Apparatus and method for adaptive channel information feedback in wireless communication system

Abstract

The objective of the present disclosure is to adaptively feed back channel information in a wireless communication system. An operation method of a terminal may comprise the steps of: receiving configuration information related to channel state information (CSI) feedback; receiving reference signals on the basis of the configuration information; generating CSI feedback information on the basis of the reference signals; and transmitting the CSI feedback information.

Description

Device and method for adaptive channel information feedback in wireless communication systems

[0001] The following description relates to a wireless communication system, and to a device and method for adaptively feeding back channel information in a wireless communication system.

[0002] Wireless access systems are widely deployed to provide various types of communication services, such as voice and data. Typically, wireless access systems are multiple access systems that support communications with multiple users by sharing available system resources (e.g., bandwidth, transmission power). Examples of multiple access systems include code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), orthogonal frequency division multiple access (OFDMA), and single-carrier frequency division multiple access (SC-FDMA).

[0003] In particular, as numerous communication devices demand greater communication capacity, enhanced mobile broadband (eMBB) communication technologies are being proposed, improving upon existing radio access technology (RAT). Furthermore, massive machine type communications (mMTC), which connects numerous devices and objects to provide diverse services anytime and anywhere, as well as communication systems that consider reliability and latency-sensitive services / user equipment (UE), are being proposed. Various technological configurations are being proposed for these purposes.

[0004] The present disclosure can provide a device and method for effectively feeding back channel state information (CSI) in a wireless communication system.

[0005] The present disclosure may provide a device and method for adaptively controlling a feedback rate of CSI in a wireless communication system.

[0006] The present disclosure may provide a device and method for performing CSI feedback using temporal correlation in a wireless communication system.

[0007] The present disclosure can provide a device and method for generating side information necessary to restore information representing a channel and channel information in a wireless communication system.

[0008] The present disclosure may provide a device and method for encoding and decoding channel information based on statistical information about the channel information in a wireless communication system.

[0009] The present disclosure may provide a device and method for generating prior information for channel information in a wireless communication system.

[0010] The present disclosure may provide a device and method for determining Doppler information of a channel in a data-driven manner in a wireless communication system.

[0011] The present disclosure may provide a device and method for providing a framework for feeding back a channel instance expressed in an image format in a wireless communication system.

[0012] The present disclosure may provide a device and method for providing a framework for encoding and decoding channel information measured using a reference signal in a wireless communication system using the temporal correlation of the channel.

[0013] The present disclosure may provide a device and method for providing a framework for feeding back channel information measured using a reference signal in a wireless communication system at a variable transmission rate based on statistical information related to the channel.

[0014] The technical objectives to be achieved in the present disclosure are not limited to those mentioned above, and other technical tasks not mentioned can be considered by a person having ordinary skill in the technical field to which the technical configuration of the present disclosure is applied from the embodiments of the present disclosure described below.

[0015] As one embodiment of the present disclosure, a method of operating a terminal in a wireless communication system may include a step of receiving configuration information related to channel state information (CSI) feedback, a step of receiving reference signals based on the configuration information, a step of generating CSI feedback information based on the reference signals, and a step of transmitting the CSI feedback information. The CSI feedback information may include channel information determined based on the reference signals and information related to a temporal correlation of the channel information.

[0016] As one embodiment of the present disclosure, a method of operating a base station in a wireless communication system may include a step of transmitting configuration information related to channel state information (CSI) feedback, a step of transmitting reference signals based on the configuration information, a step of receiving CSI feedback information corresponding to the reference signals, and a step of restoring channel information based on the CSI feedback information. The CSI feedback information may include channel information determined based on the reference signals and information related to a temporal correlation of the channel information.

[0017] As one embodiment of the present disclosure, a terminal in a wireless communication system includes a transceiver and a processor connected to the transceiver, wherein the processor receives configuration information related to channel state information (CSI) feedback, receives reference signals based on the configuration information, generates CSI feedback information based on the reference signals, and controls transmission of the CSI feedback information, wherein the CSI feedback information may include channel information determined based on the reference signals and information related to a temporal correlation of the channel information.

[0018] As one embodiment of the present disclosure, a base station in a wireless communication system includes a transceiver and a processor connected to the transceiver, wherein the processor transmits configuration information related to channel state information (CSI) feedback, transmits reference signals based on the configuration information, receives CSI feedback information corresponding to the reference signals, and controls to restore channel information based on the CSI feedback information, wherein the CSI feedback information may include channel information determined based on the reference signals and information related to a temporal correlation of the channel information.

[0019] As one embodiment of the present disclosure, a communication device includes at least one processor, and at least one computer memory coupled to the at least one processor and storing instructions that direct operations when executed by the at least one processor, wherein the operations may include: receiving configuration information related to channel state information (CSI) feedback, receiving reference signals based on the configuration information, generating CSI feedback information based on the reference signals, and transmitting the CSI feedback information. The CSI feedback information may include channel information determined based on the reference signals and information related to a temporal correlation of the channel information.

[0020] As one embodiment of the present disclosure, a non-transitory computer-readable medium storing at least one instruction includes at least one instruction executable by a processor, wherein the at least one instruction controls a device to receive configuration information related to channel state information (CSI) feedback, receive reference signals based on the configuration information, generate CSI feedback information based on the reference signals, and transmit the CSI feedback information, wherein the CSI feedback information may include channel information determined based on the reference signals and information related to a temporal correlation of the channel information.

[0021] The above-described aspects of the present disclosure are only some of the preferred embodiments of the present disclosure, and various embodiments reflecting the technical features of the present disclosure can be derived and understood by a person having ordinary skill in the art based on the detailed description of the present disclosure to be described below.

[0022] The following effects may be achieved by embodiments based on the present disclosure.

[0023] According to the present disclosure, channel information can be effectively fed back.

[0024] The effects that can be obtained from the embodiments of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned can be clearly derived and understood by those skilled in the art to which the technical configuration of the present disclosure is applied, from the description of the embodiments of the present disclosure below. In other words, unintended effects resulting from implementing the configuration described in the present disclosure can also be derived from the embodiments of the present disclosure by those skilled in the art.

[0025] The accompanying drawings are intended to aid understanding of the present disclosure and, together with detailed descriptions, may provide embodiments of the present disclosure. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with each other to form new embodiments. Reference numerals in each drawing may indicate structural elements.

[0026] Figure 1 illustrates an example of a communication system applicable to the present disclosure.

[0027] FIG. 2 illustrates an example of a wireless device applicable to the present disclosure.

[0028] FIG. 3 illustrates a method for processing a transmission signal applicable to the present disclosure.

[0029] Figure 4 illustrates a communication procedure between a terminal and a base station applicable to the present disclosure.

[0030] FIG. 5 illustrates an example of a communication structure that can be provided in a 6G (6th generation) system applicable to the present disclosure.

[0031] Figure 6 illustrates an electromagnetic spectrum applicable to the present disclosure.

[0032] FIG. 7 illustrates a THz wireless communication transceiver applicable to the present disclosure.

[0033] Figure 8 illustrates a THz signal generation method applicable to the present disclosure.

[0034] FIG. 9 illustrates a wireless communication transceiver applicable to the present disclosure.

[0035] Figure 10 illustrates a transmitter structure applicable to the present disclosure.

[0036] Figure 11 illustrates a modulator structure applicable to the present disclosure.

[0037] Figure 12 illustrates the structure of a perceptron included in an artificial neural network applicable to the present disclosure.

[0038] Figure 13 illustrates an artificial neural network structure applicable to the present disclosure.

[0039] Figure 14 illustrates an example of a functional framework for application of artificial intelligence technology applicable to the present disclosure.

[0040] Figure 15 illustrates an example of a procedure for utilizing an artificial intelligence model applicable to the present disclosure.

[0041] Figure 16 illustrates another example of a procedure for utilizing an artificial intelligence model applicable to the present disclosure.

[0042] Figure 17 illustrates another example of a procedure for utilizing an artificial intelligence model applicable to the present disclosure.

[0043] Figure 18 illustrates an AI technology-based communication procedure applicable to the present disclosure.

[0044] Figure 19 illustrates an example of a neural network structure for channel state information (CSI) feedback.

[0045] Figure 20 illustrates an example of the processing of a CSI matrix in a neural network for channel state information feedback.

[0046] Figure 21 illustrates another example of a neural network structure for channel state information feedback.

[0047] Figure 22 illustrates the concept of CSI feedback supporting variable feedback transmission rate.

[0048] Figure 23 illustrates an example of distortion imbalance between channel state information matrix instances.

[0049] Figure 24 illustrates an example of a procedure for channel state information feedback supporting variable transmission rates.

[0050] FIG. 25a illustrates an example of the structure of a transmitting device according to one embodiment of the present disclosure.

[0051] FIG. 25b illustrates an example of the structure of a receiving device according to one embodiment of the present disclosure.

[0052] FIG. 26a illustrates an example of the structure of a transmitting device using Doppler estimation and entropy coding according to one embodiment of the present disclosure.

[0053] FIG. 26b illustrates a more detailed example of the structure of a receiving device using Doppler estimation and entropy coding according to one embodiment of the present disclosure.

[0054] FIG. 26b illustrates a more detailed example of the structure of a receiving device using Doppler estimation and entropy coding according to one embodiment of the present disclosure.

[0055] FIG. 26c illustrates an example of a conceptual structure of a transmitting device and a receiving device according to one embodiment of the present disclosure.

[0056] FIG. 27a illustrates an example of an entropy bottleneck layer for entropy encoding / decoding according to one embodiment of the present disclosure.

[0057] FIG. 27b illustrates an example of a structure for bottleneck layer training according to one embodiment of the present disclosure.

[0058] FIG. 28a illustrates an example of an entropy model according to one embodiment of the present disclosure.

[0059] FIG. 28b illustrates an example of an entropy model operating based on a channel latent vector according to one embodiment of the present disclosure.

[0060] FIG. 28c illustrates an example of an entropy model operating based on a Doppler potential vector according to one embodiment of the present disclosure.

[0061] FIG. 29 illustrates an example of a procedure for transmitting channel state information (CSI) according to one embodiment of the present disclosure.

[0062] FIG. 30 illustrates an example of a procedure for generating CSI feedback information according to one embodiment of the present disclosure.

[0063] FIG. 31 illustrates an example of a procedure for obtaining CSI according to one embodiment of the present disclosure.

[0064] FIG. 32 illustrates an example of a procedure for interpreting CSI feedback information according to one embodiment of the present disclosure.

[0065] FIG. 33 illustrates an example of a CSI feedback procedure according to one embodiment of the present disclosure.

[0066] Figure 34 shows an example of the probability distribution of elements of the code space, the quantization results of those elements, and those elements combined with noise.

[0067] The following embodiments combine the components and features of the present disclosure in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented without being combined with other components or features. Furthermore, some components and / or features may be combined to form embodiments of the present disclosure. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment or may be replaced with corresponding components or features of another embodiment.

[0068] In the description of the drawings, procedures or steps that may obscure the gist of the present disclosure are not described, and procedures or steps that can be understood by a person skilled in the art are also not described.

[0069] Throughout the specification, when a part is said to "comprising" or "including" a component, this does not mean that other components may be included, but rather that other components may be excluded, unless otherwise specifically stated. In addition, terms such as "...part," "...unit," and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented by hardware, software, or a combination of hardware and software. In addition, the words "a" or "an," "one," "the," and similar related words may be used in the context of describing the present disclosure (especially in the context of the claims below) to include both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

[0070] Embodiments of the present disclosure described herein focus on the data transmission and reception relationship between a base station and a mobile station. Here, the base station is understood as a terminal node of a network that directly communicates with the mobile station. Certain operations described herein as being performed by the base station may, in some cases, be performed by an upper node of the base station.

[0071] That is, in a network consisting of multiple network nodes including a base station, various operations performed for communication with a mobile station may be performed by the base station or other network nodes other than the base station. In this case, the term 'base station' may be replaced by terms such as fixed station, Node B, eNB (eNode B), gNB (gNode B), ng-eNB, advanced base station (ABS), or access point.

[0072] Additionally, in the embodiments of the present disclosure, the term terminal may be replaced with terms such as user equipment (UE), mobile station (MS), subscriber station (SS), mobile subscriber station (MSS), mobile terminal, or advanced mobile station (AMS).

[0073] Additionally, a transmitter refers to a fixed and / or mobile node that provides data or voice services, and a receiver refers to a fixed and / or mobile node that receives data or voice services. Therefore, for uplink, a mobile station can be the transmitter, and a base station can be the receiver. Similarly, for downlink, a mobile station can be the receiver, and a base station can be the transmitter.

[0074] Embodiments of the present disclosure may be supported by standard documents disclosed in at least one of wireless access systems, such as IEEE 802.xx system, 3rd Generation Partnership Project (3GPP) system, 3GPP Long Term Evolution (LTE) system, 3GPP 5G (5th generation) NR (New Radio) system and 3GPP2 system, and in particular, embodiments of the present disclosure may be supported by 3GPP TS (technical specification) 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.321 and 3GPP TS 38.331 documents.

[0075] Furthermore, the embodiments of the present disclosure can be applied to other wireless access systems and are not limited to the systems described above. For example, they can be applied to systems implemented after the 3GPP 5G NR system and are not limited to a specific system.

[0076] That is, obvious steps or parts not described in the embodiments of the present disclosure can be explained by referring to the above documents. In addition, all terms disclosed in this document can be explained by the above standard documents.

[0077] Hereinafter, preferred embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. The detailed description set forth below, together with the accompanying drawings, is intended to illustrate exemplary embodiments of the present disclosure and is not intended to represent the only embodiments in which the technical configurations of the present disclosure may be implemented.

[0078] Additionally, specific terms used in the embodiments of the present disclosure are provided to aid in understanding of the present disclosure, and the use of such specific terms may be changed to other forms without departing from the technical spirit of the present disclosure.

[0079] The following technology can be applied to various wireless access systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access).

