Method, base station, and user equipment for transmitting and receiving signal in wireless communication system

Abstract

Provided are a method for transmitting and receiving a signal in a wireless communication system, and an apparatus therefor. The method and the apparatus may comprise: determining an artificial intelligence or machine learning-based model for channel estimation on the basis of a model identifier (ID); transmitting the model ID to a user equipment (UE); and receiving, from the UE, channel estimation information based on the model, wherein the model ID may include at least one of a signal type and a domain type of a first input signal of the model.

Description

Method for transmitting and receiving signals in a wireless communication system, base station and user equipment

[0001] This specification relates to a wireless communication system, a method for transmitting and receiving signals, a base station, and user equipment.

[0002] As wireless communication technology advances, communication devices requiring high data rates are emerging and becoming widespread. As more communication devices demand greater capacity, the need for enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low latency communication (URLLC) is emerging.

[0003] Meanwhile, the introduction of artificial intelligence (AI) technology in wireless communication systems is expected to help support low-latency, low-complexity, and high-performance communication systems. To design communication systems that take into account latency-sensitive services and / or communication devices, AI-based communication technologies that achieve system optimization are key issues to consider in next-generation communications.

[0004] With the introduction of new wireless communication technologies, the amount of data and / or control information transmitted and received by communication devices is increasing. Since the amount of radio resources available for communication is finite, new methods are needed to enable base stations and / or user devices in wireless communication systems to efficiently utilize radio resources to transmit and receive data and / or control information.

[0005] The technical tasks that this specification aims to achieve are not limited to the technical tasks mentioned above, and other technical tasks that are not mentioned will be clearly understood by those skilled in the art related to this specification from the detailed description below.

[0006] In one aspect of the present disclosure, a method for transmitting and receiving signals by a base station in a wireless communication system is provided. The method comprises: determining an artificial intelligence or machine learning-based model for channel estimation with a user equipment (UE) based on a model identifier (ID), transmitting the model ID to the UE, and receiving channel estimation information based on the model from the UE, wherein the model ID includes at least one of a signal type or a domain type of a first input signal of the model.

[0007] In another aspect of the present disclosure, a base station for transmitting and receiving signals in a wireless communication system is provided. The base station includes at least one transceiver; at least one processor; and at least one computer memory operably connected to the at least one processor and storing instructions that, when executed, cause the at least one processor to perform operations. The operations include: determining an artificial intelligence or machine learning-based model for channel estimation with a user equipment (UE) based on a model identifier (ID), transmitting the model ID to the UE, and receiving channel estimation information based on the model from the UE, wherein the model ID includes at least one of a signal type or a domain type of a first input signal of the model.

[0008] In another aspect of the present disclosure, a method for a user equipment to transmit and receive signals in a wireless communication system is provided. The method comprises: determining an artificial intelligence or machine learning-based model for channel estimation with a base station, receiving a model identifier (ID) corresponding to the model from the base station, and transmitting channel estimation information using the model to the base station, wherein the model ID includes at least one of a signal type or a domain type of a first input signal of the model.

[0009] In another aspect of the present disclosure, a user equipment for transmitting and receiving signals in a wireless communication system is provided. The user equipment includes: at least one transceiver; at least one processor; and at least one computer memory operably connected to the at least one processor and storing instructions that, when executed, cause the at least one processor to perform operations. The operations include: determining an artificial intelligence or machine learning-based model for channel estimation with a base station, receiving a model identifier (ID) corresponding to the model from the base station, and transmitting channel estimation information using the model to the base station, wherein the model ID includes at least one of a signal type or a domain type of a first input signal of the model.

[0010] In each aspect of the present specification, the method may further include transmitting to the UE at least one of a plurality of signal types or a plurality of domain types that is not included in the model ID via either radio resource control (RRC) signaling or downlink control information (DCI).

[0011] In each aspect of the present specification, the method may further include transmitting information about the second input signal of the model to the UE via either radio resource control (RRC) signaling or downlink control information (DCI).

[0012] In each aspect of the present specification, it may further include determining the signal type based on the capability of the UE among a plurality of input signal types.

[0013] In each aspect of the present specification, the method may further include receiving feedback from the UE based on transmitting the model ID, and changing the signal type of the first input signal based on the feedback.

[0014] In each aspect of the present specification, the domain type may include at least one of a plurality of domain types for channel estimation.

[0015] In each aspect of the present specification, receiving model identification information corresponding to the model from the UE or a UE other than the UE may be further included.

[0016] In each aspect of this specification, the model ID may further include information about an output signal of the model.

[0017] The above problem solving methods are only some of the examples of this specification, and various examples reflecting the technical features of this specification can be derived and understood by a person having ordinary knowledge in the relevant technical field based on the detailed description below.

[0018] According to an implementation(s) of this specification, a method for transmitting input / output of an artificial intelligence (AI) or machine learning (ML) based model in a wireless communication system is provided.

[0019] According to implementation(s) of this specification, a method for determining input / output signals and / or domains of an AI or ML-based model for channel estimation is provided.

[0020] According to the implementation(s) of this specification, wireless communication signals can be transmitted and received efficiently. Accordingly, the overall throughput of the wireless communication system can be increased.

[0021] According to implementation(s) of this specification, delay / latency occurring during wireless communication can be reduced.

[0022] The effects obtained from the present invention may not be limited to the effects described above. Furthermore, other effects not mentioned can be clearly understood by those skilled in the art from the following description.

[0023] The accompanying drawings, which are included as part of the detailed description to aid in understanding implementations of this specification, provide examples of implementations of this specification and, together with the detailed description, serve to illustrate implementations of this specification.

[0024] Figure 1 illustrates the structure of a system for 5th generation (5G) communication.

[0025] FIG. 2 is a block diagram illustrating examples of communication devices capable of performing a method according to the present specification.

