WO2022014732A1

WO2022014732A1 - Method and apparatus for performing federated learning in wireless communication system

Info

Publication number: WO2022014732A1
Application number: PCT/KR2020/009253
Authority: WO
Inventors: 박재용; 김성진
Original assignee: 엘지전자 주식회사
Priority date: 2020-07-14
Filing date: 2020-07-14
Publication date: 2022-01-20

Abstract

The present disclosure discloses operation methods of a terminal and base station in a wireless communication system, and an apparatus supporting same. According to an example of the present disclosure, an operation method of a server in a wireless communication system may comprise the steps of: transmitting information related to a global model to at least one first type apparatus from among apparatuses; receiving, from the at least one first type apparatus from among the apparatuses, information related to a result of training a local model determined from the global model; updating the global model, on the basis of the information related to the result of training; and transmitting information related to the updated global model to the at least one first type apparatus and at least one second type apparatus.

Description

Method and apparatus for performing federated learning in a wireless communication system

The following description relates to a wireless communication system, and to a method and apparatus for performing federated learning in a wireless communication system.

Wireless access systems are being 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 that can support 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) systems.

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

The present disclosure relates to a method and apparatus for more efficiently performing federated learning, which is one of machine learning, in a wireless communication system.

The present disclosure relates to a method and apparatus for performing federated learning in consideration of differences in hardware characteristics of devices participating in learning in a wireless communication system.

The technical objects to be achieved in the present disclosure are not limited to the above, and other technical problems not mentioned are common knowledge 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. can be considered by those with

As an example of the present disclosure, a method of operating a server in a wireless communication system includes transmitting information related to a global model to at least one type 1 device among devices, from at least one type 1 device among the devices Receiving information related to a result of learning for the determined local model from the global model, updating the global model based on the information related to the result of the learning, and providing information related to the updated global model to the at least transmitting to one type 1 device and at least one type 2 device.

As an example of the present disclosure, a method of operating a device in a wireless communication system includes receiving information related to a global model from a server, and learning about a local model determined from the global using data collected through a plurality of sensors. performing, transmitting information related to the result of the learning to the server, and receiving information related to the global model updated based on the information related to the result of the learning from the server. The information related to the updated global model may be provided to the other type of device for an inference operation in the other type of device that does not use at least one of the plurality of sensors.

As an example of the present disclosure, a server in a wireless communication system includes a transceiver and a processor connected to the transceiver. The processor is configured to send information related to the global model to at least one of the devices of the first type, and related to a result of learning of the local model determined from the global model from the at least one first type of devices. Receive information, update the global model based on information related to a result of the learning, and transmit information related to the updated global model to the at least one type 1 device and the at least one type 2 device can be controlled to do so.

As an example of the present disclosure, an apparatus in a wireless communication system includes a transceiver and a processor connected to the transceiver. The processor receives information related to the global model from the server, performs learning on the local model determined from the global using data collected through a plurality of sensors, and provides information related to a result of the learning to the server and control to receive information related to the global model updated based on the information related to the result of the learning from the server. The information related to the updated global model may be provided to the other type of device for an inference operation in the other type of device that does not use at least one of the plurality of sensors.

Aspects of the present disclosure described above 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 detailed descriptions of the present disclosure that will be described below by those of ordinary skill in the art can be derived and understood based on

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

According to the present disclosure, training of a machine learning model for channel estimation during operation in a wireless communication system can be effectively performed.

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

The accompanying drawings below are provided to help understanding of the present disclosure, and together with the detailed description, may provide embodiments of the present disclosure. 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 constitute a new embodiment. Reference numerals in each drawing may refer to structural elements.

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

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

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

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

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

6 is a diagram illustrating an example of a movable body applied to the present disclosure.

7 is a diagram illustrating an example of an XR device applied to the present disclosure.

8 is a diagram illustrating an example of a robot applied to the present disclosure.

9 is a diagram illustrating an example of an artificial intelligence (AI) device applied to the present disclosure.

10 is a diagram illustrating physical channels applied to the present disclosure and a signal transmission method using the same.

11 is a diagram illustrating a control plane and a user plane structure of a radio interface protocol applied to the present disclosure.

12 is a diagram illustrating a method of processing a transmission signal applied to the present disclosure.

13 is a diagram illustrating a structure of a radio frame applicable to the present disclosure.

14 is a diagram illustrating a slot structure applicable to the present disclosure.

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

16 is a diagram illustrating an electromagnetic spectrum applicable to the present disclosure.

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

18 is a diagram illustrating a THz wireless communication transceiver applicable to the present disclosure.

19 is a diagram illustrating a method for generating a THz signal applicable to the present disclosure.

20 is a diagram illustrating a wireless communication transceiver applicable to the present disclosure.

21 is a diagram illustrating a structure of a transmitter applicable to the present disclosure.

22 is a diagram illustrating a modulator structure applicable to the present disclosure.

23 is a diagram illustrating a structure of a perceptron included in an artificial neural network applicable to the present disclosure.

24 is a diagram illustrating an artificial neural network structure applicable to the present disclosure.

25 is a diagram illustrating a deep neural network applicable to the present disclosure.

26 is a diagram illustrating a convolutional neural network applicable to the present disclosure.

27 is a diagram illustrating a filter operation of a convolutional neural network applicable to the present disclosure.

28 is a diagram illustrating a neural network structure in which a cyclic loop applicable to the present disclosure exists.

29 is a diagram illustrating an operation structure of a recurrent neural network applicable to the present disclosure.

30 is a diagram showing the structure of a federated learning system applicable to the present disclosure.

31 is a diagram illustrating a procedure of associative learning applicable to the present disclosure.

32 is a diagram illustrating a structure of a first type device applicable to the present disclosure.

33 is a diagram illustrating a structure of a type 2 device applicable to the present disclosure.

34 is a diagram showing the structure of a server for controlling learning applicable to the present disclosure.

35 is a diagram illustrating an embodiment of signal exchange for associative learning applicable to the present disclosure.

36 is a diagram illustrating an embodiment of a procedure for performing federated learning in a server applicable to the present disclosure.

37 is a diagram illustrating an embodiment of a procedure for performing federated learning in a type 1 device applicable to the present disclosure.

38 is a diagram illustrating an embodiment of a procedure for performing federated learning in a type 2 device applicable to the present disclosure.

39 is a diagram illustrating another embodiment of signal exchange for associative learning applicable to the present disclosure.

40 is a diagram illustrating an embodiment of a procedure for providing a global model from a server applicable to the present disclosure to a second type device.

41 is a diagram illustrating another embodiment of signal exchange for associative learning applicable to the present disclosure.

The following embodiments combine elements and features of the present disclosure in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. In addition, some components and/or features may be combined to configure an embodiment of the present disclosure. The order of operations described in embodiments of the present disclosure may be changed. Some configurations or features of one embodiment may be included in other embodiments, or may be replaced with corresponding configurations or features of other embodiments.

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

Throughout the specification, when a part is said to "comprising or including" a certain component, it does not exclude other components unless otherwise stated, meaning that other components may be further included. do. In addition, terms such as “…unit”, “…group”, and “module” described in the specification mean a unit that processes at least one function or operation, which may be implemented as hardware or software or a combination of hardware and software. have. Also, "a or an", "one", "the" and like related terms are used differently herein in the context of describing the present disclosure (especially in the context of the following claims). Unless indicated or clearly contradicted by context, it may be used in a sense including both the singular and the plural.

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

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

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

In addition, a transmitting end refers to a fixed and/or mobile node that provides a data service or a voice service, and a receiving end refers to a fixed and/or mobile node that receives a data service or a voice service. Accordingly, in the case of uplink, the mobile station may be a transmitting end, and the base station may be a receiving end. Similarly, in the case of downlink, the mobile station may be the receiving end, and the base station may be the transmitting end.

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

Also, embodiments of the present disclosure may be applied to other wireless access systems, and are not limited to the above-described system. As an example, it may 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 in the embodiments of the present disclosure may be described with reference to the above documents. In addition, all terms disclosed in this document may be described by the standard document.

Hereinafter, preferred embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION The detailed description set forth below in conjunction with the appended drawings is intended to describe exemplary embodiments of the present disclosure, and is not intended to represent the only embodiments in which the technical constructions of the present disclosure may be practiced.

In addition, specific terms used in the embodiments of the present disclosure are provided to help the understanding of the present disclosure, and the use of these specific terms may be changed to 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), etc. It can be applied to various wireless access systems.

For clarity of the description below, although description is based on a 3GPP communication system (eg, LTE, NR, etc.), the technical spirit of the present invention is not limited thereto. LTE may mean 3GPP TS 36.xxx Release 8 or later technology. In detail, LTE technology after 3GPP TS 36.xxx Release 10 may be referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 may be referred to as LTE-A pro. 3GPP NR may refer to technology after TS 38.xxx Release 15. 3GPP 6G may refer to technology after TS Release 17 and/or Release 18. "xxx" stands for standard document detail number. LTE/NR/6G may be collectively referred to as a 3GPP system.

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

본 개시에 적용 가능한 통신 시스템Communication system applicable to the present disclosure

Although not limited thereto, the various descriptions, functions, procedures, suggestions, methods and/or operation flowcharts of the present disclosure disclosed in this document may be applied to various fields requiring wireless communication/connection (eg, 5G) between devices. have.

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

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 wireless access technology (eg, 5G NR, LTE), and may be referred to as a communication/wireless/5G device. Although not limited thereto, the wireless device may include a robot 100a, a vehicle 100b-1, 100b-2, an extended reality (XR) device 100c, a hand-held device 100d, and a home appliance. appliance) 100e, an Internet of Things (IoT) device 100f, and an artificial intelligence (AI) device/server 100g. For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous driving 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) provided in a vehicle, a television, It may be implemented in the form of a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like. The portable device 100d may include a smart phone, a smart pad, a wearable device (eg, smart watch, smart glasses), and a computer (eg, a laptop computer). 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 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 (eg, 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). Also, the IoT device 100f (eg, a sensor) may communicate directly with another IoT device (eg, a 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 uplink/downlink communication 150a and sidelink communication 150b (or D2D communication), and communication between base stations 150c (eg, relay, integrated access backhaul (IAB)). This may be achieved through radio access technology (eg, 5G NR). Through the wireless communication/

connection

150a, 150b, and 150c, the wireless device and the base station/wireless device, and the base station and the base station may transmit/receive wireless signals to each other. For example, the wireless communication/

connection

150a , 150b , 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 transmission/reception of wireless 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.

본 개시에 적용 가능한 무선 기기Wireless devices applicable to the present disclosure

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

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 flowcharts disclosed herein. For example, the processor 202a may process information in the memory 204a to generate first information/signal, and then transmit a wireless signal including the first information/signal through the transceiver 206a. In addition, the processor 202a may receive the radio signal including the second information/signal through the transceiver 206a, and then store the information obtained from the 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, the memory 204a may provide instructions for performing some or all of the processes controlled by the processor 202a, or for performing the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. may store software code including 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 via one or more antennas 208a. The transceiver 206a may include a transmitter and/or a receiver. The transceiver 206a may be used interchangeably with a radio frequency (RF) unit. In the present disclosure, a wireless device may refer to 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, proposals, methods, and/or operational flowcharts disclosed herein. For example, the processor 202b may process information in the memory 204b to generate third information/signal, and then transmit a wireless signal including the third information/signal through the transceiver 206b. In addition, the processor 202b may receive the radio signal including the fourth information/signal through the transceiver 206b, and then 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 provide instructions for performing some or all of the processes controlled by the processor 202b, or for performing the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. may store software code including 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 via one or more antennas 208b. Transceiver 206b may include a transmitter and/or receiver. Transceiver 206b may be used interchangeably with an RF unit. In the present disclosure, a wireless device may refer to a communication modem/circuit/chip.

