Electronic device and method for predicting wireless channel characteristics

Abstract

This electronic device may comprise a communication circuit, a processor, and a memory for storing instructions. The instructions may cause, when executed by the processor, the electronic device to: receive reference signals; on the basis of the reference signals, generate at least two latent representation sequences representing at least two different channel characteristics, wherein each latent representation sequence among the at least two latent representation sequences includes a plurality of latent representation indexes representing a corresponding channel characteristic among the at least two different channel characteristics; and through a latent transformer configured to acquire a correlation between the at least two latent representation sequences, output first prediction data representing future channel characteristics corresponding to a first type channel characteristic and second prediction data representing future channel characteristics corresponding to a second type channel characteristic.

Description

Electronic device and method for predicting wireless channel characteristics

[0001] The present disclosure relates to an electronic device and method for predicting wireless channel characteristics.

[0002] Fifth-generation (5G) mobile communication technology defines wide frequency bands to enable fast transmission speeds and new services. 5G technology can be implemented not only in sub-6GHz frequency bands such as 3.5 gigahertz (3.5GHz) but also in ultra-high frequency bands known as millimeter waves (mmWave), such as 28GHz and 39GHz, such as 'above 6GHz'. Furthermore, for 6G mobile communication technology, which is referred to as a system beyond 5G, implementation in terahertz bands (e.g., the 3 terahertz (3THz) band at 95GHz) is being considered to achieve transmission speeds 50 times faster and ultra-low latency reduced to one-tenth compared to 5G mobile communication technology.

[0003] In the early stages of 5G mobile communication technology, standardization for beamforming and massive multiple-input multiple-output (MIMO) was carried out with the goal of supporting services and satisfying performance requirements for enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLC), and massive machine-type communications (mMTC). The above process includes mitigating path loss of radio waves in the ultra-high frequency band and increasing the transmission distance of radio waves, supporting various numerologies for efficient utilization of ultra-high frequency resources (e.g., operation of multiple subcarrier spacings) and dynamic operation of slot formats, initial access technology to support multi-beam transmission and broadband, definition and operation of BWP (band-width part), new channel coding methods such as LDPC (low density parity check) codes for high-volume data transmission and polar codes for reliable transmission of control information, L2 pre-processing, or network slicing to provide a dedicated network specialized for a specific service.

[0004] Various technologies utilizing artificial intelligence (AI) in wireless communication systems using next-generation wireless communication technology are being researched. The accuracy of AI technology based on deep learning is improving dramatically, and research on applying AI technology in various fields is very actively underway.

[0005] Since the background technology described above is technical information held by the inventor or acquired during the process of deriving the present disclosure, it cannot necessarily be considered as prior art disclosed prior to the filing of the present disclosure.

[0006] Embodiments of the present disclosure may provide an electronic device and method for predicting wireless channel characteristics using artificial intelligence (AI).

[0007] Embodiments of the present disclosure may provide an electronic device and method for predicting future channel characteristics based on correlations between wireless channel characteristics.

[0008] An electronic device according to one embodiment of the present disclosure may include a communication circuit, at least one processor, and a memory for storing instructions. When the instructions are executed individually or collectively by the at least one processor, the electronic device may be configured to receive reference signals through the communication circuit and, based on the reference signals, generate at least two latent representation sequences representing at least two different channel characteristics, wherein each of the at least two latent representation sequences includes a plurality of latent representation indices representing the corresponding channel characteristics among the at least two different channel characteristics, and output first prediction data representing future channel characteristics corresponding to a first type of channel characteristic and second prediction data representing future channel characteristics corresponding to a second type of channel characteristic through a latent transformer configured to obtain relationships between the at least two latent representation sequences.

[0009] A method performed by an electronic device according to one embodiment of the present disclosure may include: receiving reference signals through a wireless channel; generating at least two latent representation sequences representing at least two different channel characteristics based on the received reference signals, wherein each of the at least two latent representation sequences includes a plurality of latent representation indices representing the corresponding channel characteristics among the at least two different channel characteristics; and outputting first prediction data representing future channel characteristics corresponding to a first type of channel characteristic and second prediction data representing future channel characteristics corresponding to a second type of channel characteristic through a latent transformer configured to obtain relationships between the at least two latent representation sequences.

[0010] The scope of the present disclosure is not limited to the matters mentioned above, and other aspects not mentioned above will be clearly understood by those skilled in the art from the description below.

[0011] The above and other aspects, features, and advantages according to specific embodiments of the present disclosure will become more apparent from the following description with reference to the accompanying drawings.

[0012] FIG. 1 is a block diagram of an electronic device in a network environment according to various embodiments.

[0013] FIG. 2 is a drawing showing a wireless communication system according to one embodiment of the present disclosure.

[0014] FIG. 3 is a diagram showing a channel characteristic measurement according to one embodiment of the present disclosure.

[0015] FIG. 4 is a sequence diagram illustrating a procedure for channel estimation according to one embodiment of the present disclosure.

[0016] FIG. 5 is a flowchart illustrating a procedure for predicting channel characteristics according to one embodiment of the present disclosure.

[0017] FIG. 6 is a diagram showing the configuration of a latency transformer model for channel prediction according to one embodiment of the present disclosure.

[0018] FIG. 7 shows the configuration of transformer input sequences according to one embodiment of the present disclosure.

[0019] FIG. 8 shows an example of values ​​of transformer input sequences according to one embodiment of the present disclosure.

[0020] FIG. 9 is a drawing showing an AI model including a latency transformer according to one embodiment of the present disclosure.

[0021] FIG. 10 is a diagram showing the encoder architecture of a latency transformer according to one embodiment of the present disclosure.

[0022] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In describing the embodiments of the present disclosure, specific descriptions of related known functions or configurations are omitted if it is determined that such detailed descriptions would unnecessarily obscure the essence of the present disclosure. Furthermore, terms used below are defined considering their functions in the embodiments of the present disclosure, and these may vary depending on the intent or practice of the user or operator. Therefore, such definitions should be based on the content throughout the present disclosure.

