WO2023033421A1 - Device and method for setting polar code-based encoder in wireless communication system - Google Patents

Abstract

The present invention is for setting a polar code-based encoder in a wireless communication system. An operation method of a user equipment (UE) comprises the steps of: receiving a signal including at least one codeword from a base station; decoding the at least one codeword; generating feedback information on the basis of the decoded result; and transmitting the feedback information to the base station, wherein the at least one codeword may be decoded on the basis of a polar code, and the feedback information may include information related to an erase probability for setting, in the base station, an information bit position and a frozen bit position of an encoder for the polar code.

Description

Apparatus and method for setting a polar code-based encoder in a wireless communication system

The following description relates to a wireless communication system, and relates to an apparatus and method for setting a polar code-based encoder in a wireless communication system.

A wireless access system is widely deployed to provide various types of communication services such as voice and data. In general, a wireless access system is a multiple access system capable of supporting communication with multiple users by sharing available system resources (bandwidth, transmission power, etc.). Examples of the multiple access system include a code division multiple access (CDMA) system, a frequency division multiple access (FDMA) system, a time division multiple access (TDMA) system, an orthogonal frequency division multiple access (OFDMA) system, and a single carrier frequency (SC-FDMA) system. division multiple access) system.

In particular, as many communication devices require large communication capacity, an enhanced mobile broadband (eMBB) communication technology compared to existing radio access technology (RAT) has been proposed. In addition, a communication system considering reliability and latency-sensitive services/UE (user equipment) as well as mMTC (massive machine type communications) providing various services anytime and anywhere by connecting multiple devices and objects has been proposed. . Various technical configurations for this have been proposed.

The present disclosure may provide an apparatus and method for effectively setting a polar code-based encoder in a wireless communication system.

The present disclosure may provide an apparatus and method for rapidly determining an epsilon for a channel in a wireless communication system.

The present disclosure may provide an apparatus and method for rapidly determining an erasure probability for a channel in a wireless communication system.

The present disclosure may provide an apparatus and method for determining a channel cancellation probability using Bayesian optimization in a wireless communication system.

The present disclosure may provide an apparatus and method for determining a channel cancellation probability using a tree-structured parzen estimator (TPE) in a wireless communication system.

The present disclosure may provide an apparatus and method for determining an erasure probability based on data received in a wireless communication system.

The present disclosure may provide an apparatus and method for signaling information for operating a TPE in a wireless communication system.

The present disclosure may provide an apparatus and method for signaling information related to a good point for operating a TPE in a wireless communication system.

The present disclosure may provide an apparatus and method for signaling a threshold for determining a good point for operating a TPE in a wireless communication system.

The present disclosure may provide an apparatus and method for determining an erasure probability based on information related to a good point signaled in a wireless communication system.

The present disclosure may provide an apparatus and method for a device receiving data to feed back an erasure probability in a wireless communication system.

The present disclosure may provide an apparatus and method for setting an encoder based on an erasure probability fed back in a wireless communication system.

The present disclosure may provide an apparatus and method for determining a location of a frozen bit of a polar code based on an erasure probability fed back in a wireless communication system.

The technical objectives to be achieved in the present disclosure are not limited to the above-mentioned matters, and other technical problems not mentioned are common in the technical field to which the technical configuration of the present disclosure is applied from the embodiments of the present disclosure to be described below. It can be considered by those with knowledge.

As an example of the present disclosure, a method of operating a user equipment (UE) in a wireless communication system includes receiving a signal including at least one codeword from a base station, and decoding the at least one codeword. , generating feedback information based on a result of the decoding, and transmitting the feedback information to the base station, wherein the at least one codeword is decoded based on a polar code, and the feedback information is , information related to an erasure probability for setting an information bit position of an encoder for a polar code in the base station and a frozen bit position.

As an example of the present disclosure, a method of operating a base station in a wireless communication system includes generating at least one codeword by performing encoding on a bit sequence based on a polar code, and providing a user equipment (UE) with the at least one Transmitting a signal including a codeword, receiving feedback information generated based on a decoding result of the at least one codeword, and information bit position of an encoder for the polar code based on the feedback information and setting a frozen bit position, wherein the feedback information may include information related to an information bit position of the encoder and an erase probability for setting the frozen bit position.

As an example of the present disclosure, in a wireless communication system, user equipment (UE) includes a transceiver and a processor connected to the transceiver, wherein the processor receives a signal including at least one codeword from a base station, and the decoding at least one codeword, generating feedback information based on a result of the decoding, transmitting the feedback information to the base station, and decoding the at least one codeword based on a polar code; , The feedback information may include information related to an erasure probability for setting an information bit position of an encoder and a frozen bit position for a polar code in the base station.

As an example of the present disclosure, in a wireless communication system, a base station includes a transceiver and a processor connected to the transceiver, wherein the processor generates at least one codeword by encoding a bit sequence based on a polar code. and transmits a signal including the at least one codeword to a user equipment (UE), receives feedback information generated based on a decoding result of the at least one codeword, and based on the feedback information An information bit position and a frozen bit position of an encoder for a polar code are set, and the feedback information may include information related to an erasure probability for setting the information bit position and the frozen bit position of the encoder. .

As an example of the present disclosure, an apparatus includes at least one processor, at least one computer memory connected to the at least one processor and storing instructions that direct operations as executed by the at least one processor, , The above operations include, by the apparatus, receiving a signal including at least one codeword from a base station, performing decoding on the at least one codeword, and generating feedback information based on a result of the decoding. and transmitting the feedback information to the base station, wherein the at least one codeword is decoded based on a polar code, and the feedback information includes information of an encoder for a polar code in the base station. It may include information related to an erase probability for setting a bit position and a frozen bit position.

As an example of the present disclosure, a non-transitory computer-readable medium storing at least one instruction (instructions), the at least one instruction executable by a processor (executable) Including, the at least one instruction, the apparatus, receives a signal including at least one codeword from the base station, performs decoding on the at least one codeword, and provides feedback based on a result of the decoding. generates information, transmits the feedback information to the base station, the at least one codeword is decoded based on a polar code, and the feedback information includes information bit positions of encoders for polar codes in the base station and Information related to an erase probability for setting a frozen bit position may be included.

The above-described aspects of the present disclosure are only some of the preferred embodiments of the present disclosure, and various embodiments in which the technical features of the present disclosure are reflected are the detailed descriptions of the present disclosure to be detailed below by those of ordinary skill in the art. It can be derived and understood based on the description.

The following effects may be obtained by embodiments based on the present disclosure.

According to the present disclosure, polar codes can be used more effectively.

Effects obtainable from the embodiments of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned are applied to the technical configuration of the present disclosure from the description of the embodiments of the present disclosure below. It can be clearly derived and understood by those skilled in the art. That is, unintended effects according to implementing the configuration described in the present disclosure may also be derived by those skilled in the art from the embodiments of the present disclosure.

The accompanying drawings are provided to aid understanding of the present disclosure, and may provide embodiments of the present disclosure together with a detailed description. However, the technical features of the present disclosure are not limited to specific drawings, and features disclosed in each drawing may be combined with each other to form a new embodiment. Reference numerals in each drawing may mean structural elements.

1 shows an example of a communication system applicable to the present disclosure.

2 shows an example of a wireless device applicable to the present disclosure.

3 illustrates another example of a wireless device applicable to the present disclosure.

4 shows an example of a portable device applicable to the present disclosure.

5 illustrates an example of a vehicle or autonomous vehicle applicable to the present disclosure.

6 illustrates an example of AI (Artificial Intelligence) applicable to the present disclosure.

7 illustrates a method of processing a transmission signal applicable to the present disclosure.

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

9 shows an electromagnetic spectrum applicable to the present disclosure.

10 illustrates a THz communication method applicable to the present disclosure.

11A to 11C show examples of structures of polar codes applicable to the present disclosure.

12 illustrates polarization of channel capacity according to application of a polar code applicable to the present disclosure.

13 illustrates an example of an epsilon search result for setting a polar code applicable to the present disclosure.

14 illustrates an example of sampling point search according to Bayesian optimization applicable to the present disclosure.

15A to 15C illustrate examples of sampling point search according to a tree-structured Parzen estimator (TPE) applicable to the present disclosure.

16 illustrates an example of a functional structure of devices in a wireless communication system according to an embodiment of the present disclosure.

17 illustrates an example of a procedure for setting a polar code in a wireless communication system according to an embodiment of the present disclosure.