[0080] In order to make the following description clear, the following description is based on a 3GPP communication system (e.g., LTE, NR, etc.), but the technical idea of ​​the present invention is not limited thereto. LTE may refer to technology after 3GPP TS 36.xxx Release 8. Specifically, LTE technology after 3GPP TS 36.xxx Release 10 may be referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 may be referred to as LTE-A pro. 3GPP NR may refer to technology after TS 38.xxx Release 15. 3GPP 6G may refer to technology after TS Release 17 and / or Release 18. "xxx" refers to a standard document detail number. LTE / NR / 6G may be collectively referred to as a 3GPP system.

[0081] For background information, terms, abbreviations, etc. used in this disclosure, reference may be made to standard documents published prior to the invention of the present invention. For example, reference may be made to the 36.xxx and 38.xxx standard documents.

[0082] Communication system applicable to the present disclosure

[0083] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods and / or operational flowcharts of the present disclosure disclosed in this document may be applied to various fields requiring wireless communication / connectivity (e.g., 5G) between devices.

[0084] Hereinafter, more specific examples will be provided with reference to the drawings. In the drawings / descriptions below, the same drawing reference numerals may represent identical or corresponding hardware blocks, software blocks, or functional blocks, unless otherwise described.

[0085] FIG. 1 is a diagram illustrating an example of a communication system applied to the present disclosure.

[0086] Referring to FIG. 1, a communication system (100) applied to the present disclosure includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using a wireless access technology (e.g., 5G NR, LTE) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (extended reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Things) device (100f), and an AI (artificial intelligence) device / server (100g). For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc. Here, the vehicles (100b-1, 100b-2) may include unmanned aerial vehicles (UAVs) (e.g., drones). The XR devices (100c) include augmented reality (AR) / virtual reality (VR) / mixed reality (MR) devices, and may be implemented in the form of head-mounted devices (HMDs), head-up displays (HUDs) installed in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, etc. The portable devices (100d) may include smartphones, smart pads, wearable devices (e.g., smartwatches, smart glasses), computers (e.g., laptops, etc.), etc. The home appliances (100e) may include TVs, refrigerators, washing machines, etc. The IoT devices (100f) may include sensors, smart meters, etc. For example, the base station (120) and the network (130) may also be implemented as wireless devices, and a specific wireless device (120a) may act as a base station / network node to other wireless devices.

[0087] Wireless devices (100a to 100f) can be connected to a network (130) via a base station (120). AI technology can be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (100g) via a network (130). The network (130) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, etc. The wireless devices (100a to 100f) can communicate with each other via the base station (120) / network (130), but can also communicate directly (e.g., sidelink communication) without going through the base station (120) / network (130). For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (vehicle to vehicle) / V2X (vehicle to everything) communication). In addition, IoT devices (100f) (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0088] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a to 100f) / base stations (120), and base stations (120) / base stations (120). Here, the wireless communication / connection can be established through various wireless access technologies (e.g., 5G NR) such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and communication between base stations (150c) (e.g., relay, IAB (integrated access backhaul)). Through the wireless communication / connection (150a, 150b, 150c), the wireless device and base station / wireless device, and base stations and base stations can transmit / receive wireless signals to / from each other. For example, the wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, based on various proposals of the present disclosure, at least some of various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), resource allocation processes, etc. may be performed.

[0089] Communication system applicable to the present disclosure

[0090] FIG. 2 is a diagram illustrating an example of a wireless device applicable to the present disclosure.

[0091] Referring to FIG. 2, the first wireless device (200a) and the second wireless device (200b) can transmit and receive wireless signals via various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (200a), the second wireless device (200b)} can correspond to {the wireless device (100x), the base station (120)} and / or {the wireless device (100x), the wireless device (100x)} of FIG. 1.

[0092] A first wireless device (200a) includes one or more processors (202a) and one or more memories (204a), and may further include one or more transceivers (206a) and / or one or more antennas (208a). The processor (202a) controls the memories (204a) and / or the transceivers (206a), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (202a) may process information in the memory (204a) to generate first information / signals, and then transmit a wireless signal including the first information / signals via the transceivers (206a). In addition, the processor (202a) may receive a wireless signal including second information / signals via the transceivers (206a), and then store information obtained from signal processing of the second information / signals in the memory (204a). The memory (204a) may be connected to the processor (202a) and may store various information related to the operation of the processor (202a). For example, the memory (204a) may perform some or all of the processes controlled by the processor (202a), or may store software code including instructions for performing the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. Here, the processor (202a) and the memory (204a) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206a) may be connected to the processor (202a) and may transmit and / or receive wireless signals via one or more antennas (208a). The transceiver (206a) may include a transmitter and / or a receiver. The transceiver (206a) may be used interchangeably with an RF (radio frequency) unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0093] The second wireless device (200b) includes one or more processors (202b), one or more memories (204b), and may further include one or more transceivers (206b) and / or one or more antennas (208b). The processor (202b) controls the memories (204b) and / or the transceivers (206b), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (202b) may process information in the memory (204b) to generate third information / signals, and then transmit a wireless signal including the third information / signals via the transceivers (206b). In addition, the processor (202b) may receive a wireless signal including fourth information / signals via the transceivers (206b), and then store information obtained from the signal processing of the fourth information / signals in the memory (204b). The memory (204b) may be connected to the processor (202b) and may store various information related to the operation of the processor (202b). For example, the memory (204b) may perform some or all of the processes controlled by the processor (202b), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. Here, the processor (202b) and the memory (204b) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206b) may be connected to the processor (202b) and may transmit and / or receive wireless signals via one or more antennas (208b). The transceiver (206b) may include a transmitter and / or a receiver. The transceiver (206b) may be used interchangeably with an RF unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0094] Hereinafter, hardware elements of the wireless device (200a, 200b) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (202a, 202b). For example, one or more processors (202a, 202b) may implement one or more layers (e.g., functional layers such as physical (PHY), media access control (MAC), radio link control (RLC), packet data convergence protocol (PDCP), radio resource control (RRC), and service data adaptation protocol (SDAP)). One or more processors (202a, 202b) may generate one or more Protocol Data Units (PDUs) and / or one or more Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. One or more processors (202a, 202b) may generate messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein. One or more processors (202a, 202b) 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 herein, and provide the signals to one or more transceivers (206a, 206b). One or more processors (202a, 202b) may receive signals (e.g., baseband signals) from one or more transceivers (206a, 206b) and obtain PDUs, SDUs, messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein.

[0095] One or more processors (202a, 202b) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. One or more processors (202a, 202b) 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 (202a, 202b). The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software configured to perform one or more processors (202a, 202b) or stored in one or more memories (204a, 204b) and executed by one or more processors (202a, 202b). The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and / or sets of instructions.

[0096] One or more memories (204a, 204b) may be coupled to one or more processors (202a, 202b) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. The one or more memories (204a, 204b) may be configured as read only memory (ROM), random access memory (RAM), erasable programmable read only memory (EPROM), flash memory, hard drives, registers, cache memory, computer readable storage media, and / or combinations thereof. The one or more memories (204a, 204b) may be located internally and / or externally to the one or more processors (202a, 202b). Additionally, the one or more memories (204a, 204b) may be coupled to the one or more processors (202a, 202b) via various technologies, such as wired or wireless connections.

[0097] One or more transceivers (206a, 206b) can transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or flowcharts of this document, to one or more other devices. One or more transceivers (206a, 206b) can receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts of this document, from one or more other devices. For example, one or more transceivers (206a, 206b) can be coupled to one or more processors (202a, 202b) and can transmit and receive wireless signals. For example, one or more processors (202a, 202b) can control one or more transceivers (206a, 206b) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (202a, 202b) may control one or more transceivers (206a, 206b) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (206a, 206b) may be coupled to one or more antennas (208a, 208b), and one or more transceivers (206a, 206b) may be configured to transmit and receive user data, control information, wireless signals / channels, or the like, as referred to in the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document, via one or more antennas (208a, 208b). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (206a, 206b) can convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using one or more processors (202a, 202b).One or more transceivers (206a, 206b) may convert user data, control information, wireless signals / channels, etc. processed by one or more processors (202a, 202b) from baseband signals to RF band signals. For this purpose, one or more transceivers (206a, 206b) may include an (analog) oscillator and / or filter.

[0098] Wireless device structure applicable to the present disclosure

[0099] FIG. 3 is a diagram illustrating another example of a wireless device applicable to the present disclosure.

[0100] Referring to FIG. 3, the wireless device (300) corresponds to the wireless devices (200a, 200b) of FIG. 2 and may be composed of various elements, components, units, and / or modules. For example, the wireless device (300) may include a communication unit (310), a control unit (320), a memory unit (330), and additional elements (340). The communication unit may include a communication circuit (312) and a transceiver(s) (314). For example, the communication circuit (312) may include one or more processors (202a, 202b) and / or one or more memories (204a, 204b) of FIG. 2. For example, the transceiver(s) (314) may include one or more transceivers (206a, 206b) and / or one or more antennas (208a, 208b) of FIG. 2. The control unit (320) is electrically connected to the communication unit (310), the memory unit (330), and the additional elements (340) and controls the overall operation of the wireless device. For example, the control unit (320) may control the electrical / mechanical operation of the wireless device based on the program / code / command / information stored in the memory unit (330). In addition, the control unit (320) may transmit information stored in the memory unit (330) to an external device (e.g., another communication device) via a wireless / wired interface through the communication unit (310), or store information received from an external device (e.g., another communication device) via a wireless / wired interface in the memory unit (330).

[0101] The additional element (340) may be configured in various ways depending on the type of the wireless device. For example, the additional element (340) may include at least one of a power unit / battery, an input / output unit, a driving unit, and a computing unit. Although not limited thereto, the wireless device (300) may be implemented in the form of a robot (Fig. 1, 100a), a vehicle (Fig. 1, 100b-1, 100b-2), an XR device (Fig. 1, 100c), a portable device (Fig. 1, 100d), a home appliance (Fig. 1, 100e), an IoT device (Fig. 1, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or a financial device), a security device, a climate / environmental device, an AI server / device (Fig. 1, 140), a base station (Fig. 1, 120), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0102] In FIG. 3, various elements, components, units / parts, and / or modules within the wireless device (300) may be entirely interconnected via a wired interface, or at least some may be wirelessly connected via a communication unit (310). For example, within the wireless device (300), the control unit (320) and the communication unit (310) may be wired, and the control unit (320) and the first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (310). In addition, each element, component, unit / part, and / or module within the wireless device (300) may further include one or more elements. For example, the control unit (320) may be composed of a set of one or more processors. For example, the control unit (320) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (330) may be composed of RAM, DRAM (dynamic RAM), ROM, flash memory, volatile memory, non-volatile memory, and / or a combination thereof.

[0103] Mobile devices to which the present disclosure applies

[0104] FIG. 4 is a drawing illustrating an example of a mobile device applied to the present disclosure.

[0105] Figure 4 illustrates an example of a mobile device applicable to the present disclosure. The mobile device may include a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), or a portable computer (e.g., a laptop, etc.). The mobile device may be referred to as a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS), or a wireless terminal (WT).

[0106] Referring to FIG. 4, the portable device (400) may include an antenna unit (408), a communication unit (410), a control unit (420), a memory unit (430), a power supply unit (440a), an interface unit (440b), and an input / output unit (440c). The antenna unit (408) may be configured as a part of the communication unit (410). Blocks 410 to 430 / 440a to 440c correspond to blocks 310 to 330 / 340 of FIG. 3, respectively.

[0107] The communication unit (410) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (420) can control components of the portable device (400) to perform various operations. The control unit (420) can include an AP (application processor). The memory unit (430) can store data / parameters / programs / codes / commands required for operating the portable device (400). In addition, the memory unit (430) can store input / output data / information, etc. The power supply unit (440a) supplies power to the portable device (400) and can include a wired / wireless charging circuit, a battery, etc. The interface unit (440b) can support connection between the portable device (400) and other external devices. The interface unit (440b) can include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (440c) can input or output video information / signals, audio information / signals, data, and / or information input from a user. The input / output unit (440c) may include a camera, a microphone, a user input unit, a display unit (440d), a speaker, and / or a haptic module.

[0108] For example, in the case of data communication, the input / output unit (440c) obtains information / signals (e.g., touch, text, voice, image, video) input by the user, and the obtained information / signals can be stored in the memory unit (430). The communication unit (410) can convert the information / signals stored in the memory into wireless signals, and transmit the converted wireless signals directly to other wireless devices or to a base station. In addition, the communication unit (410) can receive wireless signals from other wireless devices or base stations, and then restore the received wireless signals to the original information / signals. The restored information / signals can be stored in the memory unit (430) and then output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (440c).

[0109] Types of wireless devices to which the present disclosure applies

[0110] FIG. 5 is a drawing illustrating an example of a vehicle or autonomous vehicle to which the present disclosure applies.

[0111] Figure 5 illustrates a vehicle or autonomous vehicle applicable to the present disclosure. The vehicle or autonomous vehicle may be implemented as a mobile robot, car, train, manned / unmanned aerial vehicle (AV), ship, etc., and is not limited to the form of a vehicle.

[0112] Referring to FIG. 5, a vehicle or autonomous vehicle (500) may include an antenna unit (508), a communication unit (510), a control unit (520), a driving unit (540a), a power supply unit (540b), a sensor unit (540c), and an autonomous driving unit (540d). The antenna unit (550) may be configured as a part of the communication unit (510). Blocks 510 / 530 / 540a to 540d correspond to blocks 410 / 430 / 440 of FIG. 4, respectively.

[0113] The communication unit (510) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, roadside base stations, etc.), servers, etc. The control unit (520) can control elements of a vehicle or autonomous vehicle (500) to perform various operations. The control unit (520) can include an electronic control unit (ECU).

[0114] Figure 6 is a diagram illustrating an example of an AI device applicable to the present disclosure. For example, the AI ​​device may be implemented as a fixed or mobile device, such as a TV, projector, smartphone, PC, laptop, digital broadcasting terminal, tablet PC, wearable device, set-top box (STB), radio, washing machine, refrigerator, digital signage, robot, or vehicle.

