructions may cause the electronic device (101; 200) to check the temperature change rate for each temperature section of the electronic device (101; 200) corresponding to the second context information based on the number of the learning data being confirmed to be less than the first value.

[0221] According to one embodiment, the instructions may cause the electronic device (101; 200) to perform learning on the artificial intelligence model based on a temperature change rate for each temperature section of the electronic device (101; 200) corresponding to the second context information.

[0222] According to one embodiment, the instructions may cause the electronic device (101; 200) to determine similarity between learning data included in the first learning data set.

[0223] In one embodiment, the instructions may cause the electronic device (101; 200) to determine a first temperature change rate corresponding to the first temperature range based on the determined similarity being greater than or equal to a third value.

[0224] According to one embodiment, the first context information may include at least one of a type of an application running on the electronic device (101; 200), a type of a background application corresponding to the electronic device (101; 200), and information about a state of the electronic device (101; 200).

[0225] In one embodiment, the instructions may cause the electronic device (101; 200) to determine whether the determined similarity is less than the third value based on determining that second context information is identical to the first context information.

[0226] According to one embodiment, the instructions may cause the electronic device (101; 200) to determine a temperature change rate for each temperature section of the electronic device (101; 200) corresponding to the second context information based on the determination that the similarity is less than the third value.

[0227] According to one embodiment, the instructions may cause the electronic device (101; 200) to perform learning on the artificial intelligence model based on a temperature change rate for each temperature section of the electronic device (101; 200) corresponding to the second context information.

[0228] According to one embodiment, the electronic device (101; 200) may further include a display.

[0229] In one embodiment, the instructions may cause the electronic device (101; 200) to provide a user interface (UI) via the display for selecting whether to perform the random access operation at different operation cycles.

[0230] In one embodiment, the first communication connection may be a communication connection to a fifth-generation (5G) new radio (NR) network.

[0231] In one embodiment, the instructions may cause the electronic device (101; 200) to abort the establishment of the first communication connection and perform a long term evolution (LTE) fallback operation based on determining that the current temperature of the electronic device (101; 200) is equal to or greater than a second temperature.

[0232] A method of operating an electronic device (101; 200) according to one embodiment of the present disclosure may include an operation of checking the current temperature of the electronic device (101; 200).

[0233] An operating method according to one embodiment may include an operation of inputting first context information related to the electronic device (101; 200) into a learned artificial intelligence model based on confirming that the current temperature of the electronic device (101; 200) is equal to or higher than a first temperature.

[0234] An operating method according to one embodiment may include an operation of checking a first temperature change rate of the electronic device corresponding to a first temperature range of the electronic device related to the first context information through the learned artificial intelligence model.

[0235] An operating method according to one embodiment may include an operation of determining a first operation cycle related to a random access operation for establishing a first communication connection based on the first temperature change rate identified above.

[0236] An operating method according to one embodiment may include an operation of performing the random access operation through a communication circuit (220) based on the determined first operation cycle.

[0237] An operating method according to one embodiment may include an operation of performing the random access operation with a second operation cycle corresponding to the second temperature change rate based on the current temperature of the electronic device (101; 200) being included in the second temperature range.

[0238] An operating method according to one embodiment may include an operation of checking a first learning data set corresponding to the first context information based on checking that the current temperature of the electronic device (101; 200) is equal to or higher than the first temperature.

[0239] The operation of confirming the first temperature change rate may include an operation of inputting the first context information into the learned artificial intelligence model based on the number of learning data included in the confirmed first learning data set being greater than or equal to the first value.

[0240] The operation of confirming the first temperature change rate may include an operation of confirming the first temperature change rate using the learned artificial intelligence model. According to one embodiment, the learning data may include a temperature range corresponding to the electronic device (101; 200) and a temperature change rate corresponding to the temperature range.

[0241] An operating method according to one embodiment may include an operation of checking a third temperature range and a third temperature change rate included in the last added first learning data among the first learning data set, based on the number of learning data included in the confirmed first learning data set being less than a first value.

[0242] An operating method according to one embodiment may include an operation of performing the random access operation in a third operation cycle corresponding to the third temperature change rate based on the current temperature of the electronic device (101; 200) being included in the third temperature range.

[0243] An operating method according to one embodiment may include an operation of determining a designated operating cycle for at least one temperature section based on a number of learning data included in a first learning data set corresponding to the first context information being less than a second value.

[0244] An operating method according to one embodiment may include an operation of performing the random access operation based on a current temperature of the electronic device (101; 200) and an operation cycle specified for at least one of the identified temperature sections.

