Electronic device, method, and non-transitory computer-readable storage medium for identifying data for antenna

Abstract

This method executed in an electronic device may comprise the operations of: acquiring data including reflection coefficient values of an antenna that are defined by frequency; dividing the data into tokens for frequency intervals having the same size and mutually different frequency ranges, wherein the tokens include reflection coefficient values for the respective frequency intervals; providing the tokens to a trained model; acquiring, by means of the trained model, an image generated on the basis of the tokens; and identifying radiation efficiency data of the antenna by using the image.

Description

Electronic device, method, and non-transient computer-readable storage medium for identifying data for an antenna

[0001] The present disclosure relates to an electronic device, a method, and a non-transient computer-readable storage medium for identifying data for an antenna.

[0002] Artificial intelligence is a technology for simulating human (or biological) neural activities such as perception and / or inference, and can be implemented as hardware, software, or a combination thereof designed to perform calculations for simulating neural activities.

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

[0004] A method is described. The method may be performed within an electronic device. The method may include the operation of acquiring data containing reflection coefficient values ​​of an antenna defined by frequency. The method may include the operation of dividing the data into tokens for frequency intervals having different frequency ranges and having the same size. Each of the tokens may include reflection coefficient values ​​for each of the frequency intervals. The method may include the operation of providing the tokens to a trained model. The method may include the operation of acquiring an image generated according to the tokens using the trained model. The method may include the operation of identifying radiation efficiency data of the antenna using the image.

[0005] A non-transient computer-readable storage medium is described. The non-transient computer-readable storage medium may store one or more programs. The one or more programs may include instructions that cause the electronic device to acquire data containing reflection coefficient values ​​of an antenna defined by frequency when executed by the electronic device. The one or more programs may include instructions that cause the electronic device to divide the data into tokens for frequency intervals having different frequency ranges and having the same size when executed by the electronic device. Each of the tokens may include reflection coefficient values ​​for each of the frequency intervals. The one or more programs may include instructions that cause the electronic device to provide the tokens to a trained model when executed by the electronic device. The one or more programs may include instructions that cause the electronic device to acquire an image generated according to the tokens using the trained model when executed by the electronic device. The above one or more programs may include instructions that cause the electronic device to identify radiation efficiency data of the antenna using the image when executed by the electronic device.

[0006] An electronic device is described. The electronic device may include a memory comprising at least one processor including a processing circuit and one or more programs configured to be executed individually or collectively by the at least one processor, and one or more storage media. The instructions may cause the electronic device to acquire data including reflection coefficient values ​​of an antenna defined by frequency when executed individually or collectively by the at least one processor. The instructions may cause the electronic device to divide the data into tokens for frequency intervals having different frequency ranges and having the same size when executed individually or collectively by the at least one processor. Each of the tokens may include reflection coefficient values ​​for each of the frequency intervals. The instructions may cause the electronic device to provide the tokens to a trained model when executed individually or collectively by the at least one processor. The above instructions may cause the electronic device to acquire an image generated according to the tokens using the trained model when executed individually or collectively by the at least one processor. The above instructions may cause the electronic device to identify radiation efficiency data of the antenna using the image when executed individually or collectively by the at least one processor.

[0007] Figure 1 illustrates an example of acquiring radiation efficiency data.

[0008] Figure 2 is a simplified block diagram of an exemplary electronic device.

[0009] Figure 3 is a flowchart illustrating exemplary operations of an electronic device for training a model.

[0010] Figure 4 illustrates an example of dividing data containing antenna reflection coefficient values ​​into tokens.

[0011] Figure 5 illustrates an example of acquiring an image using radiation efficiency data.

[0012] Figure 6 illustrates an example of a chart representing the radiation efficiency values ​​of an antenna according to the reflection coefficient values ​​of the antenna.

[0013] FIG. 7 is a flowchart illustrating exemplary operations of an electronic device for identifying radiation efficiency data of an antenna using a trained model.

[0014] Figure 8 illustrates an example of identifying antenna radiation efficiency data using an image.

[0015] Figure 9a illustrates an example of a chart showing the radiation efficiency values ​​of an antenna according to frequency.

[0016] Figure 9b illustrates an example of a chart representing the reliability of antenna radiation efficiency data.

[0017] FIG. 10 is a block diagram of an electronic device in a network environment according to various embodiments.

[0018] FIG. 11 illustrates an example of a generative artificial intelligence system according to one embodiment.

[0019] Hereinafter, embodiments of the present disclosure are described in detail with reference to the drawings so that those skilled in the art can easily practice them. However, the present disclosure may be embodied in various different forms and is not limited to the embodiments described herein. In relation to 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 brevity.

[0020] Figure 1 illustrates an example of acquiring radiation efficiency data.

[0021] Referring to FIG. 1, the electronic device (110) may be described as a device available for obtaining radiation efficiency data of an external electronic device (100). For example, the electronic device (110) may be one of an antenna test chamber, an antenna measurement chamber, and an antenna analysis device that includes circuits (or circuitry) for identifying data for the antenna of the external electronic device (100). For example, the external electronic device (100) may be one of various types of mobile devices including an antenna, such as smartphones having various form factors (e.g., bar-type smartphones, foldable-type smartphones, or rollable-type smartphones), tablets, wearable devices, cellular phones, and / or other similar computing devices.

[0022] State (105) can be described as a state for obtaining radiation efficiency data of the antenna of the external electronic device (100). In state (105), the electronic device (110) may be used to obtain radiation efficiency data of the antenna of the external electronic device (100). Hereinafter, the radiation efficiency of the antenna is exemplified by parameters according to embodiments, but such exemplary description is not to be interpreted as limiting the use of other parameters. As an example that is not limiting, the radiation efficiency of the antenna of the external electronic device (100) may be replaced with total efficiency, gain, directivity, beamwidth, sidelobe level (SLL), and axial ratio. However, it is not limited thereto. The radiation efficiency data of the antenna of the external electronic device (100) may include radiation efficiency values ​​of the antenna of the external electronic device (100). The electronic device (110) can measure the radiation efficiency values ​​of the antenna of the external electronic device (100) in order to obtain radiation efficiency data of the antenna of the external electronic device (100). It may take a relatively long time for the electronic device (110) to measure the radiation efficiency values ​​of the antenna of the external electronic device (100).

[0023] For example, as the electronic device (110) measures the radiation efficiency values ​​of a plurality of antennas included in the external electronic device (100), or measures the radiation efficiency values ​​of the antennas of the plurality of external electronic devices, or remeasures the radiation efficiency values ​​of the antennas of the tuned external electronic device (100), the time required to measure the radiation efficiency values ​​of the antennas may increase. As a relatively long time is required to measure the radiation efficiency values ​​of the antennas of the external electronic device (100), the user may feel inconvenience. A solution to alleviate the inconvenience of the user may be required.

[0024] To resolve this inconvenience, data including reflection coefficient values ​​(e.g., S11 parameter) of the antenna of the external electronic device (100) may be used. For example, the time required to measure the reflection coefficient values ​​of the antenna of the external electronic device (100) may be shorter than the time required for the electronic device (110) to measure the radiation efficiency values ​​of the antenna of the external electronic device (100). Since the reflection coefficient values ​​of the antenna of the external electronic device (100) can be measured by the external electronic device (100), the electronic device (110) may not be used to measure the reflection coefficient values ​​of the antenna. The radiation efficiency data of the antenna of the external electronic device (100) may be generated using data including the reflection coefficient values ​​of the antenna of the external electronic device (100) from a trained model. A method for obtaining radiation efficiency data of the antenna of the external electronic device (100) using a trained model may be executed within an electronic device (e.g., the electronic device (200) of FIG. 2). The electronic device may perform operations exemplified within the description of FIGS. 3 through 9b to acquire radiation efficiency data of an antenna. The electronic device may include components for performing said operations. said components may be exemplified within the description of FIG. 2.

[0025] Figure 2 is a simplified block diagram of an exemplary electronic device.

[0026] Referring to FIG. 2, the electronic device (200) may be described as one or more personal computers (PCs) and / or workstations. The electronic device (200) may include at least a part of the electronic device (1001) of FIG. 10 or correspond to at least a part of the electronic device (1001) of FIG. 10. For example, the electronic device (200) may identify radiation efficiency data of an antenna of an external electronic device (e.g., external electronic device (100) of FIG. 1). For example, the electronic device (200) may identify radiation efficiency data of an antenna of an external electronic device and process the data generated. For example, the electronic device (200) may include at least one processor (210) (e.g., processor (1020) of FIG. 10) and a memory (220) (e.g., memory (1030) of FIG. 10).

[0027] At least one processor (210) may include a processing circuit. For example, at least one processor (210) may include a CPU (central processing unit) (e.g., including a processing circuit). For example, at least one processor (210) may include a GPU (graphic processing unit) (e.g., including a processing circuit) and / or an NPU (neural processing unit) (e.g., including a processing circuit). For example, at least one processor (210) may be described as an application processor. For example, at least one processor (210) may be configured to control memory (220). At least one processor (210) may be configured to execute instructions stored in memory (220) individually or collectively to cause the electronic device (200) to perform at least some of the operations illustrated in the description of FIG. 1. At least one processor (210) may be configured to execute instructions stored in memory (220) to cause the electronic device (200) to perform at least some of the operations illustrated in the description of FIGS. 3 through 9b.