[0115] Referring to FIG. 6, the AI ​​device (600) may include a communication unit (610), a control unit (620), a memory unit (630), an input / output unit (640a / 640b), a learning processor unit (640c), and a sensor unit (640d). Blocks 610 to 630 / 640a to 640d may correspond to blocks 310 to 330 / 340 of FIG. 3, respectively.

[0116] The communication unit (610) can transmit and receive wired and wireless signals (e.g., sensor information, user input, learning models, control signals, etc.) with external devices such as other AI devices (e.g., FIG. 1, 100x, 120, 140) or AI servers (FIG. 1, 140) using wired and wireless communication technology. To this end, the communication unit (610) can transmit information within the memory unit (630) to the external device or transfer a signal received from the external device to the memory unit (630).

[0117] The control unit (620) may determine at least one executable operation of the AI ​​device (600) based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the control unit (620) may control components of the AI ​​device (600) to perform the determined operation. For example, the control unit (620) may request, search, receive, or utilize data from the learning processor unit (640c) or the memory unit (630), and may control components of the AI ​​device (600) to perform a predicted operation or an operation determined to be desirable among at least one executable operation. In addition, the control unit (620) may collect history information including the operation contents of the AI ​​device (600) or user feedback on the operation, and store the collected history information in the memory unit (630) or the learning processor unit (640c), or transmit the collected history information to an external device such as an AI server (FIG. 1, 140). The collected history information may be used to update a learning model.

[0118] The memory unit (630) can store data that supports various functions of the AI ​​device (600). For example, the memory unit (630) can store data obtained from the input unit (640a), data obtained from the communication unit (610), output data of the learning processor unit (640c), and data obtained from the sensing unit (640). In addition, the memory unit (630) can store control information and / or software codes necessary for the operation / execution of the control unit (620).

[0119] The input unit (640a) can obtain various types of data from the outside of the AI ​​device (600). For example, the input unit (620) can obtain learning data for model learning, input data to which the learning model will be applied, etc. The input unit (640a) may include a camera, a microphone, and / or a user input unit. The output unit (640b) may generate output related to vision, hearing, or touch. The output unit (640b) may include a display unit, a speaker, and / or a haptic module, etc. The sensing unit (640) can obtain at least one of internal information of the AI ​​device (600), information about the surrounding environment of the AI ​​device (600), and user information using various sensors. The sensing unit (640) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, and / or a radar, etc.

[0120] The learning processor unit (640c) can train a model composed of an artificial neural network using learning data. The learning processor unit (640c) can perform AI processing together with the learning processor unit of the AI ​​server (Fig. 1, 140). The learning processor unit (640c) can process information received from an external device via the communication unit (610) and / or information stored in the memory unit (630). In addition, the output value of the learning processor unit (640c) can be transmitted to an external device via the communication unit (610) and / or stored in the memory unit (630).

[0121] FIG. 7 is a diagram illustrating a method for processing a transmission signal applied to the present disclosure. For example, the transmission signal may be processed by a signal processing circuit. At this time, the signal processing circuit (700) may include a scrambler (710), a modulator (720), a layer mapper (730), a precoder (740), a resource mapper (750), and a signal generator (760). At this time, as an example, the operations / functions of FIG. 7 may be performed in the processors (202a, 202b) and / or the transceivers (206a, 206b) of FIG. 2. Furthermore, as an example, the hardware elements of FIG. 7 may be implemented in the processors (202a, 202b) and / or the transceivers (206a, 206b) of FIG. 2. As an example, blocks 710 to 760 may be implemented in the processors (202a, 202b) of FIG. 2. Additionally, blocks 710 to 750 may be implemented in the processor (202a, 202b) of FIG. 2, and block 760 may be implemented in the transceiver (206a, 206b) of FIG. 2, and are not limited to the above-described embodiment.

[0122] The codeword can be converted into a wireless signal through the signal processing circuit (700) of FIG. 7. Here, the codeword is an encoded bit sequence of an information block. The information block may include a transport block (e.g., a UL-SCH transport block, a DL-SCH transport block). The wireless signal may be transmitted through various physical channels (e.g., a PUSCH, a PDSCH). Specifically, the codeword can be converted into a bit sequence scrambled by a scrambler (710). The scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of the wireless device, etc. The scrambled bit sequence can be modulated into a modulation symbol sequence by a modulator (720). The modulation scheme may include pi / 2-binary phase shift keying (pi / 2-BPSK), m-phase shift keying (m-PSK), m-quadrature amplitude modulation (m-QAM), etc.

[0123] A complex modulation symbol sequence can be mapped to one or more transmission layers by a layer mapper (730). The modulation symbols of each transmission layer can be mapped to the corresponding antenna port(s) by a precoder (740) (precoding). The output z of the precoder (740) can be obtained by multiplying the output y of the layer mapper (730) by an N*M precoding matrix W. Here, N is the number of antenna ports, and M is the number of transmission layers. Here, the precoder (740) can perform precoding after performing transform precoding (e.g., discrete Fourier transform (DFT) transform) on the complex modulation symbols. In addition, the precoder (740) can perform precoding without performing transform precoding.

[0124] The resource mapper (750) can map modulation symbols of each antenna port to time-frequency resources. The time-frequency resources can include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. The signal generator (760) generates a wireless signal from the mapped modulation symbols, and the generated wireless signal can be transmitted to another device through each antenna. To this end, the signal generator (760) can include an inverse fast Fourier transform (IFFT) module, a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, and the like.

[0125] The signal processing process for receiving signals in a wireless device can be configured in reverse order of the signal processing process (710 to 760) of FIG. 7. For example, a wireless device (e.g., 200a and 200b of FIG. 2) can receive wireless signals from the outside through an antenna port / transceiver. The received wireless signals can be converted into baseband signals through a signal restorer. For this purpose, the signal restorer can include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module. Thereafter, the baseband signal can be restored to a codeword through a resource demapper process, a postcoding process, a demodulation process, and a descrambling process. The codewords can be restored to the original information blocks through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler, and a decoder.

[0126] 6G communication system

[0127] The 6G (wireless communication) system aims to achieve (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) low energy consumption for battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be divided into four aspects: "intelligent connectivity," "deep connectivity," "holographic connectivity," and "ubiquitous connectivity," and the 6G system can satisfy the requirements as shown in Table 1 below. In other words, Table 1 is a table showing the requirements of the 6G system.

[0128] Per device peak data rate1 TbpsE2E latency1 msMaximum spectral efficiency100 bps / HzMobility supportup to 1000 km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0129] At this time, the 6G system may have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine type communications (mMTC), AI integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.

[0130] FIG. 10 is a diagram illustrating an example of a communication structure that can be provided in a 6G system applicable to the present disclosure.

[0131] Referring to Figure 10, 6G systems are expected to have 50 times higher simultaneous wireless connectivity than 5G wireless systems. URLLC, a key feature of 5G, is expected to become a more prominent technology in 6G communications, providing end-to-end latency of less than 1 ms. Furthermore, 6G systems will have significantly better volumetric spectral efficiency, unlike the commonly used area spectral efficiency. 6G systems can offer extremely long battery life and advanced battery technologies for energy harvesting, eliminating the need for separate charging for mobile devices in 6G systems.

[0132] Core implementation technology of 6G systems

[0133] - Artificial Intelligence (AI)

[0134] The most crucial and newly introduced technology for 6G systems is AI. 4G systems did not involve AI. 5G systems will support partial or very limited AI. However, 6G systems will fully support AI for automation. Advances in machine learning will create more intelligent networks for real-time communications in 6G. Incorporating AI into communications can streamline and improve real-time data transmission. AI can use numerous analyses to determine how complex target tasks should be performed. In other words, AI can increase efficiency and reduce processing delays.

[0135] Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly using AI. AI can also play a crucial role in machine-to-machine (M2M), machine-to-human, and human-to-machine communications. Furthermore, AI can facilitate rapid communication in brain-computer interfaces (BCIs). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0136] Recent attempts to integrate AI into wireless communication systems have focused on the application layer and network layer, particularly deep learning in wireless resource management and allocation. However, this research is increasingly evolving to the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly in the physical layer. AI-based physical layer transmission refers to applying AI-based signal processing and communication mechanisms, rather than traditional communication frameworks, in the fundamental signal processing and communication mechanisms. For example, this may include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based multiple input multiple output (MIMO) mechanisms, and AI-based resource scheduling and allocation.

[0137] Machine learning can be used for channel measurement and tracking, as well as for power allocation and interference cancellation at the physical layer of the downlink (DL). Machine learning can also be used for antenna selection, power control, and symbol detection in MIMO systems.

[0138] However, the application of deep neural networks (DNNs) for transmission at the physical layer may have the following problems.

[0139] Deep learning-based AI algorithms require a large amount of training data to optimize training parameters. However, due to limitations in obtaining training data from specific channel environments, a large amount of training data is used offline. This means that static training on training data in specific channel environments can lead to conflicts with the dynamic characteristics and diversity of the wireless channel.

[0140] Furthermore, current deep learning primarily targets real-world signals. However, signals at the physical layer of wireless communications are complex signals. Further research is needed on neural networks capable of detecting complex domain signals to match the characteristics of wireless communication signals.

[0141] Below, we will look at machine learning in more detail.

[0142] Machine learning refers to a series of operations that train machines to perform tasks that humans can or cannot perform. Machine learning requires data and a learning model. In machine learning, data learning methods can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.

[0143] Neural network training aims to minimize output errors. It involves repeatedly inputting training data into a neural network, calculating the neural network output and target error for the training data, and backpropagating the neural network error from the output layer to the input layer to update the weights of each node in the neural network to reduce the error.

[0144] Supervised learning uses labeled training data, while unsupervised learning may not have labeled training data. For example, in the case of supervised learning for data classification, the training data may be data in which each training data category is labeled. Labeled training data is input to a neural network, and the error can be calculated by comparing the output (categories) of the neural network with the training data labels. The calculated error is backpropagated through the neural network in the backward direction (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated according to the backpropagation. The amount of change in the connection weights of each updated node can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, in the early stages of training a neural network, a high learning rate can be used to quickly allow the network to achieve a certain level of performance, thereby increasing efficiency. In the later stages of training, a low learning rate can be used to increase accuracy.

[0145] Learning methods may vary depending on the characteristics of the data. For example, if the goal is to accurately predict data transmitted by a transmitter in a communication system, supervised learning is preferable to unsupervised learning or reinforcement learning.

[0146] The learning model corresponds to the human brain, and the most basic linear model can be thought of, but the machine learning paradigm that uses highly complex neural network structures, such as artificial neural networks, as learning models is called deep learning.

[0147] The neural network cores used in learning methods are largely divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent Boltzmann machines (RNN), and these learning models can be applied.

[0148] THz (Terahertz) communication

[0149] THz communications can be applied in 6G systems. For example, data transmission rates can be increased by increasing bandwidth. This can be achieved by using sub-THz communications with wide bandwidths and applying advanced massive MIMO technology.

[0150] FIG. 9 is a diagram illustrating an electromagnetic spectrum applicable to the present disclosure. For example, referring to FIG. 9, THz waves, also known as sub-millimeter radiation, typically represent a frequency band between 0.1 THz and 10 THz with a corresponding wavelength ranging from 0.03 mm to 3 mm. The 100 GHz to 300 GHz band (Sub-THz band) is considered a major portion of the THz band for cellular communications. Adding the Sub-THz band to the mmWave band will increase the capacity of 6G cellular communications. Among the defined THz bands, 300 GHz to 3 THz is in the far infrared (IR) frequency band. Although the 300 GHz to 3 THz band is part of the optical band, it is at the boundary of the optical band and lies just behind the RF band. Therefore, this 300 GHz to 3 THz band exhibits similarities to RF.

[0151] Key characteristics of THz communications include (i) the widely available bandwidth to support very high data rates and (ii) the high path loss that occurs at high frequencies (requiring highly directional antennas). The narrow beamwidths generated by highly directional antennas reduce interference. The small wavelength of THz signals allows for a significantly larger number of antenna elements to be integrated into devices and base stations operating in this band. This enables the use of advanced adaptive array technologies to overcome range limitations.

[0152] Terahertz (THz) wireless communications

[0153] FIG. 10 is a diagram illustrating a THz communication method applicable to the present disclosure.

[0154] Referring to Fig. 10, THz wireless communication is a wireless communication using THz waves with a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and may refer to terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or higher. THz waves are located between the RF (Radio Frequency) / millimeter (mm) and infrared bands, and (i) compared to visible light / infrared rays, they penetrate non-metallic / non-polarizable materials well, and compared to RF / millimeter waves, they have a shorter wavelength, so they have high straightness and can focus beams.

[0155] Artificial Intelligence (AI) system

[0156] Figure 11 illustrates the structure of a perceptron included in an artificial neural network applicable to the present disclosure. Figure 12 also illustrates the structure of an artificial neural network applicable to the present disclosure.

[0157] As described above, an artificial intelligence system can be applied in a 6G system. In this case, as an example, the artificial intelligence system can operate based on a learning model corresponding to the human brain, as described above. In this case, a machine learning paradigm that uses a highly complex neural network structure, such as an artificial neural network, as a learning model can be called deep learning. In addition, the neural network core used as a learning method can be largely divided into a deep neural network (DNN), a convolutional deep neural network (CNN), and a recurrent neural network (RNN). In this case, as an example, referring to FIG. 11, the artificial neural network can be composed of multiple perceptrons. In this case, the input vector x = {x1, x 2,…, x d} is input, each component is given a weight {W1, W 2,…, W d} are multiplied, the results are all added up, and the entire process of applying the activation function σ(·) can be called a perceptron. If a large artificial neural network structure extends the simplified perceptron structure illustrated in Figure 11, the input vector can be applied to perceptrons of different dimensions. For convenience of explanation, input values ​​or output values ​​are called nodes.