[0026] FIG. 3 illustrates a wireless communication system to which some implementations of the present specification are applied.

[0027] Figure 4 illustrates a channel in wireless communication.

[0028] Figure 5 illustrates a flow of a signal transmission and reception method according to some implementations of this specification.

[0029] Figure 6 illustrates another flow of a signal transmission and reception method according to some implementations of the present specification.

[0030] Figure 7 illustrates the structure of wireless communication based on deep learning (DL).

[0031] Figure 8 illustrates another structure of wireless communication based on deep learning (DL).

[0032] FIGS. 9 and 10 illustrate examples of a flow of a method for transmitting and receiving input / output information of an artificial intelligence (AI) and / or machine learning (ML) based model according to some implementations of the present specification.

[0033] Hereinafter, implementations according to this specification will be described in detail with reference to the attached drawings. The detailed description provided below, together with the attached drawings, is intended to describe exemplary implementations of this specification and is not intended to represent the only possible implementations of this specification. The detailed description below includes specific details to provide a thorough understanding of this specification. However, one of ordinary skill in the art will appreciate that this specification may be practiced without these specific details.

[0034] In some cases, to avoid ambiguity in the concepts of this specification, known structures and devices may be omitted or illustrated in block diagram form focusing on the core functions of each structure and device. Throughout this specification, identical components are described using the same reference numerals.

[0035] The terms and words used in the following description and drawings should not be interpreted as limited to their conventional or dictionary meanings, but should be interpreted with meanings and concepts that conform to the technical idea of ​​this specification based on the principle that the inventor can appropriately define the concept of the term to best describe his or her invention. Therefore, the examples described in this specification and the configurations illustrated in the drawings are merely the most preferred embodiments of this specification and do not represent all of the technical idea of ​​this specification. Therefore, it should be understood that there may be various equivalents and modified examples that can replace them at the time of filing this application.

[0036] Additionally, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0037] The terminology used in this specification is only used to describe specific embodiments and is not intended to limit the features, components, etc. described in the specification. The singular expression includes plural expressions unless the context clearly indicates otherwise. It should be understood that the terms "comprises" or "has" described in this specification are intended to specify the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0038] Additionally, in various embodiments of the present specification, " / " and "," should be interpreted as indicating "and / or". For example, "A / B" can mean "A and / or B". Furthermore, "A, B" can mean "A and / or B". Furthermore, "A / B / C" can mean "at least one of A, B, and / or C". Furthermore, "A, B, C" can mean "at least one of A, B, and / or C".

[0039] Additionally, terms that include ordinal numbers, such as "first," "second," etc., are used to describe various components and are only used to distinguish one component from another, not to limit said components. For example, without exceeding the scope of this specification, a second component could be referred to as a first component, and similarly, a first component could also be referred to as a second component.

[0040] The techniques, devices, and systems described below can be applied to various wireless multiple access systems. Examples of multiple access systems include code division multiple access (CDMA) systems, frequency division multiple access (FDMA) systems, time division multiple access (TDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, and single carrier frequency division multiple access (SC-FDMA) systems. CDMA can be implemented in radio technologies such as Universal Terrestrial Radio Access (UTRA) or CDMA2000. TDMA can be implemented in radio technologies such as Global System for Mobile communications (GSM) / General Packet Radio Service (GPRS) / Enhanced Data Rates for GSM Evolution (EDGE). OFDMA can be implemented in wireless technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, and Evolved UTRA (E-UTRA). UTRA is part of UMTS (Universal Mobile Telecommunications System), and 3GPP (3rd Generation Partnership Project) LTE (long term evolution) is part of E-UMTS (Evolved UMTS) that uses E-UTRA. 3GPP LTE adopts OFDMA for the downlink (DL) and SC-FDMA for the uplink (UL).LTE-A (Advanced) is an evolved form of 3GPP LTE, and 3GPP NR (New Radio or New Radio Access Technology) is an evolved form of 3GPP LTE / LTE-A.

[0041] For convenience of explanation, the following description assumes that this specification applies to 3GPP-based communication systems, such as LTE and NR. However, the technical features of this specification are not limited to this. For example, although the detailed description below is based on a mobile communication system corresponding to a 3GPP LTE / NR system, it can also be applied to any other mobile communication system, except for features specific to 3GPP LTE / NR.

[0042] For terms and technologies used in this specification that are not specifically explained, reference can be made to 3GPP-based standard documents.

[0043] In the examples of this specification described below, the expression "assumes" that a device "assumes" that the entity transmitting the channel transmits the channel in a manner consistent with the "assume." The entity receiving the channel may mean that, under the assumption that the channel was transmitted in a manner consistent with the "assume," the entity receiving the channel receives or decodes the channel in a manner consistent with the "assume."

[0044] In this specification, user equipment (UE) may be fixed or mobile, and includes various devices that communicate with a base station (BS) to transmit and / or receive user data and / or various control information. UE may be called Terminal Equipment (TE), Mobile Station (MS), Mobile Terminal (MT), User Terminal (UT), Subscriber Station (SS), wireless device, Personal Digital Assistant (PDA), wireless modem, handheld device, etc. In addition, in this specification, BS generally refers to a fixed station that communicates with UE and / or other BS, and exchanges various data and control information with UE and other BS. BS may be called by other terms such as Advanced Base Station (ABS), Node-B (NB), evolved-NodeB (eNB), Base Transceiver System (BTS), Access Point, and Processing Server (PS). In particular, the BS in UTRAN is called a Node-B, the BS in E-UTRAN is called an eNB, and the BS in a new radio access technology network is called a gNB. For convenience of explanation, the base station is referred to as a BS below, regardless of the type or version of communication technology.

[0045] Figure 1 illustrates the structure of a system for 5th generation (5G) communication.

[0046] Specifically, FIG. 1 illustrates the structure of a 5G communication system to which the method(s) described in FIGS. 5 to 6 are applied.