[0028] For example, the term “processor” as used herein, including in the claims, may include various processing circuits comprising at least one processor, and one or more of said at least one processor may be configured to perform the various functions described below in a distributed manner, individually and / or collectively. As used below, where “processor,” “at least one processor,” and “one or more processors” are described as being configured to perform various functions, these terms encompass, for example, but not limited to, situations where one processor performs some of the cited functions and another processor(s) perform other parts of the cited functions, and also situations where one processor can perform all of the cited functions. Additionally, said at least one processor may include a combination of processors that perform the enumerated / disclosed various functions, for example, in a distributed manner. At least one processor may execute program instructions to achieve or perform the various functions.

[0029] The memory (220) may include one or more storage media. For example, the memory (220) may store various data used by at least one component of the electronic device (200) (e.g., at least one processor (210)). For example, the data may include input data or output data for software and related instructions. The memory (220) may include volatile memory or non-volatile memory.

[0030] The electronic device (200) illustrated in the description of FIG. 2 may perform at least some of the operations illustrated in the descriptions of FIG. 3 through 9b. For example, the operations illustrated in the descriptions of FIG. 3 through 9b may be caused by (or within) the electronic device (200) under the control of at least one processor (210).

[0031] Figure 3 is a flowchart illustrating exemplary operations of an electronic device for training a model.

[0032] Referring to FIG. 3, in operation 300, at least one processor (210) can acquire data including values ​​of the reflection coefficient of an antenna (e.g., S11 parameter) defined by frequency. For example, at least one processor (210) can acquire data including the reflection coefficient values ​​of an antenna by receiving data including the reflection coefficient values ​​of an antenna from an external electronic device (e.g., the external electronic device (100) of FIG. 1). The antenna for the reflection coefficient values ​​may be described as the antenna of the external electronic device. The external electronic device may include a bidirectional coupler connected to (or electrically connected to, or coupled to) the antenna. The external electronic device may identify (or measure) the reflection coefficient values ​​of the antenna through the bidirectional coupler. To identify (or measure) the reflection coefficient values, a forward coupling signal (e.g., a coupling signal of a signal transmitted through the antenna) and a reverse coupling signal (e.g., a coupling signal of a signal received through the antenna) extracted through the bidirectional coupler of the external electronic device may be used.

[0033] For example, an external electronic device may transmit signals at various frequencies (or frequency bands). Based on the signals transmitted at various frequencies (or frequency bands), the external electronic device may identify (or measure) the reflection coefficient values ​​of an antenna defined by frequency. The external electronic device may transmit data including the reflection coefficient values ​​of an antenna defined by frequency to the electronic device (200).

[0034] Hereinafter, the reflection coefficient is exemplified as a parameter according to the embodiments, but such exemplary description is not to be interpreted as limiting the use of other parameters. Parameters having the same or similar technical meaning as the reflection coefficient (e.g., reflection loss, voltage standing wave ratio (VSWR)) may be used according to the embodiments.

[0035] In operation 310, at least one processor (210) may divide data containing reflection coefficient values ​​of an antenna defined by frequency into tokens for frequency intervals. Dividing data into tokens may be defined as tokenization. At least one processor (210) may perform tokenization processing of data to provide (or input) the data to a model. For example, tokenization may be a preprocessing operation on data performed so that the model can process the data efficiently. At least one processor (210) may divide the data into tokens for frequency intervals by performing tokenization of the data based on frequency intervals. Tokens for frequency intervals are exemplified in the description of FIG. 4.

[0036] Figure 4 illustrates an example of dividing data containing antenna reflection coefficient values ​​into tokens.

[0037] Referring to FIG. 4, the chart (400) represents the change in the reflection coefficient value of an antenna according to frequency. The horizontal axis (405) in the chart (400) indicates the frequency, and the vertical axis (410) in the chart (400) indicates the reflection coefficient value of the antenna. The horizontal axis (405) in the chart (400) may have units of MHz (mega Hertz), and the vertical axis (410) in the chart (400) may have units of dB (decibel).

[0038] For example, the reflection coefficient values ​​of the antenna included in the data can be represented as a line (415) in the chart (400). For example, at least one processor (210) can divide the data containing the reflection coefficient values ​​of the antenna into tokens (430) for frequency intervals (420). The frequency intervals (420) may have the same size. The frequency intervals (420) may have different frequency ranges (or frequency bands). The frequencies within each frequency interval (420) may be different from the frequencies within different frequency intervals (420). The frequencies within each frequency interval (420) may not belong to different frequency intervals (420). For example, the second frequency interval (420-2) immediately after the first frequency interval (420-1) may have a frequency range adjacent to the frequency range of the first frequency interval (420-1). For example, the third frequency interval (420-3) immediately before the fourth frequency interval (420-4) may have a frequency range adjacent to the frequency range of the fourth frequency interval (420-4).

[0039] Each of the tokens (430) for frequency intervals (420) may include reflection coefficient values ​​(425) for each of the frequency intervals (420). For example, the tokens (430) may be defined as a plurality of groups. For example, each of the reflection coefficient values ​​(425) for each of the frequency intervals (420) may be located in each of the groups within the tokens (430). For example, the groups within the tokens (430) may indicate a frequency and a reflection coefficient value of the antenna for that frequency. For example, the first token (430-1) may include first reflection coefficient values ​​(425-1) for the first frequency interval (420-1). For example, the first reflection coefficient values ​​(425-1) may be arranged sequentially within the first token (430-1) according to the frequency defining the first reflection coefficient values ​​(425-1). For example, each of the first reflection coefficient values ​​(425-1) may be located in each of the groups within the first token (430-1). For example, the number of groups in the first token (430-1) may correspond to the number of the first reflection coefficient values ​​(425-1). For example, the groups within the first token (430-1) may indicate the reflection coefficient values ​​of the antenna on the frequency and frequency, respectively. For example, the second token (430-2) may have the second reflection coefficient values ​​(425-2) for the second frequency interval (420-2). For example, the third token (430-3) may have the third reflection coefficient values ​​(425-3) for the third frequency interval (420-3). For example, the fourth token (430-4) may have fourth reflection coefficient values ​​(425-4) for the fourth frequency interval (420-4). For example, the second token (430-2), the third token (430-3), and the fourth token (430-4) may have a size corresponding to the size of the first token (430-1).

[0040] For example, the fourth frequency interval (420-4) can be described as the frequency interval having the largest frequency range among the frequency intervals (420). For example, at least some of the reflection coefficient values ​​of the antenna included in the data may be undefined within the fourth frequency interval (420-4). For example, the number of reflection coefficient values ​​within the fourth frequency interval (420-4) may be fewer than the number of reflection coefficient values ​​within other frequency intervals (e.g., the first frequency interval (420-1), the second frequency interval (420-2), and the third frequency interval (420-3)). For example, the fourth token (430-4) of the fourth frequency interval (420-4) may have the undefined reflection coefficient values ​​within the fourth frequency interval (420-4) as a value of 0 in order to have a size corresponding to the size of the other tokens (e.g., the first token (420-1), the second token (420-2), and the third token (430-3)). For example, by adding groups (435) having a value of 0 to the fourth token (430-4), the fourth token (430-4) may have a size corresponding to the size of the other tokens. For example, adding groups having a value of 0 to the token may be referred to as zero padding.

[0041] For example, the model can process data in token units. For example, as the frequency intervals (420) increase, the number of tokens (430) may decrease, or as the frequency intervals (420) decrease, the number of tokens (430) may increase. Because the model processes data in token units, the model can be trained based on the trends of the reflection coefficient values ​​(425) included in each of the tokens (430) and / or the relationships between the reflection coefficient values ​​(425). Because the model processes data in token units, the time and amount of computation required for the model to process the data may increase as the number of tokens constituting the data increases. Accordingly, at least one processor (210) can set (or determine, or change) the frequency intervals (420).

[0042] Referring again to FIG. 3, in operation 320, at least one processor (210) can obtain radiation efficiency data including radiation efficiency values ​​of an antenna defined by frequency. For example, at least one processor (210) can obtain radiation efficiency data including radiation efficiency values ​​of an antenna by receiving radiation efficiency data including radiation efficiency values ​​of an antenna of an external electronic device (e.g., external electronic device (100) of FIG. 1) from another external electronic device (e.g., electronic device (110) of FIG. 1). For example, the other external electronic device can identify (or measure) radiation efficiency values ​​of an antenna of the external electronic device defined by frequency. For example, the other external electronic device can transmit the identified (or measured) radiation efficiency values ​​of the antenna to the electronic device (200).

[0043] Radiation efficiency is exemplified as a parameter according to the embodiments, but this exemplary description is not to be interpreted as limiting the use of other parameters. As a non-limiting example, the radiation efficiency of the antenna of the external electronic device (100) may be replaced with total efficiency, gain, directivity, beamwidth, sidelobe level (SLL), and axial ratio. However, it is not limited thereto.

[0044] The frequency range in which the radiation efficiency values ​​within the radiation efficiency data are defined is included within the frequency range in which the reflection coefficient values ​​within the data are defined, or it may correspond to the frequency range in which the reflection coefficient values ​​within the data are defined.