Hereinafter, hardware elements of the

wireless devices

200a and 200b will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or

more processors

202a, 202b. For example, one or

more processors

202a, 202b may 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 a functional layer such as service data adaptation protocol (SDAP)). The one or

more processors

202a, 202b may be configured to process one or more protocol data units (PDUs) and/or one or more service data units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed herein. can create The one or

more processors

202a, 202b may generate messages, control information, data, or information according to the description, function, procedure, proposal, method, and/or flow charts disclosed herein. The one or

more processors

202a, 202b generate a signal (eg, a baseband signal) including a PDU, SDU, message, control information, data or 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 the descriptions, functions, procedures, proposals, methods, and/or flowcharts of operation disclosed herein. PDUs, SDUs, messages, control information, data, or information may be acquired according to the fields.

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, 202b. The descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations 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. The descriptions, functions, procedures, proposals, methods, and/or flow charts disclosed in this document provide that firmware or software configured to perform is included in one or

more processors

202a, 202b, or stored in one or

more memories

204a, 204b. It may be driven by the

above processors

202a and 202b. The descriptions, functions, procedures, proposals, methods, and/or flowcharts of operations disclosed herein may be implemented using firmware or software in the form of code, instructions, and/or a set 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 drives, registers, cache memory, computer readable storage media and/or It may be composed of a combination of these. One or

more memories

204a, 204b may be located inside and/or external to one or

more processors

202a, 202b. Additionally, one or

more memories

204a, 204b may be coupled to one or

more processors

202a, 202b through various technologies, such as wired or wireless connections.

The one or

more transceivers

206a, 206b may transmit user data, control information, radio signals/channels, etc. referred to in the methods and/or operational flowcharts of this document to one or more other devices. The one or

more transceivers

206a, 206b may receive user data, control information, radio signals/channels, etc. referred to in the descriptions, functions, procedures, suggestions, methods and/or flow charts, etc. disclosed herein, from one or more other devices. have. For example, one or

more transceivers

206a , 206b may be coupled to one or

more processors

202a , 202b and may transmit and receive wireless signals. For example, one or

more processors

202a, 202b may control one or

more transceivers

206a, 206b to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or

more processors

202a, 202b may control one or

more transceivers

206a, 206b to receive user data, control information, or wireless signals from one or more other devices. Further, one or

more transceivers

206a, 206b may be coupled with one or

more antennas

208a, 208b, and the one or

more transceivers

206a, 206b may be connected via one or

more antennas

208a, 208b. , may be set to transmit and receive user data, control information, radio signals/channels, etc. mentioned in procedures, proposals, methods and/or operation flowcharts. In this document, one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (eg, antenna ports). The one or

more transceivers

206a, 206b converts the received radio signal/channel, etc. from the RF band signal to process the received user data, control information, radio signal/channel, etc. using the one or

more processors

202a, 202b. It can be converted into a baseband signal. One or

more transceivers

206a, 206b may convert user data, control information, radio signals/channels, etc. processed using one or

more processors

202a, 202b from baseband signals to RF band signals. To this end, one or

more transceivers

206a, 206b may include (analog) oscillators and/or filters.

본 개시에 적용 가능한 무선 기기 구조Wireless device structure applicable 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 composed of 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 and/or one or

more memories

204a, 204b of FIG. 2 . For example, the transceiver(s) 314 may include one or

more transceivers

206a , 206b and/or one or

more antennas

208a , 208b of FIG. 2 . The control unit 320 is electrically connected to the communication unit 310 , the memory unit 330 , and the additional element 340 and controls general operations of the wireless device. For example, the controller 320 may control the electrical/mechanical operation of the wireless device based on the program/code/command/information stored in the memory unit 330 . In addition, the control unit 320 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 externally (eg, through the communication unit 310) Information received through a wireless/wired interface from another communication device) may be stored in the memory unit 330 .

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

In FIG. 3 , various elements, components, units/units, and/or modules in the wireless device 300 may be all interconnected through a wired interface, or at least some may be wirelessly connected 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 unit (eg, 130 , 140 ) are connected wirelessly through the communication unit 310 . can be connected In addition, each element, component, unit/unit, and/or module within the wireless device 300 may further include one or more elements. For example, the controller 320 may include one or more processor sets. For example, the control unit 320 may be configured as 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 a combination thereof. can be configured.

본 개시가 적용 가능한 휴대 기기Mobile device to which the present disclosure is applicable

4 illustrates a portable device applied to the present disclosure. The portable device may include a smart phone, a smart pad, a wearable device (eg, a smart watch, smart glasses), and a portable computer (eg, a laptop computer). The mobile device may be referred to as a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS), or a wireless terminal (WT).

Referring to FIG. 4 , the mobile 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 a 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 and receive signals (eg, data, control signals, etc.) with other wireless devices and base stations. The controller 420 may control components of the portable device 400 to perform various operations. 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 a connection between the portable device 400 and other external devices. The interface unit 440b may include various ports (eg, an audio input/output port and a video input/output port) for connection with an external device. 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 obtains information/signals (eg, touch, text, voice, image, video) input from the user, and the obtained information/signals are stored in the memory unit 430 . can be saved. The communication unit 410 may convert the information/signal stored in the memory into a wireless signal, and transmit the converted wireless signal directly to another wireless device or to a base station. Also, after receiving a radio signal from another radio device or base station, the communication unit 410 may restore the received radio signal to original information/signal. The restored information/signal may be stored in the memory unit 430 and output in various forms (eg, text, voice, image, video, haptic) through the input/output unit 440c.

본 개시가 적용 가능한 무선 기기 종류Types of wireless devices to which the present disclosure is applicable

5 illustrates a vehicle or an autonomous driving vehicle applied to the present disclosure. The vehicle or autonomous driving vehicle may be implemented as a mobile robot, a vehicle, a train, an aerial vehicle (AV), a ship, and the like, but is not limited to the shape of the vehicle.

Referring to FIG. 5 , the vehicle or autonomous driving 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 autonomous driving. A unit 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.) to and from external devices such as other vehicles, base stations (eg, base stations, roadside units, etc.), and servers. The controller 520 may control elements of the vehicle or the autonomous driving vehicle 500 to perform various operations. The controller 520 may include an electronic control unit (ECU). The driving unit 540a may cause the vehicle or the autonomous driving vehicle 500 to run on the ground. The driving unit 540a may include an engine, a motor, a power train, a wheel, a brake, a steering device, and the like. The power supply unit 540b supplies power to the vehicle or the autonomous driving vehicle 500 , and may include a wired/wireless charging circuit, a battery, and the like. The sensor unit 540c may obtain vehicle state, surrounding environment information, user information, and the like. The sensor unit 540c includes an inertial measurement unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight sensor, a heading sensor, a position module, and a vehicle forward movement. / may include a reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, and the like. The autonomous driving unit 540d includes a technology for maintaining a driving lane, a technology for automatically adjusting speed such as adaptive cruise control, a technology for automatically driving along a predetermined route, and a technology for automatically setting a route when a destination is set. technology can be implemented.

For example, the communication unit 510 may receive map data, traffic information data, and the like from an external server. The autonomous driving unit 540d may generate an autonomous driving route and a driving plan based on the acquired data. The controller 520 may control the driving unit 540a to move the vehicle or the autonomous driving vehicle 500 along the autonomous driving path (eg, speed/direction adjustment) according to the driving plan. During autonomous driving, the communication unit 510 may obtain the latest traffic information data from an external server non/periodically, and may acquire surrounding traffic information data from surrounding vehicles. Also, during autonomous driving, the sensor unit 540c may acquire vehicle state and surrounding environment information. The autonomous driving unit 540d may update the autonomous driving route and driving plan based on the newly acquired data/information. The communication unit 510 may transmit information about a vehicle location, an autonomous driving route, a driving plan, and the like to an external server. The external server may predict traffic information data in advance using AI technology or the like based on information collected from the vehicle or autonomous vehicles, and may provide the predicted traffic information data to the vehicle or autonomous vehicles.

Referring to FIG. 6 , the moving object applied to the present disclosure may be implemented as at least any one of means of transport, train, aircraft, and ship. In addition, the movable body applied to the present disclosure may be implemented in other forms, and is not limited to the above-described embodiment.

At this time, referring to FIG. 6 , the mobile unit 600 may include a communication unit 610 , a control unit 620 , a memory unit 630 , an input/output unit 640a , and a position measurement unit 640b . Here, blocks 610 to 630/640a to 640b correspond to blocks 310 to 330/340 of FIG. 3 , respectively.

The communication unit 610 may transmit/receive signals (eg, data, control signals, etc.) with other mobile devices or external devices such as a base station. The controller 620 may perform various operations by controlling the components of the movable body 600 . The memory unit 630 may store data/parameters/programs/codes/commands supporting various functions of the mobile unit 600 . The input/output unit 640a may output an AR/VR object based on information in the memory unit 630 . The input/output unit 640a may include a HUD. The position measuring unit 640b may acquire position information of the moving object 600 . The location information may include absolute location information of the moving object 600 , location information within a driving line, acceleration information, and location information with a surrounding vehicle. The position measuring unit 640b may include a GPS and various sensors.

For example, the communication unit 610 of the mobile unit 600 may receive map information, traffic information, and the like from an external server and store it in the memory unit 630 . The position measurement unit 640b may obtain information about the location of the moving object through GPS and various sensors and store it in the memory unit 630 . The controller 620 may generate a virtual object based on map information, traffic information, and location information of a moving object, and the input/output unit 640a may display the generated virtual object on a window inside the moving object (651, 652). Also, the control unit 620 may determine whether the moving object 600 is normally operating within the driving line based on the moving object location information. When the moving object 600 abnormally deviates from the travel line, the control unit 620 may display a warning on the glass window of the moving object through the input/output unit 640a. Also, the control unit 620 may broadcast a warning message regarding the driving abnormality to surrounding moving objects through the communication unit 610 . Depending on the situation, the control unit 620 may transmit the location information of the moving object and information on the driving/moving object abnormality to the related organization through the communication unit 610 .

7 is a diagram illustrating an example of an XR device applied to the present disclosure. The XR device may be implemented as an HMD, a head-up display (HUD) provided in a vehicle, a television, a smart phone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like.

Referring to FIG. 7 , the XR device 700a may include a communication unit 710 , a control unit 720 , a memory unit 730 , an input/output unit 740a , a sensor unit 740b , and a power supply unit 740c . . Here, blocks 710 to 730/740a to 740c may correspond to blocks 310 to 330/340 of FIG. 3 , respectively.

The communication unit 710 may transmit/receive signals (eg, media data, control signals, etc.) to/from external devices such as other wireless devices, portable devices, or media servers. Media data may include images, images, and sounds. The controller 720 may perform various operations by controlling the components of the XR device 700a. For example, the controller 720 may be configured to control and/or perform procedures such as video/image acquisition, (video/image) encoding, and metadata generation and processing. The memory unit 730 may store data/parameters/programs/codes/commands necessary for driving the XR device 700a/creating an XR object.

The input/output unit 740a may obtain control information, data, etc. from the outside, and may output the generated XR object. The input/output unit 740a may include a camera, a microphone, a user input unit, a display unit, a speaker, and/or a haptic module. The sensor unit 740b may obtain an XR device state, surrounding environment information, user information, and the like. The sensor unit 740b includes a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, a red green blue (RGB) sensor, an infrared (IR) sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone and / or radar or the like. The power supply unit 740c supplies power to the XR device 700a, and may include a wired/wireless charging circuit, a battery, and the like.