[0047] Referring to FIG. 1, a next generation radio access network (NG-RAN) may include a BS (20) that provides user plane and control plane protocol termination to a UE (10).

[0048] The example of Fig. 1 illustrates a case including only gNB. BSs (20) can be connected to each other via Xn interfaces. BSs (20) can be connected to a 5th generation core network (5GC) via an NG interface. Specifically, BSs (20) can be connected to an access and mobility management function (AMF) (30) via an NG-C interface, and can be connected to a user plane function (UPF) (30) via an NG-U interface.

[0049] According to some implementations of this specification, the functions, procedures, and / or methods described in this specification may be performed via the AMF / UPF (30). For example, according to some implementations of this specification, the operation(s) performed by the BS (20) may be processed by the AMF / UPF (30) (rather than the BS (20)).

[0050] Meanwhile, the UE (10) and / or BS (20) of FIG. 1 may correspond to the wireless device illustrated in FIG. 2.

[0051] In a wireless communication system, a UE (10) performs beam management by transmitting feedback information through uplink control information (UCI) transmitted through a physical uplink control channel (PUCCH) and / or a physical uplink shared channel (PUSCH) for a beam transmitted by a BS (20). The UCI information transmitted through the uplink may include a rank indicator (RI), a layer indicator, a channel quality indicator, a channel state information reference signal (CSI-RS) resource indicator (CRI), etc.

[0052] Meanwhile, in the next generation communication system of 5G communication, the necessity of beam management and methods for efficiently performing beam management are being discussed.

[0053] This specification proposes a method for a UE (10) and / or a BS (20) to transmit and receive signals using an artificial intelligence (AI) and / or machine learning (ML)-based model (hereinafter, referred to as an AI / ML model). For example, a method for transmitting and receiving channel estimation information based on an AI / ML model is proposed.

[0054] FIG. 2 is a block diagram illustrating examples of communication devices capable of performing a method according to the present specification.

[0055] Specifically, FIG. 2 illustrates an example of a communication device(s) that transmits and receives signals according to the method(s) described in FIGS. 5 to 6.

[0056] Referring to FIG. 2, the first wireless device (100) and the second wireless device (200) can transmit and / or receive wireless signals via various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (100), the second wireless device (200)} can correspond to {UE, BS}.

[0057] A first wireless device (100) includes one or more processors (102) and one or more memories (104), and may further include one or more transceivers (106) and / or one or more antennas (108). The processor(s) (102) controls the memories (104) and / or the transceivers (106), and may be configured to implement the functions, procedures, and / or methods described / proposed above. For example, the processor(s) (102) may process information in the memories (104) to generate first information / signals, and then transmit a wireless signal including the first information / signals via the transceivers (106). In addition, the processor(s) (102) may receive a wireless signal including second information / signal through the transceiver(s) (106), and then store information obtained from signal processing of the second information / signal in the memory(s) (104). The memory(s) (104) may be connected to the processor(s) (102) and may store various information related to the operation of the processor(s) (102). For example, the memory(s) (104) may perform some or all of the processes controlled by the processor(s) (102), or store software code including instructions for performing the procedures and / or methods described / proposed above. Here, the processor(s) (102) and the memory(s) (104) may be part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver(s) (106) may be connected to the processor(s) (102) and may transmit and / or receive wireless signals via one or more antennas (108). The transceiver(s) (106) may include a transmitter and / or a receiver. The transceiver(s) (106) may be used interchangeably with an RF (Radio Frequency) unit. In this specification, a wireless device may also mean a communication modem / circuit / chip.

[0058] The second wireless device (200) includes one or more processors (202), one or more memories (204), and may further include one or more transceivers (206) and / or one or more antennas (208). The processor(s) (202) controls the memories (204) and / or the transceivers (206), and may be configured to implement the functions, procedures, and / or methods described / proposed above. For example, the processor(s) (202) may process information in the memories (204) to generate third information / signals, and then transmit a wireless signal including the third information / signals via the transceivers (206). In addition, the processor(s) (202) may receive a wireless signal including the fourth information / signal through the transceiver(s) (206), and then store information obtained from signal processing of the fourth information / signal in the memory(s) (204). The memory(s) (204) may be connected to the processor(s) (202) and may store various information related to the operation of the processor(s) (202). For example, the memory(s) (204) may perform some or all of the processes controlled by the processor(s) (202), or store software code including instructions for performing the procedures and / or methods described / proposed above. Here, the processor(s) (202) and the memory(s) (204) may be part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver(s) (206) may be connected to the processor(s) (202) and may transmit and / or receive wireless signals via one or more antennas (208). The transceiver(s) (206) may include a transmitter and / or a receiver. The transceiver(s) (206) may be used interchangeably with an RF unit. In this specification, a wireless device may also mean a communication modem / circuit / chip.

[0059] Hereinafter, the hardware elements of the device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as a physical (PHY) layer, a medium access control (MAC) layer, a radio link control (RLC) layer, a packet data convergence protocol (PDCP) layer, a radio resource control (RRC) layer, and a service data adaptation protocol (SDAP) layer). One or more processors (102, 202) may generate one or more protocol data units (PDUs) and / or one or more service data units (SDUs) according to the functions, procedures, proposals, and / or methods disclosed in this specification. One or more processors (102, 202) may generate messages, control information, data or information according to the functions, procedures, proposals and / or methods disclosed in this specification. One or more processors (102, 202) may generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and / or methods disclosed in this specification, and provide the signals to one or more transceivers (106, 206). One or more processors (102, 202) may receive signals (e.g., baseband signals) from one or more transceivers (106, 206) and obtain PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and / or methods disclosed in this specification.