[0023] It should be noted that technical terms used in this disclosure are used merely to describe one embodiment and are not intended to limit this disclosure. Alternatively, unless specifically defined otherwise in this disclosure, technical terms used in this disclosure shall be interpreted in the sense generally understood by those skilled in the art to which this disclosure pertains, and shall not be interpreted in an overly broad or overly narrow sense. Alternatively, technical terms used in this disclosure may be understood as being replaced by other technical terms understood by those skilled in the art. General terms used in the embodiments of this disclosure shall be interpreted according to their prior definitions or according to the context, and shall not be interpreted in an overly narrow sense.

[0024] Singular expressions used in this disclosure may include plural expressions unless the context clearly indicates otherwise. In this disclosure, terms such as “composed” or “comprising” should not be interpreted as necessarily including all of the various components or operations described in the specification, and should be interpreted as some of the components or operations may not be included, or additional components or operations may be included.

[0025] Terms including ordinal numbers, such as first or second as used in this disclosure, may be used to describe various components, but said components should not be limited by said terms. Such terms may be used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the rights of this disclosure, the first component may be named the second component, and similarly, the second component may be named the first component.

[0026] When it is stated that one component is "connected" or "connected" to another component, it may be directly connected or connected to that other component, or there may be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

[0027] Hereinafter, embodiments according to the present disclosure will be described with reference to the attached drawings. Identical or similar components regardless of drawing symbols are given the same reference numeral, and redundant descriptions thereof will be omitted. In describing the embodiments of the present disclosure, detailed descriptions of related prior art are omitted if it is determined that such detailed descriptions may obscure the essence of the present disclosure. It should be noted that the attached drawings are intended only to facilitate understanding of the embodiments of the present disclosure and should not be interpreted as limiting the present disclosure. The present disclosure should be interpreted as extending to all modifications, equivalents, and substitutions in addition to the attached drawings.

[0028] In this disclosure, embodiments will be described using an electronic device as an example, but the electronic device may be referred to as a terminal, mobile station, mobile equipment (ME), user equipment (UE), user terminal (UT), subscriber station (SS), wireless device, handheld device, or access terminal (AT). In the embodiments of this disclosure, the electronic device may be a device equipped with communication functions, such as a mobile phone, personal digital assistant (PDA), smartphone, wireless modem, or laptop.

[0029] FIG. 1 is a block diagram of an electronic device (101) in a network environment (100) according to various embodiments.

[0030] Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) through a first network (198) (e.g., a short-range wireless communication network) or with an electronic device (104) or a server (108) through a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) through a server (108). According to one embodiment, the electronic device (101) may include a processor (120), memory (130), input module (150), sound output module (155), display module (160), audio module (170), sensor module (176), interface (177), connection terminal (178), haptic module (179), camera module (180), power management module (188), battery (189), communication module (190), subscriber identification module (196), or antenna module (197). In some embodiments, at least one of these components (e.g., connection terminal (178)) may be omitted from the electronic device (101), or one or more other components may be added. In some embodiments, some of these components (e.g., sensor module (176), camera module (180), or antenna module (197)) may be integrated into a single component (e.g., display module (160)).

[0031] The processor (120) can control at least one other component (e.g., hardware or software component) of the electronic device (101) connected to the processor (120) by executing software (e.g., program (140)), and can perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor (120) can store commands or data received from other components (e.g., sensor module (176) or communication module (190)) in volatile memory (132), process the commands or data stored in volatile memory (132), and store the resulting data in non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., central processing unit or application processor) or an auxiliary processor (123) that can operate independently or together with it (e.g., graphics processing unit, neural processing unit (NPU), image signal processor, sensor hub processor, or communication processor). For example, if the electronic device (101) includes a main processor (121) and an auxiliary processor (123), the auxiliary processor (123) may be configured to use lower power than the main processor (121) or to be specialized for a designated function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as part thereof.

[0032] The auxiliary processor (123) may control at least some of the functions or states associated with at least one component of the electronic device (101) (e.g., display module (160), sensor module (176), or communication module (190)) on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. According to one embodiment, the auxiliary processor (123) (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module (180) or communication module (190)). According to one embodiment, the auxiliary processor (123) (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 (101) itself where the artificial intelligence is performed, or through a separate server (e.g., server (108)). 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.

[0033] The memory (130) can store various data used by at least one component of the electronic device (101) (e.g., processor (120) or sensor module (176)). The data may include, for example, input data or output data for software (e.g., program (140)) and related commands. The memory (130) may include volatile memory (132) or non-volatile memory (134).

[0034] The program (140) may be stored as software in memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0035] The input module (150) can receive commands or data to be used for a component of the electronic device (101) (e.g., processor (120)) from outside the electronic device (101) (e.g., user). The input module (150) 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).

[0036] The sound output module (155) can output a sound signal to the outside of the electronic device (101). The sound output module (155) 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.

[0037] The display module (160) can visually provide information to an external (e.g., user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling said device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of the force generated by said touch.

[0038] The audio module (170) can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150) or output sound through the sound output module (155) or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphones) connected directly or wirelessly to the electronic device (101).

[0039] The sensor module (176) can detect the operating state of the electronic device (101) (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 (176) 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.

[0040] The interface (177) may support one or more specified protocols that can be used for the electronic device (101) to be connected directly or wirelessly to an external electronic device (e.g., electronic device (102)). According to one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0041] The connection terminal (178) may include a connector through which the electronic device (101) can be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0042] The haptic module (179) 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 (179) may include, for example, a motor, a piezoelectric element, or an electric stimulation device.

[0043] The camera module (180) can capture still images and video. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0044] The power management module (188) can manage the power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented, for example, as at least part of a power management integrated circuit (PMIC).

[0045] The battery (189) can supply power to at least one component of the electronic device (101). According to one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0046] The communication module (190) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may include one or more communication processors that operate independently of the processor (120) (e.g., application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (194) (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 (104) through a first network (198) (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (199) (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 (192) can identify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module (196).