[0045] In operation 330, at least one processor (210) can acquire an image using radiation efficiency data. For example, the image may be referred to as a two-dimensional image and / or a pixel image. At least one processor (210) can map radiation efficiency data to the image. The image with radiation efficiency data mapped to it may be used to train a model for image generation. Acquiring an image using radiation efficiency data is exemplified in the description of FIG. 5.

[0046] Figure 5 illustrates an example of acquiring an image using radiation efficiency data.

[0047] Referring to FIG. 5, the chart (500) represents the change in the radiation efficiency value of an antenna according to frequency. The horizontal axis (505) in the chart (500) indicates the frequency, and the vertical axis (510) in the chart (500) indicates the radiation efficiency value of the antenna. The horizontal axis (505) in the chart (500) may have units of MHz, and the vertical axis (510) in the chart (500) may have units of dB.

[0048] For example, the radiation efficiency values ​​of an antenna included in the radiation efficiency data can be represented as a line (515) in a chart (500). At least one processor (210) can map the radiation efficiency values ​​of an antenna represented by the line (515) to an image (535). At least one processor (210) can map the radiation efficiency values ​​of an antenna to an image (535) to train a model. For example, at least one processor (210) can convert the radiation efficiency data of an antenna into an image (535) by mapping the radiation efficiency values ​​of an antenna to an image (535). For example, converting the radiation efficiency data of an antenna into an image (535) can be defined as a 2D conversion of the radiation efficiency data of an antenna.

[0049] For example, an image (535) may be defined by parts of the image (535) (e.g., pixels). At least one processor (210) may map a frequency and a radiation efficiency value on said frequency to a part of the image (535). For example, each part of the image (535) may indicate a frequency and a radiation efficiency value on said frequency. For example, the location of a part of the image (535) within the image (535) may indicate a frequency. For example, a pixel value (or RGB value, or grayscale, or brightness) of a part of the image (535) may indicate a radiation efficiency value on said frequency.

[0050] For example, at least one processor (210) may divide the radiation efficiency data of the antenna into frequency intervals (520) to map it onto an image (535). The frequency intervals (520) may have the same size. The frequency intervals (520) may have different frequency ranges (or frequency bands). The frequencies within each frequency interval (520) may be different from the frequencies within different frequency intervals (520). The frequencies within each frequency interval (520) may not belong to different frequency intervals (520). For example, a second frequency interval (520-2) immediately following a first frequency interval (520-1) may have a frequency range adjacent to the frequency range of the first frequency interval (520-1). For example, the third frequency interval (520-3) immediately preceding the fourth frequency interval (520-4) may have a frequency range adjacent to the frequency range of the fourth frequency interval (520-4).

[0051] At least one processor (210) can map frequency and radiation efficiency values ​​(525) within frequency intervals (520) to one line (e.g., a horizontal line) of an image (535). For example, at least one processor (210) can map frequency and first radiation efficiency values ​​(525-1) within a first frequency interval (520-1) to a first line (530-1) of an image (535). For example, at least one processor (210) can map the first radiation efficiency values ​​(525-1) sequentially to the first line (530-1) of the image (535) according to frequency. For example, the number of parts of the image (535) within the first line (530-1) of the image (535) can correspond to the number of first radiation efficiency values ​​(525-1). For example, at least one processor (210) can map the frequency and second radiation efficiency values ​​(525-2) within the second frequency interval (520-2) to the second line (530-2) of the image (535). For example, at least one processor (210) can map the frequency and third radiation efficiency values ​​(525-3) within the third frequency interval (520-3) to the third line (530-3) of the image (535). For example, at least one processor (210) can map the frequency and fourth radiation efficiency values ​​(525-4) within the fourth frequency interval (520-4) to the fourth line (530-4) of the image (535). For example, the second line (530-2) of the image (535), the third line (530-3) of the image (535), and the fourth line (530-4) of the image (535) may have a size corresponding to the size of the first line (530-1) of the image (535).

[0052] For example, within the radiation efficiency data, radiation efficiency values ​​may not be defined over a frequency range exceeding the frequency range of the fourth frequency interval (520-4). The image (535) may include a portion (540) of the image (535) indicating a frequency within a frequency range exceeding the frequency range of the fourth frequency interval (520-4). For example, the portion (540) of the image (535) may be located at a position (e.g., below and / or to the right of the image) within the image (535) corresponding to a frequency within a frequency range exceeding the frequency range of the fourth frequency interval (520-4). For example, the portion (540) of the image (535) may have pixel values ​​indicating that the radiation efficiency value is not defined.

[0053] Referring again to FIG. 3, in operation 340, at least one processor (210) may provide tokens (e.g., tokens (430) of FIG. 4) and an image (e.g., an image (535) of FIG. 5) to a model. For example, the model may include a machine learning model and a deep learning model. For example, the model may include the generative AI model (1130) of FIG. 11. For example, the model may include a diffusion model. The diffusion model may be described as a generative AI model for generating an image as a text and / or image prompt is provided (or input). At least one processor (210) may perform forward diffusion of the image using the diffusion model. For example, at least one processor (210) may generate a latent variable by performing forward diffusion. For example, the latent variable may be defined as a random vector following a standard normal distribution. For example, at least one processor (210) may generate noise data (e.g., random noise) in a vector indicating an image based on the latent variable and a time step. For example, at least one processor (210) may train a model by providing the noise data and tokens for the image to a convolutional neural network (CNN) model (e.g., U-net). For example, data including the reflection coefficient values ​​of the antenna contained within the tokens may be embedded as condition parameters of the CNN model. For example, the image and tokens may be provided to the model in pairs (or in combination).

[0054] For example, the model can be trained to generate an image (e.g., output data) based on tokens (e.g., input data). For example, at least one processor (210) can train the model to obtain antenna radiation efficiency values ​​based on antenna reflection coefficient values ​​using tokens and images. For example, antenna radiation efficiency values ​​based on antenna reflection coefficient values ​​may be overfitted. For example, due to the overfitting, training the model using antenna radiation efficiency values ​​based on antenna reflection coefficient values ​​may result in errors. To resolve these errors, a method may be required to change the antenna radiation efficiency values ​​based on the overfitted antenna reflection coefficient values ​​into a linear distribution. A chart representing antenna radiation efficiency values ​​based on antenna reflection coefficient values ​​is illustrated in the description of FIG. 6.

[0055] Figure 6 illustrates an example of a chart representing the radiation efficiency values ​​of an antenna according to the reflection coefficient values ​​of the antenna.

[0056] Referring to FIG. 6, the chart (600) represents the change in the radiation efficiency value of an antenna according to the reflection coefficient value of the antenna. The horizontal axis (605) in the chart (600) indicates the radiation efficiency value of the antenna, and the vertical axis (610) in the chart (600) indicates the reflection coefficient value of the antenna. The horizontal axis (605) and the vertical axis (610) in the chart (600) may have dB units.

[0057] For example, the radiation efficiency value of an antenna according to the reflection coefficient value of the antenna can be represented as a heatmap (615) within the chart (600). For example, the dark areas of the heatmap (615) may indicate the reflection coefficient value and the radiation efficiency value of the antenna on the same frequency. For example, the reflection coefficient value of the antenna and the radiation efficiency value of the antenna may have a correlation that is mathematically undefinable. For example, since the correlation between the reflection coefficient value of the antenna and the radiation efficiency value of the antenna is mathematically undefinable, the heatmap (615) within the chart (600) may be determined experimentally.

[0058] For example, the heatmap (615) within the chart (600) may have a nonlinear shape. For example, because the heatmap (615) has a nonlinear shape, the radiation efficiency values ​​of the antenna according to the reflection coefficient values ​​of the antenna may be distributed nonlinearly. For example, the radiation efficiency values ​​of the antenna according to the reflection coefficient values ​​of the antenna distributed nonlinearly may be overfitted. For example, due to the overfitting, training a model using the radiation efficiency values ​​of the antenna according to the reflection coefficient values ​​of the antenna may result in errors. To resolve these errors, a method may be required to change the radiation efficiency values ​​of the antenna according to the overfitted reflection coefficient values ​​of the antenna into a linear distribution. For example, to change the radiation efficiency values ​​of the antenna according to the reflection coefficient values ​​of the antenna into a linear distribution, the chart (600) may be represented on a log-scale.

[0059] Chart (620) can be described as a chart that changes the signs of the reflection coefficient values ​​and radiation efficiency values ​​of the antenna of Chart (600) and represents Chart (600) on a log-scale. Chart (620) represents the change in the radiation efficiency value of the antenna according to the reflection coefficient value of the antenna. The horizontal axis (625) in Chart (620) has a changed sign and indicates the radiation efficiency value of the antenna changed on a log-scale (hereinafter, the first value), and the vertical axis (630) in Chart (620) has a changed sign and indicates the reflection coefficient value of the antenna changed on a log-scale (hereinafter, the second value).

[0060] For example, the second value corresponding to the first value can be represented as a heatmap (635) within the chart (620). For example, the dark areas of the heatmap (635) may indicate the second values ​​corresponding to the first values. For example, the heatmap (635) within the chart (620) may have a linear shape. For example, because the heatmap (635) has a linear shape, the second values ​​corresponding to the first values ​​may be distributed linearly. For example, because the second values ​​corresponding to the first values ​​are distributed linearly, at least one processor (210) can train a model using the second values ​​corresponding to the first values.