[0060] One or more processors (102, 202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or more processors (102, 202). The functions, procedures, proposals, and / or methods disclosed in this specification may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. Firmware or software configured to perform the functions, procedures, suggestions and / or methods disclosed in this specification may be included in one or more processors (102, 202) or stored in one or more memories (104, 204) and executed by one or more processors (102, 202). The functions, procedures, suggestions and / or methods disclosed in this specification may be implemented using firmware or software in the form of codes, instructions and / or sets of instructions.

[0061] The processor(s) (102, 202) may perform the method(s) and / or procedure(s) according to the present specification. For example, the processor(s) (102, 202) may transmit / receive input / output information of the AI / ML model via the transceiver(s) (106, 206). For another example, the processor(s) (102, 202) may determine the type and / or domain type of the input / output signal of the AI / ML model for channel estimation. For another example, the processor(s) (102, 202) may transmit / receive an AI / ML model identifier (ID) including the type and / or domain type of the input / output signal determined via the transceiver(s) (106, 206).

[0062] One or more memories (104, 204) may be coupled to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. The one or more memories (104, 204) may be configured as ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer-readable storage media, and / or combinations thereof. The one or more memories (104, 204) may be located internally and / or externally to the one or more processors (102, 202). Additionally, the one or more memories (104, 204) may be coupled to the one or more processors (102, 202) via various technologies, such as wired or wireless connections.

[0063] One or more transceivers (106, 206) may transmit user data, control information, wireless signals / channels, etc., as described in the methods and / or flowcharts according to some implementations of this specification to one or more other devices. One or more transceivers (106, 206) may receive user data, control information, wireless signals / channels, etc., as described in the functions, procedures, proposals, methods and / or flowcharts disclosed in this specification from one or more other devices. For example, one or more transceivers (106, 206) may be coupled to one or more processors (102, 202) and may transmit and / or receive wireless signals. For example, one or more processors (102, 202) may control one or more transceivers (106, 206) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (102, 202) may control one or more transceivers (106, 206) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be coupled to one or more antennas (108, 208), and one or more transceivers (106, 206) may be configured to transmit and / or receive user data, control information, wireless signals / channels, or the like, as referred to in the functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this specification, via one or more antennas (108, 208). In this specification, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) may convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals for processing using one or more processors (102, 202).One or more transceivers (106, 206) may convert user data, control information, wireless signals / channels, etc. processed by one or more processors (102, 202) from baseband signals to RF band signals. For this purpose, one or more transceivers (106, 206) may include an (analog) oscillator and / or filter.

[0064] FIG. 3 illustrates a wireless communication system to which some implementations of the present specification are applied.

[0065] Referring to FIG. 3, a wireless communication system may include one or more BSs and one or more UEs. With the introduction of new wireless communication technologies, the amount of data and control information transmitted and received by the BSs and / or UEs is increasing. The introduction of AI technology in wireless communication systems provides enhanced wireless communication systems by enabling system optimization. AI technology overcomes problems arising from modeling issues, implementation complexity, and / or signal distortion / interference of wireless communication system configurations, thereby supporting low-latency, low-complexity, and high-performance communication systems. For example, AI technology can determine appropriate representations for problems that are difficult to model in wireless communication system configurations. For another example, an improved AI-based wireless communication system can reduce implementation complexity by finding an ideal and computationally feasible solution, or can optimize modem parameters. As another example, improved wireless communication systems based on AI can solve the non-linearity problem of existing wireless communication through non-linear function modeling.

[0066] In particular, this specification proposes a method for transmitting and receiving wireless communication signals based on such AI. Specifically, the specification proposes a method for determining an AI / ML model based on a model ID, transmitting and receiving the corresponding model ID, and transmitting and receiving wireless communication signals based on the AI / ML model corresponding to the corresponding model ID. For example, according to some implementations of this specification, a BS and / or a UE may transmit and receive a model ID of an AI / ML model for channel estimation (CE), and transmit and receive channel estimation information based on the AI / ML model corresponding to the corresponding model ID.

[0067] Figure 4 illustrates a channel in wireless communication.

[0068] Specifically, the devices / devices shown in FIGS. 1 and 2 illustrate channels that are transmitted and received and / or estimated according to some implementations of the present specification.

[0069] In wireless communications, a channel can be represented as an image in the subcarrier and time domains. Referring to Figure 4, channel elements in wireless communications are highly correlated in the spatial, temporal, and / or frequency domains. Deep learning (DL), which is powerful for image-related tasks, can efficiently process these channel elements. For example, convolutional neural networks (CNNs) have the potential to exploit correlations between adjacent channel elements in the spatial, temporal, and / or frequency domains. In particular, DL-based channel estimation methods can improve channel estimation performance in extreme environments. For example, in cases where pilots are scarce and / or nonlinear distortions exist, DL can be utilized to implement nonlinear filters for channel estimation.

[0070] Channel estimation according to some implementations of this specification may differ from CSI feedback enhancement, beam management, and / or positioning accuracy enhancement in the following respects: In time division duplexing (TDD) mode, the UE-side AI / ML model and the network-side AI / ML model may be the same due to channel reciprocity. When the UE-side AI / ML model and the network-side AI / ML model are the same, there is an advantage in that there is no need to train the AI / ML model of either the UE or the network while training the AI / ML model of the other. For example, the BS can train the AI / ML model, transmit / forward the trained AI / ML model to the UE, and the UE can use the received / forwarded AI / ML model for channel estimation. The opposite is also possible. For example, the UE can train the AI / ML model, transmit / forward the trained AI / ML model to the BS, and the BS can use the received / forwarded AI / ML model for channel estimation. In this way, when one side trains an AI / ML model and the other side does not train an AI / ML model, it is called offline learning. However, offline learning is not limited to the fact that the UE-side AI / ML model and the network-side AI / ML model are the same. For example, the UE can receive an AI / ML model trained from the BS and additionally train the AI / ML model based on the AI / ML model received from the BS. In this way, (re)training the AI / ML model on the other side that received the AI / ML model is called online learning. In this specification, offline / online learning can also be referred to as offline / online training.