[0047] The wireless communication module (192) 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 (192) can support a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate, for example. The wireless communication module (192) 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 beam-forming, or large-scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), external electronic device (e.g., electronic device (104)), or network system (e.g., second network (199)). According to one embodiment, the wireless communication module (192) can support a Peak data rate (e.g., 20 Gbps or more) for 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.

[0048] An antenna module (197) 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 (197) 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 (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as a first network (198) or a second network (199), may be selected from the plurality of antennas, for example, by a communication module (190). A signal or power may be transmitted or received between the communication module (190) 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 (197).

[0049] According to various embodiments, the antenna module (197) 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.

[0050] 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.

[0051] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) through a server (108) connected to a second network (199). Each of the external electronic devices (102, or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations performed on the electronic device (101) may be performed on one or more of the external electronic devices (102, 104, or 108). For example, if the electronic device (101) needs to perform a function or service automatically or in response to a request from a user or another device, the electronic device (101) 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 (101). The electronic device (101) 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 (101) may provide ultra-low latency services using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server using machine learning and / or neural networks. According to one embodiment, the external electronic device (104) or the server (108) may be included within a second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0052] FIG. 2 is a drawing showing a wireless communication system according to one embodiment of the present disclosure.

[0053] Referring to FIG. 2, a wireless communication system may include a base station (210) (e.g., node B, eNB (enhanced Node B), and / or gNB (new radio (NR) Node B)) and a user terminal (101) (e.g., the electronic device (101) of FIG. 1). The base station (210) and the electronic device (101) may communicate signals through physical channels.

[0054] Physical channels may correspond to a set of time / frequency resources used for transmission of a specific transport channel. Physical channels are divided into downlink (DL) channels and uplink (UL) channels. UL may refer to a wireless link through which an electronic device (101) transmits data and / or control signals (e.g., uplink control information) to a base station (210), and DL may refer to a wireless link through which a base station (210) transmits data and / or control signals (e.g., downlink control information) to an electronic device (101).

[0055] In a wireless communication system, the quality of wireless communication between a base station (210) and an electronic device (101) may depend on the characteristics of the wireless channel. The wireless channel may be significantly affected by, for example, ambient noise, interference signals, and fading. For the base station (210) and the electronic device (101) using the wireless channel environment, accurately estimating the channel characteristics can be a factor in determining communication performance. The base station (210) and the electronic device (101) performing wireless communication may estimate various channel characteristics to improve communication performance. In one embodiment, the base station (210) and the electronic device (101) may measure channel characteristics such as signal-to-noise ratio (SNR), delay spread (DS), light of sight (LOS) / non-LOS (NLOS), beam, position, or Doppler spread, but are not limited thereto and may further measure various channel characteristics not mentioned in the present disclosure.

[0056] In one embodiment, the base station (210) may transmit a downlink reference signal (202) containing predetermined sequences through the downlink so that the electronic device (101) can estimate downlink channel characteristics. The electronic device (101) may measure or estimate various channel characteristics related to the downlink from the downlink reference signal (202).

[0057] In one embodiment, the electronic device (101) may transmit an uplink reference signal (204) containing predetermined sequences over the uplink so that the base station (210) can estimate uplink channel characteristics. The base station (210) may measure or estimate various channel characteristics related to the uplink from the uplink reference signal (204).

[0058] In one embodiment, the base station (210) may include a communication circuit (212) (e.g., a wireless communication module (192)), a memory (214) (e.g., a memory (130)), and a processor (216) (e.g., a processor (120) of FIG. 1 including at least one processing circuit). In some embodiments, the processor (216) may include one or more processors. In one embodiment, the memory (214) may store instructions, and when said instructions are executed individually or collectively by the processor (216), the base station (210) may be able to perform operations according to embodiments of the present disclosure (e.g., channel prediction).

[0059] In one embodiment, the electronic device (101) may include a communication circuit (222) (e.g., a wireless communication module (192)), a memory (224) (e.g., a memory (130)), and a processor (226) (e.g., a processor (120) of FIG. 1 including at least one processing circuit). In some embodiments, the processor (226) may include one or more processors. In one embodiment, the memory (224) may store instructions, and when said instructions are executed individually or collectively by the processor (226), the electronic device (101) may be able to perform operations according to embodiments of the present disclosure (e.g., channel prediction).

[0060] In some embodiments, for downlink communication, the base station (210) may operate as a transmitter (e.g., the transmitter (310) of FIG. 3) and the electronic device (101) may operate as a receiver (e.g., the receiver (320) of FIG. 3). In some embodiments, for uplink communication, the base station (210) may operate as a receiver (e.g., the receiver (320) of FIG. 3) and the electronic device (101) may operate as a transmitter (e.g., the transmitter (310) of FIG. 3).

[0061] FIG. 3 is a diagram showing the measurement of channel characteristics according to one embodiment of the present disclosure.

[0062] Referring to FIG. 3, a transmission signal transmitted from a transmitter (310) (e.g., a base station (210) or electronic device (101) of FIG. 2) can be received in the form of a reception signal by a receiver (320) (e.g., an electronic device (101) or base station (210) of FIG. 2) under the influence of a wireless channel H (302).

[0063] In one embodiment, the receiver (320) may use channel characteristics to detect a transmitted signal from a received signal. In one embodiment, the receiver (320) may include a channel characteristic measuring device (322). In one embodiment, the channel characteristic measuring device (322)) may measure channel characteristics associated with a wireless channel H (302) between the transmitter (310) and the receiver (320) by using a known reference signal (e.g., a downlink reference signal (202) and / or an uplink reference signal (204)) included in the transmitted signal. The measured channel characteristics may be used for various purposes, such as detecting a transmitted signal or scheduling. In one embodiment, the channel characteristic measuring device (322)) may be implemented in hardware, software, or a combination thereof.