[0061] For example, at least one processor (210) may divide data containing first values ​​into tokens for frequency intervals. For dividing data containing first values ​​into tokens for frequency intervals, reference may be made to the description in FIG. 4. For example, at least one processor (210) may acquire an image using data containing second values. For acquiring an image using data containing second values, reference may be made to the description in FIG. 5. For example, at least one processor (210) may train a model using the tokens and the image. For training a model using the tokens and the image, reference may be made to operation 340 in FIG. 3. For example, because the second values ​​according to the first values ​​are linearly distributed, the output data of a model trained based on the first values ​​and the second values ​​may have a higher reliability than the reliability of the output data of a model trained based on the reflection coefficient values ​​of the antenna and the radiation efficiency values ​​of the antenna.

[0062] For example, at least one processor (210) can identify radiation efficiency data of an antenna based on data including reflection coefficient values ​​of the antenna. For example, the trained model may be used to identify radiation efficiency data of the antenna. Identifying radiation efficiency data of the antenna using the trained model is illustrated in the description of FIG. 7.

[0063] FIG. 7 is a flowchart illustrating exemplary operations of an electronic device for identifying radiation efficiency data of an antenna using a trained model.

[0064] Referring to FIG. 7, in operation 700, at least one processor (210) may obtain data including reflection coefficient values ​​of an antenna defined by frequency. By example, without limitation, the antenna may correspond to or be different from the antenna exemplified in FIG. 3 through 6. The type of electronic device including the antenna may correspond to the type of external electronic device including the antenna exemplified in FIG. 3 through 6, but is not limited thereto. For example, operation 700 may correspond to operation 300 of FIG. 3.

[0065] In operation 710, at least one processor (210) may divide data containing reflection coefficient values ​​of an antenna defined by frequency into tokens for frequency intervals. For example, operation 700 may correspond to operation 310 of FIG. 3. For example, operation 700 may refer to the description of FIG. 4.

[0066] In operation 720, at least one processor (210) may provide (or input) tokens to the trained model. For example, the trained model may be described as a model trained by the operations illustrated in FIGS. 3 through 6.

[0067] In operation 730, at least one processor (210) can obtain an image generated according to tokens (e.g., image (800) of FIG. 8) using a trained model. For example, the trained model may include a diffusion model. For example, at least one processor (210) can perform forward diffusion of noise data using the diffusion model by providing tokens to a CNN model. For example, the noise data may be defined as Gaussian noise. For example, at least one processor (210) can predict latent variables by performing forward diffusion. For example, at least one processor (210) can remove noise from the noise data according to each step of the forward diffusion based on the predicted latent variables and time steps. For example, a vector function may be used to perform the forward diffusion. For example, at least one processor (210) can obtain an image generated according to tokens by sequentially removing noise from the noise data.

[0068] In operation 740, at least one processor (210) can identify radiation efficiency data of an antenna using an image. The image may be defined by parts of the image. For example, parts of the image may indicate a frequency and a radiation efficiency value of the antenna according to said frequency. The image may correspond to the image (535) of FIG. 5. The radiation efficiency data of the antenna may include radiation efficiency values ​​of the antenna defined by a frequency. At least one processor (210) can identify the radiation efficiency data of the antenna based on the frequency indicated by parts of the image and the radiation efficiency value of the antenna according to said frequency. Identifying the radiation efficiency data of the antenna using an image is exemplified in the description of FIG. 8.

[0069] Figure 8 illustrates an example of identifying antenna radiation efficiency data using an image.

[0070] Referring to FIG. 8, an image (800) can be defined by parts of the image (800) (e.g., pixels). Each part of the image (800) can indicate a frequency and a radiation efficiency value on said frequency. For example, the location of a part of the image (800) within the image (800) can indicate a frequency. For example, a pixel value (or RGB value, or grayscale, or brightness) of a part of the image (800) can indicate a radiation efficiency value of an antenna on said frequency.

[0071] Each of the lines (805) of the image (800) may indicate a frequency and radiation efficiency values ​​(840) within frequency intervals (835). For example, the first line (805-1) of the image (800) may indicate a frequency and first radiation efficiency values ​​(525-1) within a first frequency interval (835-1). For example, the first radiation efficiency values ​​(525-1) may be indicated sequentially according to frequency within the first line (805-1) of the image (800). For example, at least one processor (210) may identify the first radiation efficiency values ​​(525-1) defined by the frequency within the first frequency interval (835-1) based on the first line (805-1) of the image (800). For example, at least one processor (210) can identify second radiation efficiency values ​​(525-2) defined by a frequency within a second frequency interval (835-2) based on a second line (805-2) of the image (800). For example, at least one processor (210) can identify third radiation efficiency values ​​(525-3) defined by a frequency within a third frequency interval (835-3) based on a third line (805-3) of the image (800). For example, at least one processor (210) can identify fourth radiation efficiency values ​​(525-4) defined by a frequency within a fourth frequency interval (835-4) based on a fourth line (805-4) of the image (800).

[0072] At least one processor (210) can identify radiation efficiency data of an antenna based on the frequency and radiation efficiency values ​​on the frequency indicated by each part of the image (800). The radiation efficiency data of the antenna may include radiation efficiency values ​​of the antenna defined by frequency. The radiation efficiency values ​​of the antenna defined by frequency may be represented as a chart (815). The horizontal axis (820) in the chart (815) indicates the frequency, and the vertical axis (825) in the chart (815) indicates the radiation efficiency values ​​of the antenna. For example, the radiation efficiency values ​​may be represented as a line (830) in the chart (815). For example, at least one processor (210) can obtain the chart (815) by identifying the radiation efficiency data of the antenna using the image (800). The frequency range in which the radiation efficiency values ​​of the antenna are defined may correspond to the frequency range in which the reflection coefficient values ​​of the antenna are defined. The reflection coefficient values ​​of the antenna can be explained by the reflection coefficient values ​​of the antenna included in the data used to acquire the radiation efficiency data of the antenna.

[0073] Measuring the reflection efficiency values ​​of an antenna using an external electronic device (e.g., the electronic device (110) of FIG. 1) may take a relatively long time. For example, at least one processor (210) can obtain the reflection efficiency data of an antenna using data including the reflection coefficient values ​​of the antenna. At least one processor (210) can reduce the time required to obtain the reflection efficiency data of an antenna by obtaining the reflection efficiency data of an antenna using data including the reflection coefficient values ​​of the antenna.

[0074] According to another embodiment, the antenna may be tuned based on tuning conditions. For example, first data including reflection coefficient values ​​of an antenna tuned based on a first tuning condition may differ from second data including reflection coefficient values ​​of an antenna tuned based on a second tuning condition. For example, at least one processor (210) may divide the first data and the second data into tokens for frequency intervals. For example, at least one processor (210) may identify first radiation efficiency data of an antenna tuned based on a first tuning condition by providing tokens of the first data to a trained model. For example, at least one processor (210) may identify second radiation efficiency data of an antenna tuned based on a second tuning condition by providing tokens of the second data to a trained model. For example, at least one processor (210) can use the first radiation efficiency data (or the second radiation efficiency data) to bypass (or refrain from, or stop, or skip, or not perform tuning) the tuning of the antenna based on the first tuning condition (or the second tuning condition) within the antenna tuning process.

[0075] For example, in order for a user to decide whether to use antenna radiation efficiency data obtained using a trained model, a method for determining the reliability of the trained model may be required. A chart expressing the reliability of the trained model is exemplified in the description of FIGS. 9a and FIGS. 9b.

[0076] Figure 9a illustrates an example of a chart showing the radiation efficiency values ​​of an antenna according to frequency.

[0077] Figure 9b illustrates an example of a chart representing the reliability of antenna radiation efficiency data.

[0078] Referring to FIG. 9a, the chart (900) represents the change in the radiation efficiency value of an antenna according to frequency. The horizontal axis (905) in the chart (900) indicates the frequency, and the vertical axis (910) in the chart (900) indicates the radiation efficiency value of the antenna. The horizontal axis (905) in the chart (900) may have units of MHz, and the vertical axis (910) in the chart (900) may have units of dB.

[0079] For example, the radiation efficiency values ​​of an antenna identified using a trained model can be represented as a line (915) in a chart (900). The line (915) in the chart (900) may correspond to the line (830) in the chart (815) of FIG. 8. For example, the radiation efficiency values ​​of an antenna measured by an external electronic device (e.g., the electronic device (100) of FIG. 1) can be represented as a line (920) in the chart (900). For example, the radiation efficiency value of an antenna identified using a trained model at each frequency and the radiation efficiency value of an antenna measured by an external electronic device may have a difference (925). The radiation efficiency value of an antenna identified using a trained model may have an error due to this difference (925).

[0080] For example, at least one processor (210) can obtain the mean absolute percentage error (MAPE) between the radiation efficiency value of the identified antenna and the radiation efficiency value of the antenna measured by an external electronic device using a model trained according to Equation 1 below.

[0081]

[0082] The above mathematical formula 1 is merely an example to aid understanding, and embodiments of the present disclosure are not limited thereto. For example, the above mathematical formula 1 may be modified, applied, or extended in various ways.