[0071] According to some implementations of this specification, the BS and / or UE may determine an AI / ML model based on a model ID and transmit the determined model ID to the UE and / or BS. The AI / ML model corresponding to the transmitted / received model ID may be trained at the BS and / or UE that transmitted the model ID, or may be trained at the BS and / or UE that received the model ID. In other words, according to the signal transmission / reception method of this specification, both offline learning and / or online learning may be performed.

[0072] Additionally, according to some implementations of the present specification, the BS and the UE may determine an AI / ML model, wherein the BS's AI / ML model and the UE's AI / ML model may be models having a structure known to the BS and the UE. For example, the BS and / or the UE may determine an AI / ML model for channel estimation with the UE and / or the BS, receive a model ID corresponding to the AI / ML model from the UE and / or the BS, and perform channel estimation based on the AI / ML model.

[0073] Below, we specifically describe how to transmit and receive signals according to some implementations of this specification.

[0074] In this specification, determining an AI / ML model based on a model ID may also mean determining a signal type and / or domain type of an input signal and / or an output signal of the AI / ML model to be included in the model ID. For example, the BS and / or the UE may determine the signal type and / or domain type of an input signal and / or an output signal of the AI / ML model, and transmit the model ID including the determined signal type and / or domain type to the UE and / or the BS.

[0075] Figure 5 illustrates a flow of a signal transmission and reception method according to some implementations of this specification.

[0076] Specifically, FIG. 5 illustrates a flow of a method in which the devices / devices shown in FIGS. 1 and 2 transmit and receive input / output information of an AI / ML model.

[0077] Referring to FIG. 5, the BS can determine an AI / ML model based on a model ID (S510), transmit the model ID to the UE (S520), and receive feedback information based on the AI / ML model corresponding to the model ID from the UE (S530). Here, the model ID can include at least one of a signal type or domain type of an input signal and / or an output signal of the AI / ML model. For example, the BS can determine an AI / ML model for channel estimation with the UE based on the model ID, transmit the model ID including the signal type and / or domain type of the corresponding model to the UE, and receive channel estimation information based on the corresponding model from the UE. The model ID can further include an input / output signal size as input information and / or output information of the model.

[0078] As described above, in the case of channel estimation, there may be an advantage that there is no performance impact even if the UE determines the AI / ML model based on the model ID due to the reciprocity of the channel and transmits the model ID including the signal type and / or domain type of the corresponding model to the BS. As in the embodiment described above, it is possible for the BS to transmit the model ID for the AI / ML model of the UE, but in another embodiment of the present specification, the UE may also transmit the model ID for the AI / ML model of the BS in some cases. For example, the UE may determine an AI / ML model for channel estimation with the BS based on the model ID, and transmit the model ID including the signal type and / or domain type of the corresponding model to the BS. In addition, based on the transmitted model ID, channel estimation information based on the AI / ML model corresponding to the corresponding model ID may be received from the BS.

[0079] Additionally, the BS and / or UE may further include determining the signal type and / or domain type to be transmitted and received by including it in the model ID.

[0080] Additionally, the BS and / or UE may transmit at least one of a plurality of signal types and / or a plurality of domain types that is not included in the model ID to the UE and / or BS via either radio resource control (RRC) signaling or downlink control information (DCI).

[0081] Additionally, the signal type included in the model ID may be determined based on the capabilities of the UE among multiple signal types. For example, the BS and / or the UE may determine the signal type of the input signal included in the model ID based on the capabilities of the UE among multiple input signal types.

[0082] Additionally, when the BS transmits a model ID to the UE, feedback information can be received from the UE based on the transmitted model ID, and the signal type of the input signal and / or output signal included in the model ID can be changed based on the feedback information.

[0083] Additionally, the domain type included in the model ID may include at least one of multiple domain types.

[0084] Additionally, the BS and / or UE that transmitted the model ID may further include receiving model identification information corresponding to the AI / ML model from the UE and / or BS that received the model ID.

[0085] Figure 6 illustrates another flow of a signal transmission and reception method according to some implementations of the present specification.

[0086] Specifically, FIG. 6 illustrates another flow of a method in which the devices / devices shown in FIGS. 1 and 2 transmit and receive input / output information of an AI / ML model.

[0087] Referring to FIG. 6, the BS may determine an AI / ML model based on a model ID (S610), transmit the model ID to the UE (S620), transmit an additional signal (S630), and receive feedback information based on the AI / ML model corresponding to the model ID from the UE (S630). Here, the model ID may include at least one of a signal type or a domain type of an input signal and / or an output signal of the AI / ML model. For example, the BS may determine an AI / ML model for channel estimation with the UE based on the model ID, transmit the model ID including the signal type and / or the domain type of the input signal of the corresponding model to the UE, transmit an additional input signal to the UE, and receive channel estimation information based on the corresponding model from the UE. The model ID may further include an input / output signal size as input information and / or output information of the model.

[0088] As described above, in the case of channel estimation, there may be an advantage that there is no performance impact even if the UE determines the AI / ML model based on the model ID due to the reciprocity of the channel, and transmits the model ID including the signal type and / or domain type of the corresponding model to the BS. As in the embodiment described above, it is also possible for the BS to transmit the model ID, input signal and / or output signal for the AI / ML model of the UE, but in another embodiment of the present specification, the UE may also transmit the model ID, input signal and / or output signal for the AI / ML model of the BS depending on the case. For example, the UE may determine an AI / ML model for channel estimation with the BS based on the model ID, transmit the model ID including the signal type and / or domain type of the input signal of the corresponding model to the BS, and transmit an additional input signal to the BS. In addition, based on the transmitted model ID, channel estimation information based on the AI / ML model corresponding to the corresponding model ID may be received from the BS.

[0089] Additionally, the BS and / or UE may further include determining the signal type and / or domain type to be transmitted and received by including it in the model ID.