[0158] Meanwhile, the perceptron structure shown in Fig. 11 can be explained as consisting of a total of three layers based on input and output values. 1 st layer and 2 nd Between the layers, there are H perceptrons of dimension (d+1), 2 nd layer and 3 rdAn artificial neural network with K (H+1)-dimensional perceptrons between layers can be expressed as shown in Figure 12.

[0159] At this time, the layer where the input vector is located is called the input layer, the layer where the final output value is located is called the output layer, and all layers located between the input layer and the output layer are called hidden layers. For example, although three layers are disclosed in Fig. 12, when counting the number of actual artificial neural network layers, the input layer is excluded, so the artificial neural network illustrated in Fig. 12 can be understood as having a total of two layers. The artificial neural network is configured by connecting the perceptron of the basic block in two dimensions.

[0160] The aforementioned input, hidden, and output layers can be applied jointly not only to multilayer perceptrons but also to various artificial neural network structures, such as CNNs and RNNs, which will be described later. The greater the number of hidden layers, the deeper the artificial neural network. The machine learning paradigm that uses a sufficiently deep artificial neural network as a learning model is called deep learning. Furthermore, the artificial neural network used for deep learning is called a deep neural network (DNN).

[0161] Figure 13 illustrates a deep neural network applicable to the present disclosure.

[0162] Referring to Figure 13, a deep neural network can be a multilayer perceptron consisting of eight hidden layers and eight output layers. In this case, the multilayer perceptron structure can be expressed as a fully-connected neural network. In a fully-connected neural network, there is no connection between nodes located in the same layer, and a connection can exist only between nodes located in adjacent layers. DNN has a fully-connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, and can be usefully applied to identify correlation characteristics between inputs and outputs. Here, the correlation characteristic can mean the joint probability of inputs and outputs.

[0163] Figure 14 illustrates a convolutional neural network applicable to the present disclosure. Additionally, Figure 15 illustrates a filter operation of the convolutional neural network applicable to the present disclosure.

[0164] For example, depending on how multiple perceptrons are connected to each other, various artificial neural network structures different from the aforementioned DNN can be formed. At this time, the nodes located within one layer of the DNN are arranged in a one-dimensional vertical direction. However, referring to Fig. 14, it can be assumed that the nodes are arranged two-dimensionally with w nodes in width and h nodes in height. (Convolutional neural network structure of Fig. 14). In this case, since a weight is added to each connection in the connection process from one input node to the hidden layer, a total of h×w weights must be considered. Since there are h×w nodes in the input layer, there are a total of h between two adjacent layers. 2 w 2 The dog's weight may be required.

[0165] Additionally, the convolutional neural network of Fig. 14 has a problem in that the number of weights increases exponentially with the number of connections. Therefore, instead of considering all mode connections between adjacent layers, it can be assumed that a small filter exists. For example, as in Fig. 15, weighted sum and activation function operations can be performed on the overlapping portions of the filters.

[0166] At this time, one filter has a weight corresponding to the number of its size, and learning of the weight can be performed so that a specific feature on the image can be extracted as a factor and output. In Fig. 15, a 3×3 sized filter is applied to the 3×3 region at the top left of the input layer, and the output value after performing the weighted sum and activation function operation for the corresponding node is z 22 can be stored in.

[0167] At this time, the above-described filter scans the input layer and moves at a certain horizontal and vertical interval, performing weighted sum and activation function operations, and the output value can be placed at the current filter position. Since this operation method is similar to the convolution operation for images in the field of computer vision, a deep neural network with this structure is called a convolutional neural network (CNN), and the hidden layer generated as a result of the convolution operation can be called a convolutional layer. In addition, a neural network with multiple convolutional layers can be called a deep convolutional neural network (DCNN).

[0168] Furthermore, in the convolutional layer, the number of weights can be reduced by calculating the weighted sum by including only the nodes located in the area covered by the filter at the node where the current filter is located. This allows a single filter to focus on features of a local area. Accordingly, CNNs can be effectively applied to image data processing where physical distance in a two-dimensional area is an important judgment criterion. Meanwhile, CNNs can apply multiple filters immediately before the convolutional layer, and can also generate multiple output results through the convolution operation of each filter.

[0169] Meanwhile, depending on the data properties, there may be data for which sequence characteristics are important. Considering the length variability and chronological relationship of such sequence data, a structure that applies a method of inputting one element of the data sequence at each timestep and inputting the output vector (hidden vector) of the hidden layer output at a specific timestep together with the immediately following element in the sequence can be called a recurrent neural network structure.

[0170] Figure 16 illustrates a neural network structure having a recurrent loop applicable to the present disclosure. Figure 17 illustrates the operational structure of a recurrent neural network applicable to the present disclosure.

[0171] Referring to Figure 16, a recurrent neural network (RNN) is a data sequence of elements {x1 (t) , x2 (t),…, x d (t)} into a fully connected neural network, the previous time point t-1 is the hidden vector {z1 (t-1) , z2 (t-1),…, z H (t-1)} can be input together to have a structure that applies a weighted sum and an activation function. The reason for passing the hidden vector to the next time point in this way is that the information in the input vector from the previous time points is considered to be accumulated in the hidden vector of the current time point.

[0172] Also, referring to Figure 17, the recurrent neural network can operate in a predetermined time order for the input data sequence. At this time, the input vector {x1 at time 1 (t) , x2 (t),…, x d (t)} is the hidden vector {z1 when input to the recurrent neural network (1) , z2 (1),…, z H (1)} is the input vector {x1 at time point 2 (2) , x2 (2),…, x d (2)} is input, and the vector {z1 of the hidden layer is passed through the weighted sum and activation function. (2) , z2 (2),…, z H (2)} is determined. This process is repeated from time point 2, time point 3, …, up to time point T.

[0173] Meanwhile, when multiple hidden layers are placed within a recurrent neural network, it is called a deep recurrent neural network (DRNN). Recurrent neural networks are designed to be useful for processing sequence data (e.g., natural language processing).

[0174] It is a neural network core used in a learning manner, and includes various deep learning techniques such as DNN, CNN, RNN, restricted Boltzmann machine (RBM), deep belief networks (DBN), and deep Q-Network, and can be applied to fields such as computer vision, speech recognition, natural language processing, and speech / signal processing.

[0175] Recent attempts to integrate AI into wireless communication systems have focused on the application layer, network layer, and, in the case of deep learning, wireless resource management and allocation. However, this research is increasingly evolving to the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of signal processing and communication mechanisms based on AI drivers, rather than traditional communication frameworks, in the fundamental signal processing and communication mechanisms. For example, this may include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanisms, and AI-based resource scheduling and allocation.

[0176] Specific embodiments of the present invention

[0177] The present disclosure relates to a technique for adaptive channel information feedback in a wireless communication system. Specifically, the present disclosure proposes a technique for generating and transmitting CSI feedback that reflects temporal correlation while supporting variable rates in a neural network-based CSI feedback framework.

[0178] In this disclosure, lowercase or uppercase italic letters represent scalars. Lowercase boldface letters and uppercase boldface letters represent vectors and matrices, respectively. Calligraphic letters represent sets. For example, , , and stands for scalars, vectors, matrices and sets. represents the set of complex numbers, represents an m×n dimensional complex space.

[0179] In this disclosure, an artificial neural network that compresses and reconstructs CSI based on deep learning (DL) is referred to as a "CSI network." Recently, various advancements have been made in the architecture of CSI networks.

[0180] Figure 19 illustrates an example neural network structure for CSI feedback. Figure 19 illustrates CsiNet, which is an example of a CSI network structure. Referring to Figure 19, the CSI network can be viewed as consisting of a CSI encoder (1910) and a CSI decoder (1920). For example, in the downlink, where data transmission is performed from a base station to a UE, the base station may operate as a transmitter, and the UE may operate as a receiver. In the downlink, the CSI encoder may be operated by the UE, which is a receiver, and the CSI decoder may be operated by the base station, which is a transmitter. In the present disclosure, for convenience of explanation, the case of downlink communication is assumed, but the various embodiments described below are not limited to the downlink and can also be applied to other links, such as uplink and sidelink.

[0181] A CSI encoder included in a UE can compress information about a channel state. The compressed information, which is the output of the CSI encoder, is transmitted to a base station via uplink feedback. The base station inputs the received compressed information into a CSI decoder, and the CSI decoder can restore information about the channel state of the UE. In the present disclosure, for convenience of description, the compressed information, which is the output of the CSI encoder and the input of the CSI decoder, may be referred to as a CSI feedback signal, CSI feedback information, or other terms having equivalent technical meanings. In the present disclosure, the CSI feedback signal may have the form of a bit stream. Here, the bit stream means a sequence of binary digits or bits of 0 or 1, rather than a vector of floating point numbers.

[0182] The temporal correlation of a time-varying wireless channel is a first-order Markov process, which can be modeled as shown in [Mathematical Equation 1] below.

[0183]

[0184] In [Equation 1], is the time A channel instance representing channel information in is a coefficient between 0 and 1 representing the temporal correlation between two consecutive channels, refers to a term representing randomness. For example, can be a zero-mean and unit-variance complex Gaussian matrix. As an example, the channel matrix in the spatial-frequency domain can be considered. The CSI matrix in the spatial-frequency domain can be expressed as follows.

[0185] In the present disclosure, the number of transmitting antennas of the base station is The number of receiving antennas of the UE is assumed to be 1. However, the various embodiments described below are not limited to a single receiving antenna, and can be extended to a multi-antenna case as well. In addition, in the following description, An OFDM system using orthogonal subcarriers is considered.

[0186] UE The signal received through the th subcarrier can be expressed as shown in [Mathematical Formula 2] below.

[0187]

[0188] In [Equation 2], is the instantaneous channel vector in the frequency domain, is a precoding vector, is a data symbol transmitted in the downlink, is AWGN (additive white Gaussian noise), is the subcarrier index, means the number of subcarriers.

[0189] The channel vector for the th subcarrier is is estimated by the UE and can be fed back to the base station. Considering all subcarriers in total, Only when the CSI matrix, which can be expressed as , is properly fed back from the UE to the base station, can the base station correctly determine the precoding vectors. Here, can be understood as a CSI matrix in the spatial-frequency domain.

[0190] As an example, a channel matrix in the angular-delay domain, rather than a channel matrix in the space-frequency domain, can be used. The CSI matrix in the space-frequency domain is can be processed as shown in Fig. 20 below. Fig. 20 illustrates an example of a CSI matrix processing process in a neural network for channel state information feedback. Fig. 20 represents the matrix in a reversed order compared to the general way of representing the matrix. Referring to Fig. 20, preprocessing can be performed in which a two-dimensional (2D) discrete Fourier transform (DFT) and truncation along the delay axis in the angular-delay domain, and separation into real and imaginary parts are sequentially performed. Through this, a channel matrix in the angular-delay domain can be obtained.

[0191] That is, in order to utilize the CSI network, preprocessing including the following three steps can be performed, as shown in Fig. 20.

[0192] (1) 2D-DFT

[0193] CSI matrix in the angular-delay domain is the CSI matrix in the spatial-frequency domain can be obtained from. The relation is Here, and are two DFT matrices.

[0194] (2) Truncation with respect to delay-axis

[0195] Since the time delay between multipath arrivals lies within a limited period, the time delays for all subcarriers fall within a specific period. Therefore, the CSI matrix in each delay domain is only the first It has large values ​​only in the rows and close to 0 in the rest. Therefore, the CSI matrix in each delay domain The beginning of If we take only the rows of the dog, is obtained.

[0196] (3) Split into real and imaginary parts

[0197] truncated CSI matrix Each element of the matrix is ​​composed of complex numbers, but general neural networks have difficulty handling complex numbers. Therefore, for the convenience of processing in neural networks, two matrices are created by dividing the real and imaginary parts of each element, and the two matrices are stacked in the third dimension. A tensor having the size of can be constructed.

[0198] The above-described preprocessing process and the types of channel instances illustrated are only examples, and various embodiments of the present disclosure are not limited thereto. In the description of the above-described preprocessing process and FIG. 20, depending on which preprocessing process was performed or in which domain it is expressed, the channel matrix , , It is expressed as a distinction like this, but in the explanation below, time Channel instances representing channel information in are domain and preprocessed regardless of whether they are can be expressed as

[0199] Figure 21 illustrates another example of a neural network structure for channel state information feedback. Figure 21 illustrates the structure of CsiNet-LSTM (long short term memory). The neural network illustrated in Figure 21 introduces an LSTM structure on the decoder side (2110). LSTM is a type of recurrent neural network (RNN) that can be incorporated into the decoder structure to utilize the temporal correlation of the wireless channel for more effective CSI reconstruction. The role of the LSTM module (2112) is to utilize information extracted from the CSI of the previous time for the reconstruction of the CSI of the current time. By utilizing LSTM not only on the decoder side but also on the encoder side, it is possible to utilize the temporal correlation of the channel for compressing time-varying channels. In other words, the temporal correlation of the time-varying channel can be utilized for CSI compression and reconstruction.

[0200] Considering temporal correlation, time-varying channels can be treated like sequence data. For example, sequence data can be likened to video. However, conventional CSI network structures that consider temporal correlation are trained and define the loss function as the mean square error (MSE) from a distortion perspective, rather than being sufficiently studied or optimized from a rate-distortion performance perspective. Conventional CSI network structures that consider temporal correlation suffer from the problem of fixed code rates or code lengths.

[0201] Meanwhile, to address the problem that most existing deep learning-based CSI feedback techniques have fixed code lengths and support variable feedback rates, a framework utilizing an accumulable CSI feedback signal, as illustrated in Figure 22, can be applied. Figure 22 illustrates the concept of CSI feedback supporting a variable feedback rate.

[0202] Referring to FIG. 22, different CSI feedback bitstreams can be combined before being input to the decoder neural network (2220) of the CSI network. Accordingly, the input dimension of the decoder neural network (2220) can be maintained, the structure of the decoder neural network (2220) can be maintained as is, and further, the model parameter set of the decoder neural network (2220) can also be maintained as is.