For example, the memory unit 730 of the XR device 700a may include information (eg, data, etc.) necessary for generating an XR object (eg, AR/VR/MR object). The input/output unit 740a may obtain a command to operate the XR device 700a from the user, and the controller 720 may drive the XR device 700a according to the user's driving command. For example, when the user intends to watch a movie or news through the XR device 700a, the controller 720 transmits the content request information through the communication unit 730 to another device (eg, the mobile device 700b) or can be sent to the media server. The communication unit 730 may download/stream contents such as movies and news from another device (eg, the portable device 700b) or a media server to the memory unit 730 . The controller 720 controls and/or performs procedures such as video/image acquisition, (video/image) encoding, and metadata generation/processing for the content, and is acquired through the input/output unit 740a/sensor unit 740b It is possible to generate/output an XR object based on information about one surrounding space or a real object.

Also, the XR device 700a is wirelessly connected to the portable device 700b through the communication unit 710 , and the operation of the XR device 700a may be controlled by the portable device 700b. For example, the portable device 700b may operate as a controller for the XR device 700a. To this end, the XR device 700a may obtain 3D location information of the portable device 700b, and then generate and output an XR object corresponding to the portable device 700b.

8 is a diagram illustrating an example of a robot applied to the present disclosure. For example, the robot may be classified into industrial, medical, home, military, etc. according to the purpose or field of use. In this case, referring to FIG. 8 , the robot 800 may include a communication unit 810 , a control unit 820 , a memory unit 830 , an input/output unit 840a , a sensor unit 840b , and a driving unit 840c . . Here, blocks 810 to 830/840a to 840c may correspond to blocks 310 to 330/340 of FIG. 3 , respectively.

The communication unit 810 may transmit and receive signals (eg, driving information, control signals, etc.) with external devices such as other wireless devices, other robots, or control servers. The controller 820 may control components of the robot 800 to perform various operations. The memory unit 830 may store data/parameters/programs/codes/commands supporting various functions of the robot 800 . The input/output unit 840a may obtain information from the outside of the robot 800 and may output information to the outside of the robot 800 . The input/output unit 840a may include a camera, a microphone, a user input unit, a display unit, a speaker, and/or a haptic module.

The sensor unit 840b may obtain internal information, surrounding environment information, user information, and the like of the robot 800 . The sensor unit 840b may include a proximity sensor, an illumination sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a radar, and the like.

The driving unit 840c may perform various physical operations, such as moving a robot joint. Also, the driving unit 840c may cause the robot 800 to travel on the ground or to fly in the air. The driving unit 840c may include an actuator, a motor, a wheel, a brake, a propeller, and the like.

9 is a diagram illustrating an example of an AI device applied to the present disclosure. For 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 mobile device.

Referring to FIG. 9 , the AI device 900 includes a communication unit 910 , a control unit 920 , a memory unit 930 , input/output units 940a/940b , a learning processor unit 940c and a sensor unit 940d. may include Blocks 910 to 930/940a to 940d may correspond to blocks 310 to 330/340 of FIG. 3 , respectively.

The communication unit 910 uses wired/wireless communication technology to communicate with external devices such as other AI devices (eg, FIGS. 1, 100x, 120, 140) or an AI server ( FIGS. 1 and 140 ) and wired/wireless signals (eg, sensor information, user input, learning model, control signal, etc.). To this end, the communication unit 910 may transmit information in the memory unit 930 to an external device or transmit a signal received from the external device to the memory unit 930 .

The controller 920 may determine at least one executable operation of the AI device 900 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the controller 920 may control the components of the AI device 900 to perform the determined operation. For example, the control unit 920 may request, search, receive, or utilize the data of the learning processor unit 940c or the memory unit 930, and may be a predicted operation among at least one executable operation or determined to be preferable. Components of the AI device 900 may be controlled to execute the operation. In addition, the control unit 920 collects history information including user feedback on the operation contents or operation of the AI device 900 and stores it in the memory unit 930 or the learning processor unit 940c, or the AI server ( 1 and 140), and the like may be transmitted to an external device. The collected historical information may be used to update the learning model.

The memory unit 930 may store data supporting various functions of the AI device 900 . For example, the memory unit 930 may store data obtained from the input unit 940a , data obtained from the communication unit 910 , output data of the learning processor unit 940c , and data obtained from the sensing unit 940 . Also, the memory unit 930 may store control information and/or software codes necessary for the operation/execution of the control unit 920 .

The input unit 940a may acquire various types of data from the outside of the AI device 900 . For example, the input unit 920 may obtain training data for model learning, input data to which the learning model is applied, and the like. The input unit 940a may include a camera, a microphone, and/or a user input unit. The output unit 940b may generate an output related to sight, hearing, or touch. The output unit 940b may include a display unit, a speaker, and/or a haptic module. The sensing unit 940 may obtain at least one of internal information of the AI device 900 , surrounding environment information of the AI device 900 , and user information by using various sensors. The sensing unit 940 may include a proximity sensor, an illumination 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. have.

The learning processor unit 940c may train a model composed of an artificial neural network by using the training data. The learning processor unit 940c may perform AI processing together with the learning processor unit of the AI server ( FIGS. 1 and 140 ). The learning processor unit 940c may process information received from an external device through the communication unit 910 and/or information stored in the memory unit 930 . Also, the output value of the learning processor unit 940c may be transmitted to an external device through the communication unit 910 and/or stored in the memory unit 930 .

물리 채널들 및 일반적인 신호 전송Physical channels and general signal transmission

In a radio access system, a terminal may receive information from a base station through downlink (DL) and transmit information to a base station through uplink (UL). Information transmitted and received between the base station and the terminal includes general data information and various control information, and various physical channels exist according to the type/use of the information they transmit and receive.

In a state in which the power is turned off, the power is turned on again, or a terminal newly entering a cell performs an initial cell search operation such as synchronizing with the base station in step S1011. To this end, the terminal receives a primary synchronization channel (P-SCH) and a secondary synchronization channel (S-SCH) from the base station, synchronizes with the base station, and obtains information such as cell ID. .

Thereafter, the terminal may receive a physical broadcast channel (PBCH) signal from the base station to obtain intra-cell broadcast information. Meanwhile, the UE may receive a downlink reference signal (DL RS) in the initial cell search step to check the downlink channel state. After completing the initial cell search, the UE receives a physical downlink control channel (PDCCH) and a physical downlink control channel (PDSCH) according to physical downlink control channel information in step S1012 and receives a little more Specific system information can be obtained.

Thereafter, the terminal may perform a random access procedure, such as steps S1013 to S1016, to complete access to the base station. To this end, the UE transmits a preamble through a physical random access channel (PRACH) (S1013), and RAR for the preamble through a physical downlink control channel and a corresponding physical downlink shared channel (S1013). random access response) may be received (S1014). The UE transmits a physical uplink shared channel (PUSCH) using scheduling information in the RAR (S1015), and a contention resolution procedure such as reception of a physical downlink control channel signal and a corresponding physical downlink shared channel signal. ) can be performed (S1016).

After performing the procedure as described above, the terminal receives a physical downlink control channel signal and/or a physical downlink shared channel signal (S1017) and a physical uplink shared channel as a general uplink/downlink signal transmission procedure thereafter. channel, PUSCH) signal and/or a physical uplink control channel (PUCCH) signal may be transmitted ( S1018 ).

Control information transmitted by the terminal to the base station is collectively referred to as uplink control information (UCI). UCI is HARQ-ACK / NACK (hybrid automatic repeat and request acknowledgment / negative-ACK), SR (scheduling request), CQI (channel quality indication), PMI (precoding matrix indication), RI (rank indication), BI (beam indication) ) information, etc. In this case, the UCI is generally transmitted periodically through the PUCCH, but may be transmitted through the PUSCH according to an embodiment (eg, when control information and traffic data are to be transmitted at the same time). In addition, according to a request/instruction of the network, the UE may aperiodically transmit the UCI through the PUSCH.

Referring to FIG. 11 , entity 1 may be a user equipment (UE). In this case, the term "terminal" may be at least one of a wireless device, a portable device, a vehicle, a mobile body, an XR device, a robot, and an AI to which the present disclosure is applied in FIGS. 1 to 9 described above. In addition, the terminal refers to a device to which the present disclosure can be applied and may not be limited to a specific device or device.

Entity 2 may be a base station. In this case, the base station may be at least one of an eNB, a gNB, and an ng-eNB. In addition, the base station may refer to an apparatus for transmitting a downlink signal to the terminal, and may not be limited to a specific type or apparatus. That is, the base station may be implemented in various forms or types, and may not be limited to a specific form.

Entity 3 may be a network device or a device performing a network function. In this case, the network device may be a core network node (eg, a mobility management entity (MME), an access and mobility management function (AMF), etc.) that manages mobility. In addition, the network function may mean a function implemented to perform a network function, and entity 3 may be a device to which the function is applied. That is, the entity 3 may refer to a function or device that performs a network function, and is not limited to a specific type of device.

The control plane may refer to a path through which control messages used by a user equipment (UE) and a network to manage a call are transmitted. In addition, the user plane may mean a path through which data generated in the application layer, for example, voice data or Internet packet data, is transmitted. In this case, the physical layer, which is the first layer, may provide an information transfer service to a higher layer by using a physical channel. The physical layer is connected to the upper medium access control layer through a transport channel. In this case, data may be moved between the medium access control layer and the physical layer through the transport channel. Data can be moved between the physical layers of the transmitting side and the receiving side through a physical channel. In this case, the physical channel uses time and frequency as radio resources.

A medium access control (MAC) layer of the second layer provides a service to a radio link control (RLC) layer, which is an upper layer, through a logical channel. The RLC layer of the second layer may support reliable data transmission. The function of the RLC layer may be implemented as a function block inside the MAC. The packet data convergence protocol (PDCP) layer of the second layer may perform a header compression function that reduces unnecessary control information in order to efficiently transmit IP packets such as IPv4 or IPv6 in a narrow-bandwidth air interface. . A radio resource control (RRC) layer located at the bottom of the third layer is defined only in the control plane. The RRC layer may be in charge of controlling logical channels, transport channels and physical channels in relation to configuration, re-configuration, and release of radio bearers (RBs). RB may mean a service provided by the second layer for data transfer between the terminal and the network. To this end, the UE and the RRC layer of the network may exchange RRC messages with each other. A non-access stratum (NAS) layer above the RRC layer may perform functions such as session management and mobility management. One cell constituting the base station may be set to one of various bandwidths to provide downlink or uplink transmission services to multiple terminals. Different cells may be configured to provide different bandwidths. The downlink transmission channel for transmitting data from the network to the terminal includes a broadcast channel (BCH) for transmitting system information, a paging channel (PCH) for transmitting a paging message, and a downlink shared channel (SCH) for transmitting user traffic or control messages. have. In the case of a downlink multicast or broadcast service traffic or control message, it may be transmitted through a downlink SCH or may be transmitted through a separate downlink multicast channel (MCH). Meanwhile, as an uplink transmission channel for transmitting data from the terminal to the network, there are a random access channel (RACH) for transmitting an initial control message and an uplink shared channel (SCH) for transmitting user traffic or a control message. A logical channel that is located above the transport channel and is mapped to the transport channel includes a broadcast control channel (BCCH), a paging control channel (PCCH), a common control channel (CCCH), a multicast control channel (MCCH), and a multicast (MTCH) channel. traffic channels), etc.