[0083] In Equation 1, MAPE represents the mean absolute percentage error between the antenna radiation efficiency value identified using the trained model and the antenna radiation efficiency value measured by an external electronic device, n represents the number of antenna radiation efficiency values ​​(e.g., the number of antenna radiation efficiency values ​​identified using the trained model and / or the number of antenna radiation efficiency values ​​measured by an external electronic device), xi represents the antenna radiation efficiency value identified using the trained model, and x represents the antenna radiation efficiency value measured by an external electronic device. For example, the mean absolute percentage error between the antenna radiation efficiency value identified using the trained model and the antenna radiation efficiency value measured by an external electronic device may be proportional to the absolute value of the difference between the antenna radiation efficiency value identified using the trained model and the antenna radiation efficiency value measured by an external electronic device. For example, the accuracy of the antenna radiation efficiency value identified using the trained model may be defined as 100 minus MAPE.

[0084] For example, the margin score of the radiation efficiency value of an antenna identified using a trained model can be defined according to Equation 2 below.

[0085]

[0086] The above mathematical formula 2 is merely an example to aid understanding, and embodiments of the present disclosure may not be limited thereto. For example, the above mathematical formula 2 may be modified, applied, or extended in various ways.

[0087] In Equation 2, M represents the margin score of the antenna radiation efficiency value identified using the trained model, n represents the number of antenna radiation efficiency values ​​(e.g., the number of antenna radiation efficiency values ​​identified using the trained model and / or the number of antenna radiation efficiency values ​​measured by an external electronic device), and Si represents the score of each of the antenna radiation efficiency values ​​identified using the trained model. For example, if the difference between the antenna radiation efficiency value identified using the trained model and the antenna radiation efficiency value measured by an external electronic device is less than 0.5 dB, Si may be 1; if the difference is greater than 0.5 and less than 1, Si may be 0.8; if the difference is greater than 1 and less than 2, Si may be 0.5; if the difference is greater than 2 and less than 3, Si may be 0.2; and if the difference is greater than 3, Si may be 0.

[0088] Referring to FIG. 9b, the chart (930) represents the margin score of the radiation efficiency data of the antenna identified using the trained model according to the accuracy of the radiation efficiency data of the antenna identified using the trained model. The horizontal axis (935) in the chart (930) indicates the accuracy of the radiation efficiency data of the antenna identified using the trained model, and the vertical axis (940) in the chart (930) indicates the margin score of the radiation efficiency data of the antenna identified using the trained model.

[0089] For example, the margin score of the radiation efficiency data of the antenna identified using the trained model, based on the accuracy of the radiation efficiency data of the antenna identified using the trained model, can be represented as a point (945) in the chart (930). For example, the greater the accuracy of the radiation efficiency data of the antenna identified using the trained model and the margin score of the radiation efficiency data of the antenna identified using the trained model, the smaller the difference between the radiation efficiency data of the antenna identified using the trained model and the radiation efficiency data of the antenna measured using an external electronic device. For example, the greater the accuracy of the radiation efficiency data of the antenna identified using the trained model and the margin score of the radiation efficiency data of the antenna identified using the trained model, the more reliable the radiation efficiency data of the antenna identified using the trained model can be.

[0090] For example, the greater the accuracy of the radiation efficiency data of the antenna identified using the trained model, the point (945) may be located relatively to the right within the chart (930). For example, the greater the margin score of the radiation efficiency data of the antenna identified using the trained model, the point (945) may be located relatively to the top within the chart (930). For example, points (945) representing relatively large accuracy and margin scores may be located within area (950) within the chart (930). For example, points (945) representing relatively small accuracy and margin scores may be located within area (955) within the chart (930).

[0091] For example, the radiation efficiency data of an antenna obtained using a trained model for a point (945) located within the area (950) is relatively reliable. For example, at least one processor (210) may replace the radiation efficiency data of an antenna obtained using the trained model with the radiation efficiency data of an external electronic device. For example, the radiation efficiency data of an antenna obtained using a trained model for a point (945) located within the area (955) may be relatively unreliable. For example, at least one processor (210) may guide the acquisition of the radiation efficiency data of an antenna using an external electronic device based on the point (945) located within the area (955), or guide the retraining of the trained model.

[0092] FIG. 10 is a block diagram of an electronic device in a network environment according to various embodiments.

[0093] Referring to FIG. 10, in a network environment (1000), an electronic device (1001) may communicate with an electronic device (1002) through a first network (1098) (e.g., a short-range wireless communication network) or with at least one of an electronic device (1004) or a server (1008) through a second network (1099) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (1001) may communicate with the electronic device (1004) through a server (1008). According to one embodiment, the electronic device (1001) may include a processor (1020), memory (1030), input module (1050), sound output module (1055), display module (1060), audio module (1070), sensor module (1076), interface (1077), connection terminal (1078), haptic module (1079), camera module (1080), power management module (1088), battery (1089), communication module (1090), subscriber identification module (1096), or antenna module (1097). In some embodiments, at least one of these components (e.g., connection terminal (1078)) may be omitted from the electronic device (1001), or one or more other components may be added. In some embodiments, some of these components (e.g., sensor module (1076), camera module (1080), or antenna module (1097)) may be integrated into a single component (e.g., display module (1060)).

[0094] The processor (1020) can, for example, execute software (e.g., program (1040)) to control at least one other component (e.g., hardware or software component) of the electronic device (1001) connected to the processor (1020) and can perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor (1020) can store commands or data received from other components (e.g., sensor module (1076) or communication module (1090)) in volatile memory (1032), process the commands or data stored in volatile memory (1032), and store the resulting data in non-volatile memory (1034). According to one embodiment, the processor (1020) may include a main processor (1021) (e.g., a central processing unit or an application processor) or an auxiliary processor (1023) that can operate independently or together with it (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device (1001) includes a main processor (1021) and an auxiliary processor (1023), the auxiliary processor (1023) may be configured to use lower power than the main processor (1021) or to be specialized for a specified function. The auxiliary processor (1023) may be implemented separately from the main processor (1021) or as part thereof.

[0095] The auxiliary processor (1023) may control at least some of the functions or states associated with at least one component of the electronic device (1001) (e.g., display module (1060), sensor module (1076), or communication module (1090)) on behalf of the main processor (1021) while the main processor (1021) is in an inactive (e.g., sleep) state, or together with the main processor (1021) while the main processor (1021) is in an active (e.g., application execution) state. According to one embodiment, the auxiliary processor (1023) (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module (1080) or communication module (1090)). According to one embodiment, the auxiliary processor (1023) (e.g., neural network processing unit) may include a hardware structure specialized for processing an artificial intelligence model. The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, on the electronic device (1001) itself where the artificial intelligence model is executed, or through a separate server (e.g., server (1008)). The learning algorithm may 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 may include a plurality of artificial neural network layers.An artificial neural network may be 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 the hardware structure, the artificial intelligence model may include a software structure, either additionally or substantially.

[0096] The memory (1030) can store various data used by at least one component of the electronic device (1001) (e.g., processor (1020) or sensor module (1076)). The data may include, for example, input data or output data for software (e.g., program (1040)) and related commands. The memory (1030) may include volatile memory (1032) or non-volatile memory (1034).

[0097] The program (1040) may be stored as software in memory (1030) and may include, for example, an operating system (1042), middleware (1044), or an application (1046).

[0098] The input module (1050) can receive commands or data to be used for a component of the electronic device (1001) (e.g., processor (1020)) from outside the electronic device (1001) (e.g., user). The input module (1050) may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0099] The sound output module (1055) can output a sound signal to the outside of the electronic device (1001). The sound output module (1055) may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as multimedia playback or recording playback. The receiver may be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part thereof.

[0100] The display module (1060) can visually provide information to an external (e.g., user) of the electronic device (1001). The display module (1060) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling said device. According to one embodiment, the display module (1060) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of the force generated by said touch.

[0101] The audio module (1070) can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module (1070) can acquire sound through the input module (1050) or output sound through the sound output module (1055) or an external electronic device (e.g., electronic device (1002)) (e.g., speaker or headphones) connected directly or wirelessly to the electronic device (1001).

[0102] The sensor module (1076) can detect the operating state of the electronic device (1001) (e.g., power or temperature) or the external environmental state (e.g., user state) and generate an electrical signal or data value corresponding to the detected state. According to one embodiment, the sensor module (1076) may include, for example, a gesture sensor, a gyroscope sensor, a barometric pressure sensor, a magnetic sensor, an accelerometer sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biosensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0103] The interface (1077) may support one or more specified protocols that can be used for the electronic device (1001) to be connected directly or wirelessly to an external electronic device (e.g., electronic device (1002)). According to one embodiment, the interface (1077) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0104] The connection terminal (1078) may include a connector through which the electronic device (1001) can be physically connected to an external electronic device (e.g., electronic device (1002)). According to one embodiment, the connection terminal (1078) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0105] The haptic module (1079) can convert an electrical signal into a mechanical stimulus (e.g., vibration or movement) or an electrical stimulus that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module (1079) may include, for example, a motor, a piezoelectric element, or an electric stimulation device.

[0106] The camera module (1080) can capture still images and video. According to one embodiment, the camera module (1080) may include one or more lenses, image sensors, image signal processors, or flashes.