[0245] An operating method according to one embodiment may include an operation of performing the random access operation with an operation cycle corresponding to the fourth temperature range, based on the current temperature of the electronic device (101; 200) being included in a fourth temperature range among the at least one temperature range.

[0246] An operating method according to one embodiment may include an operation of performing the random access operation with an operation cycle corresponding to the fifth temperature range, based on the temperature of the electronic device (101; 200) being included in a fifth temperature range different from the fourth temperature range among the at least one temperature range.

[0247] According to one embodiment, the first context information may include at least one of the type of an application running on the electronic device (101; 200), the type of a background application corresponding to the electronic device (101; 200), and information on the status of the electronic device (101; 200).

[0248] A method of operation according to one embodiment may include an operation of checking whether the number of learning data included in the first learning data set is less than the first value based on confirmation of second context information identical to the first context information.

[0249] An operating method according to one embodiment may include an operation of checking a temperature change rate for each temperature section of the electronic device (101; 200) corresponding to the second context information based on the number of the learning data being confirmed to be less than the first value.

[0250] An operating method according to one embodiment may include an operation of performing learning on the artificial intelligence model based on a temperature change rate for each temperature section of the electronic device (101; 200) corresponding to the second context information.

[0251] An operating method according to one embodiment may include an operation of checking similarity between learning data included in the first learning data set.

[0252] An operating method according to an embodiment may include an operation of confirming a first temperature change rate corresponding to the first temperature range based on the confirmed similarity being confirmed to be equal to or greater than a third value.

[0253] In a storage medium storing computer-readable instructions according to one embodiment of the present disclosure, the instructions, when executed by at least one processor (230) of an electronic device (101; 200), can cause the electronic device (101; 200) to check the current temperature of the electronic device (101; 200).

[0254] In one embodiment, the instructions may cause the electronic device (101; 200) to input first context information related to the electronic device (101; 200) into a learned artificial intelligence model based on determining that the current temperature of the electronic device (101; 200) is equal to or greater than a first temperature.

[0255] According to one embodiment, the instructions may cause the electronic device (101; 200) to determine, through the learned artificial intelligence model, a first temperature change rate of the electronic device corresponding to a first temperature range of the electronic device related to the first context information.

[0256] According to one embodiment, the instructions may cause the electronic device (101; 200) to determine a first operation cycle associated with a random access procedure for establishing a first communication connection based on the identified first temperature change rate.

[0257] According to one embodiment, the instructions may cause the electronic device (101; 200) to perform the random access operation based on the determined first operation cycle.

[0258] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned will be clearly understood by a person having ordinary skill in the art to which the present disclosure pertains.

[0259] As used herein, the term “if” will be understood to mean “when, upon,” “in response to deciding,” or “in response to detecting,” depending on the context. Similarly, “if it is decided to do,” or “if [the stated condition or event] is detected,” will optionally be understood to mean “upon deciding,” or “in response to deciding,” “upon detecting [the stated condition or event],” or “in response to detecting [the stated condition or event].”

[0260] The devices described above may be implemented as hardware components, software components, and / or a combination of hardware components and software components. For example, the devices and components described in the embodiments may be implemented using one or more general-purpose computers or special-purpose computers, such as a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them. A processing device (or processing circuit) may execute an operating system (OS) and one or more software applications running on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used alone; however, one of ordinary skill in the art will recognize that the processing device may include multiple processing elements and / or multiple types of processing elements. For example, a processing unit may include multiple processors, or a processor and a controller. Other processing configurations, such as parallel processors, are also possible.

[0261] Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing device to perform a desired operation or may independently or collectively command the processing device. The software and / or data may be embodied in any type of machine, component, physical device, computer storage medium, or device for interpretation by the processing device or for providing instructions or data to the processing device. The software may also be distributed over networked computer systems and stored or executed in a distributed manner. The software and data may be stored on one or more computer-readable recording media.

[0262] The method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. In this case, the medium may be one that continuously stores a computer-executable program or one that temporarily stores it for execution or download. In addition, the medium may be various recording or storage means in the form of a single or multiple hardware combinations, and is not limited to a medium directly connected to a computer system, but may also be distributed over a network. Examples of the medium may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and those configured to store program commands, including ROM, RAM, and flash memory. In addition, examples of other media may include an app store that distributes applications, a site that supplies or distributes various software, or a recording or storage medium managed by a server.