12 is a diagram illustrating a method of processing a transmission signal applied to the present disclosure. As an example, the transmission signal may be processed by a signal processing circuit. In this case, the signal processing circuit 1200 may include a scrambler 1210 , a modulator 1220 , a layer mapper 1230 , a precoder 1240 , a resource mapper 1250 , and a signal generator 1260 . In this case, as an example, the operation/function of FIG. 12 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. 12 may be implemented in the

processors

202a and 202b and/or the

transceivers

206a and 206b of FIG. 2 . As an example, blocks 1010 to 1060 may be implemented in the

processors

202a and 202b of FIG. 2 . In addition, blocks 1210 to 1250 may be implemented in the

processors

202a and 202b of FIG. 2 , and block 1260 may be implemented in the

transceivers

206a and 206b of FIG. 2 , and the embodiment is not limited thereto.

The codeword may be converted into a wireless signal through the signal processing circuit 1200 of FIG. 12 . Here, the codeword is a coded bit sequence of an information block. The information block may include a transport block (eg, a UL-SCH transport block, a DL-SCH transport block). The radio signal may be transmitted through various physical channels (eg, PUSCH, PDSCH) of FIG. 10 . Specifically, the codeword may be converted into a scrambled bit sequence by the scrambler 1210 . 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, and the like. The scrambled bit sequence may be modulated by a modulator 1220 into a modulation symbol sequence. 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 a layer mapper 1230 . Modulation symbols of each transport layer may be mapped to corresponding antenna port(s) by the precoder 1240 (precoding). The output z of the precoder 1240 may be obtained by multiplying the output y of the layer mapper 1230 by the precoding matrix W of N*M. Here, N is the number of antenna ports, and M is the number of transport layers. Here, the precoder 1240 may perform precoding after performing transform precoding (eg, discrete fourier transform (DFT) transform) on the complex modulation symbols. Also, the precoder 1240 may perform precoding without performing transform precoding.

The resource mapper 1250 may map modulation symbols of each antenna port to a time-frequency resource. The time-frequency resource may include a plurality of symbols (eg, a CP-OFDMA symbol, a DFT-s-OFDMA symbol) in the time domain and a plurality of subcarriers in the frequency domain. The signal generator 1260 generates a radio signal from the mapped modulation symbols, and the generated radio signal may be transmitted to another device through each antenna. To this end, the signal generator 1260 may include an inverse fast fourier transform (IFFT) module and 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 of the signal processing process 1210 to 1260 of FIG. 12 . For example, the wireless device (eg, 200a or 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 descrambling process. The codeword may be restored to the original information block through decoding. Accordingly, the signal processing circuit (not shown) for the received signal may include a signal restorer, a resource de-mapper, a post coder, a demodulator, a descrambler, and a decoder.

Uplink and downlink transmission based on the NR system may be based on a frame as shown in FIG. 13 . In this case, one radio frame has a length of 10 ms and may be defined as two 5 ms half-frames (HF). One half-frame may be defined as 5 1ms subframes (subframe, SF). One subframe is divided into one or more slots, and the number of slots in a subframe may depend on subcarrier spacing (SCS). In this case, each slot may include 12 or 14 OFDM(A) symbols according to a cyclic prefix (CP). When a normal CP (normal CP) is used, each slot may include 14 symbols. When an extended CP (CP) is used, each slot may include 12 symbols. Here, the symbol may include an OFDM symbol (or a CP-OFDM symbol) and an SC-FDMA symbol (or a DFT-s-OFDM symbol).

Table 1 shows the number of symbols per slot, the number of slots per frame, and the number of slots per subframe according to the SCS when the normal CP is used, and Table 2 shows the number of slots per slot according to the SCS when the extended CSP is used. Indicates the number of symbols, the number of slots per frame, and the number of slots per subframe.


00	1414	1010	1One
1One	1414	2020	22
22	1414	4040	44
33	1414	8080	88
44	1414	160160	1616
55	1414	320320	3232


22	1212	4040	44

In Tables 1 and 2, N ^slot _symb may indicate the number of symbols in a slot, N ^{frame, μ} _slot _{may indicate the number of slots} in a frame ^{, and N subframe, μ} slot may indicate the number of slots in a subframe. .

In addition, in a system to which the present disclosure is applicable, OFDM(A) numerology (eg, SCS, CP length, etc.) may be set differently between a plurality of cells merged into one UE. Accordingly, an (absolute time) interval of a time resource (eg, SF, slot, or TTI) (commonly referred to as a TU (time unit) for convenience) composed of the same number of symbols may be set differently between the merged cells.

NR may support multiple numerology (or subcarrier spacing (SCS)) to support various 5G services. For example, when SCS is 15kHz, it supports a wide area in traditional cellular bands, and when SCS is 30kHz/60kHz, dense-urban, lower latency and a wider carrier bandwidth, and when the SCS is 60 kHz or higher, it can support a bandwidth greater than 24.25 GHz to overcome phase noise.

The NR frequency band is defined as a frequency range of two types (FR1, FR2). FR1 and FR2 may be configured as shown in the table below. In addition, FR2 may mean a millimeter wave (mmW).

Frequency Range designationFrequency Range designation	Corresponding frequency rangeCorresponding frequency range	Subcarrier SpacingSubcarrier Spacing
FR1FR1	410MHz - 7125MHz410MHz - 7125MHz	15, 30, 60kHz15, 30, 60 kHz
FR2FR2	24250MHz - 52600MHz24250MHz - 52600MHz	60, 120, 240kHz60, 120, 240 kHz

Also, as an example, in a communication system to which the present disclosure is applicable, the above-described pneumatic numerology may be set differently. For example, a terahertz wave (THz) band may be used as a higher frequency band than the above-described FR2. In the THz band, the SCS may be set to be larger than that of the NR system, and the number of slots may be set differently, and it is not limited to the above-described embodiment. The THz band will be described later.

One slot includes a plurality of symbols in the time domain. For example, in the case of a normal CP, one slot may include 7 symbols, but in the case of an extended CP, one slot may include 6 symbols. A carrier (carrier) includes a plurality of subcarriers (subcarrier) in the frequency domain. A resource block (RB) may be defined as a plurality of (eg, 12) consecutive subcarriers in the frequency domain.

In addition, a bandwidth part (BWP) is defined as a plurality of consecutive (P)RBs in the frequency domain, and may correspond to one numerology (eg, SCS, CP length, etc.).

A carrier may include a maximum of N (eg, 5) BWPs. Data communication is performed through the activated BWP, and only one BWP can be activated for one terminal. Each element in the resource grid is referred to as a resource element (RE), and one complex symbol may be mapped.

6G 통신 시스템 6G communication system

6G (wireless) systems have (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 reduce energy consumption of battery-free IoT devices, (vi) ultra-reliable connections, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system may have four aspects such as "intelligent connectivity", "deep connectivity", "holographic connectivity", and "ubiquitous connectivity", and the 6G system may satisfy the requirements shown in Table 4 below. That is, Table 4 is a table showing the requirements of the 6G system.

Per device peak data ratePer device peak data rate	1 Tbps1 Tbps

E2E latencyE2E latency	1 ms1 ms
Maximum spectral efficiencyMaximum spectral efficiency	100 bps/Hz100 bps/Hz
Mobility supportMobility support	up to 1000 km/hrup to 1000 km/hr
Satellite integrationSatellite integration	FullyFully
AIAI	FullyFully
Autonomous vehicleautonomous vehicle	FullyFully
XRXR	FullyFully
Haptic CommunicationHaptic Communication	FullyFully

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

Referring to FIG. 15 , the 6G system is expected to have 50 times higher simultaneous wireless communication connectivity than the 5G wireless communication system. URLLC, a key feature of 5G, is expected to become an even more important technology by providing an end-to-end delay of less than 1 ms in 6G communication. At this time, the 6G system will have much better volumetric 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. In addition, new network characteristics in 6G may be as follows.

- Satellites integrated network: 6G is expected to be integrated with satellites to provide a global mobile population. The integration of terrestrial, satellite and public networks into one wireless communication system could be very important for 6G.

- Connected intelligence: Unlike previous generations of wireless communication systems, 6G is revolutionary and will update the evolution of wireless from “connected things” to “connected intelligence”. AI may be applied in each step of a communication procedure (or each procedure of signal processing to be described later).

- Seamless integration wireless information and energy transfer: The 6G wireless network will deliver power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.

- Ubiquitous super 3-dimemtion connectivity: access to networks and core network functions of drones and very low-Earth orbit satellites will create super 3D connectivity in 6G ubiquitous.

In the above new network characteristics of 6G, some general requirements may be as follows.

- Small cell networks: The idea of small cell networks was introduced to improve the received signal quality as a result of improved throughput, energy efficiency and spectral efficiency in cellular systems. As a result, small cell networks are essential characteristics for communication systems beyond 5G and Beyond 5G (5GB). Accordingly, the 6G communication system also adopts the characteristics of the small cell network.

- Ultra-dense heterogeneous network: Ultra-dense heterogeneous networks will be another important characteristic of 6G communication system. A multi-tier network composed of heterogeneous networks improves overall QoS and reduces costs.

- high-capacity backhaul: The backhaul connection is characterized as a high-capacity backhaul network to support high-capacity traffic. High-speed fiber optics and free-space optics (FSO) systems may be possible solutions to this problem.

- Radar technology integrated with mobile technology: High-precision localization (or location-based service) through communication is one of the functions of the 6G wireless communication system. Therefore, the radar system will be integrated with the 6G network.

- Softwarization and virtualization: Softening and virtualization are two important functions that underlie the design process in 5GB networks to ensure flexibility, reconfigurability and programmability. In addition, billions of devices can be shared in a shared physical infrastructure.

6G 시스템의 핵심 구현 기술Core implementation technology of 6G system

- 인공 지능(artificial Intelligence, AI)- Artificial Intelligence (AI)

The most important and newly introduced technology for 6G systems 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. Incorporating AI into communications can simplify and enhance real-time data transmission. AI can use numerous 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 handovers, network selection, and resource scheduling can be performed instantly by using AI. AI can also play an important role in M2M, machine-to-human and human-to-machine communication. In addition, AI can be a rapid communication in the BCI (brain computer interface). 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, attempts have been made to integrate AI with wireless communication systems, but these are the application layer, network layer, and especially deep learning focused on wireless resource management and allocation. come. However, these studies are gradually developing into the MAC layer and the physical layer, and in particular, attempts to combine deep learning with wireless transmission in the physical layer are appearing. 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 a fundamental signal processing and communication mechanism. 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 physical layer of a downlink (DL). In addition, machine learning may 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.

Deep learning-based AI algorithms require large amounts of training data to optimize training parameters. However, due to a limitation 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 wireless channel.

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

Hereinafter, machine learning will be described in more detail.

Machine learning refers to a set of operations that trains a machine to create a machine that can perform tasks that humans can or cannot do. Machine learning requires data and a learning model. In machine learning, data learning methods can be roughly divided into three types: supervised learning, unsupervised learning, and reinforcement learning.

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

Supervised learning uses training data in which the correct answer is labeled in the training data, and in unsupervised learning, the correct answer may not be labeled in the training data. That is, for example, learning data in the case of supervised learning related to data classification may be data in which categories are labeled for each of the training data. The labeled training data is input to the neural network, and an error can be calculated by comparing the output (category) of the neural network with the label of the training data. The calculated error is back propagated in the 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 change amount of the connection weight of each node to be updated may be determined according to a learning rate. The computation of the neural network on the input data and the backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stage of learning a neural network, a high learning rate can be used to increase the efficiency by allowing the neural network to quickly obtain a certain level of performance, and in the late learning period, a low learning rate can be used to increase the accuracy.

The learning method may vary depending on the characteristics of the data. For example, when the purpose of accurately predicting data transmitted from a transmitter in a communication system is at a receiver, 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) methods. and such a learning model can be applied.