[0090] Additionally, in S630, the BS and / or the UE may transmit an additional input signal and / or an additional output signal to the UE and / or the BS via either RRC signaling or DCI. For example, the BS and / or the UE may transmit a model ID including a signal type and a domain type of a first input signal to the UE and / or the BS, and transmit a second input signal to the UE and / or the BS via either RRC signaling or DCI.

[0091] Additionally, the BS and / or the UE may transmit to the UE and / or the BS at least one of the plurality of signal types and / or the plurality of domain types, which is not included in the model ID, via either RRC signaling or DCI. For example, the BS and / or the UE may transmit the signal type of the first input signal and / or the domain type of the plurality of domain types, which is not included in the model ID, by including the signal type of the first input signal and / or the domain type of the plurality of domain types in the model ID, and may transmit other signal types and / or domain types, which are not included in the model ID, via either RRC signaling or DCI.

[0092] Additionally, the signal type included in the model ID may be determined based on the capabilities of the UE among multiple signal types. For example, the BS and / or the UE may determine the signal type of the input signal included in the model ID based on the capabilities of the UE among multiple input signal types.

[0093] Additionally, when the BS transmits a model ID to the UE, feedback information can be received from the UE based on the transmitted model ID, and the signal type of the input signal and / or output signal included in the model ID can be changed based on the feedback information.

[0094] Additionally, the domain type included in the model ID may include at least one of multiple domain types.

[0095] Additionally, the BS and / or UE that transmitted the model ID may further include receiving model identification information corresponding to the AI / ML model from the UE and / or BS that received the model ID.

[0096] S610, S620, and S640 of FIG. 6 may correspond to S510 to S530 of FIG. 5.

[0097] According to some implementations of the present specification, a method is provided for transmitting and receiving an input signal, an output signal, and / or a domain type of an AI / ML model using a model ID in a wireless communication system. Through this, a BS and / or a UE can efficiently transmit and receive wireless communication signals. For example, according to some implementations of the present specification, a BS and / or a UE that transmits and receives an AI / ML model ID for channel estimation can perform inference of the AI / ML model based on input / output signal information and / or domain information included in the model ID, and perform channel estimation based on the AI / ML model, thereby reducing implementation complexity, computational load, time delay, etc.

[0098] Figure 7 illustrates the structure of wireless communication based on deep learning (DL).

[0099] Specifically, FIG. 7 is an example of the structure of a DL-based wireless communication system to which the method(s) described in FIGS. 5 to 6 are applied.

[0100] Referring to FIG. 7, a communication system can be designed block-by-block. A DL-based block-structured communication system can be composed of multiple blocks to partition signal processing. For example, a DL-based block-structured communication system can be composed of a channel encoding / decoding block (702), a modulation / demodulation block (704), a radio frequency (RF) transceiver (706), a channel estimation block (708), and a signal detection block (710). Here, each block(s) can be a processing block(s) individually / independently optimized based on DL for stable communication according to some implementations of the present specification.

[0101] In particular, the channel estimation block (708) may perform channel state information (CSI) estimation and / or direction of arrival (DOA) estimation according to some implementations of the present specification to improve the wireless communication environment. For example, according to some implementations of the present specification, the channel estimation block (708) may transmit and receive a model ID for channel estimation, and perform CSI estimation, DOA estimation, etc. based on an AI / ML model corresponding to the model ID, thereby improving the implementation complexity and the performance of the wireless communication system for a small number of pilot symbols.

[0102] Figure 8 illustrates another structure of wireless communication based on deep learning (DL).

[0103] Specifically, FIG. 8 is an example of the structure of a DL-based wireless communication system to which the method(s) described in FIGS. 5 to 6 are applied.

[0104] Block-structured communication systems allow for individual / independent optimization of blocks, but do not guarantee overall system performance. As an alternative to such block-structured communication systems, the DL-based end-to-end communication system illustrated in Figure 8 is trained based on DL, enabling optimization of the entire system.

[0105] Some implementations of this specification are applicable to DL-based end-to-end communication systems, depending on their embodiment.

[0106] Below, we specifically describe methods for determining and transmitting the model ID of an AI / ML model according to the implementations of this specification. Specifically, we describe methods for transmitting and receiving input / output information and / or domain information of an AI / ML model.

[0107] FIGS. 9 and 10 illustrate examples of a flow of a method for transmitting and receiving input / output information of an artificial intelligence (AI) and / or machine learning (ML) based model according to some implementations of the present specification.

[0108] Specifically, FIGS. 9 to 10 are examples of a method for transmitting and receiving input / output information and / or domain information of an AI / ML model based on the method(s) described in FIGS. 5 to 6.

[0109] According to some implementations of this specification, a BS and / or a UE may transmit and receive a model ID of an AI / ML model. Input information and / or output information (e.g., input / output signal types and / or input / output signal magnitudes, etc.) of an AI / ML model are important information for performing inference of the AI / ML model. Performing inference of a model means performing model training and applying new input data to the trained model to produce a result. In other words, model inference may mean inputting new data to a trained model and performing result prediction. This specification proposes a method for transmitting and receiving such input information and / or output information of an AI / ML model. Specifically, a method is proposed for determining and transmitting and receiving input information, output information (e.g., input / output signal types, input / output signal magnitudes) of a model, which are important information for performing channel estimation based on an AI / ML model, and the same.

[0110] 1. Proposal 1

[0111] The following multiple signal types can be defined as input signal types for AI / ML models for channel estimation.

[0112] - Type 1a: Received pilot signal

[0113] - Type 1b: Estimated channel using the least-squares (LS) method corresponding to the pilot signal location.

[0114] - Type 2a: Estimated channel using least-squares (LS) method and linear interpolation method for channel grid (also called resource grid)

[0115] - Type 2b: Estimated channel using minimum mean square error (MMSE) for the channel grid.