18 illustrates an example of a procedure for feeding back an erasure probability in a wireless communication system according to an embodiment of the present disclosure.

19 illustrates an example of a procedure for performing communication between devices using polar codes in a wireless communication system according to an embodiment of the present disclosure.

20 illustrates an example of a procedure for searching epsilon in a wireless communication system according to an embodiment of the present disclosure.

21 and 22 show performance of an erasure probability search technique according to an embodiment of the present disclosure.

The following embodiments combine elements and features of the present disclosure in some form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form not combined with other components or features. In addition, an embodiment of the present disclosure may be configured by combining some elements and/or features. 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.

In the description of the drawings, procedures or steps that may obscure the gist of the present disclosure have not been described, and procedures or steps that can be understood by those skilled in the art have not been described.

Throughout the specification, when a part is said to "comprising" or "including" a certain element, it means that it may further include other elements, not excluding other elements, unless otherwise stated. do. In addition, terms such as “… unit”, “… unit”, and “module” described in the specification mean a unit that processes at least one function or operation, which is hardware or software or a combination of hardware and software. can be implemented as Also, "a or an", "one", "the" and similar related words in the context of describing the present disclosure (particularly in the context of the claims below) Unless indicated or otherwise clearly contradicted by context, both the singular and the plural can be used.

Embodiments of the present disclosure in this specification have been described with a focus on a data transmission/reception relationship between a base station and a mobile station. Here, a base station has meaning as a terminal node of a network that directly communicates with a mobile station. A specific operation described as being performed by a base station in this document may be performed by an upper node of the base station in some cases.

That is, in a network composed of a plurality of network nodes including a base station, various operations performed for communication with a mobile station may be performed by the base station or network nodes other than the base station. At this time, the 'base station' is a term such as a fixed station, Node B, eNode B, gNode B, ng-eNB, advanced base station (ABS), or access point. can be replaced by

In addition, in the embodiments of the present disclosure, a terminal includes a user equipment (UE), a mobile station (MS), a subscriber station (SS), and a mobile subscriber station (MSS). , may be replaced with terms such as mobile terminal or advanced mobile station (AMS).

In addition, the transmitting end refers to a fixed and/or mobile node providing data service or voice service, and the receiving end refers to a fixed and/or mobile node receiving data service or voice service. Therefore, in the case of uplink, the mobile station can be a transmitter and the base station can be a receiver. Similarly, in the case of downlink, the mobile station may be a receiving end and the base station may be a transmitting end.

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

In addition, embodiments of the present disclosure may be applied to other wireless access systems, and are not limited to the above-described systems. For example, it may also be applicable to a system applied after the 3GPP 5G NR system, and is not limited to a specific system.

That is, obvious steps or parts not described among the embodiments of the present disclosure may be described with reference to the above documents. In addition, all terms disclosed in this document can be explained by the standard document.

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 in conjunction with the accompanying drawings is intended to describe 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 practiced.

In addition, specific terms used in the embodiments of the present disclosure are provided to aid understanding of the present disclosure, and the use of these specific terms may be changed in other forms without departing from the technical spirit of the present disclosure.

The following technologies include code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), orthogonal frequency division multiple access (OFDMA), single carrier frequency division multiple access (SC-FDMA), and the like. It can be applied to various wireless access systems.

In the following, in order to clarify the following description, the description is based on the 3GPP communication system (e.g. (eg, LTE, NR, etc.), but the technical spirit of the present invention is not limited thereto. LTE is 3GPP TS 36.xxx Release 8 or later In detail, LTE technology after 3GPP TS 36.xxx Release 10 is 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 mean technology after TS 38.xxx Release 15. 3GPP 6G may mean technology after TS Release 17 and/or Release 18. "xxx" means a standard document detail number. LTE/NR/6G may be collectively referred to as a 3GPP system.

For background art, terms, abbreviations, etc. used in the present disclosure, reference may be made to matters described in standard documents published prior to the present invention. As an example, 36.xxx and 38.xxx standard documents may be referred to.

Communication systems applicable to the present disclosure

Although not limited thereto, 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 / connection (eg, 5G) between devices. there is.

Hereinafter, it will be exemplified in more detail with reference to the drawings. In the following drawings/description, the same reference numerals may represent the same or corresponding hardware blocks, software blocks or functional blocks unless otherwise specified.

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

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 means a device that performs communication using a radio access technology (eg, 5G NR, LTE), and may be referred to as a communication/wireless/5G device. Although not limited thereto, the wireless device includes a robot 100a, a vehicle 100b-1 and 100b-2, an extended reality (XR) device 100c, a hand-held device 100d, and a home appliance. appliance) 100e, Internet of Thing (IoT) device 100f, and artificial intelligence (AI) 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 inter-vehicle communication, and the like. Here, the vehicles 100b-1 and 100b-2 may include an unmanned aerial vehicle (UAV) (eg, a drone). The XR device 100c includes augmented reality (AR)/virtual reality (VR)/mixed reality (MR) devices, and includes a head-mounted device (HMD), a head-up display (HUD) installed in a vehicle, a television, It may be implemented in the form of smart phones, computers, wearable devices, home appliances, digital signage, vehicles, robots, and the like. The mobile device 100d may include a smart phone, a smart pad, a wearable device (eg, a smart watch, a smart glass), a computer (eg, a laptop computer), and the like. The home appliance 100e may include a TV, a refrigerator, a washing machine, and the like. The IoT device 100f may include a sensor, a smart meter, and the like. For example, the base station 120 and the network 130 may also be implemented as a wireless device, and a specific wireless device 120a may operate as a base station/network node to other wireless devices.

The wireless devices 100a to 100f may be connected to the network 130 through the base station 120 . AI technology may be applied to the wireless devices 100a to 100f, and the wireless devices 100a to 100f may be connected to the AI server 100g through the network 130. The network 130 may be configured using a 3G network, a 4G (eg LTE) network, or a 5G (eg NR) network. The wireless devices 100a to 100f may communicate with each other through the base station 120/network 130, but communicate directly without going through the base station 120/network 130 (e.g., sidelink communication). You may. For example, the vehicles 100b-1 and 100b-2 may perform direct communication (eg, vehicle to vehicle (V2V)/vehicle to everything (V2X) communication). In addition, the IoT device 100f (eg, sensor) may directly communicate with other IoT devices (eg, sensor) or other wireless devices 100a to 100f.

Wireless communication/ connection 150a, 150b, and 150c may be performed between the wireless devices 100a to 100f/base station 120 and the base station 120/base station 120. Here, wireless communication/connection includes various types of uplink/downlink communication 150a, sidelink communication 150b (or D2D communication), and inter-base station communication 150c (eg relay, integrated access backhaul (IAB)). This can be done through radio access technology (eg 5G NR). Through the wireless communication/ connection 150a, 150b, and 150c, a wireless device and a base station/wireless device, and a base station and a base station can transmit/receive radio signals to each other. For example, the wireless communication/ connections 150a, 150b, and 150c may transmit/receive signals through various physical channels. To this end, based on various proposals of the present disclosure, various configuration information setting processes for transmitting / receiving radio signals, various signal processing processes (eg, channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.) At least a part of a resource allocation process may be performed.

Communication systems applicable to the present disclosure

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

Referring to FIG. 2 , a first wireless device 200a and a second wireless device 200b may transmit and receive radio signals through various wireless access technologies (eg, LTE and NR). Here, {the first wireless device 200a, the second wireless device 200b} denotes the {wireless device 100x and the base station 120} of FIG. 1 and/or the {wireless device 100x and the wireless device 100x. } can correspond.

The 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 memory 204a and/or the transceiver 206a and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flow diagrams disclosed herein. For example, the processor 202a may process information in the memory 204a to generate first information/signal, and transmit a radio signal including the first information/signal through the transceiver 206a. In addition, the processor 202a may receive a radio signal including the second information/signal through the transceiver 206a and store information obtained from signal processing of the second information/signal 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, memory 204a may perform some or all of the processes controlled by processor 202a, or instructions for performing the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. It may store software codes including them. Here, the processor 202a and the memory 204a may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR). The transceiver 206a may be coupled to the processor 202a and may transmit and/or receive wireless signals through one or more antennas 208a. The transceiver 206a may include a transmitter and/or a receiver. The transceiver 206a may be used interchangeably with a radio frequency (RF) unit. In the present disclosure, a wireless device may mean a communication modem/circuit/chip.