THz(Terahertz) 통신THz (Terahertz) communication

THz communication may be applied in the 6G system. For example, the data rate may be increased by increasing the bandwidth. This can be accomplished by using sub-THz communication with a wide bandwidth and applying advanced large-scale MIMO technology.

16 is a diagram illustrating an electromagnetic spectrum applicable to the present disclosure. As an example, referring to FIG. 16 , a THz wave, also known as sub-millimeter radiation, generally represents a frequency band between 0.1 THz and 10 THz with a corresponding wavelength in the range of 0.03 mm-3 mm. The 100GHz-300GHz band range (Sub THz band) is considered a major part of the THz band for cellular communication. Sub-THz band Addition to mmWave band increases 6G cellular communication capacity. Among the defined THz bands, 300GHz-3THz is in the far-infrared (IR) frequency band. The 300GHz-3THz band is part of the broadband, but at the edge of the wideband, just behind the RF band. Thus, this 300 GHz-3 THz band shows similarities to RF.

The main characteristics of THz communication include (i) widely available bandwidth to support very high data rates, and (ii) high path loss occurring at high frequencies (high 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 integrated into devices and BSs operating in this band. This allows the use of advanced adaptive nesting techniques that can overcome range limitations.

광 무선 기술(optical wireless technology)optical wireless technology

Optical wireless communication (OWC) technology is envisaged for 6G communication in addition to RF-based communication for all possible device-to-access networks. These networks connect to network-to-backhaul/fronthaul network connections. OWC technology has already been used since the 4G communication system, but will be used more widely to meet the needs of the 6G communication system. OWC technologies such as light fidelity, visible light communication, optical camera communication, and free space optical (FSO) communication based on a light band are well known technologies. Communication based on optical radio technology can provide very high data rates, low latency and secure communication. Light detection and ranging (LiDAR) can also be used for ultra-high-resolution 3D mapping in 6G communication based on a wide band.

FSO 백홀 네트워크FSO backhaul network

The transmitter and receiver characteristics of an FSO system are similar to those of a fiber optic network. Thus, data transmission in an FSO system is similar to that of a fiber optic system. Therefore, FSO can be a good technology to provide backhaul connectivity in 6G systems along with fiber optic networks. Using FSO, very long-distance communication is possible even at distances of 10,000 km or more. FSO supports high-capacity backhaul connections for remote and non-remote areas such as sea, space, underwater, and isolated islands. FSO also supports cellular base station connectivity.

대규모 MIMO 기술Massive MIMO technology

One of the key technologies to improve spectral efficiency is to apply MIMO technology. As MIMO technology improves, so does the spectral efficiency. Therefore, large-scale MIMO technology will be important in 6G systems. Since the MIMO technology uses multiple paths, a multiplexing technique and a beam generation and operation technique suitable for the THz band should also be considered important so that a data signal can be transmitted through one or more paths.

블록 체인blockchain

Blockchain will become an important technology for managing large amounts of data in future communication systems. Blockchain is a form of distributed ledger technology, which is a database distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger. The blockchain is managed as a peer-to-peer (P2P) network. It can exist without being managed by a centralized authority or server. Data on the blockchain is collected together and organized into blocks. Blocks are linked together and protected using encryption. Blockchain in nature perfectly complements IoT at scale with improved interoperability, security, privacy, reliability and scalability. Therefore, blockchain technology provides several functions such as interoperability between devices, traceability of large amounts of data, autonomous interaction of different IoT systems, and large-scale connection stability of 6G communication systems.

3D 네트워킹3D Networking

The 6G system integrates terrestrial and public networks to support vertical expansion of user communications. 3D BS will be provided via low orbit satellites and UAVs. Adding a new dimension in terms of elevation and associated degrees of freedom makes 3D connections significantly different from traditional 2D networks.

양자 커뮤니케이션quantum communication

In the context of 6G networks, unsupervised reinforcement learning of networks is promising. Supervised learning methods cannot label the massive amounts of data generated by 6G. Unsupervised learning does not require labeling. Thus, this technique can be used to autonomously build representations of complex networks. Combining reinforcement learning and unsupervised learning allows networks to operate in a truly autonomous way.

무인 항공기drone

Unmanned aerial vehicles (UAVs) or drones will become an important element in 6G wireless communications. In most cases, high-speed data wireless connections are provided using UAV technology. A base station entity is installed in the UAV to provide cellular connectivity. UAVs have certain features not found in fixed base station infrastructure, such as easy deployment, strong line-of-sight links, and degrees of freedom with controlled mobility. During emergencies such as natural disasters, the deployment of terrestrial communications infrastructure is not economically feasible and sometimes cannot provide services in volatile environments. A UAV can easily handle this situation. UAV will become a new paradigm in the field of wireless communication. This technology facilitates the three basic requirements of wireless networks: eMBB, URLLC and mMTC. UAVs can also serve several purposes, such as improving network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, incident monitoring, and more. Therefore, UAV technology is recognized as one of the most important technologies for 6G communication.

셀-프리 통신(cell-free Communication)Cell-free Communication

Tight integration of multiple frequencies and heterogeneous communication technologies is very important in 6G systems. As a result, users can seamlessly move from one network to another without having to make any manual configuration on the device. The best network is automatically selected from the available communication technologies. This will break the limitations of the cell concept in wireless communication. Currently, user movement from one cell to another causes too many handovers in high-density networks, causing handover failures, handover delays, data loss and ping-pong effects. 6G cell-free communication will overcome all of this and provide better QoS. Cell-free communication will be achieved through multi-connectivity and multi-tier hybrid technologies and different heterogeneous radios of devices.

무선 정보 및 에너지 전송 통합(wireless information and energy transfer, WIET)Wireless information and energy transfer (WIET)

WIET uses the same fields and waves as wireless communication systems. In particular, the sensor and smartphone will be charged using wireless power transfer during communication. WIET is a promising technology for extending the life of battery-charging wireless systems. Therefore, devices without batteries will be supported in 6G communication.

센싱과 커뮤니케이션의 통합Integration of sensing and communication

An autonomous wireless network is a function that can continuously detect dynamically changing environmental conditions and exchange information between different nodes. In 6G, sensing will be tightly integrated with communications to support autonomous systems.

액세스 백홀 네트워크의 통합Consolidation of access backhaul networks

The density of access networks in 6G will be enormous. Each access network is connected by backhaul connections such as fiber optic and FSO networks. To cope with a very large number of access networks, there will be tight integration between the access and backhaul networks.

홀로그램 빔포밍holographic beamforming

Beamforming is a signal processing procedure that adjusts an antenna array to transmit a radio signal in a specific direction. A smart antenna or a subset of an advanced antenna system. Beamforming technology has several advantages, such as high signal-to-noise ratio, interference prevention and rejection, and high network efficiency. Hologram beamforming (HBF) is a new beamforming method that is significantly different from MIMO systems because it uses a software-defined antenna. HBF will be a very effective approach for efficient and flexible transmission and reception of signals in multi-antenna communication devices in 6G.

빅 데이터 분석Big Data Analytics

Big data analytics is a complex process for analyzing various large data sets or big data. This process ensures complete data management by finding information such as hidden data, unknown correlations and customer propensity. Big data is gathered from a variety of sources such as videos, social networks, images and sensors. This technology is widely used to process massive amounts of data in 6G systems.

LIS(large intelligent surface)LIS (large intelligent surface)

In the case of the THz band signal, the linearity is strong, so there may be many shaded areas due to obstructions. By installing the LIS near these shaded areas, the LIS technology that expands the communication area, strengthens communication stability and enables additional additional services becomes important. The LIS is an artificial surface made of electromagnetic materials, and can change the propagation of incoming and outgoing radio waves. LIS can be viewed as an extension of massive MIMO, but has a different array structure and operation mechanism from that of massive MIMO. In addition, LIS is low in that it operates as a reconfigurable reflector with passive elements, that is, only passively reflects the signal without using an active RF chain. It has the advantage of having power consumption. Also, since each of the passive reflectors of the LIS must independently adjust the phase shift of the incoming signal, it can be advantageous for a wireless communication channel. By properly adjusting the phase shift via the LIS controller, the reflected signal can be gathered at the target receiver to boost the received signal power.

테라헤르츠(THz) 무선통신terahertz (THz) wireless communication

Referring to FIG. 17, THz wireless communication uses a THz wave having a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and uses a very high carrier frequency of 100 GHz or more. It can mean communication. THz wave is located between RF (Radio Frequency)/millimeter (mm) and infrared band, (i) It transmits non-metal/non-polar material better than visible light/infrared light, and has a shorter wavelength than RF/millimeter wave, so it has high straightness. Beam focusing may be possible.

In addition, since the photon energy of the THz wave is only a few meV, it is harmless to the human body. The frequency band expected to be used for THz wireless communication may be a D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz) band with low propagation loss due to absorption of molecules in the air. Standardization discussion on THz wireless communication is being discussed centered on IEEE 802.15 THz working group (WG) in addition to 3GPP, and standard documents issued by TG (task group) (eg, TG3d, TG3e) of IEEE 802.15 are described in this specification. It can be specified or supplemented. THz wireless communication may be applied to wireless recognition, sensing, imaging, wireless communication, THz navigation, and the like.

Specifically, referring to FIG. 17 , a THz wireless communication scenario may be classified into a macro network, a micro network, and a nanoscale network. In a macro network, THz wireless communication can be applied to a vehicle-to-vehicle (V2V) connection and a backhaul/fronthaul connection. THz wireless communication in micro networks is applied to indoor small cells, fixed point-to-point or multi-point connections such as wireless connections in data centers, and near-field communication such as kiosk downloading. can be Table 5 below is a table showing an example of a technique that can be used in the THz wave.

Transceivers DeviceTransceivers Device	Available immature: UTC-PD, RTD and SBDAvailable immature: UTC-PD, RTD and SBD
Modulation and codingModulation and coding	Low order modulation techniques (OOK, QPSK), LDPC, Reed Soloman, Hamming, Polar, TurboLow order modulation techniques (OOK, QPSK), LDPC, Reed Soloman, Hamming, Polar, Turbo
AntennaAntenna	Omni and Directional, phased array with low number of antenna elementsOmni and Directional, phased array with low number of antenna elements
BandwidthBandwidth	69 GHz (or 23 GHz) at 300 GHz69 GHz (or 23 GHz) at 300 GHz
Channel modelsChannel models	PartiallyPartially
Data rate data rate	100 Gbps100 Gbps
Outdoor deploymentoutdoor deployment	NoNo
Fee space lossFee space loss	HighHigh
CoverageCoverage	Low Low

Radio MeasurementsRadio Measurements	300 GHz indoor300 GHz indoor
Device sizeDevice size	Few micrometersFew micrometers

Referring to FIG. 18 , THz wireless communication may be classified based on a method for generating and receiving THz. The THz generation method can be classified into an optical device or an electronic device-based technology.

In this case, the method of generating THz using an electronic device is a method using a semiconductor device such as a resonant tunneling diode (RTD), a method using a local oscillator and a multiplier, a compound semiconductor HEMT (high electron mobility transistor) based There are a monolithic microwave integrated circuit (MMIC) method using an integrated circuit, a method using a Si-CMOS-based integrated circuit, and the like. In the case of FIG. 18 , a doubler, tripler, or multiplier is applied to increase the frequency, and it is radiated by the antenna through the sub-harmonic mixer. Since the THz band forms a high frequency, a multiplier is essential. Here, the multiplier is a circuit that has an output frequency that is N times that of the input, matches the desired harmonic frequency, and filters out all other frequencies. Also, an array antenna or the like may be applied to the antenna of FIG. 18 to implement beamforming. In FIG. 18 , IF denotes an intermediate frequency, tripler, and multiplier denote a multiplier, PA denotes a power amplifier, and LNA denotes a low noise amplifier. ), PLL represents a phase-locked loop.