[0064] In one embodiment, the channel characteristic measuring instrument (322) can measure or predict various channel characteristics using AI technology. The AI ​​technology may be based on deep learning. In a wireless communication system, channel characteristics such as SNR, DS, or LOS / NLOS are closely related to one another, and these channel characteristics can have a significant impact on system performance. For example, increasing SNR, reducing DS, and securing an LOS environment can improve the performance of the wireless communication system. In one embodiment, the channel characteristic measuring instrument (322) can predict future channel characteristics based on the relationship between channel characteristics.

[0065] FIG. 4 is a sequence diagram illustrating a procedure for channel estimation according to one embodiment of the present disclosure. Here, a procedure is illustrated in which an electronic device (402) (e.g., the electronic device (101) of FIG. 2 or the base station (210)) predicts channel characteristics for one external electronic device (404) (e.g., the base station (210) of FIG. 2 or the electronic device (101)), but in some embodiments, the electronic device (402) may perform the following operations to predict channel characteristics for one or more external electronic devices.

[0066] Referring to FIG. 4, in operation 412, an external electronic device (404) may transmit a reference signal (e.g., the downlink reference signal (202) or uplink reference signal (204) of FIG. 2) containing a predetermined sequence to enable the electronic device (402) to predict channel characteristics. In operation 414, the electronic device (402) may analyze the reference signal to generate N latent representation indexes corresponding to N different channel characteristics. In one embodiment, the N latent representation indexes may include at least a first latent representation index corresponding to a first type of channel characteristic and a second latent representation index corresponding to a second type of channel characteristic. The first type of channel characteristic and the second type of channel may be different from each other. In one embodiment, the N latent representation indexes may be generated by a latent transformer model.

[0067] In some embodiments, a latent representation (i.e., latent space) in a transformer model may refer to a compressed form of input data that captures features (e.g., important features) of the input data. Through the said latent space, the transformer model learns relationships and patterns within the input data and can generate latent representation indices through an encoder (e.g., encoders (604, 606, 608) of FIG. 6) that converts the input data into a low-dimensional vector representation. The encoder can identify and extract the most relevant features from the input data and encode them into a latent representation (e.g., latent representation indices).

[0068] In operation 416, the external electronic device (404) may transmit a reference signal (e.g., the downlink reference signal (202) or the uplink reference signal (204) of FIG. 2). The reference signal transmitted in operation 416 and the reference signal transmitted in operation 412 may be the same or different from each other. In operation 418, the electronic device (402) may analyze the reference signal transmitted in operation 416 to generate N latent representation indices corresponding to N different channel characteristics. In one embodiment, the electronic device (402) may repeat the operation of generating N latent representation indices (e.g., operation 414 or operation 418) a specified number of times (e.g., T) to generate N latent representation sequences of length T corresponding to different time-series channel characteristics. In some embodiments, each latent representation sequence may include T latent representation indices.

[0069] In operation 420, the electronic device (402) may generate forecasted data representing future channel characteristics corresponding to each channel characteristic based on the correlation between a plurality (e.g., N) of latency representation indices corresponding to different channel characteristics. In one embodiment, the forecasted data may include at least first forecasted data corresponding to a first type of channel characteristic and second forecasted data corresponding to a second type of channel characteristic. The first type of channel characteristic and the second type of channel characteristic may be different from each other. In one embodiment, the electronic device (402) may generate first forecasted data representing future channel characteristics corresponding to a first type of channel characteristic and second forecasted data representing future channel characteristics corresponding to a second type of channel characteristic. In one embodiment, the forecasted data may include at least one of predicted SNR data, predicted DS data, or predicted LOS / NLOS data.

[0070] In operation 422, the prediction data may be used to improve communication performance between the electronic device (402) and the external electronic device (404). In one embodiment, the electronic device (402) may perform scheduling for the external electronic device (404) based on the prediction data, transmit the prediction data to the external electronic device (404), and / or report the prediction data to a server or core network.

[0071] FIG. 5 is a flowchart illustrating a procedure for predicting channel characteristics according to one embodiment of the present disclosure. At least one of the operations described below may be omitted, modified, or executed in a different order depending on the embodiments. In one embodiment, at least one of the operations described below may be executed by a processor (e.g., processor (216) or processor (226) of FIG. 2) of an electronic device (402) (e.g., base station (210) or electronic device (101) of FIG. 2). In one embodiment, a memory of the electronic device (402) (e.g., memory (214) or memory (224) of FIG. 2) may store instructions that cause the electronic device (402) to perform at least one of the operations described below when executed individually or collectively by the processor (216) or processor (226).

[0072] Referring to FIG. 5, in operation 502, the electronic device (402) may receive reference signals (e.g., the downlink reference signal (202) or the uplink reference signal (204) of FIG. 2) containing a predetermined sequence from an external electronic device (e.g., the external electronic device (404) of FIG. 4). In operation 504, the electronic device (402) may generate at least two latent representation sequences representing at least two different types of channel characteristics based on the reference signals. In one embodiment, the at least two latent representation sequences may include at least one of a first latent representation sequence representing SNR, a second latent representation sequence representing DS, or a third latent representation sequence representing LOS / NLOS. In one embodiment, each latent representation sequence may include a plurality (e.g., T) of latent representation indices representing the corresponding channel characteristics. In one embodiment, the electronic device (402) can generate T latency representation indices by repeating the operation of receiving reference signals and measuring channel characteristics T times.

[0073] In operation 506, the electronic device (402) can generate prediction data representing future channel characteristics corresponding to each channel characteristic based on a latency transformer model that obtains relationships between the at least two latency representation sequences. In one embodiment, the prediction data may include at least a first prediction data representing future channel characteristics corresponding to a first type of channel characteristic and a second prediction data representing future channel characteristics corresponding to a second type of channel characteristic. The first type of channel characteristic and the second type of channel characteristic may be different from each other. In one embodiment, the prediction data may be used for the electronic device (402) to communicate with an external electronic device (404).