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 memory 204b and/or the transceiver 206b and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flow diagrams disclosed herein. For example, the processor 202b may process information in the memory 204b to generate third information/signal, and transmit a radio signal including the third information/signal through the transceiver 206b. In addition, the processor 202b may receive a radio signal including the fourth information/signal through the transceiver 206b and store information obtained from signal processing of the fourth information/signal 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 instructions for performing the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. It may store software codes including them. Here, the processor 202b and the memory 204b may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR). The transceiver 206b may be coupled to the processor 202b and may transmit and/or receive wireless signals through 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 mean a communication modem/circuit/chip.

Hereinafter, hardware elements of the wireless devices 200a and 200b will be described in more detail. Although not limited to this, one or more protocol layers may be implemented by one or more processors 202a, 202b. For example, the one or more processors 202a and 202b may include one or more layers (eg, PHY (physical), MAC (media access control), RLC (radio link control), PDCP (packet data convergence protocol), RRC (radio resource) control) and functional layers such as 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 flow charts disclosed herein. can create 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 flow diagrams disclosed herein. One or more processors 202a, 202b generate PDUs, SDUs, messages, control information, data or signals (eg, baseband signals) containing information according to the functions, procedures, proposals and/or methods disclosed herein , may be provided to one or more transceivers 206a and 206b. One or more processors 202a, 202b may receive signals (eg, baseband signals) from one or more transceivers 206a, 206b, and descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein PDUs, SDUs, messages, control information, data or information can be obtained according to these.

One or more processors 202a, 202b may be referred to as a controller, microcontroller, microprocessor or 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 and 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, and the like. Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods and/or operational flow charts disclosed in this document may be included in one or more processors 202a or 202b or stored in one or more memories 204a or 204b. It can be driven by the above processors 202a and 202b. The descriptions, functions, procedures, suggestions, methods and/or operational flow charts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and/or sets of instructions.

One or more memories 204a, 204b may be coupled to one or more processors 202a, 202b and may store various types of data, signals, messages, information, programs, codes, instructions and/or instructions. One or more memories 204a, 204b may include read only memory (ROM), random access memory (RAM), erasable programmable read only memory (EPROM), flash memory, hard drive, registers, cache memory, computer readable storage media, and/or It may consist of a combination of these. One or more memories 204a, 204b may be located internally and/or externally to one or more processors 202a, 202b. In addition, one or more memories 204a, 204b may be connected to one or more processors 202a, 202b through various technologies such as wired or wireless connections.

One or more transceivers 206a, 206b may transmit user data, control information, radio signals/channels, etc. referred to in the methods and/or operational flow charts of this document to one or more other devices. One or more transceivers 206a, 206b may receive user data, control information, radio signals/channels, etc. referred to in descriptions, functions, procedures, proposals, methods and/or operational flow charts, etc. disclosed herein from one or more other devices. there is. For example, one or more transceivers 206a and 206b may be connected to one or more processors 202a and 202b and transmit and receive radio signals. For example, one or more processors 202a, 202b may control one or more transceivers 206a, 206b to transmit user data, control information, or radio signals to one or more other devices. In addition, one or more processors 202a, 202b may control one or more transceivers 206a, 206b to receive user data, control information, or radio signals from one or more other devices. In addition, 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 connected to one or more antennas 208a, 208b to achieve the descriptions, functions disclosed in this document. , procedures, proposals, methods and / or operation flowcharts, etc. can be set to transmit and receive user data, control information, radio signals / channels, etc. In this document, one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (eg, antenna ports). One or more transceivers (206a, 206b) in order to process the received user data, control information, radio signal / channel, etc. using one or more processors (202a, 202b), the received radio signal / channel, etc. in the RF band signal It can be converted into a baseband signal. One or more transceivers 206a and 206b may convert user data, control information, and radio signals/channels processed by one or more processors 202a and 202b from baseband signals to RF band signals. To this end, one or more transceivers 206a, 206b may include (analog) oscillators and/or filters.

Wireless device structure applicable to the present disclosure

3 is a diagram illustrating another example of a wireless device applied to the present disclosure.

Referring to FIG. 3, a wireless device 300 corresponds to the wireless devices 200a and 200b of FIG. 2, and includes various elements, components, units/units, and/or modules. ) can be configured. For example, the wireless device 300 may include a communication unit 310, a control unit 320, a memory unit 330, and an additional element 340. The communication unit may include communication circuitry 312 and transceiver(s) 314 . For example, communication circuitry 312 may include one or more processors 202a, 202b of FIG. 2 and/or one or more memories 204a, 204b. For example, transceiver(s) 314 may include one or more transceivers 206a, 206b of FIG. 2 and/or one or more antennas 208a, 208b. The control unit 320 is electrically connected to the communication unit 310, the memory unit 330, and the additional element 340 and controls overall operations of the wireless device. For example, the control unit 320 may control electrical/mechanical operations of the wireless device based on programs/codes/commands/information stored in the memory unit 330. In addition, the control unit 320 transmits the information stored in the memory unit 330 to the outside (eg, another communication device) through the communication unit 310 through a wireless/wired interface, or transmits the information stored in the memory unit 330 to the outside (eg, another communication device) through the communication unit 310. Information received through a wireless/wired interface from other communication devices) may be stored in the memory unit 330 .

The additional element 340 may be configured in various ways according to the type of 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 a robot (FIG. 1, 100a), a vehicle (FIG. 1, 100b-1, 100b-2), an XR device (FIG. 1, 100c), a mobile device (FIG. 1, 100d) ), home appliances (FIG. 1, 100e), IoT devices (FIG. 1, 100f), digital broadcasting terminals, hologram devices, public safety devices, MTC devices, medical devices, fintech devices (or financial devices), security devices, climate/ It may be implemented in the form of an environment device, an AI server/device (FIG. 1, 140), a base station (FIG. 1, 120), a network node, and the like. Wireless devices can be mobile or used in a fixed location depending on the use-case/service.

In FIG. 3 , various elements, components, units/units, and/or modules in the wireless device 300 may be entirely interconnected through a wired interface or at least partially connected wirelessly through the communication unit 310 . For example, in the wireless device 300, the control unit 320 and the communication unit 310 are connected by wire, and the control unit 320 and the first units (eg, 130 and 140) are connected wirelessly through the communication unit 310. can be connected Additionally, each element, component, unit/unit, and/or module within wireless device 300 may further include one or more elements. For example, the control unit 320 may be composed of one or more processor sets. For example, the control unit 320 may include a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphic processing processor, a memory control processor, and the like. As another example, the memory unit 330 may include RAM, dynamic RAM (DRAM), ROM, flash memory, volatile memory, non-volatile memory, and/or combinations thereof. can be configured.

Mobile device to which the present disclosure is applicable

4 is a diagram illustrating an example of a portable device applied to the present disclosure.

4 illustrates a portable device applied to the present disclosure. A portable device may include a smart phone, a smart pad, a wearable device (eg, smart watch, smart glasses), and a portable computer (eg, a laptop computer). A 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).

Referring to FIG. 4 , a portable device 400 includes 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. ) may be included. The antenna unit 408 may be configured as part of the communication unit 410 . Blocks 410 to 430/440a to 440c respectively correspond to blocks 310 to 330/340 of FIG. 3 .

The communication unit 410 may transmit/receive signals (eg, data, control signals, etc.) with other wireless devices and base stations. The controller 420 may perform various operations by controlling components of the portable device 400 . The controller 420 may include an application processor (AP). The memory unit 430 may store data/parameters/programs/codes/commands necessary for driving the portable device 400 . Also, the memory unit 430 may store input/output data/information. The power supply unit 440a supplies power to the portable device 400 and may include a wired/wireless charging circuit, a battery, and the like. The interface unit 440b may support connection between the mobile device 400 and other external devices. The interface unit 440b may include various ports (eg, audio input/output ports and video input/output ports) for connection with external devices. The input/output unit 440c may receive or output image information/signal, audio information/signal, 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.

For example, in the case of data communication, the input/output unit 440c acquires information/signals (eg, touch, text, voice, image, video) input from the user, and the acquired information/signals are stored in the memory unit 430. can be stored The communication unit 410 may convert the information/signal stored in the memory into a wireless signal, and directly transmit the converted wireless signal to another wireless device or to a base station. In addition, the communication unit 410 may receive a radio signal from another wireless device or base station and then restore the received radio signal to original information/signal. After the restored information/signal is stored in the memory unit 430, it may be output in various forms (eg, text, voice, image, video, or haptic) through the input/output unit 440c.