[0203] Regardless of the number of CSI feedback bit strings that are combined before being input to the decoder neural network (2220), the same decoder neural network (2220) model can always be used. The number of feedback bits increases in proportion to the number of CSI feedback bit strings that are added and input to the decoder neural network (2220). For example, if the length of the CSI feedback bit string that can be input to the decoder neural network (2220) alone is 256 bits, the number of feedback bits will increase to 512, 768, and 1024 as the number of CSI feedback bit strings becomes 2, 3, and 4, respectively.

[0204] Meanwhile, even if the same decoder neural network (2220) model is used, the CSI reconstruction performance may improve as the number of CSI feedback bit-strings input to the decoder neural network (2220) increases. Fig. 21 metaphorically expresses the improvement in CSI reconstruction performance as the number of CSI feedback bit-strings increases through the change in resolution of the reconstructed Lenna image. Specifically, the second image (2292) reconstructed based on two CSI feedback bit-strings (2201, 2202) has a higher resolution than the first image (2291) reconstructed based on one CSI feedback bit-string (2201). Similarly, it is confirmed that the resolution gradually increases in the order of the third image (2293) reconstructed based on three CSI feedback bit-strings (2201 to 2203) and the fourth image (2294) reconstructed based on four CSI feedback bit-strings (2201 to 2204).

[0205] However, in Fig. 22, what is expressed as an image of Lena is purely a metaphor, and information such as the CSI matrix that can actually be restored from the CSI network is not recognized by the human eye like an image. Expressing the increase in CSI restoration performance as an increase in image resolution is also merely a metaphor, and should not be understood as meaning that the CSI restoration performance improves in terms of image resolution. This is because, due to the nature of deep learning, it is difficult to precisely explain what the different CSI feedback bit strings input and added to the decoder neural network (2220) mean and what role they play as signals.

[0206] In FIG. 22, a plurality of CSI feedback bit streams (2201 to 2204) having different roles are combined before being input to the decoder neural network (2220). The first CSI feedback bit stream (2201) may be a signal capable of CSI restoration even when input to the decoder neural network (2220) alone. The second CSI feedback bit stream (2202) may be a signal capable of CSI restoration only when input to the decoder neural network (2220) together with the first CSI feedback bit stream (2201). In this way, the CSI feedback bit streams generated by the encoder neural network (2210) may be classified into a CSI feedback bit stream that enables CSI restoration even when input to the decoder neural network (2220) alone and a CSI feedback bit stream that enables CSI restoration only when combined. At this time, the former CSI feedback bit string may be referred to as an 'independent CSI bit string', and the latter CSI feedback bit string may be referred to as a 'dependent CSI bit string'.

[0207] In the expressions intended to combine bit strings included in a CSI feedback signal, such as “added before being input to a decoder neural network,” “added and input to a decoder neural network,” or “added and input to a decoder neural network,” used in the present disclosure, the operation of “added” can be understood as not only summation, but also one of a weighted sum, a weighted average, or various numerical processing that can be derived therefrom.

[0208] As described above, when neural network structures and signaling procedures that can operate different feedback transmission rates through the same neural network model, different from the general CSI network, are applied, adaptive feedback transmission rate operation according to the downlink channel environment and uplink resource status becomes possible.

[0209] There are several studies to support variable transmission rates. Figure 23 illustrates an example of distortion imbalance between channel state information matrix instances. Referring to Figure 23, it can be seen that even when using the same number of CSI feedback bits, the distortion between the reconstructed CSI matrices and the original CSI matrices can vary depending on the CSI pattern. Noting this distortion imbalance, a variable transmission rate CSI feedback framework, as shown in Figure 24, has been proposed. Figure 24 illustrates an example of a procedure for channel state information feedback supporting a variable transmission rate. Referring to Figure 24, an estimator for predicting distortion is configured as a neural network, an appropriate codeword length is selected based on the distortion values ​​predicted by the estimator, and the channel can be compressed / decompressed using an encoder and decoder with the selected codeword length. However, the aforementioned techniques do not consider the temporal correlation of the channel.

[0210] The present disclosure proposes a CSI feedback framework that considers the temporal correlation of time-varying channels and supports variable feedback transmission rates from a rate-distortion performance perspective. In other words, the present disclosure describes various embodiments that support variable feedback transmission rates based on the temporal correlation of time-varying channels.

[0211] FIG. 25A illustrates an example of the structure of a transmitter according to one embodiment of the present disclosure. FIG. 25A illustrates the functional structure of the transmitter. The transmitter can perform functions such as those shown in FIG. 25A based on components such as those shown in FIG. 2.

[0212] Referring to FIG. 25A, the transmitting device includes a CSI encoder (2510), a variable transmission rate and temporal correlation processing unit (2520), and a transmitter (2530). The CSI encoder (2510) and the variable transmission rate and temporal correlation processing unit (2520) may be processors designed to implement the functions described below, or may be processors executing instructions or programs for the functions described below.

[0213] The CSI encoder (2510) encodes channel information. The CSI encoder (2510) can compress channel information generated based on a reference signal. According to one embodiment, the CSI encoder (2510) may have a neural network structure including multiple layers. That is, the CSI encoder (2510) can generate a latent vector representing a channel instance using the encoder neural network.

[0214] The variable transmission rate and temporal correlation processing unit (2520) generates feedback information including channel information by converting encoded channel information into a bit string. Specifically, the variable transmission rate and temporal correlation processing unit (2520) can estimate the temporal correlation of the channel and convert a latent vector representing a channel instance into a bit string based on the temporal correlation. In addition, the variable transmission rate and temporal correlation processing unit (2520) can support a variable transmission rate by adaptively adjusting the size of the bit string representing the channel information. To reflect the temporal correlation, the variable transmission rate and temporal correlation processing unit (2520) can estimate the Doppler characteristic of the channel based on channel instances determined at adjacent time points and encode a latent vector representing the channel instance based on the Doppler characteristic. At this time, since the feedback information is generated based on the temporal correlation, the receiving device requires information related to the temporal correlation to restore the channel information. Accordingly, the feedback information may further include information related to the temporal correlation in addition to the channel information. According to one embodiment, the variable transmission rate and temporal correlation processing unit (2520) may perform entropy encoding using an entropy model based on the Doppler characteristics of the channel.

[0215] The transmitter (2530) transmits feedback information. The transmitter (2530) may perform physical layer processing, such as modulation and resource mapping, on the feedback information and then transmit it via a wireless channel. Here, the feedback information may include channel information and information related to the temporal correlation of the channel.

[0216] FIG. 25b illustrates an example of the structure of a receiving device according to one embodiment of the present disclosure. FIG. 25b illustrates the functional structure of the receiving device. The receiving device can perform functions such as those shown in FIG. 25b based on components such as those shown in FIG. 2.

[0217] Referring to FIG. 25b, the receiving device includes a receiver (2560), a variable transmission rate and temporal correlation processing unit (2570), and a CSI decoder (2580). The variable transmission rate and temporal correlation processing unit (2570) and the CSI decoder (2580) may be processors designed to implement the functions described below, or may be processors executing instructions or programs for the functions described below.

[0218] The receiver (2560) receives feedback information. The transmitter (2560) can obtain feedback information by performing physical layer processing, such as resource demapping and demodulation, on a signal received via a wireless channel. Here, the feedback information may include channel information and information related to the temporal correlation of the channel.

[0219] The variable rate and temporal correlation processing unit (2570) can determine a latent vector representing a channel instance based on feedback information. Specifically, the variable rate and temporal correlation processing unit (2570) can verify the temporal correlation of the channel based on the feedback information, and obtain a latent vector representing the channel instance from a bit string representing channel information based on the temporal correlation. To consider the temporal correlation, the variable rate and temporal correlation processing unit (2570) can verify a Doppler characteristic based on the feedback information, and decode the bit string representing the channel information based on at least one of the Doppler characteristic and a previously restored channel instance. According to one embodiment, the variable rate and temporal correlation processing unit (2570) can perform entropy decoding using an entropy model based on the Doppler characteristic of the channel.

[0220] The CSI decoder (2580) recovers channel information by decoding a latent vector representing a channel instance. In one embodiment, the CSI decoder (2580) may have a neural network structure including multiple layers.

[0221] FIG. 26a illustrates an example of the structure of a transmitting device using Doppler estimation and entropy coding according to one embodiment of the present disclosure. FIG. 26a is an example of the detailed structure of FIG. 25a.

[0222] Referring to FIG. 26A, the transmitting device includes a CSI encoder (2510), a variable transmission rate and temporal correlation processing unit (2520), and a transmitter (2530). Here, the variable transmission rate and temporal correlation processing unit (2520) includes a first quantization and entropy encoder (2602), a first entropy decoding and dequantizer (2604), a CSI decoder (2606), a Doppler estimator (2608), a Doppler encoder (2610), a second quantization and entropy encoder (2612), a second entropy decoding and dequantizer (2614), a Doppler decoder (2616), a context generator (2618), a first entropy model (2620), and a second entropy model (2622).

[0223] Channel information is measured based on a reference signal and provided to a CSI encoder (2510) and a Doppler estimator (2608). The channel information may include channel instances expressed in an image format in the angle-delay domain or the space-frequency domain. The channel information input to the CSI encoder (2510) is intended to be transmitted in the current feedback occasion, and the channel information input to the Doppler estimator (2608) is intended to determine the temporal correlation for the channel information to be transmitted in the current feedback occasion.

[0224] The first quantization and entropy encoder (2602) performs quantization and entropy encoding on a latent vector (hereinafter referred to as a "channel latent vector") representing a channel instance generated by the CSI encoder (2510). Accordingly, the first quantization and entropy encoder (2602) generates a bitstream corresponding to the channel instance.

[0225] The first entropy decoding and dequantization unit (2604) performs entropy decoding and dequantization on the bitstream generated by the first quantization and entropy encoder (2602), thereby restoring the channel latent vector. The CSI decoder (2606) restores channel information (e.g., channel instances) based on the channel latent vector. To generate information related to temporal correlation, channel instances from previous time points may be used. Accordingly, the first entropy decoding and dequantization unit (2604) and the CSI decoder (2606) restore the channel information. Although not illustrated in FIG. 26A, a component that adds noise corresponding to channel noise may be further included between the first quantization and entropy encoder (2602) and the first entropy decoding and dequantization unit (2604).

[0226] The Doppler estimator (2608) generates Doppler information by estimating the Doppler characteristics of the channel based on the current channel information and the previous channel information. Doppler characteristics are related to temporal correlation. The current channel information includes the channel instance to be fed back. The previous channel information, as a previously fed back channel instance, can be obtained from the CSI decoder (2606).

[0227] The Doppler encoder (2610) encodes Doppler information to generate a latent vector (hereinafter referred to as a "Doppler latent vector") representing the Doppler information. The second quantization and entropy encoder (2612) performs quantization and entropy coding on the Doppler latent vector to generate a bit string representing temporal correlation. The bit string representing the temporal correlation is provided to the transmitter (2530) to be included in the feedback information.

[0228] The second entropy decoding and dequantization unit (2614) performs entropy decoding and dequantization on a bit string expressing temporal correlation, thereby restoring a Doppler latent vector. The Doppler decoder (2616) restores Doppler information based on the Doppler latent vector. Although not illustrated in FIG. 26a, a component that adds noise corresponding to channel noise may be further included between the second quantization and entropy encoder (2612) and the second entropy decoding and dequantization unit (2614).

[0229] The context generator (2618) generates context information for the first entropy model (2620) based on the Doppler information recovered by the Doppler decoder (2616). In addition, the context information generated by the context generator (2618) may be provided to the CSI encoder (2510) and the CSI decoder (2606). For example, the context information may include a channel instance predicted from a previous channel instance based on the Doppler information. The context information may be generated in at least one of various domains (e.g., each-delay domain, feature domain, etc.). The context generator (2618) may have a neural network structure including a plurality of layers.

[0230] The first entropy model (2620) generates an entropy model for the entropy encoding operation of the first quantization and entropy encoder (2602) and the entropy decoding operation of the first entropy decoding and dequantizer (2604). The first entropy model (2620) generates at least one prior (e.g., a temporal prior, a hierarchical prior, etc.) based on context information and a channel latent vector, and generates statistical parameters (e.g., a mean, a standard deviation, etc.) for the generated at least one prior. In addition, the first entropy model (2620) can provide information determined in the process of generating at least one prior (e.g., a result of encoding and quantizing a hierarchical prior for a channel latent vector) to the transmitter (2530), thereby providing the information to the receiving device. Through this, at least one prior required for entropy decoding can be determined in the receiving device.

[0231] The second entropy model (2622) generates an entropy model for the entropy encoding operation of the second quantization and entropy encoder (2612) and the entropy decoding operation of the second entropy decoding and dequantizer (2614). The second entropy model (2622) generates at least one prior (e.g., a hierarchical prior, etc.) based on the Doppler latent vector, and generates statistical parameters (e.g., a mean, a standard deviation, etc.) for the generated at least one prior. In addition, the second entropy model (2622) can transmit information determined in the process of generating at least one prior (e.g., a result of encoding and quantizing the hierarchical prior for the Doppler latent vector) to the transmitter (2530) and thereby transmit the information to the receiving device. Through this, at least one prior required for entropy decoding can be determined in the receiving device.

[0232] FIG. 26b illustrates a more detailed example of the structure of a receiving device utilizing Doppler estimation and entropy coding according to one embodiment of the present disclosure. FIG. 26b is an example of the detailed structure of FIG. 25b.

[0233] Referring to FIG. 26b, the receiving device includes a receiver (2560), a variable transmission rate and temporal correlation processing unit (2570), and a CSI decoder (2580). Here, the variable transmission rate and temporal correlation processing unit (2570) includes a first entropy decoding and dequantization unit (2662), a second entropy decoding and dequantization unit (2664), a Doppler decoder (2666), a context generator (2668), a first entropy model (2670), and a second entropy model (2672).

[0234] The receiver (2560) receives feedback information. Then, the receiver (2560) provides channel information included in the feedback information to a first entropy decoding and dequantization unit (2662) and a first entropy model (2670), and provides information related to temporal correlation included in the feedback information to a second entropy decoding and dequantization unit (2664) and a second entropy model (2672).