19 is a diagram illustrating a method for generating a THz signal applicable to the present disclosure. In addition, FIG. 20 is a diagram illustrating a wireless communication transceiver applicable to the present disclosure.

19 and 20 , the optical device-based THz wireless communication technology refers to a method of generating and modulating a THz signal using an optical device. The optical element-based THz signal generation technology is a technology that generates a high-speed optical signal using a laser and an optical modulator, and converts it into a THz signal using an ultra-high-speed photodetector. In this technology, it is easier to increase the frequency compared to the technology using only electronic devices, it is possible to generate a high-power signal, and it is possible to obtain a flat response characteristic in a wide frequency band. As shown in FIG. 19 , a laser diode, a broadband optical modulator, and a high-speed photodetector are required to generate an optical device-based THz signal. In the case of FIG. 19 , light signals of two lasers having different wavelengths are multiplexed to generate a THz signal corresponding to a difference in wavelength between the lasers. In FIG. 19 , an optical coupler refers to a semiconductor device that transmits electrical signals using light waves to provide coupling with electrical insulation between circuits or systems, and UTC-PD (uni-travelling carrier photo- The detector) is one of the photodetectors, which uses electrons as active carriers and reduces the movement time of electrons by bandgap grading. UTC-PD is capable of photodetection above 150GHz. In FIG. 20 , an erbium-doped fiber amplifier (EDFA) indicates an erbium-doped optical fiber amplifier, a photo detector (PD) indicates a semiconductor device capable of converting an optical signal into an electrical signal, and the OSA indicates various optical communication functions (eg, .

21 is a diagram illustrating a structure of a transmitter applicable to the present disclosure. Also, FIG. 22 is a diagram illustrating a modulator structure applicable to the present disclosure.

Referring to FIGS. 21 and 22 , in general, a phase of a signal may be changed by passing an optical source of a laser through an optical wave guide. At this time, data is loaded by changing electrical characteristics through microwave contact or the like. Accordingly, an optical modulator output is formed as a modulated waveform. The photoelectric modulator (O/E converter) is an optical rectification operation by a nonlinear crystal (nonlinear crystal), photoelectric conversion (O / E conversion) by a photoconductive antenna (photoconductive antenna), a bunch of electrons in the light beam (bunch of) THz pulses can be generated by, for example, emission from relativistic electrons. A terahertz pulse (THz pulse) generated in the above manner may have a length in units of femtoseconds to picoseconds. An O/E converter performs down conversion by using non-linearity of a device.

Considering the THz spectrum usage, a number of contiguous GHz bands for fixed or mobile service use for the terahertz system are used. likely to use According to the outdoor scenario standard, available bandwidth may be classified based on oxygen attenuation of 10^2 dB/km in a spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is composed of several band chunks may be considered. As an example of the framework, if the length of a terahertz pulse (THz pulse) for one carrier is set to 50 ps, the bandwidth (BW) becomes about 20 GHz.

Effective down conversion from the infrared band to the THz band depends on how the nonlinearity of the O/E converter is exploited. That is, in order to down-convert to a desired terahertz band (THz band), the O/E converter having the most ideal non-linearity for transfer to the terahertz band (THz band) is design is required. If an O/E converter that does not fit the target frequency band is used, there is a high possibility that an error may occur with respect to the amplitude and phase of the corresponding pulse.

A terahertz transmission/reception system may be implemented using one photoelectric converter in a single carrier system. Although it depends on the channel environment, as many photoelectric converters as the number of carriers may be required in a far-carrier system. In particular, in the case of a multi-carrier system using several broadbands according to the above-described spectrum usage-related scheme, the phenomenon will become conspicuous. In this regard, a frame structure for the multi-carrier system may be considered. The down-frequency-converted signal based on the photoelectric converter may be transmitted in a specific resource region (eg, a specific frame). The frequency domain of the specific resource region may include a plurality of chunks. Each chunk may be composed of at least one component carrier (CC).

인공 지능(Artificial Intelligence) 시스템Artificial Intelligence System

23 is a diagram illustrating a structure of a perceptron included in an artificial neural network applicable to the present disclosure. Also, FIG. 24 is a diagram illustrating an artificial neural network structure applicable to the present disclosure.

As described above, an artificial intelligence system may be applied in the 6G system. In this case, as an example, the artificial intelligence system may operate based on a learning model corresponding to the human brain, as described above. In this case, a paradigm of machine learning that uses a neural network structure with high complexity such as artificial neural networks as a learning model can be called deep learning. In addition, the neural network cord used as a learning method is largely a deep neural network (DNN), a convolutional deep neural network (CNN), and a recurrent neural network (RNN). There is a way. In this case, as an example, referring to FIG. 23 , the artificial neural network may be composed of several perceptrons. In this case, the input vector x={x ₁ , x ₂ , … , x _d } is entered, each component is given a weight {W ₁ , W ₂ , … , W _d } is multiplied, and the results are summed up, and then the whole process of applying the activation function σ(·) can be called a perceptron. If the huge artificial neural network structure extends the simplified perceptron structure shown in FIG. 23, input vectors can be applied to different multidimensional perceptrons. For convenience of description, an input value or an output value is referred to as a node.

Meanwhile, the perceptron structure shown in FIG. 23 may be described as being composed of a total of three layers based on an input value and an output value. 1 ^st layer and 2 ^nd layer between, the (d + 1) pieces perceptron H of D, 2, (H + 1) between ^nd layer and a 3 ^rd layer level perceptron is to be described as an artificial neural network 24 present the K can

In this case, the layer where the input vector is located is called an input layer, the layer where the final output value is located is called the output layer, and all layers located between the input layer and the output layer are called hidden layers. As an example, although three layers are disclosed in FIG. 24 , when counting the number of actual artificial neural network layers, the input layers are counted, so the artificial neural network illustrated in FIG. 24 can be understood as a total of two layers. The artificial neural network is constructed by connecting the perceptrons of the basic blocks in two dimensions.

The aforementioned input layer, hidden layer, and output layer can be jointly applied in various artificial neural network structures such as CNN and RNN to be described later as well as multi-layer perceptron. As the number of hidden layers increases, the artificial neural network becomes deeper, and a machine learning paradigm that uses a sufficiently deep artificial neural network as a learning model can be called deep learning. Also, an artificial neural network used for deep learning may be referred to as a deep neural network (DNN).

Referring to FIG. 25 , the deep neural network may be a multilayer perceptron composed of eight hidden layers + eight output layers. In this case, the multilayer perceptron structure may be expressed as a fully-connected neural network. In a fully connected neural network, a connection relationship does not exist between nodes located in the same layer, and a connection relationship can exist only between nodes located in adjacent layers. DNN has a fully connected neural network structure and is composed of a combination of a number of hidden layers and activation functions, so it can be usefully applied to figure out the correlation between input and output. Here, the correlation characteristic may mean a joint probability of input/output.

26 is a diagram illustrating a convolutional neural network applicable to the present disclosure. 27 is a diagram illustrating a filter operation of a convolutional neural network applicable to the present disclosure.

For example, according to how a plurality of perceptrons are connected to each other, various artificial neural network structures different from the above-described DNN may be formed. In this case, in the DNN, nodes located inside one layer are arranged in a one-dimensional vertical direction. However, referring to FIG. 26 , it may be assumed that the nodes are two-dimensionally arranged with w horizontally and h vertical nodes. (Convolutional neural network structure in Fig. 26). In this case, since a weight is added per connection in the connection process from one input node to the hidden layer, a total of h×w weights must be considered. Since there are h×w nodes in the input layer, a total of h ² w ² weights may be required between two adjacent layers.

In addition, the convolutional neural network of FIG. 26 has a problem in that the number of weights increases exponentially according to the number of connections, so instead of considering the connection of all modes between adjacent layers, it is assumed that a filter with a small size exists. can As an example, as shown in FIG. 27 , a weighted sum and activation function operation may be performed on a portion where the filters overlap.

In this case, one filter has a weight corresponding to the number corresponding to its size, and learning of the weight can be performed so that a specific feature on the image can be extracted and output as a factor. In FIG. 27 , a 3×3 filter is applied to the upper left 3×3 region of the input layer, and an output value obtained by performing weighted sum and activation function operations on the corresponding node may be stored _{in z 22 .}

In this case, the above-described filter may perform weighted sum and activation function calculations while moving horizontally and vertically at regular intervals while scanning the input layer, and the output value may be disposed at the current filter position. Since this operation method is similar to a convolution operation on an image in the field of computer vision, a deep neural network with such a structure is called a convolutional neural network (CNN), and the result of the convolution operation is The hidden layer may be referred to as a convolutional layer. Also, a neural network having a plurality of convolutional layers may be referred to as a deep convolutional neural network (DCNN).

In addition, in the convolution layer, the number of weights can be reduced by calculating the weighted sum by including only nodes located in the region covered by the filter in the node where the filter is currently located. Due to this, one filter can be used to focus on features for a local area. Accordingly, CNN can be effectively applied to image data processing in which physical distance on a two-dimensional domain is an important criterion. Meanwhile, in CNN, a plurality of filters may be applied immediately before the convolution layer, and a plurality of output results may be generated through the convolution operation of each filter.

Meanwhile, there may be data whose sequence characteristics are important according to data properties. Considering the length variability and precedence relationship of the sequence data, one element in the data sequence is input at each timestep, and the output vector (hidden vector) of the hidden layer output at a specific time is input together with the next element in the sequence. A structure in which this method is applied to an artificial neural network can be called a recurrent neural network structure.

28 is a diagram illustrating a neural network structure in which a cyclic loop applicable to the present disclosure exists. 29 is a diagram illustrating an operation structure of a recurrent neural network applicable to the present disclosure.

Referring to FIG. 28 , a recurrent neural network (RNN) is an element {x ₁ ^(t) , x ₂ ^(t) , . , x _d ^(t) } in the process of input to the fully connected neural network, the immediately preceding time point t-1 is the hidden vector {z ₁ ^(t-1) , z ₂ ^(t-1) , … , z _H ^(t-1) } can be input together to have a structure in which a weighted sum and an activation function are applied. The reason why the hidden vector is transferred to the next time point in this way is that information in the input vector at previous time points is considered to be accumulated in the hidden vector of the current time point.

Also, referring to FIG. 29 , the recurrent neural network may operate in a predetermined time sequence with respect to an input data sequence. In this case, the input vector {x ₁ ^(t) , x ₂ ^(t) , ... , x _d ^(t) } when the hidden vector {z ₁ ⁽¹⁾ , z ₂ ⁽¹⁾ , … , z _H ⁽¹⁾ } is the input vector {x ₁ ⁽²⁾ , x ₂ ⁽²⁾ , … , x _d ⁽²⁾ }, the vector of the hidden layer {z ₁ ⁽²⁾ , z ₂ ⁽²⁾ , … , z _H ⁽²⁾ } is determined. These processes are time point 2, time point 3, ... , iteratively until time point T.

On the other hand, when a plurality of hidden layers are arranged in a recurrent neural network, this is called a deep recurrent neural network (DRNN). The recurrent neural network is designed to be usefully applied to sequence data (eg, natural language processing).

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

In recent years, attempts have been made to integrate AI with a wireless communication system, but this is an application layer, a network layer, and in particular, in the case of deep learning, wireless resource management and allocation. has been focused on the field. However, these studies are gradually developing into the MAC layer and the physical layer, and in particular, attempts to combine deep learning with wireless transmission in the physical layer are appearing. 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 a fundamental signal processing and communication mechanism. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanism, AI-based resource scheduling ( scheduling) and allocation may be included.