[0263] Although the embodiments described above have been described by way of limited examples and drawings, those skilled in the art will appreciate that various modifications and variations can be made based on the above teachings. For example, appropriate results can still be achieved even if the described techniques are performed in a different order than described, and / or components such as the described systems, structures, devices, and circuits are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents.

[0264] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims described below.

[0265] Electronic devices according to the various embodiments disclosed in this document may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to the embodiments of this document are not limited to the aforementioned devices.

[0266] The various embodiments of this document and the terminology used therein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among those phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0267] The term "module" used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0268] Various embodiments of the present document may be implemented as software (e.g., a program (140)) including one or more instructions stored in a storage medium (e.g., an internal memory (136) or an external memory (138)) readable by a machine (e.g., an electronic device (101)). For example, a processor (e.g., a processor (120)) of the machine (e.g., an electronic device (101)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0269] According to one embodiment, the method according to various embodiments disclosed in this document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smart phones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0270] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and arranged in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the 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.

Claims

1. In an electronic device (101; 200), At least one processor (230) comprising a processing circuit; Communication circuit (220); and Includes a memory (210) for storing instructions, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device (101; 200) to: Check the current temperature of the above electronic device (101; 200), Based on confirming that the current temperature of the electronic device (101; 200) is higher than the first temperature, first context information related to the electronic device (101; 200) is input into the learned artificial intelligence model, Through the learned artificial intelligence model, the first temperature change rate of the electronic device corresponding to the first temperature section of the electronic device related to the first context information is confirmed, Based on the first temperature change rate confirmed above, a first operation cycle related to a random access operation for establishing a first communication connection is determined, An electronic device (101; 200), characterized in that it includes instructions that cause the random access operation to be performed through the communication circuit (220) based on the determined first operation cycle.

2. In paragraph 1, The above instructions cause the electronic device (101; 200) to: By inputting the first context information into the learned artificial intelligence model, the first temperature change rate and the second temperature change rate corresponding to a second temperature range different from the first temperature range are respectively confirmed, Based on the current temperature of the electronic device (101; 200) being included in the first temperature range, the random access operation is performed in the first operation cycle, An electronic device (101; 200), characterized in that it includes instructions that cause the random access operation to be performed in a second operation cycle corresponding to the second temperature change rate, based on the current temperature of the electronic device (101; 200) being included in the second temperature range.

3. In paragraph 1 or 2, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device (101; 200) to: Based on confirming that the current temperature of the electronic device (101; 200) is higher than the first temperature, a first learning data set corresponding to the first context information is confirmed, Based on the fact that the number of learning data included in the first learning data set confirmed above is greater than or equal to the first value, the first context information is input into the learned artificial intelligence model, An electronic device (101; 200), characterized in that it includes instructions that cause the first temperature change rate to be confirmed through the learned artificial intelligence model.

4. In any one of paragraphs 1 to 3, The above learning data includes a temperature range corresponding to the electronic device (101; 200) and a temperature change rate corresponding to the temperature range, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device (101; 200) to: Based on the fact that the number of learning data included in the first learning data set confirmed above is less than the first value, the third temperature range and the third temperature change rate included in the first learning data added last among the first learning data sets are confirmed, An electronic device (101; 200), characterized in that it includes instructions that cause the random access operation to be performed in a third operation cycle corresponding to the third temperature change rate, based on the current temperature of the electronic device (101; 200) being included in the third temperature range.

5. In any one of paragraphs 1 to 4, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device (101; 200) to: Based on the number of learning data included in the first learning data set corresponding to the first context information being less than the second value, determining a designated operation cycle for at least one temperature section, An electronic device (101; 200), characterized in that it includes instructions causing the random access operation to be performed based on the current temperature of the electronic device (101; 200) and the determined at least one temperature interval-specific designated operation cycle.

6. In any one of paragraphs 1 to 5, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device (101; 200) to: Based on the current temperature of the electronic device (101; 200) being included in a fourth temperature section among the at least one temperature section, the random access operation is performed with an operation cycle corresponding to the fourth temperature section, An electronic device (101; 200), characterized in that it includes instructions that cause the random access operation to be performed with an operation cycle corresponding to the fifth temperature range, based on the current temperature of the electronic device (101; 200) being included in a fifth temperature range different from the fourth temperature range among the at least one temperature range.