[0074] In operation 508, the electronic device (402) can communicate with an external electronic device (404) based on the predicted data.

[0075] FIG. 6 is a diagram showing the configuration of a latency transformer model for channel prediction according to one embodiment of the present disclosure.

[0076] Referring to FIG. 6, the electronic device (402) may include a plurality of encoders (604, 606, 608) and a latency transformer (612). In one embodiment, the electronic device (402) may be configured to store instructions for representing or executing an AI model including a plurality of encoders (604, 606, 608) and a latency transformer (612) in memory (e.g., memory (214 or 224) of FIG. 2) and / or to execute the AI ​​model by a processor (e.g., processor (216 or 226) of FIG. 2).

[0077] In one embodiment, a plurality of encoders (604, 606, 608) may operate as a channel characteristic generator (e.g., a channel characteristic measuring device) that takes received reference signals (602) (e.g., the downlink reference signal (202) or the uplink reference signal (204) of FIG. 2) as input and outputs latency expression sequences (604a, 606a, 608a) that include latency expression indices corresponding to the corresponding channel characteristics. In one embodiment, a first encoder (Enc-1) (604) may generate a latency expression sequence (604a) that includes latency expression indices representing a first type of channel characteristic (e.g., SNR). In one embodiment, a second encoder (Enc-2) (606) may generate a latency expression sequence (606a) that includes latency expression indices representing a second type of channel characteristic (e.g., DS). In one embodiment, the Nth encoder (Enc-N) (608) may generate a latency expression sequence (608a) including latency expression indices representing the Nth channel characteristics (e.g., LOS / NLOS). In one embodiment, the LOS / NLOS channel characteristics may include information (e.g., LOS probability and / or NLOS probability) indicating whether the wireless channel between the electronic device (402) and the external electronic device (404) is close to LOS or close to NLOS.

[0078] In one embodiment, a plurality of encoders (604, 606, 608) may be implemented using one or more AI models or using conventional signal processing methods. Since algorithms for measuring channel characteristics through conventional signal processing methods are well known to those skilled in the art, detailed descriptions may be omitted in this disclosure. As an example, the plurality of encoders (604, 606, 608) may determine each channel characteristic based on specified threshold values ​​according to a known existing algorithm.

[0079] In one embodiment, each latency representation sequence (604a, 606a, 608a) can be used as an input to a latency transformer (612), enabling the latency transformer (612) to predict future channel characteristics 614. The latency transformer (612) applies scalability and utilizes the time-to-time correlation between the latency representation sequences (604a, 606a, 608a) representing various time-series channel characteristics, and can predict channel characteristics with higher accuracy through the time-to-time correlation between the latency representation sequences (604a, 606a, 608a).

[0080] In one embodiment, an AI model including encoders (604, 606, 608) and a latency transformer (612) can be implemented as a neural processing unit (NPU) or a graphic processing unit (GPU) included in a base station (e.g., base station (210) of FIG. 2) or a user terminal (e.g., electronic device (101) of FIG. 1), and can predict various wireless channel characteristics in real time at once using received reference signals.

[0081] In one embodiment of the present disclosure, the electronic device (402) can generate latency representation indices such as <Equation 1> below by operating channel characteristic generators (e.g., encoders (604, 606, 608)) corresponding to N channel characteristics from a reference signal received from an external electronic device (e.g., external electronic device (404)).

[0082]

[0083] Here, L(f) can represent the latency representation index corresponding to the f-th channel characteristic.

[0084] To track channel characteristics that change over time, the electronic device (402) may repeat the operation of receiving a reference signal and generating N latency expression indices (e.g., operation 412 and operation 414) T times, and obtain time-dependent latency expression sequences such as <Equation 2> below. Multiple (e.g., N) latency expression sequences may have the same dimension (e.g., T).

[0085]

[0086] Here, L(f,t) may represent the t-th latency representation index corresponding to the f-th channel characteristic. For example, the f-th latency representation sequence can be expressed as {L(f,1), ..., L(f,T)}.

[0087] In one embodiment, the plurality (e.g., N) latency expression sequences (e.g., NT latency expression indices) that can each correspond to a plurality (e.g., N) different channel characteristics can be inputs to a latency transformer model (e.g., the latency transformer (612) of FIG. 6).

[0088] FIG. 7 shows the configuration of transformer input sequences according to one embodiment of the present disclosure.

[0089] Referring to FIG. 7, a latency expression sequence (702) (e.g., latency expression sequence (604a)) corresponding to a first type of channel characteristic (e.g., SNR) may be {L(1,1), L(1,2), ..., L(1,T)}, a latency expression sequence (704) (e.g., latency expression sequence (606a)) corresponding to a second type of channel characteristic (e.g., DS) may be {L(2,1), L(2,2), ..., L(2,T)}, and a latency expression sequence (706) (e.g., latency expression sequence (608a)) corresponding to an Nth channel characteristic (e.g., LOS / NLOS) may be {L(N,1), L(N,2), ..., L(N,T)}.

[0090] In one embodiment, an electronic device (402) (e.g., a latency transformer (612)) can predict N (or fewer than N) future latency expression indices through correlation between a plurality of latency expression sequences (702, 7004, 706) and predict future channel characteristics from the future latency expression indices.

[0091] In one embodiment, the electronic device (402) may utilize channel models defined in a wireless communication system (e.g., RMa (rural macrocells), UMa (urban macrocells), or UMi (urban microcells), and / or utilize a CDL (clustered delay line) channel model or a TDL (tapped delay line) channel model to train an AI model comprising a plurality of encoders (e.g., encoders (604, 606, 608)) and a latency transformer (612). In some embodiments, the AI ​​model may include one or more AI models. The electronic device (402) may perform inference on channel characteristics in an actual communication environment based on an AI model that includes information learned in an offline environment based on the aforementioned channel models.

[0092] FIG. 8 shows an example of values ​​of transformer input sequences according to one embodiment of the present disclosure.