[0235] The first entropy decoding and dequantization unit (2662) performs entropy decoding and dequantization on a bit string representing channel information included in the feedback information, thereby restoring a channel latent vector. The channel latent vector is provided to the CSI decoder (2580) and used to restore channel information (e.g., channel instances).

[0236] The second entropy decoding and dequantization unit (2664) performs entropy decoding and dequantization on a bit string expressing temporal correlation, thereby restoring the Doppler latent vector. The Doppler decoder (2666) restores Doppler information based on the Doppler latent vector.

[0237] The context generator (2668) generates context information for the first entropy model (2670) based on the Doppler information restored by the Doppler decoder (2666). In addition, the context information generated by the context generator (2668) may be provided to the CSI decoder (2580). The context generator (2668) may have a neural network structure including multiple layers.

[0238] The first entropy model (2670) generates an entropy model for the entropy decoding operation of the first entropy decoding and dequantizer (2662). The first entropy model (2670) generates at least one prior (e.g., a temporal prior, a hierarchical prior, etc.) based on context information and a channel latent vector, and generates statistical parameters (e.g., a mean, a standard deviation, etc.) for the generated at least one prior. To this end, the first entropy model (2670) can perform entropy decoding and hierarchical prior decoding on information received from a transmitting device.

[0239] The second entropy model (2672) generates an entropy model for the entropy decoding operation of the second entropy decoding and dequantization unit (2664). The second entropy model (2672) generates at least one prior (e.g., a hierarchical prior, etc.) based on the Doppler latent vector, and generates statistical parameters (e.g., a mean, a standard deviation, etc.) for the generated at least one prior.

[0240] According to the structure of the transmitting device illustrated in Fig. 26a and the structure of the receiving device illustrated in Fig. 26c, there are functional blocks that perform similar functions in the transmitting device and the receiving device. If the functional blocks that perform similar functions are expressed as a single block, the transmitting device and the receiving device can be expressed as in Fig. 26c. Fig. 26c illustrates an example of the conceptual structure of the transmitting device and the receiving device according to one embodiment of the present disclosure. All blocks illustrated in Fig. 26 can be understood as transmitting devices, and the portion enclosed in a dotted box can be understood as receiving devices.

[0241] Referring to Figure 26c, the encoder includes a Doppler estimator. The Doppler information is the output of the Doppler estimator. is a Doppler latent vector through a Doppler encoder neural network. is encoded as a Doppler potential vector can be converted into a bit string through an entropy bottleneck layer. The quantized Doppler potential vector is the reconstructed Doppler information through the Doppler decoder neural network. can be decoded. The Doppler-related information and the Doppler potential vector expressed in the present disclosure are intermediate information or signals obtained in a data-driven manner from the channel information of the current time and the channel information of the previous time, and it is not easy to clearly identify or accurately explain the physical meaning of the signal due to the characteristics of neural networks and deep learning. However, since the above-mentioned information or signals include information related to the Doppler shift, the present disclosure refers to them as Doppler-related information and the Doppler potential vector. However, in the present disclosure, the Doppler potential vector, which is a signal transmitted and / or received between a terminal and a base station, may actually include information related to temporal correlation other than the Doppler shift. Unlike traditional Doppler estimators based on mathematical model-driven methods, Doppler-related information, which is the output of a Doppler estimator that can be implemented as a data-driven neural network according to various embodiments of the present disclosure, can be extracted so that higher-dimensional, rich temporal correlation information that can be expressed in the feature domain can be better reflected.

[0242] In the present disclosure, the Doppler estimator takes as input a channel instance decoded at a previous time and a channel instance measured at a current time, expressed in each-delay domain, and has as output Doppler-related information extracted from each-delay domain or a specific feature domain, which is different from a Doppler estimator that generally takes as input a received signal and outputs the frequency shift itself. However, if a traditional Doppler estimator is utilized, and an appropriate modification is applied, the traditional Doppler estimator can also be used as a Doppler estimator according to an embodiment of the present disclosure. Thereafter, the restored Doppler information and the CSI matrix decoded in the previous time From context is obtained. If the CSI matrix is ​​generated in the angular-delay domain, is the predicted CSI matrix can be treated as . In this case, the residual - can be encoded to be fed back. On the other hand, If it is generated in the feature domain, Is can be used as a condition related to encoding (e.g., conditional coding). In this case, the context of the feature domain The higher dimension of It can provide richer and more correlated conditions for encoding. and From context The obtained operation can be performed by the context generator of Fig. 26. The context generator can be implemented based on a neural network. To implement the context generator, it is also possible to benchmark techniques used in video compression (e.g., warping functions). is input as a condition to the contextual encoder / decoder neural network, and the CSI instance to the potential vector or encoded as From can be used to decode.

[0243] In one embodiment, the system according to various embodiments may utilize an entropy bottleneck layer. The entropy bottleneck layer is an example of the first quantization and entropy encoder (2602), the first entropy model (2620), and the first entropy decoding and dequantizer (2604) of FIG. 26A, or an example of the second quantization and entropy encoder (2612), the second entropy model (2622), and the second entropy decoding and dequantizer (2614). The entropy bottleneck layer is a neural network designed to jointly optimize transmission rate and distortion. The entropy bottleneck layer has been introduced for deep learning-based CSI feedback, and the entropy bottleneck layer may be adopted for CSI compression / reconstruction according to various embodiments. An example of the structure of the entropy bottleneck layer is shown in Fig. 27a.

[0244] FIG. 27A illustrates an example of an entropy bottleneck layer for entropy encoding / decoding according to an embodiment of the present disclosure. Referring to FIG. 27A, the entropy bottleneck layer includes a quantizer (2712), an entropy encoder (2714), an entropy decoder (2722), a dequantizer (2724), and an entropy model (2726).

[0245] Referring to Fig. 27a, a quantizer (2712) quantizes an input vector, and an entropy encoder (2714) entropy-codes the quantized vector. This generates a bit string representing the input vector. The bit string passes through a channel and is input to an entropy decoder (2722), which entropy-decodes the bit string. A dequantizer (2724) dequantizes the decoded information. An entropy model (2726) provides probability distribution information for entropy encoding / decoding.

[0246] Channel matrix A compressed latent vector through an encoder neural network can be generated. The entropy bottleneck layer is Take as input, is an approximation of or Prints out. Here, is the output during training. It is the value with uniform noise added to it, is the output of inference. It can be obtained by performing quantization in Fig. 27a. Is can be obtained by performing dequantization again. In the case of the quantization operation performed during inference, since the gradient is 0 in almost all areas and the differentiation is undefined at the remaining points, problems may occur in the backpropagation operation, which is the process of training the neural network.

[0247] To solve the problem, the entropy bottleneck layer can be transformed into a structure as shown in Fig. 27b during training. Fig. 27b illustrates an example of a structure for training the bottleneck layer according to an embodiment of the present disclosure. Referring to Fig. 27b, the entropy bottleneck layer can be transformed into a structure as shown in Fig. 27b during training. , and outputs a probability distribution is stored as an entropy model. The stored entropy model is the inference time It is used to perform entropy coding / decoding. PMF (probability mass function) required for entropy coding / decoding is saved can be obtained from the compressed latent vector during inference through the entropy bottleneck layer. can be expressed as a bit stream.

[0248] An entropy model may have a structure as shown in FIG. 28A. FIG. 28A illustrates an example of an entropy model according to an embodiment of the present disclosure. Referring to FIG. 28A, the entropy model includes a temporal prior encoder (2812), a hierarchical prior encoder (2814), and a prior fusion unit (2816). Additionally, the entropy model may further include a spatial prior encoder (2818).

[0249] The temporal prior encoder (2812) generates temporal priors based on context information. The context information is generated based on the temporal correlation of the channel, for example, the Doppler characteristic. Furthermore, the temporal prior encoder (2812) generates information related to the prior distribution of the context information. The temporal prior encoder (2812) may have a neural network structure.

[0250] The hierarchical prior encoder (2814) generates a hierarchical prior based on a latent vector representing a channel (hereinafter, referred to as a “channel latent vector”). Specifically, the hierarchical prior encoder (2814) may include a prior distribution related to at least one parameter of a prior distribution for the latent vector, for example, a prior distribution related to a mean and / or a standard deviation. To this end, according to one embodiment, the hierarchical prior encoder (2814) may include at least one of a hierarchical prior encoder, a quantizer, an entropy encoder, an entropy decoder, and a hierarchical prior decoder. For example, the hierarchical prior encoder (2814) included in a transmitting device may include a hierarchical prior encoder, a quantizer, an entropy encoder, an entropy decoder, and a hierarchical prior decoder, and the hierarchical prior encoder (2814) included in a receiving device may include an entropy decoder and a hierarchical prior decoder. For the operation of the hierarchical prior encoder (2814) of the receiving device, the transmitting device may transmit a value including the output of the quantizer as side information to the receiving device. Specifically, the information transmitted as side information may include the output of the hierarchical prior encoder that has undergone quantization and entropy coding. Here, the hierarchical prior encoder and the hierarchical prior decoder may have a neural network structure.

[0251] The prior fusion unit (2816) combines temporal priors and hierarchical priors. That is, the prior fusion unit (2816) can generate parameters (e.g., a mean vector, a standard deviation vector, etc.) of the prior distribution of the channel latent vector as distribution information used for entropy encoding / decoding. Here, the prior fusion unit (2816) can have a neural network structure.

[0252] Additionally, a spatial prior encoder (2818) may be further included. The spatial prior encoder (2818) may be used in conjunction with the temporal prior encoder (2812) or the hierarchical prior encoder (2814) to perform a corresponding role, and may generate a spatial prior based on the quantization result for the latent vector. When the spatial prior encoder (2818) is further included, a prior fusion unit (2816) fuses the temporal prior, the hierarchical prior, and the spatial prior.

[0253] The structure of the entropy model is described with reference to FIG. 28a. Meanwhile, referring to FIGS. 26a and 26b, each of the transmitting device and the receiving device includes two entropy models. According to various embodiments, each of the two entropy models may have the structure illustrated in FIG. 28a, or one of the entropy models may have a structure different from the structure illustrated in FIG. 28a. For example, one of the entropy models may be composed solely of the hierarchical prior encoder (2814) illustrated in FIG. 28a.

[0254] According to one embodiment, the first entropy model (2620) or the first entropy model (2670) may have a structure as shown in FIG. 28b. FIG. 28b illustrates an example of an entropy model that operates based on a channel latent vector according to one embodiment of the present disclosure. In FIG. 28b, Q performs quantization, AE / AD (arithmetic encoding / arithmetic decoding) performs entropy coding / decoding, and HPE / HPD (hierarchical prior encoder / decoder) encodes / decodes hierarchical priors. Here, HPE and HPD can be implemented as neural networks. Context, which is information corresponding to temporal correlation is encoded as a temporal prior through a temporal prior encoder neural network. The temporal prior is used for prior fusion together with the hierarchical prior. Prior fusion can also be implemented as a neural network. By fusing the temporal prior and hierarchical prior through a prior fusion neural network, Parameters of the prior distribution (e.g., mean vector) and standard deviation vector ) can be obtained. In prior fusion, in addition to hierarchical priors and temporal priors, a third prior (e.g., spatial correlation) can also be a target of fusion.

[0255] According to one embodiment, the second entropy model (2622) or the first entropy model (2672) may have a structure as shown in FIG. 28C. FIG. 28C illustrates an example of an entropy model operating based on a Doppler latent vector according to one embodiment of the present disclosure. In FIG. 28C, Q performs quantization, EC / ED performs entropy coding / decoding, and HPE / HPD encodes / decodes hierarchical priors. Here, HPE and HPD may be implemented as neural networks. Hierarchical Priors is the Doppler potential vector Information about the prior distribution (e.g., the mean vector) and standard deviation vector ) may be included. Therefore, If you decode it through HPD, and This can be obtained. and can be utilized for entropy coding / decoding of Doppler potential vectors.

[0256] As described above, systems according to various embodiments may utilize hyperprior or hierarchical priors. Latent representation The concept of hyperprior can be applied to model the dependency between elements. Hyperprior is Prior distribution for Prior distribution for the parameters (e.g. mean vector μ and standard deviation vector σ) It means. At this time, is the latent vector Another latent vector obtained by encoding has information about the hyperprior. by inputting it into another encoder neural network. get , is input into another entropy bottleneck layer and is printed A bit string can be generated for HyperFryer Because the structure for obtaining it is hierarchical, may be referred to as a hierarchical prior. In the present disclosure, Since other priors (e.g. temporal priors) are also used, to avoid confusion, is referred to as a hierarchical prior.

[0257] As described above, systems according to various embodiments can utilize temporal priors and contexts. The temporal priors can be extracted as follows. First, the channel matrix is assumed to be expressed in the angular-delay domain. It is known that Doppler terms of different paths in the angular-delay domain are less entangled with each other than in the space-frequency domain. However, various embodiments of the present disclosure are not limited to a channel matrix in a specific domain such as the space-frequency domain or the angular-delay domain. For example, the channel matrix may be expressed in a 3D tensor format in a 2D space-1D frequency domain, or in a tensor format in a 3D angular-delay domain consisting of an elevation angle, an azimuth angle, and a tap of delay. Alternatively, the channel matrix according to various embodiments of the present disclosure may be expressed in other domains.