[0107] The power management module (1088) can manage power supplied to the electronic device (1001). According to one embodiment, the power management module (1088) can be implemented, for example, as at least part of a power management integrated circuit (PMIC).

[0108] The battery (1089) can supply power to at least one component of the electronic device (1001). According to one embodiment, the battery (1089) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0109] The communication module (1090) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (1001) and an external electronic device (e.g., electronic device (1002), electronic device (1004), or server (1008)), and the performance of communication through the established communication channel. The communication module (1090) may include one or more communication processors that operate independently of the processor (1020) (e.g., application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (1090) may include a wireless communication module (1092) (e.g., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (1094) (e.g., LAN (local area network) communication module, or power line communication module). The corresponding communication module among these communication modules can communicate with an external electronic device (1004) through a first network (1098) (e.g., a short-range communication network such as Bluetooth, Wi-Fi (wireless fidelity) direct or IrDA (infrared data association)) or a second network (1099) (e.g., 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 may 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 (1092) can identify or authenticate the electronic device (1001) within a communication network such as the first network (1098) or the second network (1099) using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module (1096).

[0110] The wireless communication module (1092) can support 5G networks and next-generation communication technologies following 4G networks, for example, new radio access technology. NR access technology can support high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and connection of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low-latency communications (URLLC)). The wireless communication module (1092) can support a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate, for example. The wireless communication module (1092) can support various technologies for securing performance in the high-frequency band, such as beamforming, massive MIMO (multiple-input and multiple-output), full-dimensional MIMO (FD-MIMO), array antenna, analog beamforming, or large-scale antenna. The wireless communication module (1092) can support various requirements specified in the electronic device (1001), external electronic device (e.g., electronic device (1004)), or network system (e.g., second network (1099)). According to one embodiment, the wireless communication module (1092) can support a Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mMTC, or U-plane latency (e.g., downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) for realizing URLLC.

[0111] An antenna module (1097) can transmit a signal or power to or from an external source (e.g., an external electronic device). According to one embodiment, the antenna module (1097) may include an antenna comprising a radiator made of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). According to one embodiment, the antenna module (1097) 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 a first network (1098) or a second network (1099), may be selected from the plurality of antennas, for example, by a communication module (1090). A signal or power may be transmitted or received between the communication module (1090) and an external electronic device through the selected at least one antenna. According to some embodiments, in addition to the radiator, other components (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module (1097).

[0112] According to various embodiments, the antenna module (1097) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first surface (e.g., bottom surface) of the printed circuit board and capable of supporting a specified high frequency band (e.g., mmWave band), and a plurality of antennas (e.g., array antennas) disposed on or adjacent to a second surface (e.g., top surface or side surface) of the printed circuit board and capable of transmitting or receiving a signal of the specified high frequency band.

[0113] At least some of the above components can be connected to each other via a communication method between peripheral devices (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)) and exchange signals (e.g., commands or data) with each other.

[0114] According to one embodiment, commands or data may be transmitted or received between the electronic device (1001) and an external electronic device (1004) through a server (1008) connected to a second network (1099). Each of the external electronic devices (1002, or 1004) may be the same or a different type of device as the electronic device (1001). According to one embodiment, all or part of the operations performed on the electronic device (1001) may be performed on one or more of the external electronic devices (1002, 1004, or 1008). For example, if the electronic device (1001) needs to perform a function or service automatically or in response to a request from a user or another device, the electronic device (1001) may request one or more external electronic devices to perform at least part of the function or service instead of performing the function or service itself or additionally. One or more external electronic devices that receive the above request may execute at least part of the requested function or service, or additional function or service related to the request, and transmit the result of the execution to the electronic device (1001). The electronic device (1001) may provide the result as is or additionally processed as at least part of the response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device (1001) may provide ultra-low latency services using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (1004) may include an Internet of Things (IoT) device. The server (1008) may be an intelligent server using machine learning and / or neural networks.According to one embodiment, an external electronic device (1004) or server (1008) may be included within the second network (1099). The electronic device (1001) may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0115] The electronic device according to the various embodiments disclosed in this document may be of various forms. The electronic device may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a consumer electronics device. The electronic device according to the embodiments of this document is not limited to the devices described above.

[0116] The various embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various modifications, equivalents, or substitutions of said 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 said items unless the relevant context clearly indicates otherwise. In this document, phrases such as "A or B," "at least one of A and B," "at least one of A or B," "A, B or C," "at least one of A, B and C," and "at least one of A, B, or C" may each include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used simply to distinguish said components from other said components and do not limit said components in any other aspect (e.g., importance or order). Where any (e.g., 1st) component is referred to as "coupled" or "connected" to another (e.g., 2nd) component, with or without the terms "functionally" or "communicationly," it means that said any component may be connected to said other component directly (e.g., via a wire), wirelessly, or through a third component.

[0117] The term “module” as used in the 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, for example. A module may be a component formed integrally, or a minimum unit of said component or a part thereof 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).

[0118] Various embodiments of the present document may be implemented as software (e.g., program (1040)) comprising one or more instructions stored in a storage medium (e.g., internal memory (1036) or external memory (1038)) readable by a machine (e.g., electronic device (1001)). For example, a processor (e.g., processor (1020)) of the machine (e.g., electronic device (1001)) may call at least one of the one or more instructions stored from the storage medium and execute it. This enables the machine to be operated 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 that can be executed by an interpreter. The storage medium readable by the machine may be provided in the form of a non-transitory storage medium. Here, 'non-transient' simply means that the storage medium is a tangible device and does not contain a signal (e.g., electromagnetic waves), and this term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily.

[0119] According to one embodiment, the method according to the various embodiments disclosed herein may be provided by being included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0120] According to various embodiments, each component (e.g., module or program) of the components described above may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, one or more of the components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as those performed by the corresponding component among the multiple components prior to integration. According to various embodiments, operations performed by the module, program, or other components 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.

[0121] The technical problems to be solved in this disclosure are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this disclosure pertains.

[0122] FIG. 11 illustrates an example of a generative artificial intelligence system according to one embodiment.

[0123] Referring to FIG. 11, the AI ​​system (1100) may include an input / output interface (1110), an AI framework (1120), a generative AI model (1130), and / or a knowledge repository (1190).

[0124] The input / output interface (1110) can receive input. The input may include user input and / or data obtained or generated by an electronic device (e.g., the electronic device (200) or electronic device (1001) described above). The above data may include images, videos, and / or sensor data generated by at least one processor of the electronic device (e.g., at least one processor (210) or processor (1020)) (e.g., illumination data around the electronic device obtained from a sensor or sensor hub (e.g., auxiliary processor (1023), posture data (or orientation data) of the electronic device, temperature inside the electronic device (e.g., temperature of at least one processor (210)), and / or images obtained through an image sensor of the electronic device (e.g., included in a camera module (1080)). The user input may include natural language, touch data obtained through a touch circuit included in the display panel (e.g., used to identify input from a finger and / or stylus), images displayed (and / or to be displayed) on the display panel, and / or videos. As an example, but not limited to, the user input may be received by an input / output interface (1110) along with context information. The context information is additional information obtained in relation to the user input. It may be described. The above situation information may relate to the state at the time the user input is received (e.g., the state of the electronic device and / or the state of the surroundings of the electronic device (e.g., the user state)). For example, the above situation information may include information about one or more software applications executed within the electronic device at the time the user input is received. For example, the above situation information may include information about the location of the electronic device (or the location of the user of the electronic device) at the time the user input is received.For example, the user input can be integrated with the situation information. For example, the user input with the situation information integrated into it can be received by the input / output interface (1110).

[0125] The input / output interface (1110) may transmit (or provide) an output. The output may include a result (or result information) generated or obtained by the AI ​​system (1100) based on at least part of the input. The format of the output may vary. For example, the output may include natural language. For example, the output may include content (e.g., media content and / or multimedia content). For example, the output may include an action related to the user of the electronic device. For example, the output may have a format according to the user settings of the electronic device.

[0126] The AI ​​framework (1120) can be used to obtain information (or data) about the input from the input / output interface (1110) and to control one or more components related to the AI ​​system (1100) using the obtained information.

[0127] For example, a prompt design component (1121) within an AI framework (1120) can generate or obtain prompts for a generative AI model (1130) (e.g., including a large language model (LLM) or a large multimodal model (LMM)) using the acquired information. For example, the prompt design component (1121) may be described as an AI component that uses a learning algorithm and / or a neural network to provide prompts that are enhanced over time. For example, the prompt design component (1121) can generate or obtain prompts by accessing a knowledge component (e.g., a knowledge repository (1190)) containing user preference data, a prompt library, and / or prompt examples using the acquired information. The generated prompts may be provided to the generative AI model (1130) (e.g., including an LLM or LMM).

[0128] For example, an API / plugin management component (1122) within the AI ​​framework (1120) may be used to support communication for additional information requested (or induced) in relation to the prompt provided (or to be provided) to the generative AI model (1130). For example, the API / plugin management component (1122) may be used to create or establish a channel for communication with various data sources (e.g., knowledge repository (1190)). For example, the API / plugin management component (1122) may support access to at least some of the data sources. For example, the API / plugin management component (1122) may be used to request another component (e.g., application / service component (1180)) that performs feedback (or response) according to the prompt. As a non-limiting example, information obtained (or generated) through the API / plugin management component (1122) may be provided to the prompt design component (1121) for generating a prompt. As a non-limiting example, information obtained (or generated) through the API / plugin management component (1122) may be provided to the generative AI model (1130).