[0093] Referring to FIG. 8, the electronic device (402) can extract three (N=3) latency representation indices representing SNR, DS, and LOS / NLOS, respectively, through encoders (e.g., encoders (604, 606, 608)) at each iteration. During T iterations, the electronic device (402) can utilize eight (T=8) past values ​​for each channel characteristic and provide a total of 24 latency representation indices as inputs to the latency transformer (612).

[0094] In one embodiment, latent expression indices constituting a latent expression sequence corresponding to one channel characteristic may have values ​​within a first range, and latent expression indices constituting a latent expression sequence corresponding to another channel characteristic may have values ​​within a second range, and the second range may be continuous without overlapping with the first range.

[0095] In one embodiment, the latency expression sequence (802) corresponding to LOS / NLOS may be {L(1,1), L(1,2), ..., L(1,8)}, the latency expression sequence (804) corresponding to DS may be {L(2,1), L(2,2), ..., L(2,8)}, and the latency expression sequence (806) corresponding to SNR may be {L(3,1), L(3,2), ..., L(3,8)}.

[0096] In one embodiment, each latency representation index has a value between 0 and 23, and each latency representation index for LOS / NLOS may have a value between 0 and 7, each latency representation index for DS may have a value between 8 and 15, and each latency representation index for SNR may have a value between 16 and 23.

[0097] In one embodiment, the electronic device (402) can utilize a latency transformer (612) to simultaneously consider all or one or more correlations between latency expression sequences corresponding to different channel characteristics, and through the correlations, can flexibly reflect whether a latency expression sequence corresponding to a specific channel characteristic has a strong or weak correlation with a latency expression sequence corresponding to another channel characteristic to predict each future channel characteristic.

[0098] In one embodiment, the electronic device (402) can generate latency expression sequences corresponding to future channel characteristics as the output of the latency transformer (612), and can predict future channel characteristics based on the latency expression sequences. In one embodiment, the electronic device (402) can generate a plurality of latency expression sequences corresponding to future channel characteristics by setting a latency expression index corresponding to future channel characteristics to an arbitrary value and inputting it to the latency transformer (612).

[0099] FIG. 9 is a drawing showing an AI model including a latency transformer according to one embodiment of the present disclosure.

[0100] Referring to FIG. 9, the AI ​​model (900) for channel prediction may include a plurality of encoders (604, 606, 608) (e.g., the encoders (604, 606, 608) of FIG. 6) corresponding to different channel characteristics and a latency transformer (612) (e.g., the latency transformer (612) of FIG. 6). In one embodiment, the encoders (604, 606, 608) may generate latency representation sequences (604a, 606a, 608a) corresponding to each channel characteristic, using channel data (902) corresponding to a received reference signal (e.g., downlink reference signal (202) or uplink reference signal (204)) as input.

[0101] In one embodiment, the channel data (902) may include an in-phase (I) signal component and a quadrature-phase (Q) signal component obtained from a received reference signal, and each signal component may include signal values ​​corresponding to a plurality of antennas and a plurality of resource blocks (RB).

[0102] In one embodiment, the latency transformer (612) can output predicted DS data (904) and predicted LOS / NLOS data (906) by using a latency expression sequence (604a) for SNR, a latency expression sequence (606a) for DS, and a latency expression sequence (608a) for LOS / NLOS as inputs.

[0103] FIG. 10 is a diagram showing the encoder architecture of a latency transformer according to one embodiment of the present disclosure.

[0104] Referring to FIG. 10, the latency transformer (612) may have a transformer encoder architecture composed of at least one of an embedding layer (1002), an attention layer (1004), a feed forward layer (1006), or at least one linear layer (1008).

[0105] In one embodiment, for channel characteristic prediction based on eight past values ​​for three channel characteristics, hyperparameters for controlling the learning process of the latency transformer (612) and determining the values ​​of the model parameters that the learning algorithm ultimately learns may include at least one of the following.

[0106] - n_seq = 24

[0107] - d_embedding = 24

[0108] - d_model = 24

[0109] - d_feedforward = 96

[0110] - n_layer = 3

[0111] - n_head = 1

[0112] Here, n_seq represents the total number of latent representation indices used as transformer inputs, d_embedding represents the embedding dimension for the embedding layer (1002), d_model represents the size of the fixed input and output in the latent transformer (612), d_feedforward represents the hidden dimension in the feedforward layer (1006), n_layer represents how many layers the encoder architecture in the latent transformer (612) consists of, and n_head represents the number of heads of the multi-head attention performed in the attention layer (1004).

[0113] In one embodiment, the latent transformer (612) includes an embedding layer (1002), an attention layer (1004), and a feed-forward layer (1006), and may further include a linear layer (1008) depending on the channel characteristics to be predicted. In one embodiment, the latent transformer (612) may further include an activation function such as a sigmoid depending on the channel characteristics to be predicted (e.g., LOS / NLOS). In one embodiment, the latent transformer (612) may further include a positioning encoding layer between the embedding layer (1002) and the attention layer (1004).

[0114] In one embodiment, the embedding layer (102) has dimensions such as d_model, and the attention layer (1004) and the feed-forward layer (1006) may consist of n_layer layers. In one embodiment, the feed-forward layer (1006) may have a hidden dimension such as d_feedforward. In one embodiment, the linear layer (1008) may have dimensions such as d_model. In one embodiment, one linear layer for LOS / NLOS and one linear layer for DS may be included in the linear layer (1008).

[0115] In one embodiment, the latency transformer (612) can obtain soft values ​​between 0 and 1 by applying an activation function (e.g., a sigmoid function) to the output of the linear layer (1008), and based on the soft values, predict whether the LOS / NLOS among future channel characteristics is closer to LOS or closer to NLOS, and / or predict whether DS is shorter or longer than a specified threshold, and generate prediction data (e.g., predicted DS data (904) and / or predicted LOS / NLOS data (906)) representing the predicted result.