[0258] The Doppler effect can be extracted in the angular-delay domain. For convenience of explanation, is represented as a matrix, but for planar arrays, to estimate the Doppler effect, it is represented as a 3D angular-domain tensor where each component corresponds to the elevation angle, azimuth angle, and tap of delay. can be used. Conceptually, information related to Doppler is and corresponds to the difference between the two. Therefore, as a conceptually simple method, Doppler information To extract , a subtraction operation can be performed on two consecutive channel matrices, i.e., the current channel matrix Previous channel matrix in A subtraction operation can be performed to subtract the previous CSI matrix. Instead, the previously decoded CSI matrix Because you can use it, at By subtracting, we can obtain Doppler-related information. That is, may be. However, in other embodiments, more complex operations and processes other than simple subtraction may be required, and for example, neural networks may be utilized. A Doppler estimator according to one embodiment of the present disclosure may be used to estimate the current CSI matrix. and the CSI matrix decoded in the previous time Takes as input, and Doppler information It may include a neural network that outputs a Doppler estimator. The Doppler estimator can be implemented based on a conventional channel prediction neural network. In addition, non-deep learning-based channel prediction approaches (e.g., Prony-based, autoregressive, matrix pencil, etc.) can be considered as a means for implementing the Doppler estimator.

[0259] As described above, according to the framework for the CSI feedback method according to one embodiment, the transmitting device includes a Doppler estimator. Doppler information, which is the output of the Doppler estimator is a Doppler latent vector through a Doppler encoder neural network. is encoded as a Doppler potential vector can be converted into a bit string through an entropy bottleneck layer. The quantized Doppler potential vector is the reconstructed Doppler information through the Doppler decoder neural network. can be decoded as

[0260] The Doppler-related information and Doppler potential vector expressed in the present disclosure are intermediate information or signals obtained in a data-driven manner from channel information of the current time and channel information of the previous time. Due to the characteristics of neural networks and deep learning, it is not easy to clearly identify or accurately describe the physical meaning of the signal. However, since the aforementioned information or signals include information related to the Doppler shift, the present disclosure refers to them as Doppler-related information and Doppler potential vector. However, in the present disclosure, the Doppler potential vector, which is a signal transmitted and / or received between a terminal and a base station, may actually include information related to temporal correlation other than the Doppler shift. Unlike traditional Doppler estimators based on mathematical model-driven methods, Doppler-related information, which is the output of a Doppler estimator that can be implemented as a data-driven neural network according to various embodiments of the present disclosure, can be extracted so that higher-dimensional, rich temporal correlation information that can be expressed in the feature domain can be better reflected.

[0261] In the present disclosure, the Doppler estimator takes as input a previously decoded channel instance and a current measured channel instance, expressed in the respective delay domain, and outputs Doppler-related information extracted from the respective delay domain or a specific feature domain. This differs from a Doppler estimator that typically takes as input a received signal and outputs the frequency shift itself. However, if a traditional Doppler estimator is utilized, and appropriate modifications are applied, the traditional Doppler estimator can also be used as the Doppler estimator according to an embodiment of the present disclosure.

[0262] Afterwards, the restored Doppler information and the CSI matrix decoded in the previous time From context is obtained. Context If the context is generated in the angular-delay domain, as is the case with the CSI matrix, is the predicted CSI matrix can be treated as . In this case, the residual can be decoded to provide feedback. On the other hand, the context If the context is generated from the feature domain, Is can be used as a condition related to encoding (i.e., conditional coding). In this case, the context of the feature domain The higher dimension of It can provide richer and more correlated conditions for encoding. and From context The operation of obtaining a context can be performed by the context generator (2618) of Fig. 26a. The context generator can be implemented based on a neural network. In order to implement the context generator (2618), it is also possible to benchmark techniques used in video compression (e.g., warping function). Context is input as a condition to the contextual encoder / decoder neural network, and the CSI instance to the potential vector or encoded as From can be used to decode.

[0263] As described above, according to various embodiments, the context is a CSI instance Channel potential vector can be used to encode or decode. Furthermore, the context is a latent vector It can also be used to represent a bit string. Specifically, a context generated through a context generator is a latent vector This is information required for the entropy model of the entropy bottleneck layer for transmission and reception. An example of the entropy model according to one embodiment of the present disclosure is as described above with reference to Figure 28a. That is, the channel potential vector An example of an entropy model required for entropy coding (e.g., arithmetic coding) for transmission and reception is as described above with reference to FIG. 28a.

[0264] Context, which is information corresponding to temporal correlation is encoded as a temporal prior through a temporal prior encoder neural network. The temporal prior is used for prior fusion together with the hierarchical prior. Prior fusion can also be implemented as a neural network. By fusing the temporal prior and hierarchical prior through the prior fusion neural network, Parameters of the prior distribution (e.g., mean vector) and standard deviation vector ) can be obtained. In prior fusion, in addition to hierarchical priors and temporal priors, a third prior (e.g., spatial correlation) can also be a target of fusion.

[0265] FIG. 29 illustrates an example of a procedure for transmitting CSI according to one embodiment of the present disclosure. FIG. 29 also illustrates an operation method of a terminal.

[0266] Referring to FIG. 29, in step S2901, the terminal receives configuration information related to CSI feedback. The configuration information may include at least one of information related to reference signals transmitted for channel measurement (e.g., resources, sequences, etc.), information related to channel measurement operations, and information related to feedback (e.g., formats, resources, feedback counts, cycles, etc.). According to various embodiments, the configuration information may be generated based on temporal correlation and may indicate CSI feedback having a variable transmission rate. Specifically, according to various embodiments, the configuration information may indicate feedback of a latent vector representing a channel and information related to temporal correlation (e.g., Doppler information).

[0267] In step S2903, the terminal receives reference signals. The terminal receives the reference signals based on the configuration information. That is, the terminal can receive reference signals based on the sequence indicated by the configuration information through the resources indicated by the configuration information. Through this, the terminal can obtain reception values ​​or measurement values ​​for the reference signals. The reception values ​​or measurement values ​​for the reference signals can be understood as measurement information for the channel, i.e., channel information. Here, the channel information can be expressed in the time-frequency domain or in the angular-delay domain.

[0268] In step S2905, the terminal generates CSI feedback information. According to various embodiments, the CSI feedback information may be generated by at least one encoding operation on channel information expressed in an image format. Here, at least one encoding includes an inference operation using a trained neural network and is performed based on information related to the temporal correlation of the channel (e.g., Doppler information), thereby generating a value indicating the channel (hereinafter, "channel value"). In addition, at least one encoding may include entropy coding.

[0269] In step S2907, the terminal transmits CSI feedback information. The terminal may transmit the CSI feedback information based on the configuration information received in step S2901. The CSI feedback information includes the channel value generated in step S2905. Since the channel value is generated based on temporal correlation, information related to temporal correlation is required to restore channel information from the channel value. Therefore, according to one embodiment, the CSI feedback information may further include a value indicating temporal correlation (hereinafter, “temporal correlation value”) in addition to the channel value. According to another embodiment, the temporal correlation value may be transmitted through separate signaling from the CSI feedback information. For example, the temporal correlation value may be transmitted at a longer period or at a longer time interval than the channel value.

[0270] FIG. 30 illustrates an example of a procedure for generating CSI feedback information according to one embodiment of the present disclosure. FIG. 30 also illustrates an operating method of a terminal.

[0271] Referring to FIG. 30, in step S3001, the terminal generates Doppler information for a channel. According to various embodiments, the Doppler information may be generated based on a channel instance at the current time point and a channel instance at a previous time point, and may include information related to a Doppler shift. Specifically, the terminal may estimate the Doppler information using a channel instance restored from a latent vector of a channel instance measured at a previous time point and a channel instance measured at the current time point. For example, the Doppler information may include Doppler-related characteristics extracted from each delay domain or a specific feature domain.

[0272] In step S3003, the terminal generates a Doppler potential vector. According to one embodiment, the terminal may generate the Doppler potential vector from Doppler information using a trained neural network. That is, the terminal may generate the Doppler potential vector through an inference operation using the trained neural network.

[0273] In step S3005, the terminal performs entropy coding on the Doppler latent vector. To this end, the terminal may determine an entropy model for entropy coding on the Doppler latent vector. For example, the terminal may generate an entropy model based on the Doppler latent vector, i.e., information related to a probability distribution, such as a mean and a standard deviation, for the Doppler latent vector. Specifically, the terminal may determine information related to a probability distribution, such as a mean and a standard deviation, for the Doppler latent vector as an entropy model through at least one operation among hierarchical prior encoding, quantization, entropy encoding, entropy decoding, dequantization, and hierarchical prior decoding on the Doppler latent vector. The terminal performs entropy encoding using the determined entropy model, and according to one embodiment, an entropy bottleneck layer may be used for entropy coding.

[0274] In step S3007, the terminal generates context information based on temporal correlation for the channel. Specifically, the terminal generates context information based on temporal correlation for the channel based on a Doppler latent vector. The context information is used to determine an entropy model for entropy coding of the channel latent vector, and according to one embodiment, may be generated based on information related to temporal correlation to reflect time-varying characteristics of the channel. Specifically, the terminal entropy decodes the Doppler latent vector from a bit string representing a temporal correlation value, inputs the decoded Doppler latent vector into a Doppler decoder neural network to restore the Doppler information, and then uses the restored Doppler information and / or channel instances from previous time points to generate a predicted channel instance or a condition for conditional coding as context information. According to one embodiment, the terminal may generate the context information using a trained neural network. That is, the terminal may generate the context information through an inference operation using the trained neural network.

[0275] In step S3009, the terminal generates a channel latent vector. The terminal can generate the channel latent vector using a trained neural network based on channel information measured based on a reference signal received from a base station. At this time, the channel information includes a channel instance at the current time point and, according to one embodiment, can be expressed in the form of an image in the angular-delay domain. Specifically, the terminal can use the context information generated in step S3007 as an additional input to a CSI encoder neural network (e.g., a neural network for generating a channel latent vector). That is, the context can be used as a condition for encoding the channel latent vector. Here, the context can be referred to as assistance information or a conditional input of the CSI encoder neural network.

[0276] In step S3011, the terminal performs entropy coding on the channel latent vector based on context information. To this end, the terminal may determine an entropy model for entropy coding on the channel latent vector. For example, the terminal may generate an entropy model, i.e., information related to a probability distribution, such as the mean and standard deviation, of the prior distribution of the channel latent vector, based on the context information and / or the channel latent vector. Specifically, the terminal may generate a hierarchical prior for the channel latent vector based on the channel latent vector, generate a temporal prior based on the context information, and then determine the entropy model by fusing the hierarchical prior and the temporal prior. Then, the terminal performs entropy encoding using the determined entropy model. In one embodiment, an entropy bottleneck layer may be used for entropy coding.

[0277] FIG. 31 illustrates an example of a procedure for obtaining CSI according to one embodiment of the present disclosure. FIG. 31 illustrates an operating method of a base station.

[0278] Referring to FIG. 31, in step S3101, the base station transmits configuration information related to CSI feedback. The configuration information may include at least one of information related to reference signals transmitted for channel measurement (e.g., resources, sequences, etc.), information related to channel measurement operations, and information related to feedback (e.g., formats, resources, feedback counts, cycles, etc.). According to various embodiments, the configuration information may be generated based on temporal correlation and may indicate CSI feedback having a variable transmission rate. Specifically, according to various embodiments, the configuration information may indicate feedback of a latent vector representing a channel and information related to temporal correlation (e.g., Doppler information).

[0279] In step S3103, the base station transmits reference signals. The base station transmits reference signals based on the configuration information. That is, the base station can transmit reference signals based on the sequence indicated by the configuration information through the resources indicated by the configuration information.

[0280] In step S3105, the base station receives CSI feedback information. That is, the base station receives CSI feedback information generated based on the transmitted reference signals. According to various embodiments, the CSI feedback information may include a value indicating a channel (hereinafter, “channel value”) generated by at least one encoding operation for channel information expressed in an image format. Here, the channel value may be generated by an encoding operation performed based on information related to the temporal correlation of the channel (e.g., Doppler information). Furthermore, according to one embodiment, the CSI feedback information may further include a value indicating the temporal correlation (hereinafter, “temporal correlation value”) in addition to the channel value. According to another embodiment, the temporal correlation value may be transmitted through separate signaling from the CSI feedback information. For example, the temporal correlation value may be transmitted at a longer period or at a longer time interval than the channel value.

[0281] In step S3107, the base station acquires channel information. In other words, the base station reconstructs the channel information based on at least one of the channel value and / or temporal correlation value included in the CSI feedback information. Specifically, according to one embodiment, the base station acquires Doppler information of the channel based on the temporal correlation value, acquires a channel potential vector based on the Doppler information, and then determines the channel information based on the channel potential vector. To this end, the base station may perform at least one decoding operation, and the decoding operation may include an inference operation using a trained neural network.

[0282] FIG. 32 illustrates an example of a procedure for interpreting CSI feedback information according to one embodiment of the present disclosure. FIG. 32 illustrates an operating method of a base station.

[0283] Referring to FIG. 32, in step S3201, the base station obtains a Doppler latent vector through entropy decoding. The base station can obtain the Doppler latent vector by performing entropy decoding on the temporal correlation values ​​included in the feedback information received from the terminal. To this end, the base station can determine an entropy model for entropy decoding. The base station can receive information necessary to determine the entropy model from the terminal, and generate an entropy model, i.e., information related to a probability distribution, such as the mean information and standard deviation, for the Doppler latent vector, based on the received information. Specifically, the base station can determine information related to a probability distribution, such as the mean information and standard deviation, for the Doppler latent vector as an entropy model, through at least one operation among entropy decoding, dequantization, and hierarchical prior decoding for values ​​included in the received information (e.g., the result of hierarchical prior encoding, quantization, and entropy coding for the Doppler latent vector). The terminal performs entropy decoding using the determined entropy model, and in one embodiment, an entropy bottleneck layer may be used for entropy decoding.

[0284] In step S3203, the base station generates context information based on temporal correlation for the channel. The base station generates context information based on temporal correlation for the channel based on a Doppler latent vector. The context information is used to determine an entropy model for entropy decoding the channel latent vector, and according to one embodiment, may be generated based on information related to temporal correlation to reflect the time-varying characteristics of the channel. Specifically, the base station entropy-decodes a Doppler latent vector from a bit string representing a received temporal correlation value, inputs the decoded Doppler latent vector into a Doppler decoder neural network to restore the Doppler information, and then uses the restored Doppler information and / or channel instances from previous time points to generate a predicted channel instance or a condition for conditional coding as context information. According to one embodiment, the base station may generate the context information using a trained neural network. That is, the base station may generate the context information through an inference operation using the trained neural network.