[0116] Additionally, the domain type for channel estimation includes any one of the time domain, frequency domain, spatial domain, angle domain, and / or delay domain. The domain type may also be defined as a combination of at least one of these multiple domain types.

[0117] (1) Proposal 1-(1)

[0118] Referring to FIG. 9, the BS and / or UE can determine the signal type and / or domain type of the input / output signal of the AI / ML model (S910).

[0119] Here, the signal type of the input / output signal may be any one of a plurality of signal types. The BS and / or the UE may determine the signal type of the corresponding signal based on the capability of the UE among the plurality of signal types. For example, among signal types 1a to 2b, type 1a or type 1b may be selected / determined / indicated based on the UE's low capability. For another example, among signal types 1a to 2b, type 2a or type 2b may be selected / determined / indicated based on the UE's high capability.

[0120] Additionally, the domain type may include at least one of a plurality of domain types for channel estimation. The BS and / or the UE may determine at least one of the plurality of domain types and / or a combination of at least one or more of the plurality of domain types as the domain type of the AI / ML model. Here, the BS and / or the UE may select / determine / indicate a domain type among the plurality of domain types that has a high correlation with the AI / ML model and / or the channel between the BS and the UE.

[0121] Additionally, the BS and / or UE may include the determined signal type and / or the determined domain type in the model ID and transmit it to the UE and / or BS (S920).

[0122] Additionally, the BS and / or UE that transmitted the model ID may further include receiving model identification information corresponding to the AI / ML model from the UE and / or BS that received the model ID.

[0123] In performing channel estimation based on an AI / ML model according to some implementations of this specification, due to channel reciprocity, the AI / ML model on the UE side that transmits and receives the model ID and the AI / ML model on the BS side that transmits and receives the model ID may be the same, and the BS and / or the UE may perform inference on the AI / ML model corresponding to the transmitted and received model ID. For example, the UE may receive a model ID including input / output information and / or domain information from the BS, and perform channel estimation based on the model corresponding to the model ID. The opposite is also true.

[0124] (2) Proposal 1-(2)

[0125] Referring to FIG. 10, the BS and / or the UE may determine a signal type and / or a domain type of an input / output signal of an AI / ML model (S1010). Here, the signal type of the input / output signal may be any one of a plurality of signal types. The BS and / or the UE may determine the signal type of the corresponding signal based on the capability of the UE among the plurality of signal types. In addition, the domain type may include at least one of a plurality of domain types for channel estimation. The BS and / or the UE may determine at least one of the plurality of domain types and / or a combination of at least one of the plurality of domain types as the domain type of the AI / ML model. Here, the BS and / or the UE may select / indicate a domain type having a high correlation between the AI / ML model and / or the channel between the BS and the UE among the plurality of domain types. For example, if the channel between the BS and the UE is highly correlated in the time domain (i.e., the UE does not move quickly), the domain of the AI / ML model may be selected / determined / indicated as the time domain. For another example, if the channel between the BS and the UE is highly correlated in the frequency domain (i.e., the multipath channel is small), the domain of the AI / ML model may be selected / determined / indicated as the frequency domain.

[0126] Additionally, the BS and / or UE may include the determined signal type and / or the determined domain type in the model ID and transmit it to the UE and / or BS (S1020).

[0127] In addition, the BS and / or the UE may transmit signal type(s) and / or domain type(s) not included in the model ID among the plurality of signal types and / or the plurality of domain types to the UE and / or the BS (S1030). Here, the signal type(s) and / or domain type(s) not included in the model ID may be transmitted via any one of RRC signaling and / or DCI. For example, the BS may transmit to the UE any one of the signal type or domain type of an input / output signal of an AI / ML model by including it in the model ID, and may transmit to the UE at least any one of the plurality of signal types or the plurality of domain types not included in the model ID to the UE via any one of RRC signaling and / or DCI.

[0128] Additionally, the BS and / or UE that transmitted the model ID may further include receiving model identification information corresponding to the AI / ML model from the UE and / or BS that received the model ID.

[0129] (3) Proposal 1-(3)

[0130] The signal type selected / determined / indicated by the BS and / or UE may be changed based on the UE capability. For example, after Type 1a or Type 1b is selected / determined / indicated among signal types 1a to 2b, the signal type may be changed to Type 2a or Type 2b based on the UE's higher capability. For another example, after Type 2a or Type 2b is selected / determined / indicated among signal types 1a to 2b, the signal type may be changed to Type 1a or Type 1b based on the UE's lower capability.

[0131] The BS and / or the UE may receive feedback from the UE and / or the BS that received the model ID based on the model ID including input / output information and / or domain information transmitted, and may change signal information of the AI / ML model corresponding to the model ID based on the feedback. The feedback may include information related to the capability of the UE and / or information related to the signal information. For example, the BS may determine the signal type of the input signal of the AI / ML model, transmit the model ID including the determined signal type to the UE, and receive feedback thereon from the UE. The UE that received the feedback may change the signal type of the input signal to a signal type that is highly correlated with the capability of the UE based on the feedback. The opposite is also true.

[0132] 2. Proposal 2

[0133] The BS and / or UE may transmit a model ID including input / output signal information and / or domain information of the AI / ML model to the UE and / or BS, and transmit / instruct additional input / output signals to the UE and / or BS.

[0134] For example, the BS may determine an AI / ML model for channel estimation with the UE based on a model ID, transmit the model ID including the signal type and / or domain type of the first input signal of the model to the UE, and additionally transmit / instruct the second input signal to the UE. The reverse is also true.

[0135] AI / ML models can operate even without these additional signals. For example, the BS and / or UE can perform channel estimation using the AI / ML model without any additional signals, such as through predefined default values ​​or RRC signaling. However, additional signals may be necessary to improve the performance and / or robustness of the AI / ML model. For example, additional input signals may include the received signal-to-noise ratio (SNR), beam-related information, and / or the UE's velocity.