[0129] For example, an improvement component (1123) within the AI ​​framework (1120) can at least partially tune (or adjust) (or change) the result (e.g., content) obtained (or output) from the generative AI model (1130). For example, the improvement component (1123) can determine or verify whether the content obtained from the generative AI model (1130) is related to the input. For example, the improvement component (1123) can determine or verify whether the content obtained from the generative AI model (1130) contains biased content. For example, the improvement component (1123) can determine or verify whether the content obtained from the generative AI model (1130) contains harmful content. For example, the improvement component (1123) can support or assist in performing additional processing to improve the content obtained from the generative AI model (1130). For example, the improvement component (1123) may support providing a hint to the user to improve the content.

[0130] A generative AI model (1130) can be described as an artificial intelligence neural network that generates feedback in response to a prompt. For example, the feedback may include additional data and / or information relative to the prompt, but relative to the prompt. For example, the feedback may include new content relative to the prompt. For example, the generative AI model (1130) may include a model that generates images and / or a model that generates language. For example, the model that generates images may include a generative adversarial network (GAN) and / or a variational autoencoder (VAE). For example, the model that generates images may include a diffusion-based generative model (e.g., a transformer VAE). For example, the model that generates language may include CHAT-GPT 3 and / or CHAT-GPT 4. For example, the generative AI model (1130) may include an LMM that generates the feedback by recognizing text, images, and / or speech.

[0131] As an example without limitation, the AI ​​framework (1120) and / or generative AI model (1130) may be included within an AI module (e.g., including a processing circuit) within the electronic device (200). For example, the AI ​​module may be operatively coupled with at least one processor of the electronic device (200) (e.g., at least one processor (210) or processor (1020)). For example, the AI ​​module may be operatively coupled with a display driving circuit of the electronic device. For example, the AI ​​module may be operatively coupled with a sensor hub of the electronic device for one or more sensors within the electronic device.

[0132] The technical problems to be solved in this disclosure are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this disclosure pertains.

[0133] The above method as described above may be performed within an electronic device (e.g., the electronic device (200) of FIG. 2). The method may include the operation of acquiring data including reflection coefficient values ​​of an antenna defined by frequency. The method may include the operation of dividing the data into tokens (e.g., tokens (430) of FIG. 4) for frequency intervals (e.g., frequency intervals (420) of FIG. 4) having the same size and different frequency ranges. Each of the tokens may include reflection coefficient values ​​for each of the frequency intervals. The method may include the operation of providing the tokens to a trained model. The method may include the operation of acquiring an image (e.g., image (800) of FIG. 8) generated according to the tokens using the trained model. The method may include the operation of identifying radiation efficiency data of the antenna using the image.

[0134] For example, the above image may be defined as a plurality of parts including a first part and a second part. The first part of the image may indicate a first frequency and a first radiation efficiency value of the antenna on the first frequency. The second part of the image may indicate a second frequency different from the first frequency and a second radiation efficiency value of the antenna on the second frequency.

[0135] In the above image, the location of the first part of the image may indicate the first frequency. In the above image, the location of the second part of the image may indicate the second frequency. The pixel value of the first part of the image may indicate the first radiation efficiency value of the antenna. The pixel value of the second part of the image may indicate the second radiation efficiency value of the antenna.

[0136] For example, the radiation efficiency data of the antenna may include radiation efficiency values ​​of the antenna defined by frequency.

[0137] For example, the reflection coefficient values ​​of the antenna included in the above data may be defined by frequency within a first frequency range. The radiation efficiency values ​​of the antenna included in the radiation efficiency data may be defined by frequency within a second frequency range corresponding to the first frequency range.

[0138] For example, the above method may include the operation of obtaining a chart that expresses the radiation efficiency values ​​of the antenna included in the radiation efficiency data of the antenna according to frequency, based on identifying the radiation efficiency data of the antenna using the image.

[0139] For example, the above method may include an operation of acquiring other data including reflection coefficient values ​​of another antenna defined by frequency. The above method may include an operation of dividing the other data into other tokens for the frequency intervals. Each of the other tokens may include the reflection coefficient values ​​of the other antenna for each of the frequency intervals. The above method may include an operation of acquiring other radiation efficiency data including radiation efficiency values ​​of the other antenna defined by frequency. The above method may include an operation of acquiring another image using the other radiation efficiency data. The above method may include an operation of training the trained model by providing the other tokens and the other image to the trained model.

[0140] For example, the other image may be defined as a plurality of parts including a first part and a second part. The first part of the other image may indicate a first frequency and a first radiation efficiency value of the other antenna on the first frequency. The second part of the other image may indicate a second frequency different from the first frequency and a second radiation efficiency value of the other antenna on the second frequency.

[0141] For example, the location of the first part of the other image within the other image may indicate the first frequency. The location of the second part of the other image within the other image may indicate the second frequency. The pixel value of the first part of the other image may indicate the first radiation efficiency value of the other antenna. The pixel value of the second part of the other image may indicate the second radiation efficiency value of the other antenna.

[0142] For example, the other images mentioned above may be provided to the trained model in pairs with the other tokens mentioned above.

[0143] For example, the type of the first external electronic device including the antenna may correspond to the type of the second external electronic device including the other antenna.

[0144] The reflection coefficient values ​​of the other antenna included in the other data can be expressed on a log-scale. The radiation efficiency values ​​of the other antenna included in the other radiation efficiency data can be expressed on a log-scale.

[0145] The above-described non-transient computer-readable storage medium may store one or more programs. The one or more programs may include instructions that cause the electronic device to acquire data containing reflection coefficient values ​​of an antenna defined by frequency when executed by the electronic device. The one or more programs may include instructions that cause the electronic device to divide the data into tokens for frequency intervals having different frequency ranges and having the same size when executed by the electronic device. Each of the tokens may include reflection coefficient values ​​for each of the frequency intervals. The one or more programs may include instructions that cause the electronic device to provide the tokens to a trained model when executed by the electronic device. The one or more programs may include instructions that cause the electronic device to acquire an image generated according to the tokens using the trained model when executed by the electronic device. The above one or more programs may include instructions that cause the electronic device to identify radiation efficiency data of the antenna using the image when executed by the electronic device.

[0146] For example, the above image may be defined as a plurality of parts including a first part and a second part. The first part of the image may indicate a first frequency and a first radiation efficiency value of the antenna on the first frequency. The second part of the image may indicate a second frequency different from the first frequency and a second radiation efficiency value of the antenna on the second frequency.

[0147] In the above image, the location of the first part of the image may indicate the first frequency. In the above image, the location of the second part of the image may indicate the second frequency. The pixel value of the first part of the image may indicate the first radiation efficiency value of the antenna. The pixel value of the second part of the image may indicate the second radiation efficiency value of the antenna.

[0148] For example, the radiation efficiency data of the antenna may include radiation efficiency values ​​of the antenna defined by frequency.

[0149] For example, the reflection coefficient values ​​of the antenna included in the above data may be defined by frequency within a first frequency range. The radiation efficiency values ​​of the antenna included in the radiation efficiency data may be defined by frequency within a second frequency range corresponding to the first frequency range.

[0150] For example, the above one or more programs may include instructions that cause the electronic device to obtain a chart representing the radiation efficiency values ​​of the antenna included in the radiation efficiency data of the antenna according to frequency, based on identifying the radiation efficiency data of the antenna using the image when executed by the electronic device.

[0151] For example, the one or more programs may include instructions that cause the electronic device to obtain other data, including reflection coefficient values ​​of another antenna defined by frequency, when executed by the electronic device. The one or more programs may include instructions that cause the electronic device to divide the other data into other tokens for the frequency intervals when executed by the electronic device. Each of the other tokens may include the reflection coefficient values ​​of the other antenna for each of the frequency intervals. The one or more programs may include instructions that cause the electronic device to obtain other radiation efficiency data, including radiation efficiency values ​​of the other antenna defined by frequency, when executed by the electronic device. The one or more programs may include instructions that cause the electronic device to obtain another image using the other radiation efficiency data when executed by the electronic device. The above one or more programs may include instructions that cause the electronic device to train the trained model by providing the other tokens and the other images to the trained model when executed by the electronic device.

[0152] For example, the other image may be defined as a plurality of parts including a first part and a second part. The first part of the other image may indicate a first frequency and a first radiation efficiency value of the other antenna on the first frequency. The second part of the other image may indicate a second frequency different from the first frequency and a second radiation efficiency value of the other antenna on the second frequency.

[0153] For example, the location of the first part of the other image within the other image may indicate the first frequency. The location of the second part of the other image within the other image may indicate the second frequency. The pixel value of the first part of the other image may indicate the first radiation efficiency value of the other antenna. The pixel value of the second part of the other image may indicate the second radiation efficiency value of the other antenna.

[0154] For example, the other images mentioned above may be provided to the trained model in pairs with the other tokens mentioned above.