[0285] In step S3205, the base station obtains a channel latent vector through entropy decoding. To this end, the base station may determine an entropy model for entropy decoding the channel latent vector using context information. The base station may receive information necessary to determine the entropy model from the terminal, and based on the received information, may generate an entropy model, i.e., information related to a probability distribution, such as the mean and standard deviation, of the prior distribution of channel information. Specifically, the base station may generate a hierarchical prior for the channel latent vector based on the channel latent vector, generate a temporal prior based on the context information, and then determine the entropy model by fusing the hierarchical prior and the temporal prior. Then, the base station performs entropy decoding using the determined entropy model. In one embodiment, an entropy bottleneck layer may be used for entropy coding.

[0286] In step S3207, the base station can obtain channel information based on the channel potential vector. The base station can reconstruct the channel information using a neural network trained from the channel potential vector. The channel information includes the channel instance at the current point in time, and according to one embodiment, can be expressed in the form of an image in the angular-delay domain.

[0287] In the embodiment described with reference to FIG. 32, information required to determine an entropy model is received from a terminal. According to one embodiment, the information required to determine the entropy model may be included in CSI feedback information. In other words, the information required to determine the entropy model may be received as side information added to a channel value or temporal correlation value. According to another embodiment, the information required to determine the entropy model may be received through a separate procedure. In this case, the information required to determine the entropy model may be received at a different cycle from the CSI feedback.

[0288] Figure 33 illustrates an example of a CSI feedback procedure according to one embodiment of the present disclosure. Figure 33 illustrates an example of CSI feedback. The CSI feedback procedure according to various embodiments does not necessarily include all of the illustrated operations and may include only a portion of the procedures described below. For example, the order of signaling may vary, some signaling may be omitted, or additional signaling may be performed beyond what is described. In Figure 33, the terminal (3310) includes an encoder neural network as the encoder side, and the base station (3320) includes a decoder neural network as the decoder side.

[0289] Referring to FIG. 33, in steps S3301-1 to S3301-N, the base station (3320) transmits reference signals, for example, CSI-RSs, to the terminal (3310). The CSI-RSs may be transmitted periodically, aperiodicly, or semi-persistently. Although not illustrated in FIG. 33, the base station (3320) may transmit CSI-RSs after transmitting configuration information for the CSI-RSs to the terminal (3310).

[0290] In step S3303, the terminal (3310) determines a channel instance based on the CSI-RS. And, in step S3305, the terminal (3310) measures the channel potential vector and Doppler potential vector In step S3307, the base station (3320) estimates the channel instance. Restores.

[0291] At step S3309, the terminal (3310) determines a channel instance based on the CSI-RS. And, in step S3311, the terminal (3310) measures context information and channel instances Based on the channel latent vector Outputs the channel latent vector Hierarchical fryer for can be obtained. In step S3313, the terminal (3310) obtains Doppler information Based on the Doppler potential vector Outputs the Doppler potential vector. Hierarchical fryer for can be obtained.

[0292] At step S3315, the terminal (3310) quantizes and entropy-codes the channel latent vector and hierarchical fryer In step S3317, the terminal (3310) transmits a quantized and entropy-coded Doppler potential vector and hierarchical fryer Sends.

[0293] That is, the terminal (3310) is a channel potential vector and Doppler potential vector After quantizing and entropy coding, it is transmitted to the base station (3320), and also and Hierarchical priors for and After quantizing and entropy-coding, the terminal (3310) transmits the data to the base station (3320). That is, the terminal (3310) transmits the data to the base station (3320). and As a side note about and In this process, the channel potential vector and Hierarchical priors for , and the Doppler potential vector and Hierarchical priors for Each is converted into a sequence of symbols, such as a bitstream, by entropy coding. For convenience of explanation, the present disclosure considers the bitstream as a sequence of symbols, but binary transformation is only an example, and embodiments of the present disclosure are not limited to binary transformation.

[0294] At step S3319, the base station (3320) restores the Doppler potential vector Doppler information based on In step S3321, the base station (3320) restores the restored Doppler information. and restored channel instances Based on context In step S3323, the base station (3320) generates a context and restored channel latent vectors Channel instances based on Restores.

[0295] As described above, CSI feedback according to various embodiments can be performed using various components comprised of neural networks. From receiving channel information based on a reference signal at a transmitter to restoring the channel information at a receiver, the channel information passes through at least one neural network. For this CSI feedback, training of at least one neural network is required. Training is performed over the entire CSI feedback path from the transmitter to the receiver, and the channel information measured at the transmitter can be used as input and / or labels, and the channel information restored at the receiver can be used as output. At this time, backpropagation is difficult to perform due to the quantizer and dequantizer included in the CSI feedback path. This is because the gradient is 0 in most areas where the quantization operation is performed, and the differentiation is undefined at the remaining points. Therefore, when performing training, the quantizer and dequantizer, or the section of the entropy bottleneck layer including them, can be replaced with a component that generates uniform noise.

[0296] The concepts of prior, hyperprior, and context

[0297] In general, a prior is a concept that contrasts with a posterior probability. The posterior probability is proportional to the prior multiplied by the likelihood. The prior probability refers to the probability before a specific event occurs, meaning the probability before any observational information about that specific event is reflected.

[0298] In this disclosure, the prior is a probabilistic model for the quantized representation required for entropy coding, i.e., an entropy model. or corresponds to. The reason why the probability model, i.e. the probability distribution, is called a prior is because it is still a channel instance. or Doppler information Because it is not given. In contrast, the channel instance or Doppler information is the conditional probability given or corresponds to the posterior probability. Furthermore, in this disclosure, or Each of them or Because we assume that we get it from the latent representation or Prior probability distribution for and Or and All of them can be treated as fryers.

[0299] Priors for parameters of an entropy model are called hyperpriors. Since the entropy model is called a prior, the term "hyperprior" can be used in the sense of "prior for priors." That is, a hyperprior means information about parameters of the entropy model (e.g., standard deviation, mean, etc.). The hierarchical priors and temporal priors in the present disclosure can be understood as a type of hyperprior.

[0300] Additionally, in the present disclosure, context refers to information that can be provided as a conditional input to a function block to conditionally operate the function block. For example, in the case of a CSI encoder, the same channel instance Even if the context is entered, Depending on what value is given, the output of the CSI encoder can vary. That is, the behavior of the function block depends on the context. may vary depending on the context. In particular, according to various embodiments of the present disclosure, high-dimensional context in the feature domain can be extracted through learning so that the context can contain rich information that can assist in the operation of the functional block.

[0301] Obtaining an entropy model

[0302] Entropy model in this disclosure or is a parameter or can be obtained from. This can be explained through the graph of Figure 34 below. For convenience of explanation, go Explain the reasons that can be given from From The method of obtaining it is the same, so it is omitted.

[0303] Referring to Figure 34, , and Each one is , and Each random It means the th element, , and Each one is , and It means the probability distribution for each. Is This is the quantization result value. If the quantization bin size is 1 and the median of each bin is used as the representative value for quantization, then if the median of each bin is set as an integer, =round( ) can be expressed as an arbitrary integer. For each quantization bin, the interval corresponding to About The result of the definite integral is an integer In matches the value. Here, is an integral variable.

[0304] Is It can be obtained by adding uniform noise. At this time, the interval length of the uniform distribution is 1, like the quantization bin size. Since the probability distribution of a random variable defined as the sum of two random variables is expressed as a convolution of each of the two random variables, is a uniform distribution with a width of 1 and is obtained by convolution operation. A uniform distribution with an interval length of 1 is the same as a constant function with a function value of 1 within an interval of width 1 due to the characteristics of PDF. Therefore, a uniform distribution with a width of 1 and Performing a convolution operation on is to move a boxcar function of unit width with function value 1 over the overlapping interval. is equivalent to integrating . Therefore, the moving integration interval is exactly If located in, Is About It is calculated as a definite integral, and this value is an integer In Matches a value, i.e. any integer About Silver integer In It is equal to the value.

[0305] It is clear that the examples of the proposed methods described above can also be considered as a type of proposed methods, as they can be included as one of the implementation methods of the present disclosure. Furthermore, the proposed methods described above can be implemented independently, but they can also be implemented in the form of a combination (or merge) of some of the proposed methods. Information regarding the applicability of the proposed methods (or information regarding the rules of the proposed methods) can be defined by a rule such that the base station notifies the terminal of the application of the proposed methods through a predefined signal (e.g., a physical layer signal or a higher layer signal).

[0306] The present disclosure may be embodied in other specific forms without departing from the technical ideas and essential features described herein. Therefore, the above detailed description should not be construed as limiting in all respects but rather as illustrative. The scope of the present disclosure should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present disclosure are intended to be included within the scope of the present disclosure. Furthermore, claims that are not explicitly cited in the claims may be combined to form an embodiment or incorporated into a new claim through a post-filing amendment.

[0307] Embodiments of the present disclosure can be applied to various wireless access systems. Examples of various wireless access systems include the 3rd Generation Partnership Project (3GPP) or 3GPP2 systems.

[0308] The embodiments of the present disclosure can be applied not only to the various wireless access systems described above, but also to all technical fields that utilize these various wireless access systems. Furthermore, the proposed method can also be applied to mmWave and THz communication systems utilizing ultra-high frequency bands.

[0309] Additionally, embodiments of the present disclosure can be applied to various applications such as autonomous vehicles and drones.

Claims

1. In a method of operating a terminal in a wireless communication system, A step of receiving configuration information related to CSI (channel state information) feedback; A step of receiving reference signals based on the above setting information; A step of generating CSI feedback information based on the above reference signals; and A step of transmitting the above CSI feedback information is included, A method in which the CSI feedback information includes channel information determined based on the reference signals and information related to the temporal correlation of the channel information.

2. In claim 1, A method wherein the channel information includes the result of entropy coding for a latent vector of a first channel instance measured using the reference signals.

3. In claim 2, A method further comprising the step of transmitting hierarchical prior information determined based on the latent vector of the first channel instance.

4. In claim 2, A method in which the entropy coding is performed using an entropy model determined based on a hierarchical prior related to a channel determined based on a latent vector of the first channel instance and a temporal prior determined based on Doppler information related to the channel.

5. In claim 4, A method in which the temporal prior is determined based on context information determined based on the channel instance restored from the Doppler information and the latent vector of the second channel instance measured at a previous point in time.

6. In claim 1, A method in which information related to the above temporal correlation includes the result of entropy coding for a latent vector of Doppler information estimated based on the first channel instance.

7. In claim 6, A method further comprising the step of estimating the Doppler information based on a channel instance restored from a latent vector of a second channel instance measured at a previous point in time and the first channel instance measured at the current point in time.

8. In claim 6, A method further comprising the step of transmitting hierarchical prior information related to the latent vector of the above Doppler information.

9. In claim 1, The step of generating the above CSI feedback information is: A step of estimating Doppler information for a channel; A step of generating a latent vector of the above Doppler information; A step of generating context information based on a latent vector of the above Doppler information; A step of generating a latent vector of a first channel instance measured using the above reference signals based on the context information; and A method comprising the steps of performing quantization and entropy coding on a latent vector of the first channel instance based on the context information.

10. In claim 9, A method wherein the first channel instance comprises information expressed in one of the following forms: an image format in an angular-delay domain, a 3D tensor format in a two-dimensional space-one-dimensional frequency domain, or a tensor format in a three-dimensional angular-delay domain consisting of an elevation angle, an azimuth angle, and a tap of delay.

11. In a method of operating a base station in a wireless communication system, A step of transmitting configuration information related to CSI (channel state information) feedback; A step of transmitting reference signals based on the above setting information; A step of receiving CSI feedback information corresponding to the above reference signals; and A step of restoring channel information based on the above CSI feedback information is included, A method in which the CSI feedback information includes channel information determined based on the reference signals and information related to the temporal correlation of the channel information.

12. In claim 11, A method further comprising the step of receiving hierarchical prior information determined based on a latent vector of a first channel instance measured using the reference signals included in the channel information.

13. In claim 11, A method further comprising the step of receiving hierarchical prior information related to a latent vector of Doppler information included in the above temporal correlation.

14. In a wireless communication system, at a terminal, Transmitter and receiver; and A processor connected to the above transmitter and receiver is included, The above processor, Receive configuration information related to CSI (channel state information) feedback, Receive reference signals based on the above setting information, Generate CSI feedback information based on the above reference signals, Control to transmit the above CSI feedback information, A terminal including the CSI feedback information, the terminal including channel information determined based on the reference signals and information related to the temporal correlation of the channel information.

15. In a base station in a wireless communication system, Transmitter and receiver; and A processor connected to the above transmitter and receiver is included, The above processor, Transmit configuration information related to CSI (channel state information) feedback, Transmit reference signals based on the above setting information, Receive CSI feedback information corresponding to the above reference signals, Control to restore channel information based on the above CSI feedback information, A base station, wherein the CSI feedback information includes channel information determined based on the reference signals and information related to the temporal correlation of the channel information.

16. In communication devices, At least one processor; At least one computer memory connected to said at least one processor and storing instructions that direct operations when executed by said at least one processor, The above actions are, A step of receiving configuration information related to CSI (channel state information) feedback; A step of receiving reference signals based on the above setting information; A step of generating CSI feedback information based on the above reference signals; and A step of transmitting the above CSI feedback information is included, A communication device wherein the CSI feedback information includes channel information determined based on the reference signals and information related to the temporal correlation of the channel information.

17. In a non-transitory computer-readable medium storing at least one instruction, comprising at least one instruction executable by the processor, At least one of the above commands causes the device to: Receive configuration information related to CSI (channel state information) feedback, Receive reference signals based on the above setting information, Generate CSI feedback information based on the above reference signals, Control to transmit the above CSI feedback information, The CSI feedback information is a non-transitory computer-readable medium including channel information determined based on the reference signals and information related to the temporal correlation of the channel information.

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