Types of wireless devices to which this disclosure is applicable

5 is a diagram illustrating an example of a vehicle or autonomous vehicle to which the present disclosure applies.

5 illustrates a vehicle or autonomous vehicle to which the present disclosure is applied. A vehicle or an autonomous vehicle may be implemented as a mobile robot, vehicle, train, manned/unmanned aerial vehicle (AV), ship, etc., and is not limited to a vehicle type.

Referring to FIG. 5 , a vehicle or autonomous vehicle 500 includes 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. A portion 540d may be included. The antenna unit 550 may be configured as a part of the communication unit 510 . Blocks 510/530/540a to 540d respectively correspond to blocks 410/430/440 of FIG. 4 .

The communication unit 510 may transmit/receive signals (eg, data, control signals, etc.) with external devices such as other vehicles, base stations (eg, base stations, roadside base units, etc.), servers, and the like. The controller 520 may perform various operations by controlling elements of the vehicle or autonomous vehicle 500 . The controller 520 may include an electronic control unit (ECU).

6 is a diagram illustrating an example of an AI device applied to the present disclosure. As an example, AI devices include TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, vehicles, etc. It may be implemented as a device or a movable device.

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

The communication unit 610 communicates wired and wireless signals (eg, sensor information, user data) with external devices such as other AI devices (eg, FIG. 1, 100x, 120, and 140) or AI servers (Fig. input, learning model, control signal, etc.) can be transmitted and received. To this end, the communication unit 610 may transmit information in the memory unit 630 to an external device or transmit a signal received from the external device to the memory unit 630 .

The controller 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. And, the controller 620 may perform the determined operation by controlling components of the AI device 600 . For example, the control unit 620 may request, search for, receive, or utilize data from the learning processor unit 640c or the memory unit 630, and may perform a predicted operation among at least one feasible operation or an operation determined to be desirable. Components of the AI device 600 may be controlled to execute an operation. In addition, the control unit 620 collects history information including user feedback on the operation contents or operation of the AI device 600 and stores it in the memory unit 630 or the running processor unit 640c, or the AI server ( 1, 140) can be transmitted to an external device. The collected history information can be used to update the learning model.

The memory unit 630 may store data supporting various functions of the AI device 600 . For example, the memory unit 630 may 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. Also, the memory unit 630 may store control information and/or software codes required for operation/execution of the controller 620 .

The input unit 640a may obtain various types of data from the outside of the AI device 600. For example, the input unit 620 may obtain learning data for model learning and input data to which the learning model is to be applied. The input unit 640a may include a camera, a microphone, and/or a user input unit. The output unit 640b may generate an output related to sight, hearing, or touch. The output unit 640b may include a display unit, a speaker, and/or a haptic module. The sensing unit 640 may obtain at least one of internal information of the AI device 600, surrounding environment information of the AI device 600, and user information by 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, an optical sensor, a microphone, and/or a radar. there is.

The learning processor unit 640c may learn a model composed of an artificial neural network using learning data. The running processor unit 640c may perform AI processing together with the running processor unit of the AI server (FIG. 1, 140). The learning processor unit 640c may process information received from an external device through the communication unit 610 and/or information stored in the memory unit 630 . In addition, the output value of the learning processor unit 640c may be transmitted to an external device through the communication unit 610 and/or stored in the memory unit 630.

7 is a diagram illustrating a method of processing a transmission signal applied to the present disclosure. For example, the transmitted signal may be processed by a signal processing circuit. In this case, 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 operation/function of FIG. 7 may be performed by the processors 202a and 202b and/or the transceivers 206a and 206b of FIG. 2 . Also, as an example, the hardware elements of FIG. 7 may be implemented in the processors 202a and 202b and/or the transceivers 206a and 206b of FIG. 2 . As an example, blocks 710 to 760 may be implemented in the processors 202a and 202b of FIG. 2 . Also, blocks 710 to 750 may be implemented in the processors 202a and 202b of FIG. 2 , and block 760 may be implemented in the transceivers 206a and 206b of FIG. 2 , and are not limited to the above-described embodiment.

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

The complex modulation symbol sequence may be mapped to one or more transport layers by the layer mapper 730. Modulation symbols of each transport layer may be mapped to corresponding antenna port(s) by the precoder 740 (precoding). The output z of the precoder 740 can be obtained by multiplying the output y of the layer mapper 730 by the N*M precoding matrix W. Here, N is the number of antenna ports and M is the number of transport layers. Here, the precoder 740 may perform precoding after transform precoding (eg, discrete fourier transform (DFT)) on complex modulation symbols. Also, the precoder 740 may perform precoding without performing transform precoding.

The resource mapper 750 may map modulation symbols of each antenna port to time-frequency resources. The time-frequency resource may include a plurality of symbols (eg, CP-OFDMA symbols and DFT-s-OFDMA symbols) in the time domain and a plurality of subcarriers in the frequency domain. The signal generator 760 generates a radio signal from the mapped modulation symbols, and the generated radio signal can be transmitted to other devices through each antenna. To this end, the signal generator 760 may 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. .

The signal processing process for the received signal in the wireless device may be configured in reverse to the signal processing process 710 to 760 of FIG. 7 . For example, a wireless device (eg, 200a and 200b of FIG. 2 ) may receive a wireless signal from the outside through an antenna port/transceiver. The received radio signal may be converted into a baseband signal through a signal restorer. To this end, the signal restorer may 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 may be restored to a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a de-scramble process. The codeword may be restored to an original information block through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource demapper, a postcoder, a demodulator, a descrambler, and a decoder.

6G communication system

6G (radio communications) systems are characterized by (i) very high data rates per device, (ii) very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) battery- It aims to lower energy consumption of battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be four aspects such as "intelligent connectivity", "deep connectivity", "holographic connectivity", and "ubiquitous connectivity", and the 6G system can satisfy the requirements shown in Table 1 below. That is, Table 1 is a table showing the requirements of the 6G system.

Per device peak data rate	1 Tbps
E2E latency	1ms
Maximum spectral efficiency	100 bps/Hz
Mobility support	up to 1000 km/hr
Satellite integration	Fully
AI	Fully
Autonomous vehicles	Fully
XR	Fully
Haptic Communication	Fully

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

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

Referring to FIG. 10 , a 6G system is expected to have 50 times higher simultaneous wireless communication connectivity than a 5G wireless communication system. URLLC, a key feature of 5G, is expected to become a more mainstream technology by providing end-to-end latency of less than 1 ms in 6G communications. At this time, the 6G system will have much better volume spectral efficiency, unlike the frequently used area spectral efficiency. 6G systems can provide very long battery life and advanced battery technology for energy harvesting, so mobile devices in 6G systems may not need to be charged separately.

Core implementation technology of 6G system

- Artificial Intelligence (AI)

The most important and newly introduced technology for the 6G system is AI. AI was not involved in the 4G system. 5G systems will support partial or very limited AI. However, the 6G system will be AI-enabled for full automation. Advances in machine learning will create more intelligent networks for real-time communication in 6G. Introducing AI in communications can simplify and enhance real-time data transmission. AI can use a plethora of analytics to determine how complex target tasks are performed. In other words, AI can increase efficiency and reduce processing delays.

Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly by using AI. AI can also play an important role in machine-to-machine, machine-to-human and human-to-machine communications. In addition, AI can be a rapid communication in the brain computer interface (BCI). 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.

Recently, there have been attempts to integrate AI with wireless communication systems, but these are focused on the application layer, network layer, and especially deep learning, wireless resource management and allocation. come. However, such research is gradually developing into the MAC layer and the physical layer, and in particular, attempts to combine deep learning with wireless transmission are appearing in the physical layer. AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in fundamental signal processing and communication mechanisms. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based multiple input multiple output (MIMO) mechanism, It may include AI-based resource scheduling and allocation.

Machine learning may be used for channel estimation and channel tracking, and may be used for power allocation, interference cancellation, and the like in a downlink (DL) physical layer. Machine learning can also be used for antenna selection, power control, symbol detection, and the like in a MIMO system.

However, the application of DNN for transmission in the physical layer may have the following problems.

AI algorithms based on deep learning require a lot of training data to optimize training parameters. However, due to limitations in acquiring data in a specific channel environment as training data, a lot of training data is used offline. This is because static training on training data in a specific channel environment may cause a contradiction between dynamic characteristics and diversity of a radio channel.