[0116] In one embodiment, the electronic device (402) may train an AI model including encoders (604, 606, 608) and a latency transformer (612) based on RMa, UMa, and / or UMi channel models. As an example, when the electronic device (402) is a base station (e.g., base station (210) in FIG. 2) and the external electronic device (404) is a user equipment (UE) (e.g., electronic device (101) in FIG. 2), the electronic device (402) may utilize the trained AI model to predict LOS / NLOS characteristics and DS characteristics among future channel characteristics for each user equipment. In one embodiment, the electronic device (402) receives a reference signal (e.g., uplink reference signal (204) in FIG. 2) that is periodically transmitted by a plurality of user equipment, infers the received reference signal based on the trained AI model, and predicts LOS / NLOS and DS for each user equipment based on the inferred result.

[0117] Wireless channel characteristics can be predicted with high accuracy through the embodiments of the present disclosure. An electronic device (402) based on an AI model learned according to the embodiments of the present disclosure can predict LOS / NLOS channel characteristics and DS channel characteristics with an accuracy of 95% or higher regardless of SNR. In addition, an electronic device (402) based on an AI model learned according to the embodiments of the present disclosure can further improve communication performance by utilizing the correlation between LOS / NLOS channel characteristics and DS channel characteristics.

[0118] An electronic device (402) according to one embodiment of the present disclosure may include a communication circuit (212; 222), at least one processor (216; 226), and a memory (214; 224) for storing instructions. When the above instructions are executed individually or collectively by the at least one processor, the electronic device may be configured to receive reference signals through the communication circuit and, based on the received reference signals, generate at least two latent representation sequences representing at least two different channel characteristics, wherein each of the at least two latent representation sequences includes a plurality of latent representation indices representing the corresponding channel characteristics, and to obtain relationships between the at least two latent representation sequences, thereby outputting at least first prediction data representing future channel characteristics corresponding to a first type of channel characteristic and second prediction data representing future channel characteristics corresponding to a second type of channel characteristic.

[0119] In one embodiment, each of the plurality of latency expression indices can indicate a plurality of time series channel characteristic values.

[0120] In one embodiment, the at least two latency expression sequences may include at least a first latency expression sequence including first latency expression indices representing the first type of channel characteristics, and a second latency expression sequence including second latency expression indices representing the second type of channel characteristics. Each of the first latency expression indices has a value within a first range, and each of the second latency expression indices has a value within a second range that is different from the first range, and the second range may be consecutively connected to the first range.

[0121] In one embodiment, the latency transformer may include an embedding layer, an attention layer, a feed-forward layer, and at least one linear layer.

[0122] In one embodiment, the at least one linear layer may include a first linear layer for predicting LOS / NLOS channel characteristics and a second linear layer for predicting DS channel characteristics.

[0123] In one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may generate a soft value indicating whether the LOS / NLOS channel characteristic is close to LOS or close to NLOS by applying a sigmoid function to the output of the first linear layer.

[0124] In one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may apply a sigmoid function to each of the output of the first linear layer and the output of the second linear layer.

[0125] In one embodiment, at least one of the at least two channel characteristics may include any one of SNR (signal to noise ratio), DS (delay spread), and LOS (line of sight) / NLOS (non-LOS).

[0126] In one embodiment, the latency transformer may be configured to predict future channel characteristics based on information learned based on an RMa (rural macrocells) channel model, a UMa (urban macrocells) channel model, a UMi (urban microcells) channel model, a CDL (clustered delay line) channel model, and / or a TDL (tapped delay line) channel model.

[0127] In one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device generates the latency representation sequences by inputting channel data including in-phase (I) signal components and quadrature-phase (Q) signal components obtained from the received reference signals, and each of the I signal component and the Q signal component may include signal values ​​corresponding to a plurality of antennas and a plurality of resource blocks (RB).

[0128] A method performed by an electronic device (402) according to one embodiment of the present disclosure may include: an operation (502) of receiving reference signals through a wireless channel; an operation (504) of generating at least two latent representation sequences representing at least two different channel characteristics based on the received reference signals, wherein each of the at least two latent representation sequences includes a plurality of latent representation indices representing the corresponding channel characteristics, and an operation (506) of at least outputting first prediction data representing future channel characteristics corresponding to a first type of channel characteristic and second prediction data representing future channel characteristics corresponding to a second type of channel characteristic through a latent transformer configured to obtain relationships between the at least two latent representation sequences.

[0129] In one embodiment, each of the plurality of latency expression indices can indicate a plurality of time series channel characteristic values.

[0130] In one embodiment, the at least two latency expression sequences may include at least a first latency expression sequence including first latency expression indices representing the first type of channel characteristics, and a second latency expression sequence including second latency expression indices representing the second type of channel characteristics. Each of the first latency expression indices has a value within a first range, and each of the second latency expression indices has a value within a second range that is different from the first range, and the second range may be consecutively connected to the first range.

[0131] In one embodiment, the latency transformer may include an embedding layer, an attention layer, a feed-forward layer, and at least one linear layer.

[0132] In one embodiment, the at least one linear layer may include a first linear layer for predicting LOS / NLOS channel characteristics and a second linear layer for predicting DS channel characteristics.

[0133] In one embodiment, the method may further include an operation of generating a soft value indicating whether the LOS / NLOS channel characteristic is close to LOS or close to NLOS by applying a sigmoid function to the output of the first linear layer.

[0134] In one embodiment, the method may further include the operation of applying a sigmoid function to each of the output of the first linear layer and the output of the second linear layer.

[0135] In one embodiment, at least one of the at least two channel characteristics may include any one of SNR (signal to noise ratio), DS (delay spread), and LOS (line of sight) / NLOS (non-LOS).

[0136] In one embodiment, the latency transformer may be configured to predict future channel characteristics based on information learned based on an RMa (rural macrocells) channel model, a UMa (urban macrocells) channel model, a UMi (urban microcells) channel model, a CDL (clustered delay line) channel model, and / or a TDL (tapped delay line) channel model.