7. In any one of paragraphs 1 to 6, The first context information includes at least one of the type of application running on the electronic device (101; 200), the type of background application corresponding to the electronic device (101; 200), and information about the status of the electronic device (101; 200). The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device (101; 200) to: Based on the confirmation that the second context information is identical to the first context information, it is confirmed whether the number of learning data included in the first learning data set is less than the first value, Based on the fact that the number of the above learning data is confirmed to be less than the first value, the temperature change rate for each temperature section of the electronic device (101; 200) corresponding to the second context information is confirmed, An electronic device (101; 200), characterized in that it includes instructions that cause learning of the artificial intelligence model to be performed based on the temperature change rate for each temperature section of the electronic device (101; 200) corresponding to the second context information.

8. In any one of paragraphs 1 to 7, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device (101; 200) to: Check the similarity between the learning data included in the first learning data set, An electronic device (101; 200), characterized in that it includes instructions that cause a first temperature change rate corresponding to the first temperature range to be confirmed based on the confirmed similarity being greater than or equal to a third value.

9. In any one of paragraphs 1 to 8, The first context information includes at least one of the type of application running on the electronic device (101; 200), the type of background application corresponding to the electronic device (101; 200), and information about the status of the electronic device (101; 200). The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device (101; 200) to: Based on the confirmation that the second context information is identical to the first context information, it is confirmed that the confirmed similarity is less than the third value, Based on the confirmation that the similarity is less than the third value, the temperature change rate for each temperature section of the electronic device (101; 200) corresponding to the second context information is confirmed, An electronic device (101; 200), characterized in that it includes instructions that cause learning of the artificial intelligence model to be performed based on the temperature change rate for each temperature section of the electronic device (101; 200) corresponding to the second context information.

10. In any one of paragraphs 1 to 9, The electronic device further comprises a display, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device (101; 200) to: An electronic device (101; 200), characterized by including instructions that cause a user interface (UI) to be provided through the display for selecting whether to perform the random access operation with different operation cycles.

11. In any one of paragraphs 1 to 10, The first communication connection includes a communication connection to a 5G (fifth-generation) NR (new radio) network, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device (101; 200) to: An electronic device (101; 200), characterized in that it includes an instruction that causes establishment of the first communication connection to be stopped and an LTE (long term evolution) fallback operation to be performed based on determining that the current temperature of the electronic device (101; 200) is equal to or greater than a second temperature.

12. In the operating method of an electronic device (101; 200), An operation of checking the current temperature of the above electronic device (101; 200); An operation of inputting first context information related to the electronic device (101; 200) into a learned artificial intelligence model based on confirming that the current temperature of the electronic device (101; 200) is higher than a first temperature; An operation of confirming a first temperature change rate of the electronic device corresponding to a first temperature section of the electronic device related to the first context information through the learned artificial intelligence model; An operation for determining a first operation cycle related to a random access operation for establishing a first communication connection based on the first temperature change rate confirmed above; and An operating method of an electronic device, characterized in that it includes an operation of performing the random access operation through a communication circuit (220) based on the first operation cycle determined above.

13. In paragraph 12, The above operation method is: An operation of inputting the first context information into the learned artificial intelligence model to check the first temperature change rate and the second temperature change rate corresponding to a second temperature range different from the first temperature range; An operation of performing the random access operation in the first operation cycle based on the current temperature of the electronic device (101; 200) being included in the first temperature range, and An operating method of an electronic device, characterized in that it includes an operation of performing the random access operation with a second operation cycle corresponding to the second temperature change rate based on the current temperature of the electronic device (101; 200) being included in the second temperature range.

14. In paragraph 12 or 13, The above operating method further includes an operation of checking a first learning data set corresponding to the first context information based on checking that the current temperature of the electronic device (101; 200) is equal to or higher than the first temperature, The operation of checking the above first temperature change rate is: An operation of inputting the first context information into the learned artificial intelligence model based on the number of learning data included in the first learning data set confirmed above being greater than or equal to the first value, and An operating method of an electronic device, characterized in that it includes an operation of checking the first temperature change rate through the learned artificial intelligence model.

15. In a non-transitory recording medium (130) configured to store computer-readable instructions, the instructions, when executed by at least one processor (230) of an electronic device (101; 200), cause the electronic device (101; 200) to: Check the current temperature of the above electronic device (101; 200), Based on confirming that the current temperature of the electronic device (101; 200) is higher than the first temperature, first context information related to the electronic device (101; 200) is input into the learned artificial intelligence model, Through the learned artificial intelligence model, the first temperature change rate of the electronic device corresponding to the first temperature section of the electronic device related to the first context information is confirmed, Based on the first temperature change rate confirmed above, a first operation cycle related to a random access procedure for establishing a first communication connection is determined, and A non-transitory recording medium (130), characterized in that it includes instructions that cause the random access operation to be performed based on the determined first operation cycle.

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