In addition, current deep learning mainly targets real signals. However, the signals of the physical layer of wireless communication are complex signals. In order to match the characteristics of a wireless communication signal, further research is needed on a neural network that detects a complex domain signal.

Hereinafter, machine learning will be described in more detail.

Machine learning refers to a set of actions that train a machine to create a machine that can do tasks that humans can or cannot do. Machine learning requires data and a running model. In machine learning, data learning methods can be largely classified into three types: supervised learning, unsupervised learning, and reinforcement learning.

Neural network training is aimed at minimizing errors in the output. Neural network learning repeatedly inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and backpropagates the error of the neural network from the output layer of the neural network to the input layer in a direction to reduce the error. ) to update the weight of each node in the neural network.

Supervised learning uses training data in which correct answers are labeled in the learning data, and unsupervised learning may not have correct answers labeled in the learning data. That is, for example, learning data in the case of supervised learning related to data classification may be data in which each learning data is labeled with a category. Labeled training data is input to the neural network, and an error may be calculated by comparing the output (category) of the neural network and the label of the training data. The calculated error is back-propagated in a reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back-propagation. The amount of change in the connection weight of each updated node may be determined according to a learning rate. The neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate is used in the early stages of neural network learning to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and a low learning rate can be used in the late stage to increase accuracy.

The learning method may vary depending on the characteristics of the data. For example, in a case where the purpose of the receiver is to accurately predict data transmitted by the transmitter in a communication system, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

The learning model corresponds to the human brain, and the most basic linear model can be considered. ) is called

The neural network cord used as a learning method is largely divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent boltzmann machine (RNN). and this learning model can be applied.

Terahertz (THz) communication

THz communication can be applied in 6G systems. For example, the data transmission rate can be increased by increasing the bandwidth. This can be done using sub-THz communication with wide bandwidth and applying advanced massive MIMO technology.

9 is a diagram showing an electromagnetic spectrum applicable to the present disclosure. As an example, referring to FIG. 9 , THz waves, also known as sub-millimeter radiation, generally represent a frequency band between 0.1 THz and 10 THz with corresponding wavelengths in the range of 0.03 mm-3 mm. The 100 GHz-300 GHz band range (sub THz band) is considered a major part of the THz band for cellular communications. Adding to the sub-THz band mmWave band will increase 6G cellular communications capacity. Among the defined THz bands, 300 GHz-3 THz is in the far infrared (IR) frequency band. The 300 GHz-3 THz band is part of the broad band, but is at the border of the wide band, just behind the RF band. Thus, this 300 GHz-3 THz band exhibits similarities to RF.

The main characteristics of THz communications include (i) widely available bandwidth to support very high data rates, and (ii) high path loss at high frequencies (highly directional antennas are indispensable). The narrow beamwidth produced by the highly directional antenna reduces interference. The small wavelength of the THz signal allows a much larger number of antenna elements to be incorporated into devices and BSs operating in this band. This enables advanced adaptive array technology to overcome range limitations.

Terahertz (THz) wireless communication

10 is a diagram illustrating a THz communication method applicable to the present disclosure.

Referring to FIG. 10, THz wireless communication uses wireless communication using a THz wave having a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and a terahertz (THz) band radio using a very high carrier frequency of 100 GHz or more. can mean communication. THz waves are located between RF (Radio Frequency)/millimeter (mm) and infrared bands, and (i) transmit non-metal/non-polarizable materials better than visible light/infrared rays, and have a shorter wavelength than RF/millimeter waves and have high straightness. Beam focusing may be possible.

Specific embodiments of the present disclosure

The present disclosure relates to a technique for setting an encoder based on a polar code in a wireless communication system and for searching for epsilon, that is, erasure probability, which is one of channel characteristics. That is, the present disclosure relates to an efficient epsilon search technique of a polar code, which is a channel coding scheme adopted for new radio (NR).

The polar code is a code using a channel polarization phenomenon first proposed by Arikan in 2008. Channel polarization refers to a phenomenon in which two channels having the same capacity change to have different capacities. A polar code has been proposed as a channel coding scheme that repeatedly uses capacitive polarization, and has been adopted in the NR standard of 3GPP.

It was theoretically analyzed that if the block size is increased through channel polarization, the channel capacity is achieved in the basic communication channel situation. However, poly codes still have various limitations of existing channel coding schemes. As representative limitations, there are color noise, fading loss, reception decoding complexity, and the like. Since these characteristics change according to the space-time channel environment, the heuristic search epsilon table method designed in the laboratory has a limitation in that it is difficult to reflect the changing channel environment. In addition, according to the method, there is a disadvantage in that the time required for searching for epsilon is rather long.

11A to 11C show examples of structures of polar codes applicable to the present disclosure. 11A illustrates a structure in which two channels having the same capacitance I(W) are polarized to have different capacitances, that is, a W ₂ channel including channel combination with N=2. Referring to FIG. 11A , x ₁ =u ₁ +u2 and x ₂ =u ₂ are determined from input bits u ₁ and u ₂ , and x ₁ and x ₂ are transmitted through a channel having capacity I(W). At this time, the channel capacity I(W _N ⁽¹⁾ ) experienced by u ₁ and the channel capacity I(W _N ⁽²⁾ ) experienced by u ₂ are I(W _N ⁽¹⁾ )=I(W _N/2 ) ² and I(W _N ⁽²⁾ )=2I(W _N/2 )-I(W _N/2 ) ² respectively. That is, u ₁ and u ₂ are in the same state as passing channels of different channel capacities.

FIG. 11B illustrates a W ₄ channel including a channel combination where N=4 polarizes the four channels by taking the W ₂ channel illustrated in FIG. 11A as a basic structure. The W ₄ channel includes a shuffle block of length 4 and two W ₂ channels. A polar code can be implemented by repeatedly using a structure for polarizing capacitance. As shown in FIG. 11c, using two W _N/2 channels and shuffling through R _N of length N, W _N channels can be implemented.

When N is 1024, the channel capacitance can be polarized as shown in FIG. 12 below. 12 illustrates polarization of channel capacity according to application of a polar code applicable to the present disclosure. 12 illustrates capacity per channel index in a binary erasure channel (BEC) in which epsilon is 0.5. Referring to FIG. 12 , channels having a low index generally have a capacity of 0, and channels having a high index generally have a capacity of 1. Some channels have a capacity between 0 and 1. In general, a polar code can be designed by calculating channel capacities for N input bits u _i and selecting K indices having the highest channel capacities among them. N-K bits that are not selected are mapped to a predetermined value (eg, 0), and they are called frozen bits. When bits at positions corresponding to the selected K indices are mapped to values of message bits, the output N bits are a codeword, which is a polar code of K/N code rate.

An operation of selecting K bits mapped with information bits may be understood as designing or setting a polar code. And, the design of the polar code can be understood as the setting of an encoder or decoder. Also, an operation of selecting K bits mapped with an information bit may be understood as an operation of selecting N-K frozen bits, which may be referred to as a polar frozen set design.

As an example, the polar freezing set design calculates the reliability order of the channel using the Bhattacharyya parameter, and based on the results heuristically tabulated in a greedy search method. can be performed by An example of a heuristically tabulated result is the table defined in Table 5.3.1.2-1 of 3GPP NR 38.212. That is, the table of Table 5.3.1.2-1 of 3GPP NR 38.212 includes tuning parameter values designed in the laboratory.

For example, as in the W ₂ channel of FIG. 11A, when N=2, K=1, and an erasure probability of 0.5, the polar freeze set design is as follows. The channel capacity experienced by u ₁ is I(W _N ⁽¹⁾ )=I(W _N/2 ) ² =0.5×0.5=0.25, and the channel capacity experienced by u ₂ is I(W _N ⁽²⁾ )=2I( W _N/2 )-I(W _N/2 ) ² =2×0.5-0.5 ² =0.75. Accordingly, the channel capacity I = [0.25 0.75], and NK = 2-1 = 1 channel is frozen. For example, a channel having a channel capacity of 0.25 is frozen, and information bits are mapped to a channel having a channel capacity of 0.75.

As another example, as in the W ₄ channel of FIG. 11B, when N=4, K=1, and an erasure probability of 0.5, the polar freeze set design is as follows. I(W _N ⁽¹⁾ )=I(W _N/2 ) ² =[0.25 0.75]×[0.25 0.75]=[0.0625 0.5625], I(W _N ⁽²⁾ )=2I(W _N/2 )- I(W _N/2 ) ² = 2×([0.25 0.75])-([0.25 0.75]) ² = [0.4375 0.9375]. Accordingly, the channel capacity I = [0.0625 0.5625 0.4375 0.9375], and NK = 4-1 = 3 channels are frozen. For example, channels having channel capacities of 0.4375, 0.5625, and 0.0625 are frozen, and information bits are mapped to channels having channel capacities of 0.9375.