[0155] For example, the type of the first external electronic device including the antenna may correspond to the type of the second external electronic device including the other antenna.

[0156] The reflection coefficient values ​​of the other antenna included in the other data can be expressed on a log-scale. The radiation efficiency values ​​of the other antenna included in the other radiation efficiency data can be expressed on a log-scale.

[0157] The electronic device described above may include at least one processor comprising a processing circuit and a memory comprising one or more storage media for storing one or more programs configured to be executed individually or collectively by the at least one processor. The instructions may cause the electronic device to acquire data including reflection coefficient values ​​of an antenna defined by frequency when executed individually or collectively by the at least one processor. The instructions may cause the electronic device to divide the data into tokens for frequency intervals having different frequency ranges and having the same size when executed individually or collectively by the at least one processor. Each of the tokens may include reflection coefficient values ​​for each of the frequency intervals. The instructions may cause the electronic device to provide the tokens to a trained model when executed individually or collectively by the at least one processor. The above instructions may cause the electronic device to acquire an image generated according to the tokens using the trained model when executed individually or collectively by the at least one processor. The above instructions may cause the electronic device to identify radiation efficiency data of the antenna using the image when executed individually or collectively by the at least one processor.

[0158] For example, the above image may be defined as a plurality of parts including a first part and a second part. The first part of the image may indicate a first frequency and a first radiation efficiency value of the antenna on the first frequency. The second part of the image may indicate a second frequency different from the first frequency and a second radiation efficiency value of the antenna on the second frequency.

[0159] In the above image, the location of the first part of the image may indicate the first frequency. In the above image, the location of the second part of the image may indicate the second frequency. The pixel value of the first part of the image may indicate the first radiation efficiency value of the antenna. The pixel value of the second part of the image may indicate the second radiation efficiency value of the antenna.

[0160] For example, the radiation efficiency data of the antenna may include radiation efficiency values ​​of the antenna defined by frequency.

[0161] For example, the reflection coefficient values ​​of the antenna included in the above data may be defined by frequency within a first frequency range. The radiation efficiency values ​​of the antenna included in the radiation efficiency data may be defined by frequency within a second frequency range corresponding to the first frequency range.

[0162] For example, when the above instructions are executed individually or collectively by the at least one processor, the electronic device may obtain a chart that represents the radiation efficiency values ​​of the antenna included in the radiation efficiency data of the antenna according to frequency, based on identifying the radiation efficiency data of the antenna using the image.

[0163] For example, the instructions may cause the electronic device to acquire other data, including reflection coefficient values ​​of another antenna defined by frequency, when executed individually or collectively by the at least one processor. The instructions may cause the electronic device to divide the other data into other tokens for the frequency intervals when executed individually or collectively by the at least one processor. Each of the other tokens may include the reflection coefficient values ​​of the other antenna for each of the frequency intervals. The instructions may cause the electronic device to acquire other radiation efficiency data, including radiation efficiency values ​​of the other antenna defined by frequency, when executed individually or collectively by the at least one processor. The instructions may cause the electronic device to acquire another image using the other radiation efficiency data when executed individually or collectively by the at least one processor. The above instructions may cause the electronic device to train the trained model by providing the other tokens and the other images to the trained model when executed individually or collectively by the at least one processor.

[0164] For example, the other image may be defined as a plurality of parts including a first part and a second part. The first part of the other image may indicate a first frequency and a first radiation efficiency value of the other antenna on the first frequency. The second part of the other image may indicate a second frequency different from the first frequency and a second radiation efficiency value of the other antenna on the second frequency.

[0165] For example, the location of the first part of the other image within the other image may indicate the first frequency. The location of the second part of the other image within the other image may indicate the second frequency. The pixel value of the first part of the other image may indicate the first radiation efficiency value of the other antenna. The pixel value of the second part of the other image may indicate the second radiation efficiency value of the other antenna.

[0166] For example, the other images mentioned above may be provided to the trained model in pairs with the other tokens mentioned above.

[0167] For example, the type of the first external electronic device including the antenna may correspond to the type of the second external electronic device including the other antenna.

[0168] The reflection coefficient values ​​of the other antenna included in the other data can be expressed on a log-scale. The radiation efficiency values ​​of the other antenna included in the other radiation efficiency data can be expressed on a log-scale.

[0169] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs.

Claims

1. A method executed within an electronic device, wherein the method comprises: An operation to acquire data including reflection coefficient values ​​of an antenna defined by frequency; The operation of dividing the above data into tokens for frequency intervals having the same size and different frequency ranges, wherein each of the tokens includes reflection coefficient values ​​for each of the frequency intervals; The action of providing the above tokens to a trained model; The operation of acquiring an image generated according to the tokens using the above-mentioned trained model; and A method including identifying radiation efficiency data of the antenna using the above image, method.

2. In Claim 1, The image above is, Defined as a plurality of parts including a first part and a second part, and The first part of the above image is, Indicating a first frequency and a first radiation efficiency value of the antenna on the first frequency, and The second part of the above image is, Indicating a second frequency different from the first frequency and a second radiation efficiency value of the antenna on the second frequency, method.

3. In Claim 2, The location of the first part of the image within the image above is, Indicating the above first frequency, The location of the second part of the image within the image above is, Indicating the above second frequency, The pixel value of the first part of the above image is, Indicating the first radiation efficiency value of the above antenna, and The pixel value of the second part of the above image is, Indicating the second radiation efficiency value of the above antenna, method.

4. In Claim 1, The radiation efficiency data of the above antenna is, Includes radiation efficiency values ​​of the antenna defined by frequency, method.

5. In Claim 4, The reflection coefficient values ​​of the antenna included in the above data are, Defined by frequency within the first frequency range, and The radiation efficiency values ​​of the antenna included in the radiation efficiency data of the antenna are, Defined by frequency within a second frequency range corresponding to the first frequency range above, method.

6. In claim 1, the method comprises: Based on identifying the radiation efficiency data of the antenna using the above image, the operation of obtaining a chart that expresses the radiation efficiency values ​​of the antenna included in the radiation efficiency data of the antenna according to frequency, method.

7. In claim 1, the method comprises: An operation to acquire other data including reflection coefficient values ​​of other antennas defined by frequency; The operation of dividing the above other data into different tokens for the above frequency intervals, each of the above different tokens including reflection coefficient values ​​of the above different antenna for each of the above frequency intervals; An operation to acquire other radiation efficiency data including radiation efficiency values ​​of the other antenna defined by frequency; The operation of acquiring another image using the above-mentioned other radiation efficiency data; and The operation of training the trained model by providing the other tokens and the other images to the trained model, method.

8. In Claim 7, The other image above is, Defined as a plurality of parts including a first part and a second part, and The first part of the above other image is, Indicating a first frequency and a first radiation efficiency value of the other antenna on the first frequency, and The second part of the above other image is, Indicating a second frequency different from the first frequency and a second radiation efficiency value of the other antenna on the second frequency, method.

9. In Claim 8, The location of the first part of the other image within the other image is, Indicating the above first frequency, The location of the second part of the other image within the other image is, Indicating the above second frequency, The pixel value of the first part of the above other image is, Indicating the first radiation efficiency value of the above other antenna, and The pixel value of the second part of the above other image is, Indicating the second radiation efficiency value of the above other antenna, method.

10. In Claim 7, The other image above is, Provided to the trained model in pairs with the other tokens mentioned above, method.

11. In Claim 7, The type of first external electronic device including the above antenna is, Corresponding to the type of second external electronic device including the above other antenna, method.

12. In Claim 7, The reflection coefficient values ​​of the other antenna included in the other data above are, Expressed on a log-scale, and The radiation efficiency values ​​of the other antenna included in the other radiation efficiency data above are, Expressed on a log-scale, method.

13. In a non-transient computer-readable storage medium storing one or more programs, When one or more of the above programs are executed by an electronic device: Acquire data including reflection coefficient values ​​of an antenna defined by frequency; The above data is divided into tokens for frequency intervals having the same size and different frequency ranges, and each of the tokens includes reflection coefficient values ​​for each of the frequency intervals; Provide the above tokens to the trained model; Using the above-mentioned trained model, obtain an image generated according to the above-mentioned tokens; and To identify radiation efficiency data of the antenna using the above image, Including instructions that cause the above electronic device, Non-transient computer-readable storage media.

14. In Claim 13, The image above is, Defined as a plurality of parts including a first part and a second part, and The first part of the above image is, Indicating a first frequency and a first radiation efficiency value of the antenna on the first frequency, and The second part of the above image is, Indicating a second frequency different from the first frequency and a second radiation efficiency value of the antenna on the second frequency, Non-transient computer-readable storage media.

15. In electronic devices, Memory that stores instructions and includes one or more storage media; and It includes at least one processor comprising a processing circuit, and When the above instructions are executed individually or collectively by the at least one processor: Acquire data including reflection coefficient values ​​of an antenna defined by frequency; The above data is divided into tokens for frequency intervals having the same size and different frequency ranges, and each of the tokens includes reflection coefficient values ​​for each of the frequency intervals; Provide the above tokens to the trained model; Using the above-mentioned trained model, obtain an image generated according to the above-mentioned tokens; and To identify radiation efficiency data of the antenna using the above image, causing the above electronic device, Electronic device.

Images