본 개시의 구체적인 실시 예Specific embodiments of the present disclosure

The present disclosure relates to a method and apparatus for performing federated learning, which is a type of machine learning, in a wireless communication system. Specifically, the present disclosure describes a technique for performing federated learning in an environment in which devices having different hardware characteristics coexist.

Various embodiments to be described below can be easily applied when cooperative learning is performed in an environment in which devices of various specifications (eg, mounted sensors, hardware performance, etc.) are mixed in terms of connected intelligence. For example, the following embodiments may be used for efficient federated learning of an artificial intelligence-based virtual sensing device.

The autonomous operation method and system discussed recently can be divided into a method of operating only on a device and a method of dividing and operating a task between a server and a device. However, as the number of sensors (eg, light detection and ranging (LiDAR), radar, camera, etc.) installed for autonomous driving of devices and operation of devices such as unmanned drones/Internet of things (IoT)/robots increases, Accordingly, limitations on work processing methods between devices according to a complex task processing algorithm and a difference in types of installed sensors are exposed.

For example, with the advent of autonomous/unmanned devices, identification of surrounding objects (e.g., whether it is a person, a mobile device, or an object), understanding of the driving environment (e.g. 3D of surrounding and local areas), object detection and speed measurement Functions such as braking and collision avoidance are required. These functions require very complex calculations and accurate inferences. However, due to battery and hardware limitations of the device, there is a limit to performance optimization.

In addition, due to changes in specifications (eg hardware specifications, sensor mounting support types, etc.) according to the device's product target (eg premium, high-tier, no-tier), the device There is a limit to the amount of information collected and business processing capabilities. As a result, performance degradation may occur for identifying surrounding objects, identifying driving environments, detecting objects, and measuring speed.

In addition, there may be an imbalance between the amount of information collected and the quality of information depending on whether or not multiple sensors are installed. This imbalance may greatly affect the performance degradation of autonomous/unmanned devices. Currently, we try to overcome performance degradation by using organic operation with the server based on distributed learning and federated learning to overcome the imbalance in the amount of information collected according to the battery, hardware constraints, and specifications of the device. Because there is also an impact on hardware and task processing power, performance degradation may occur.

Accordingly, the present disclosure proposes an organic operation method between a device and a server capable of solving the above-described limitations and increasing the reliability of learning and the efficiency of resources.

Federated learning is a type of machine learning technique that trains algorithms on multiple devices or servers holding local data samples without exchanging data samples. Federated learning allows multiple devices to build a common powerful machine learning model without sharing data, thus addressing critical issues such as data privacy, data security, data access rights and heterogeneous data access. known as technology. The general principle of federated learning is to train a local model on a local data sample, and pass the parameters of the local model (e.g. the weights of the neural network) to the server and exchange them to create a global model.

30 is a diagram showing the structure of a federated learning system applicable to the present disclosure. In FIG. 30 , a situation in which three or more devices participate in federated learning is exemplified, but embodiments described below may be applied even when two or less devices participate in federated learning.

Referring to FIG. 30 , a plurality of

devices

3010a, 3010b, and 3010c and a base station 3020 may perform joint learning. For example, the

devices

3010a , 3010b , and 3010c may be vehicles having an autonomous driving function. The base station 3020 may perform and control federated learning by including a server, performing a function of a server, or interworking with a server. For joint learning, the plurality of

devices

3010a, 3010b, 3010c and the base station 3020 are connected through various communication means, and operate organically with each other.

For federated learning, a global model and a local model are used. A

local model

3012a , 3012b , 3012c resides in the

devices

3010a , 3010b , 3010c , and a global model 3022 resides in the base station 3020 .

Local models

3012a , 3012b , 3012c are derived from global model 3022 . The initial

local models

3012a, 3012b, and 3012c are the same as the global model 3022, and may vary for each device by learning in each device. Base station 3020 provides information w ^BS representing global model 3022 to

devices

3010a, 3010b, 3010c, and

devices

3010a, 3010b, 3010c represent

local models

3012a, 3012b, 3012c The information w ₁ to w _u is transmitted to the base station 3020 .

The procedure of federated learning using the

local models

3012a, 3012b, and 3012c and the global model 3022 is shown in FIG. 31 below. 31 is a diagram illustrating a procedure of associative learning applicable to the present disclosure. 31 illustrates an apparatus and a method of operation of a base station.

Referring to FIG. 31 , in step S3101, the device updates the local model using local data. Here, the local data means data generated or acquired by the device. In step S3103, the device sends the local model to the server. Here, the server may include a base station. In this case, the transmitted information may include information (eg, w _u ) related to weights of connections included in the local model. In step S3105, the server updates the global model based on the local model. In step S3107, the server sends the global model to the device.

In step S3109, the server determines whether the global model is converged. For example, the server may perform an evaluation on a cost function. If the global model is not converged, the procedure returns to step S3101. That is, the above-described steps may be repeated until the result of the evaluation converges.

Here, the cost function may be defined in various ways. For example, the cost function may be defined as in [Equation 1] below.

In [Equation 1], j(w) is a cost function, d _k is an actual value, x _k (w) is an inferenced value using a weight w, f _NN () is a neural network model, s _k is the input value. Here, w may be optimized to satisfy a condition such as [Equation 2]

In [Equation 2], w denotes a weight of the neural network model, and j(w) denotes a cost function.

The present disclosure considers a scenario in which devices having different hardware characteristics use a global model determined by federated learning. For example, the properties (eg, accuracy, quality, resolution, etc.) of the acquired local data may differ from device to device due to differences in characteristics or performance of a means (eg, a sensor) for acquiring local data mounted on the devices. can In this case, it is preferable that federated learning proceeds in consideration of the properties of local data available for each device. In the following description, devices may be classified into two or more classes among a premium device, a high tier device, and a naive device according to a difference in obtainable local data. For convenience of explanation, the present disclosure divides the devices into two classes, and a device having a relatively high performance is a first type device and a device having a relatively low performance is a second type (second type) device. ) as a device. However, the embodiments described below may be applied even when devices are classified into three or more classes.

32 is a diagram illustrating a structure of a type 1 device applicable to the present disclosure.

Referring to FIG. 32 , the first type device includes a first sensor 3210a , a second sensor 3210b , a transceiver 3220 , and a processor 3230 .

The first sensor 3210a and the second sensor 3210b are means for acquiring local data. For example, the first sensor 3210a and the second sensor 3210b may acquire sensing data for recognizing objects around the device. In this case, the second sensor 3210b has higher performance than the first sensor 3210a. For example, the first sensor 3210a may be a camera, and the second sensor 3210b may be a LiDAR. As another example, the first sensor 3210a may be a radar, an infrared sensor, a sound sensor, or the like. Alternatively, the first sensor 3210a may be a set of a plurality of types of sensors.

The transceiver 3220 provides an interface for transmitting and receiving signals with an external device. The transceiver 3220 may transmit or receive a signal under the control of the processor 3230 .

The processor 3230 controls overall functions of the device. According to an embodiment, the processor 3230 may include a virtual neural network 3232 , a neural network learner 3234 , and an application 3236 . Here, the virtual neural network 3232 , the neural network learning unit 3234 , and the application 3236 may be hardware circuitry or a temporarily existing instruction set. The virtual neural network 3232 includes a local model, and the neural network learning unit 3234 learns the local model through a back-propagation operation. The virtual neural network 3232 may perform an operation of identifying objects existing in the vicinity of the device based on the sensed data. The neural network learning unit 3234 updates the weights of the virtual neural network 3232 by learning the virtual neural network 3232 . The application 3236 performs a necessary function using an inference result (eg, x[k]). For example, the inference result includes information indicating at least one of locations of surrounding objects, types of objects (eg, people, vehicles, buildings, facilities), and characteristics of objects (eg, fixed, movable, and moving speed, etc.) can do.

The output s[k] of the data obtained from the first sensor 3210a is input to the virtual neural network 3232 . Also, the neural network learning unit 3234 provides the learning result w[k] to the virtual neural network 3232 . The virtual neural network 3232 infers and outputs the output x[k] from s[k] using w[k], and the output x[k] is input to the neural network learning unit 3234 for learning. For example, x[k] is information representing at least one of locations of surrounding objects, types of objects (eg, people, vehicles, buildings, facilities), and characteristics of objects (eg, fixed, movable, and moving speed, etc.) may include The neural network learning unit 3234 updates w[k] by performing learning using the local data d[k] provided from the second sensor 3210b, and provides w[k] to the virtual neural network 3232 . . For example, the neural network learning unit 3234 may perform learning based on the difference between x[k] and d[k]. This cycle process may be repeated until the local model converges.

Referring to FIG. 33 , the first type device includes a sensor 3310 , a transceiver 3320 , and a processor 3330 .

The sensor 3310 is a means for acquiring local data. For example, the sensor 3310 may acquire sensing data for recognizing objects around the device. For example, the sensor 3310 may be a camera. As another example, the sensor 3310 may be a radar, an infrared sensor, a sound sensor, or the like. Alternatively, the sensor 3310 may be a set of a plurality of types of sensors.

The transceiver 3320 provides an interface for transmitting and receiving signals with an external device. The transceiver 3320 may transmit or receive a signal under the control of the processor 3330 .

The processor 3330 controls overall functions of the device. According to an embodiment, the processor 3330 may include a virtual neural network 3332 and an application 3334 . Here, the virtual neural network 3332 and the application 3334 may be hardware or a temporarily existing instruction set. The virtual neural network 3232 may perform an operation of identifying objects existing in the vicinity of the device based on the sensed data.

The virtual neural network 3332 may include a network model generated based ^{on the information w BS} related to the global model received through the transceiver 3320 . The output s[k] of the data obtained from the sensor 3310 is input to the virtual neural network 3332 . The virtual neural network 3332 infers the output x[k] from s[k] using the ^{weight w BS} received through the transceiver 3220 , and provides it to the application 3334 . The application 3334 performs a necessary function using x[k]. For example, x[k] is information representing at least one of the locations of surrounding objects, types of objects (eg, people, vehicles, buildings, facilities), and characteristics of objects (eg, fixed, movable, moving speed, etc.) may include

34 is a diagram showing the structure of a server for controlling learning applicable to the present disclosure. The structure shown in FIG. 34 may be understood as an independent entity or included in a base station as a part of a station.

Referring to FIG. 34 , the server includes a transceiver 3410 and a processor 3420 .

The transceiver 3410 provides an interface for transmitting and receiving signals with an external device. The transceiver 3410 may transmit or receive a signal under the control of the processor 3420 .

The processor 3420 controls overall functions of the server. According to an embodiment, the processor 3420 may include a virtual neural network 3422 and a neural network updater 3424 . Here, the virtual neural network 3422 and the neural network updater 3424 may be hardware or a temporarily existing instruction set. The neural network updater 3424 updates the global model included in the virtual neural network 3422 by using information related to local models of devices collected through the transceiver 3410 . When the global model included in the virtual neural network 3422 is updated, information related to the updated global model may be transmitted to devices through the transceiver 3410 .

35 is a diagram illustrating an embodiment of signal exchange for associative learning applicable to the present disclosure. 35 illustrates signal exchange between two (2) or

more devices

3510a, 3510b and a server 3520 .

Referring to FIG. 35 , in steps S3501-1 and S3501-2, each of the

devices

3510a and 3510b transmits information related to a local model trained using local data. The information related to the local model may include _{a weight w u of the local model.} In step S3503, the server 3520 updates the global model using the received information. For example, the server 3520 can determine ^{the weight w BS} of the updated global model by averaging the _{received weights w u .} In step S3505 , the server 3520 transmits information related to the updated global model to the

devices

3510a and 3510b. For example, the information related to the updated global model may include the updated weight w ^BS .