For polar freezing set design, the erasure probability, i.e. the epsilon value, is used. The epsilon value may be determined as a value that minimizes a block error measured in an additive white Gaussian noise (AWGN) channel. 13 illustrates an example of an epsilon search result for setting a polar code applicable to the present disclosure. 13 illustrates epsilon search results in the case of K = 3286, N = 4095, and code rate R = 4/5. As a result of performing 99 greedy searches, epsilon values according to block errors can be obtained as shown in FIG. 13 .

Bayesian optimization (BO) is useful for optimizing functions that are expensive to evaluate. Bayesian optimization uses a surrogate function and an acquisition function to update the posterior probability based on prior knowledge, thereby providing an optimal experimental path through minimal experiments. and can achieve optimization of complex problems. In addition, Bayesian optimization is known as an efficient search methodology for optimizing an objective function by using data obtained so far as prior knowledge of a task that takes a long time to evaluate. Here, the surrogate function is a model used to approximate the target function f. As a replacement function, a Gaussian process (GP), a tree-structured Parzen estimator (TPE), or the like can be used. The acquisition function provides the criteria for determining the next evaluation point. As an acquisition function, techniques such as probability of improvement (PI), expected improvement (EI), upper confidence bound (UCB), and the like can be used. The Bayesian optimization algorithm described above can be expressed as shown in Table 2 below.

Algorithm: Bayesian Optimization

for t=1,2,… do: Find the next sampling point x t by optimizing the acquisition function over the GP : x t =argmax x u(x|D 1:t-1 ) Sample the objective function f : y t = f(x t )+ε t . Add the sample to previous samples D 1:t ={D 1:t-1 ,(x t ,y t )} and update the GP. end for

An example of a process of searching for a sampling point according to the Bayesian optimization algorithm is shown in FIG. 14 below. 14 illustrates an example of sampling point search according to Bayesian optimization applicable to the present disclosure. 14 shows time points t=1, . . . , 6 illustrates the sample point change. In FIG. 14, the dotted line is the target function, the upper solid line is the substitution function, and the lower solid line is the EI-based acquisition function. Referring to the case of t=1, prior knowledge is updated based on the sample 1412 output from the target function. The next sampling point 1414 is selected using an acquisition function indicative of sampling into an area that is likely to be improved over the current best observation. Then, the result (x _t , y _t ) 1416 of the target function for the selected sampling point 1414 is obtained, and as the result 1416 is added to the previous sample domain, D _1:t posterior probability is updated. . After that, t=2,... Similar operations are repeated in ,6. As a result, at t=6, it is confirmed that the surrogate function approaches the target function.

TPE is a kind of substitution function available in Bayesian optimization. TPE uses a surrogate model that is modeled using p(x|y) and p(y) based on Bayes' rule.

In [Equation 1], x is the input domain, y is the output domain, p(x) is the distribution of x in the target function, p(y) is the distribution of y in the target function, and p(x|y) is the substitution function In TPE model,

is a hyper-parameter, where ℓ(x) is the density of good points, g(x) is the density of bad points, and y* is the quantile-based good point and bad point It means the standard value for classification. here,

Since is a constant, EI is proportional to ℓ(x)/g(x).

Examples of next sampling pointer search using TPE are shown in FIGS. 15A to 15C. 15A to 15C show examples of sampling point search according to TPE applicable to the present disclosure. Referring to FIGS. 15A-15C , in a first state 1510, an initial set of loss scores is first obtained using a random sample of x. A good point group is selected based on the loss threshold y ^* , and the rest are designated as bad point groups. To model p(x|y) in two groups, two density distributions are defined: l(x) corresponding to good points and g(x) corresponding to bad points. ℓ(x) denotes the density of hyperparameter terms of ‘good’ losses, and g(x) denotes the density of ‘bad’ loss terms. g(x) can be modeled as p(x|y≥y ^* ), and ℓ(x) can be modeled as p(x|y<y ^* ). According to ℓ(x) and g(x), as in the second state 1520, the acquisition function EI is updated, and the next sampling point may be determined based on the acquisition function EI. Each time the TPE process iterates, the sampled hyperparameter configuration will be evaluated and, as in the third state 1530, the density will be adjusted to approximate the actual loss function.

When the above-described Bayesian optimization using TPE is applied to the epsilon search of polar codes, the domain y may correspond to an error rate for data (eg, block error rate), and the domain y may correspond to epsilon. Accordingly, a data point can be expressed as {epsilon, error rate}. The target function can be understood as an error rate for data according to epsilon, and searching for epsilon that minimizes the error rate will be the result of optimization. Bayesian optimization requires the execution of an objective function, which can be understood as an error rate measurement for data passing through an actual channel. In addition, epsilon is a channel parameter related to a binary erase channel, but a procedure for searching for epsilon described below may be applied even to a non-binary erase channel.

16 illustrates an example of a functional structure of devices in a wireless communication system according to an embodiment of the present disclosure. 16 illustrates a functional structure of a first device 1610 that performs encoding and a second device 1620 that performs decoding.

Referring to FIG. 16 , a first device 1610 includes a polar encoder 1612, a BEC block 1614, and a BO hyperparameter management block 1616, and a second device 1620 includes a polar decoder 1622, BO TPE block 1624.

The polar encoder 1612 generates a codeword by encoding an input information bit (eg, s) based on a polar code. The codeword is converted into a transmission signal (eg, x) through processing such as modulation and RF conversion. The transmitted signal is received by the second device 1620 after passing through a channel, for example, an AWGN channel. The second device 1620 receives a received signal (eg, y) passing through the channel and performs processing such as demodulation on the received signal. And, the polar decoder 1622 performs decoding based on the polar code to estimate information bits (e.g.,

) to determine

For the estimation of the codeword, the data error rate, i.e. the block error rate, can be measured. For example, a block error rate may be calculated by a cyclic redundancy check (CRC) for a codeword. The block error rate is provided to the BO TPE block 1624, which indicates the epsilon for the polar code used in at least one of the polar encoder 1612 and the polar decoder 1622, that is, the erasure probability. Determine information (e.g. epsilon control signal). According to one embodiment, BO TPE block 1624 may determine epsilon according to a Bayesian optimization technique using TPE as a surrogate function. Here, the block error rate may be measured by the polar encoder 1622 or by a separate block.

To this end, the BO TPE block 1624 provides information (e.g., quantiles, which are hyperparameters related to Bayesian optimization) from the BO hyper-parameter management block 1616 of the first device 1610.

), and information indicating an erasure probability may be generated based on the information related to the quartile. here,

Is a hyperparameter and indicates the quantile of a good point. That is, the BO hyper-parameter management block 1616 is used in the second device 1620.

can manage According to one embodiment, in RX group 1

=0.4, to receive group 2

After assigning = 0.2, the performance of each reception group can be evaluated through error retransmission rate evaluation. of the receiving group with relatively good performance.

Bayesian optimization can be performed by applying t to the different receiving groups.

The BEC block 1614 determines the frozen bit position of the polar code based on information indicating the erase probability (eg, an epsilon control signal) fed back from the second device 1620 . Specifically, the BEC block 1614 checks the erasure probability, determines the channel capacity of each of the channels formed by the polar code based on the erasure probability, and then maps the frozen bits among the channels based on the channel capacities. Determine at least one channel. Accordingly, the polar code 1612 may map a frozen bit to at least one channel determined by the BEC block 1614 and an information bit (eg, s) to the remaining channels.

17 illustrates an example of a procedure for setting a polar code in a wireless communication system according to an embodiment of the present disclosure. 17 illustrates an operating method of an encoding device (eg, the first device 1610 of FIG. 16 ).

Referring to FIG. 17 , in step S1701, the device performs encoding based on polar codes. That is, the device generates at least one codeword including transmission bits through encoding. Specifically, the device adds CRC bits to each of the code blocks divided from the information bit stream, and performs encoding based on a polar code set for each bit sequence input including the code block and the CRC bits. At this time, according to the setting of the polar code, the device maps a frozen bit (eg, 0) to at least one channel at a designated position, and maps bits included in the bit sequence input to the remaining channels. If necessary, the device may further perform operations such as interleaving and rate matching.