[0137] In one embodiment, the method further includes the operation of generating the latency representation sequences by inputting channel data including in-phase (I) signal components and quadrature-phase (Q) signal components obtained from the received reference signals, and each of the I signal component and the Q signal component may include signal values ​​corresponding to a plurality of antennas and a plurality of resource blocks (RB).

[0138] In a non-transient computer-readable storage medium storing one or more programs according to one embodiment of the present disclosure, the one or more programs may include instructions configured to, when executed by at least one processor (216; 224) of an electronic device (402), cause the electronic device to receive reference signals and, based on the received reference signals, generate at least two latent representation sequences representing at least two different channel characteristics, wherein each of the at least two latent representation sequences includes a plurality of latent representation indices representing the corresponding channel characteristics, and to at least output first prediction data representing future channel characteristics corresponding to a first type of channel characteristic and second prediction data representing future channel characteristics corresponding to a second type of channel characteristic through a latent transformer configured to obtain relationships between the at least two latent representation sequences.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] Various embodiments of the present document may be implemented as software (e.g., program (140)) comprising one or more instructions stored in a storage medium (e.g., internal memory (136) or external memory (138)) readable by a machine (e.g., electronic device (101)). For example, a processor (e.g., processor (120)) of the machine (e.g., electronic device (101)) 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-temporary' simply means that the storage medium is a tangible device and does not contain a signal (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily.

[0143] 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 an application store (e.g., Play Store). TM It can be distributed online (e.g., downloaded or uploaded) through ) 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.

[0144] 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.

Claims

1. In an electronic device (402), Communication circuit (212; 222); At least one processor (216; 226); and The electronic device includes a memory (214; 224) for storing instructions, wherein the instructions are executed individually or collectively by the at least one processor, Receive reference signals through the above communication circuit, and Based on the above reference signals, at least two latent representation sequences representing at least two different channel characteristics are generated—wherein each of the at least two latent representation sequences includes a plurality of latent representation indices representing the corresponding channel characteristics among the at least two different channel characteristics, and An electronic device that outputs first prediction data representing future channel characteristics corresponding to a first type of channel characteristic and second prediction data representing future channel characteristics corresponding to a second type of channel characteristic through a latent transformer configured to acquire relationships between at least two latent expression sequences.

2. In claim 1, each latency expression index among the plurality of latency expression indices is an electronic device indicating a plurality of time series channel characteristic values.

3. In claim 1 or 2, the at least two latency expression sequences include a first latency expression sequence including first latency expression indices representing the first type of channel characteristics, and a second latency expression sequence including second latency expression indices representing the second type of channel characteristics, and Each of the above first latency expression indices has a value within a first range, and Each of the above second latency expression indices has a value within a second range that is different from the first range, The above second range is an electronic device that continues in succession to the above first range.

4. An electronic device according to any one of claims 1 to 3, wherein the latency transformer comprises an embedding layer, an attention layer, a feedforward layer, and at least one linear layer.

5. An electronic device according to claim 4, wherein the at least one linear layer comprises a first linear layer for predicting LOS / NLOS channel characteristics and a second linear layer for predicting DS channel characteristics.

6. In claim 5, when the instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that generates a soft value indicating whether the LOS / NLOS channel characteristic is close to LOS or close to NLOS by applying a sigmoid function to the output of the first linear layer.

7. In claim 5, when the instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that applies a sigmoid function to each of the output of the first linear layer and the output of the second linear layer.

8. In any one of claims 1 to 7, at least one of the at least two different channel characteristics is, An electronic device comprising any one of SNR (signal to noise ratio), DS (delay spread), LOS (line of sight) / NLOS (non-LOS).

9. In any one of claims 1 to 8, the latency transformer is, An electronic device configured to predict future channel characteristics based on information learned based on at least one of an RMa (rural macrocells) channel model, a UMa (urban macrocells) channel model, a UMi (urban microcells) channel model, a CDL (clustered delay line) channel model, or a TDL (tapped delay line) channel model.

10. In any one of claims 1 to 9, when the instructions are executed individually or collectively by the at least one processor, the electronic device, Based on channel data including in-phase (I) signal components and quadrature-phase (Q) signal components obtained from the received reference signals, the at least two latency representation sequences are generated, and An electronic device in which each of the above I signal component and Q signal component includes signal values ​​corresponding to a plurality of antennas and a plurality of resource blocks (RB).

11. In a method performed by an electronic device (402), Operation of receiving reference signals through a wireless channel; An operation of generating at least two latent representation sequences representing at least two different channel characteristics based on the received reference signals above—wherein each of the at least two latent representation sequences includes a plurality of latent representation indices representing the corresponding channel characteristic among the at least two different channel characteristics; and A method comprising the operation of outputting first prediction data representing future channel characteristics corresponding to a first type of channel characteristic and second prediction data representing future channel characteristics corresponding to a second type of channel characteristic through a latent transformer configured to obtain relationships between at least two latent expression sequences.

12. In claim 11, each latency expression index among the plurality of latency expression indices indicates a plurality of time series channel characteristic values.

13. In claim 11 or 12, the at least two latency expression sequences include a first latency expression sequence including first latency expression indices representing the first type of channel characteristics, and a second latency expression sequence including second latency expression indices representing the second type of channel characteristics, and Each of the above first latency expression indices has a value within a first range, and Each of the above second latency expression indices has a value within a second range that is different from the first range, The above second range is a method that continues in succession to the above first range.

14. In any one of claims 11 to 13, the latency transformer comprises an embedding layer, an attention layer, a feedforward layer, and at least one linear layer, and A method in which at least one linear layer comprises a first linear layer for predicting LOS / NLOS channel characteristics and a second linear layer for predicting DS channel characteristics.

15. In Paragraph 14, An operation to generate a soft value indicating whether the LOS / NLOS channel characteristic is close to LOS or close to NLOS by applying a sigmoid function to the output of the first linear layer, and A method further comprising the operation of applying a sigmoid function to each of the output of the first linear layer and the output of the second linear layer.

Images