[0136] The BS and / or UE may transmit a model ID containing information about input / output signals of the AI / ML model and / or domain information, and may transmit / indicate additional signals via either RRC signaling and / or DCI.

[0137] Additionally, the BS and / or UE may select one or more inputs from the pre-defined parameter(s) as additional inputs and transmit them to the UE and / or BS via RRC signaling or DCI.

[0138] By transmitting and receiving additional inputs and outputs, the performance of AI / ML models and / or operations utilizing them can be improved. For example, inputting received SNR can improve the performance of an AI / ML model for channel estimation.

[0139] The methods according to Proposals 1 and 2 are examples for transmitting and receiving input / output information and / or domain information of an AI / ML model according to some implementations of this specification, and are not limited thereto, and an AI / ML model may be transmitted and received by a method combining each of the methods included in Proposals 1 and 2.

[0140] In this specification, a computer-readable storage medium can store at least one instruction or computer program, which when executed by at least one processor can cause the at least one processor to perform operations according to some embodiments or implementations of this specification.

[0141] In this specification, a computer program or computer program product may be recorded on at least one computer-readable (non-volatile) storage medium and may contain instructions that, when executed, cause (at least one processor) to perform operations according to some embodiments or implementations of this specification.

[0142] In this specification, a processing device or apparatus may include at least one processor and at least one computer memory connectable to the at least one processor. The at least one computer memory may store instructions or programs, which, when executed, cause at least one processor operably connected to the at least one memory to perform operations according to some embodiments or implementations of the present specification.

[0143] As described above, the examples disclosed in this specification are provided to enable those skilled in the relevant technical fields to implement and practice this specification. While the examples of this specification have been described above with reference to the examples, those skilled in the relevant technical fields can modify and adapt the examples of this specification in various ways. For example, those skilled in the art can utilize the individual components described in the examples of this specification in combination with each other.

Claims

1. When a base station transmits and receives signals in a wireless communication system, Determine the user equipment (UE) and artificial intelligence or machine learning-based model for channel estimation based on the model identifier (ID), Transmit the above model ID to the UE, and Including receiving channel estimation information based on the model from the UE, The above model ID includes at least one of a signal type or a domain type of a first input signal of the model. Method of transmitting and receiving signals.

2. In paragraph 1, Further comprising transmitting to the UE at least one of a plurality of signal types or a plurality of domain types that is not included in the model ID via either radio resource control (RRC) signaling or downlink control information (DCI). Method of transmitting and receiving signals.

3. In paragraph 1, Further comprising transmitting information about the second input signal of the model to the UE via either radio resource control (RRC) signaling or downlink control information (DCI). Method of transmitting and receiving signals.

4. In paragraph 1, Further comprising determining the signal type based on the capability of the UE among a plurality of input signal types. Method of transmitting and receiving signals.

5. In paragraph 1, Based on the transmission of the above model ID, feedback is received from the UE, Further comprising changing the signal type of the first input signal based on the above feedback. Method of transmitting and receiving signals.

6. In paragraph 1, The domain type includes at least one of a plurality of domain types for channel estimation, Method of transmitting and receiving signals.

7. In paragraph 1, Further comprising receiving model identification information corresponding to the model from the UE or a UE other than the UE, Method of transmitting and receiving signals.

8. In paragraph 1, The above model ID further includes information about the output signal of the model. Method of transmitting and receiving signals.

9. In a base station that transmits and receives signals in a wireless communication system, At least one transmitter / receiver; at least one processor; and At least one computer memory operably connected to said at least one processor and storing instructions that, when executed, cause said at least one processor to perform operations, said operations comprising: Determine the user equipment (UE) and artificial intelligence or machine learning-based model for channel estimation based on the model identifier (ID), Transmit the above model ID to the UE, and Including receiving channel estimation information based on the model from the UE, The above model ID includes at least one of a signal type or a domain type of a first input signal of the model. Base station.

10. In paragraph 9, The above actions are: Further comprising transmitting to the UE at least one of a plurality of signal types or a plurality of domain types that is not included in the model ID via either radio resource control (RRC) signaling or downlink control information (DCI). Base station.

11. In paragraph 9, The above actions are: Further comprising transmitting information about the second input signal of the model to the UE via either radio resource control (RRC) signaling or downlink control information (DCI). Base station.

12. In paragraph 9, The above actions are: Further comprising determining the signal type based on the capability of the UE among a plurality of input signal types. Base station.

13. In paragraph 9, Based on the transmission of the above model ID, feedback is received from the UE, Further comprising changing the signal type of the first input signal based on the above feedback. Base station.

14. In paragraph 9, The domain type includes at least one of a plurality of domain types for channel estimation, Base station.

15. In paragraph 9, The above actions are: Further comprising receiving model identification information corresponding to the model from the UE or a UE other than the UE, Base station.

16. In paragraph 9, The above model ID further includes information about the output signal of the model. Base station.

17. In a method for a user device to transmit and receive signals in a wireless communication system, Determine an artificial intelligence or machine learning-based model for base station and channel estimation, Receive a model identifier (ID) corresponding to the above model from the base station, and Including transmitting channel estimation information through the above model to the base station, The above model ID includes at least one of a signal type or a domain type of the first input signal of the model. Method of transmitting and receiving signals.

18. In a user device that transmits and receives signals in a wireless communication system, At least one transmitter / receiver; at least one processor; and At least one computer memory operably connected to said at least one processor and storing instructions that, when executed, cause said at least one processor to perform operations, said operations comprising: Determine an artificial intelligence or machine learning-based model for base station and channel estimation, Receive a model identifier (ID) corresponding to the above model from the base station, and Including transmitting channel estimation information through the above model to the base station, The above model ID includes at least one of a signal type or a domain type of the first input signal of the model. User device.

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