In step S1703, the device transmits a signal including at least one codeword. The device may generate a transmission signal through physical layer processing and transmit the generated transmission signal through at least one antenna. For example, physical layer processing may include at least one of modulation, layer mapping, frequency-time resource mapping, precoding, and OFDM modulation.

In step S1705, the device receives feedback information. The device may receive feedback information about the transmitted at least one codeword. Here, the feedback information may include information for each codeword or information for each burst constituting the transmission signal. According to various embodiments, the feedback information may include a plurality of feedback signals, and the plurality of feedback signals may be received as separate control signals. For example, the feedback information may include feedback requesting retransmission (eg, acknowledgment (ACK)/negative-ACK (NACK)). According to an embodiment, the feedback information is information for setting a polar code, and may include information (eg, epsilon information) indicating an erase probability of a binary erase channel.

In step S1707, the device sets a polar code based on the feedback information. The device may determine an erasure probability of the polar code based on the feedback information, and determine indexes of channels to which frozen bits are mapped based on the erasure probability. In this case, according to various embodiments, the erasure probability may be derived from the feedback information or may be explicitly or implicitly indicated by the feedback information. After the polar code is set, the device may perform an encoding operation based on the set polar code.

In one embodiment described with reference to FIG. 17 , the device may receive information indicating an erasure probability as feedback information. In this case, although not shown in FIG. 17, the device may signal information necessary for determining an erase probability, if necessary. For example, when the device of the counterpart determines the erase probability using the Bayesian optimization technique, the device may transmit information related to quantiles for classifying good points and bad points. For example, information related to the quantile may be included in downlink control information (DCI) for scheduling of codewords, a MAC control element (CE), or included in an RRC message. Alternatively, as a method in which a plurality of signalings are combined, candidate values may be set through an RRC message, and one of the candidate values may be indicated through DCI or MAC CE.

For example, the above-described operation of transmitting information necessary for determining the erasure probability may be performed when the device performing the operations of FIG. 17 is a base station. On the other hand, when the device performing the operations of FIG. 17 is a UE, the erasure probability may be directly determined by the counterpart device, that is, the base station. In this case, an operation of transmitting information necessary for determining an erasure probability may be omitted.

18 illustrates an example of a procedure for feeding back an erasure probability in a wireless communication system according to an embodiment of the present disclosure. 18 illustrates an operating method of a device performing decoding (eg, the second device 1620 of FIG. 16 ).

Referring to FIG. 18 , in step S1801, the device receives a signal including at least one codeword based on a polar code. To this end, prior to receiving a signal, the device may transmit or receive a control signal for scheduling of at least one codeword. For example, if the device is a UE, the device may receive a control signal (eg, DCI) for scheduling. As another example, when the device is a base station, the device may transmit a control signal (eg, DCI) for scheduling.

In step S1803, the device performs decoding on at least one codeword. After performing physical layer processing on the received signal, the device performs decoding based on the polar code on the received values of the codeword to restore the bit sequence before encoding. In addition, the device may determine whether decoding is successful by performing a CRC check on the restored bit sequence.

In step S1805, the device transmits feedback information. The device may receive feedback information on the received at least one codeword. Here, the feedback information may include information for each codeword or information for each burst constituting the transmission signal. According to various embodiments, the feedback information may include a plurality of feedback signals, and the plurality of feedback signals may be received as separate control signals. For example, the feedback information may include feedback requesting retransmission (eg, ACK/NACK). According to an embodiment, the feedback information is information for setting a polar code, and may include information (eg, epsilon information) indicating an erase probability of a binary erase channel.

In one embodiment described with reference to FIG. 18 , the device may transmit information indicating an erasure probability as feedback information. In this case, although not shown in FIG. 18, the device may receive information necessary for determining an erase probability, if necessary. For example, when a device determines an erasure probability using a Bayesian optimization technique, the device may receive information related to quantiles for classifying good points and bad points. For example, information related to quantiles may be included in a DCI for scheduling of codewords, a MAC CE, or an RRC message. Alternatively, as a method in which a plurality of signalings are combined, candidate values may be set through an RRC message, and one of the candidate values may be indicated through DCI or MAC CE.

For example, the above-described operation of receiving information necessary for determining the erasure probability may be performed when the device performing the operations of FIG. 18 is a UE. On the other hand, if the device performing the operations of FIG. 18 is a base station, the erasure probability may be directly determined by the base station. In this case, an operation of receiving information necessary to determine an erasure probability may be omitted.

19 illustrates an example of a procedure for performing communication between devices using polar codes in a wireless communication system according to an embodiment of the present disclosure. 19 illustrates signaling between a first device 1910 and a second device 1920.

Referring to FIG. 19 , in step S1901, the first device 1910 transmits a message s to the second device 1920. Message s contains at least one codeword obtained through polar encoding of a bit sequence representing information.

In step S1903, the second device 1920 determines a block error rate for message s. The block error rate is the message s transmitted from the first device 1910 and the estimated message after passing through the channel

means a loss for For example, the block error rate may be determined through CRC check. In some cases, a block error rate may be determined for a plurality of messages.

In step S1905, the first device 1910 transmits information indicating a quantile to the second device 1920. The quantile is a hyper-parameter for Bayesian optimization and may be determined according to a separate setting. According to one embodiment, the quartile may be set to a predefined value. According to another embodiment, the quantiles may be adjusted according to the channel model. In this case, the first device 1910 checks channel characteristics between the current first device 1910 and the second device 1920, checks a channel model corresponding to the checked channel characteristics, and then based on the checked channel model. to determine and indicate quantiles.

In step S1907, the second device 1920 performs BO TPE. In other words, the second device 1920 determines the epsilon, that is, the cancellation probability of the binary cancellation channel, according to a Bayesian optimization technique using TPE as a substitution function. To this end, the second device 1920 may use all data points examined so far, that is, {epsilon, block error rate}. In this case, the second device 1920 may determine epsilon based on the information indicating the quartile received from the first device 1910 .

In step S1909, the second device 1920 transmits an epsilon control signal to the first device 1910. In other words, the second device 1920 transmits a control signal including information related to the epsilon determined in step S1907. Accordingly, the first device 1910 may set a polar code and then perform encoding based on the set polar code.

In the procedure described with reference to FIG. 19 , the first device 1910 transmits information related to the quantile to the second device 1920, and the second device 1920 transmits information related to epsilon to the first device 1910. transmit However, according to another embodiment, an operation of transmitting information related to quantiles may be omitted. In this case, the second device 1920 may determine the quantile according to a predefined rule and then determine the epsilon based on the determined quantile.

In the description with reference to FIG. 19, various embodiments for determining quantiles have been presented. Additionally, a method of classifying a plurality of devices performing decoding into a plurality of groups, transmitting different quantiles for each group, and adjusting one according to performance may be applied. In this case, according to an embodiment, the first device 1910 transmits a first value as a quantile to at least one device belonging to the first group, and transmits a second value as a quantile to at least one device belonging to the second group. send Thereafter, the first device 1910 sets encoders for each group based on epsilon fed back from devices belonging to each group, and checks performance (eg, data error rate, block error rate, etc.) for each group. Also, the first device 1910 may apply the quantile applied to the group with the highest performance to other groups. To this end, the first device 1910 may transmit information related to the quartile to at least one device belonging to another group.

Here, if K is the number of groups,

denotes the good point quantile for group k.

The above procedure is explained again using . The first device 1910 determines the good point quantiles for each group.

is transmitted to each group, and the performance of each group is evaluated by measuring the error retransmission rate. group that performed well

After sending to another group, Bayesian optimization can be performed.

According to various embodiments described above, a Bayesian optimization technique using TPE as a surrogate function may be used to determine an erasure probability of a binary erase channel related to a polar code. Bayesian optimization using TPE for determining the erasure probability can be expressed as shown in Table 3 below.

Algorithm: Polar epsilon search based on BO TPE

for t=1,2,… do: Find the next sampling point x_t by optimizing the acquisition function over the TPE : x t = arg max x EI(x|D 1:t-1 ) Sample the objective function f : y t = f(x t )+ε t . (Perform BEC+AWGN channel Block Error measurement) Add the sample to previous samples D 1:t ={D 1:t-1 ,(x t ,y t )} and update the TPE. end for

A detailed description of the epsilon determination procedure according to various embodiments is shown in FIG. 20 below. 20 illustrates an example of a procedure for searching epsilon in a wireless communication system according to an embodiment of the present disclosure. 20 illustrates an operating method of devices searching for epsilon. Some of the operations illustrated in FIG. 20 may be performed by a device performing encoding (hereinafter referred to as 'first device'), and others may be performed by a device performing decoding (hereinafter referred to as 'second device').

First, an initial random search step 2010 is performed. An initial random search may be performed n times. For example, the random search number Random_Search_n may be set to 5. To this end, the target function is executed. Here, execution of the target function corresponds to the following operation. For each search trial, an epsilon value is randomly selected (2011), and channel reliability is calculated through a BEC block (2012). By ordering the channel reliability in ascending order, the frozen channel of the polar code is determined (2013), and the block error rate in the AWGN channel through which encoded data passes is measured based on the polar code to which the determined frozen channel is applied (2014 ). Accordingly, data {epsilon, block error rate} can be obtained.

After an initial random search, a Bayesian optimization step 2020 is performed. Quantiles of Good Points

After an initial random search, a good distribution and a bad distribution are classified (eg, the number of good points = 5). In other words, by including the data points {epsilon, block error rate} obtained through the initial random search in prior knowledge, the data domain D is constructed (after 2021, the good distribution l(x) and the bad distribution g An EI acquisition operation is performed using (x) (2022), and the next sampling point, epsilon, is searched for (2023). The searched epsilon is transmitted from the receiving device to the transmitting device through signaling. The transmitting device selects the frozen channel of the polar code using the BEC formula (2024) and executes the BEC and AWGN channel block error measurement object function for the sampling point. By doing so, sampling is performed (2025). A new data point is updated in the posterior distribution TPE (2026). After that, it is determined whether optimization is completed (2027), and if not completed, the above-described operations are repeated. and, if completed, the optimization ends (2028).

21 and 22 show performance of an erasure probability search technique according to an embodiment of the present disclosure. 21 and 22 show the results according to the proposed technology. Referring to FIGS. 21 and 22 , when K=3276 and N=4096, the proposed technology can reduce the time required for epsilon search by about 1/5 compared to the greedy search method. As such, when using the technique according to various embodiments proposed in the present disclosure, an efficient epsilon search of a polar code is possible.

It is obvious that examples of the proposed schemes described above may also be included as one of the implementation methods of the present disclosure, and thus may be regarded as a kind of proposed schemes. In addition, the above-described proposed schemes may be implemented independently, but may also be implemented in a combination (or merged) form of some proposed schemes. Information on whether the proposed methods are applied (or information on the rules of the proposed methods) may be defined so that the base station informs the terminal through a predefined signal (eg, a physical layer signal or a higher layer signal). .

The present disclosure may be embodied in other specific forms without departing from the technical ideas and essential characteristics described in the present disclosure. Accordingly, the above detailed description should not be construed as limiting in all respects and should be considered illustrative. The scope of the present disclosure should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent range of the present disclosure are included in the scope of the present disclosure. In addition, claims that do not have an explicit citation relationship in the claims may be combined to form an embodiment or may be included as new claims by amendment after filing.

Embodiments of the present disclosure may be applied to various wireless access systems. As an example of various wireless access systems, there is a 3rd Generation Partnership Project (3GPP) or 3GPP2 system.

Embodiments of the present disclosure may be applied not only to the various wireless access systems, but also to all technical fields to which the various wireless access systems are applied. Furthermore, the proposed method can be applied to mmWave and THz communication systems using ultra-high frequency bands.

Additionally, embodiments of the present disclosure may be applied to various applications such as free-running vehicles and drones.

Claims (17)

1. In a method of operating a user equipment (UE) in a wireless communication system,

   Receiving a signal including at least one codeword from a base station;

   performing decoding on the at least one codeword;

   generating feedback information based on a result of the decoding; and

   Transmitting the feedback information to the base station,

   The at least one codeword is decoded based on a polar code,

   The feedback information includes information related to an erasure probability for setting an information bit position and a frozen bit position of an encoder for a polar code in the base station.
2. The method of claim 1,

   The method further comprising receiving information related to a quantile needed to determine the erasure probability.
3. The method of claim 2,

   determining a good point distribution and a bad point distribution based on the quantiles;

   determining a next sampling point based on the good point distribution and the bad point distribution; and

   The method further comprising determining information related to the erasure probability based on the next sampling point.
4. The method of claim 3,

   The step of determining the next sampling point,

   determining an acquisition function based on the good point distribution and the bad point distribution; and

   and determining the next sampling point based on the acquisition function.
5. The method of claim 1,

   The method further comprising determining information related to the cancellation probability based on Bayesian optimization.
6. The method of claim 5,

   The Bayesian optimization method uses a tree-structured Parzen estimator (TPE) as a surrogate function.
7. The method of claim 5,

   The sampling point for the Bayesian optimization comprises an epsilon of the polar code and a data error rate for a codeword encoded according to the polar code set based on the epsilon.
8. The method of claim 5,

   receiving at least one codeword encoded according to a polar code set based on a randomly selected epsilon for an initial random search of the Bayesian optimization;

   measuring a data error rate for the at least one codeword; and

   obtaining prior knowledge including an initial search sampling point consisting of the randomly selected epsilon and the data error rate.
9. In the method of operating a base station in a wireless communication system,

   generating at least one codeword by encoding a bit sequence based on the polar code;

   Transmitting a signal including the at least one codeword to a user equipment (UE);

   Receiving feedback information generated based on a result of decoding the at least one codeword; and

   Setting an information bit position and a frozen bit position of an encoder for the polar code based on the feedback information,

   The feedback information includes information related to an erase probability for setting an information bit position and a frozen bit position of the encoder.
10. The method of claim 9,

    The method further comprising transmitting information related to a quantile needed to determine the erasure probability.
11. The method of claim 10,

    The quantile is determined based on characteristics of a channel between the UE and the base station.
12. The method of claim 9,

    Setting the information bit position and the freezing bit position of the encoder,

    determining channel reliability values for each channel of the polar code based on the erasure probability; and

    determining the frozen bit position based on the channel reliability values.
13. The method of claim 9,

    Transmitting a first value as information related to a quantile necessary for determining the erasure probability to the UE;

    Transmitting a second value as information related to a quantile necessary for determining the erasure probability to another UE; and

    If the performance of the first encoder configured based on the feedback information from the UE is superior to the performance of the second encoder configured based on the feedback information from the other UE, transmitting the first value to the other UE. How to include.
14. In a user equipment (UE) in a wireless communication system,

    transceiver; and

    It includes a processor connected to the transceiver,

    the processor,

    Receiving a signal including at least one codeword from a base station;

    Decoding the at least one codeword;

    Generate feedback information based on a result of the decoding;

    Transmitting the feedback information to the base station;

    The at least one codeword is decoded based on a polar code,

    The feedback information includes information related to an erasure probability for setting an information bit position of an encoder and a frozen bit position for a polar code in the base station.
15. In a base station in a wireless communication system,

    transceiver; and

    It includes a processor connected to the transceiver,

    the processor,

    Generating at least one codeword by performing encoding on a bit sequence based on a polar code;

    Transmitting a signal including the at least one codeword to a user equipment (UE);

    Receiving feedback information generated based on a decoding result for the at least one codeword;

    Based on the feedback information, setting an information bit position and a freezing bit position of an encoder for the polar code,

    The feedback information includes information related to an erase probability for setting an information bit position and a frozen bit position of the encoder.
16. In the device,

    at least one processor;

    at least one computer memory connected to the at least one processor and storing instructions directing operations as executed by the at least one processor;

    The operations, the device,

    Receiving a signal including at least one codeword from a base station;

    performing decoding on the at least one codeword;

    generating feedback information based on a result of the decoding; and

    Transmitting the feedback information to the base station,

    The at least one codeword is decoded based on a polar code,

    The feedback information includes information related to an erasure probability for setting an information bit position of an encoder and a frozen bit position for a polar code in the base station.
17. In a non-transitory computer-readable medium storing at least one instruction,

    comprising the at least one instruction executable by a processor;

    The at least one instruction, the device,

    Receiving a signal including at least one codeword from a base station;

    Decoding the at least one codeword;

    Generate feedback information based on a result of the decoding;

    Transmitting the feedback information to the base station;

    The at least one codeword is decoded based on a polar code,

    The feedback information includes information related to an erasure probability for setting an information bit position and a frozen bit position of an encoder for a polar code in the base station.

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