The procedure described with reference to FIG. 35 is applicable when the

devices

3510a and 3510b have the same grade. On the other hand, the procedure described with reference to FIG. 35 may be applied even when the

devices

3510a and 3510b have different grades. In this case, when the server 3520 updates the global model based on the information related to the local model provided from the

devices

3510a and 3510b, unlike the example of FIG. 35 , the server 3520 treats the information related to the local model differently depending on the grade. can For example, with respect to information provided from high-grade devices (eg, type I devices), a higher weight may be given when combining (eg, averaging).

Federated learning may be performed according to the procedure as described above. For example, federated learning may be performed to learn a network model used to perform autonomous driving. In this case, the server is responsible for identifying surrounding objects (e.g. whether it is a person, a mobile device, or an object), determining the driving environment (e.g., 3D surrounding and area), object detection and speed measurement (e.g. braking and collision avoidance). The global model to be trained is transmitted to devices equipped with the same sensor (eg, camera, lidar, radar, etc.). Devices that have received the global model learn the local model using the information acquired through the sensor. Each device sends parameters (eg, weights of deep neural networks) updated through learning to the server. The server uses the parameters collected from the devices to update the global model, and then evaluates it based on the cost function. After repeating learning until the evaluation result is converged, the server may provide information related to the converged global model to the devices.

As described above, in a situation where a plurality of devices are mixed, there is a difference in specifications (eg hardware performance, sensor installation and type, etc.) Due to this, differences may occur in the local data collected by the device. Information asymmetry can cause several problems. In order to solve information imbalance, the present disclosure will describe embodiments in which information imbalance is resolved by transferring a global model used in a device equipped with an advanced sensor to a device equipped with a general sensor. That is, various embodiments to be described below relate to resolving information imbalance through information exchange between a device equipped with an advanced sensor (eg, a type 1 device) and a device equipped with a general sensor (eg, a type 2 device).

36 is a diagram illustrating an embodiment of a procedure for performing federated learning in a server applicable to the present disclosure. 36 illustrates an operating method of a server. An operating subject of the procedure illustrated in FIG. 36 is described as a 'server', where the server may be an independent entity or may be understood as a part of a base station.

Referring to FIG. 36 , in step S3601, the server transmits information related to the global model to the first type device. For example, the information related to the global model includes information indicating the structure of the global model (eg, the number of layers, the number of nodes per layer, whether or not connections are made between nodes, etc.) and weights of connections included in the global model. can do. Through the information related to the global model, the first-class device may specify a target for training.

In step S3603, the server receives information related to the local model from the first type device. The local model is a network model updated through training in a first-class device. Here, the information transmitted by the type 1 device is derived from a training result using local data obtained through a sensor (eg, lidar) of a type not used in the type 2 device.

In step S3605, the server updates the global model based on the local model. The server may receive information related to local models from a plurality of first-class devices, and may update connections and weights of the global model in consideration of connections and weights of connections included in the plurality of local models. have. For example, the server may determine the weights of the new global model by averaging the weights of the local models for each connection.

In step S3607, the server transmits information related to the updated global model to the first type device. The server transmits information related to the updated global model to the first type device for subsequent iterative learning. The information related to the updated global model includes information capable of constructing the updated global model in the first type device. For example, information related to the updated global model includes values of structures and variables (eg, weights of connections, biases of nodes, etc.) of the updated global model, or differences from previously provided global models It may include information indicating (eg, connections added, connections deleted, amount of weight change, etc.).

In step S3609, the server determines whether the learning results converge. For example, the server may determine whether weights of the global model are optimized using the cost function. As another example, the server may determine whether to converge based on the degree of change between the previous global model and the current global model. If the learning result is not converged, the server returns to step S3603.

If the learning result is converged, in step S3611, the server transmits information related to the global model to the second type device. Since the converged global model is determined, the server provides the global model to the second type device so that not only the first type device but also the second type device can perform an inference operation using the global model.

In the procedure described with reference to FIG. 36, if the global model does not converge, learning is repeated. In this case, although not shown in FIG. 36 , additional signaling may be performed to help determine the type I device. For example, when the learning results are not converged, the server may transmit a message instructing to repeat the learning to the first type device. Alternatively, when repetition of learning is defined as a basic procedure, and when the learning results are converged, the server may transmit a message instructing the first type device to stop learning. Here, according to an embodiment, a message instructing to repeat or stop learning may be transmitted together with information related to the global model. In this case, step S3607 may be performed after step S3609.

37 is a diagram illustrating an embodiment of a procedure for performing federated learning in a type 1 device applicable to the present disclosure. 37 illustrates an operation method of a type I device (eg, the device of FIG. 32 ). The operating subject of the procedure illustrated in FIG. 37 is described as a 'device'.

Referring to FIG. 37 , in step S3701, the device receives information related to the global model from the server. For example, the information related to the global model may include information indicating the structure of the global model (eg, the number of layers, the number of nodes per layer, whether or not connections are made between nodes, etc.) and weights of connections included in the global model. . Through the information related to the global model, the first-class device may specify a target for training. The device may generate a local model based on the received information about the global model. The local model is initially identical to the global model, but can be updated through later training.

In step S3703, the device collects training data and trains the local model. The device collects learning data using at least one mounted sensor. The sensor is for recognizing the physical environment around the device, and includes, for example, at least one of a camera, a lidar, a radar, an infrared sensor, and a sound sensor. Then, the device updates the weights of the local model by performing learning using the collected training data. In this case, according to an embodiment, the device performs learning using data acquired through a sensor (eg, lidar) that is not used in the second type device (eg, the device of FIG. 33 ).

In step S3705, the device transmits information related to the learned local model to the server. The information related to the learned local model includes information related to weights updated by learning. Information related to the learned local model may be used in the base station to update the global model. Here, the global model may be updated based on information related to a plurality of local models collected from a plurality of devices.

In step S3707, the device receives information related to the updated global model from the server. The information related to the updated global model includes information capable of constructing the updated global model in the device. For example, information related to the updated global model includes values of structures and variables (eg, weights of connections, biases of nodes, etc.) of the updated global model, or differences from previously provided global models (eg, information indicating an added connection, a deleted connection, a change in weight, etc.).

In step S3709, the device performs an inference operation using the global model. The device may estimate the surrounding physical environment using the global model, and may perform necessary functions based on the estimation result. At this time, although not shown in FIG. 37 , the device may repeat steps S3703 to S3707 under the control of the server. Steps S3703 to S3707 may be repeated until convergence.

In the procedure described with reference to FIG. 37 , the device performs learning on the local model. At this time, the device is a type of sensor not used in the second type device (hereinafter referred to as a 'differential sensor') in addition to the type of sensor used in the second type device (hereinafter referred to as a 'common sensor') ) is further used to perform learning. In this case, the common sensor and the differential sensor may be used in various ways.

According to an embodiment, the device may perform learning by using sensing data input through a common sensor and sensing data input through a differential sensor as inputs. In this case, since abundant input data is secured compared to the second type device, more accurate and effective learning can be performed compared to the second type device.

According to another embodiment, the device may configure labeled data by configuring a pair having sensing data input through a common sensor as an input and a result measured through a differential sensor as an output. . In other words, the device may configure the labeled data by pairing a result estimated based on the sensing data obtained through the differential sensor and the sensing data obtained through the common sensor. Then, the device may perform supervised learning using the labeled data.

According to another embodiment, the device performs primary learning using a common sensor, and compares a result inferred using the primary learned local model and a result measured through the differential sensor. Through the comparison operation, learning data for secondary learning is obtained. Then, the device may perform secondary learning on the local model using the comparison result, that is, data for secondary learning. Secondary learning can be understood as a correction process for primary learning.

In addition to the above-described application examples, the differential sensor may be utilized for learning in various ways.

38 is a diagram illustrating an embodiment of a procedure for performing federated learning in a type 2 device applicable to the present disclosure. 38 illustrates a method of operation of a type 2 device. An operating subject of the procedure illustrated in FIG. 38 is described as a 'device'.

Referring to FIG. 38 , in step S3801 , the device receives information related to the global model updated based on the local model of the first type device from the server. The information related to the updated global model includes information for building a global model in the device. For example, information related to the updated global model includes values of structures and variables (eg, weights of connections, biases of nodes, etc.) of the updated global model, or differences from previously provided global models (eg, information indicating an added connection, a deleted connection, a change in weight, etc.).

In step S3803, the device performs an inference operation using the global model. The device may estimate the surrounding physical environment using the global model, and may perform necessary functions based on the estimation result.

39 is a diagram illustrating another embodiment of signal exchange for associative learning applicable to the present disclosure. FIG. 39 exemplifies signal exchange between type 1

devices

3912a and 3912b and type 2

devices

3914a and 3914b having different specifications, and a server 3920 .

Referring to FIG. 39 , in steps S3901-1 and S3901-2, each of the

first type devices

3912a and 3912b transmits information related to a local model trained using local data. For example, in order to operate as an autonomous/unmanned device, the first-

class devices

3912a and 3912b acquire sensing data for environment identification, object identification around an object, driving detection and speed measurement, etc. through advanced sensors, and obtain It learns using the sensed data. The parameters (eg, weight values) of the local model updated by learning are transmitted to the server 3920 . Although not shown in FIG. 39 , prior to steps S3901-1 and S3901-2, the server 3920 may transmit information related to the global model to the

first type devices

3912a and 3912b.

In step S3903, the server 3520 updates the global model using the received information. In this case, an evaluation of the cost function may be performed, and the update of the global model may be repeatedly performed until the results are converged. For example, the server 3520 may determine ^{the weight w BS} of the updated global model by averaging the _{weights w 1} to w _u received from the first-

type devices

3912a and 3912b .

In steps S3905-1 to S3905-4, the server 3920 transmits information related to the updated global model to the first-

type devices

3912a and 3912b and the second-

type devices

3914a and 3914b. For example, the information related to the updated global model may include the updated weight w ^BS . That is, the server 3920 provides converged result values not only to the high-grade

type I devices

3912a and 3912b but also to the low-grade type 2

devices

3914a and 3914b.

Providing the result value converged through learning not only to the first-type devices but also to the second-type devices may provide an effect of resolving information imbalance. However, if the results derived from the local models of the first-type devices are directly applied to the operation of the second-type devices, performance degradation may occur due to hardware restrictions (eg, restrictions on computing power). Accordingly, it may be necessary to simplify the global model to a level that a second-class device can process, by performing model compression on the data so that inference is not a problem.

40 is a diagram illustrating an embodiment of a procedure for providing a global model from a server applicable to the present disclosure to a second type device. 40 illustrates an operating method of a server. The operating subject of the procedure illustrated in FIG. 40 is described as a 'server', where the server may be an independent entity or may be understood as a part of a base station.

Referring to FIG. 40 , in step S4001, the server checks the hardware characteristics of the second type device. The hardware characteristics identified relate to the resources or capabilities used for inference operations using the global model. For example, the server may include the amount of memory available for the reasoning operation in the second type device, the processing speed of the computing device, the amount of available power, and the like. To this end, the server retains hardware characteristic information according to the device type in advance, and may check the hardware characteristic of the second type device by using the device type information obtained when the second type device is connected.

In step S4003, the server processes the global model based on the hardware characteristics. The server compresses the global model based on the hardware characteristics of the second type device. In other words, the server may simplify the global model determined by the learning performed in the first type device to be used in the second type device. According to an embodiment, the server performs pruning of at least one connection (eg, a connection whose weight is less than or equal to a threshold) among connections included in the determined global model, clustering of weights, and quantization of weights using a codebook By performing one operation of quantizing and encoding of weights, or an operation in which two or more are combined, the global model can be processed.

In step S4005, the server transmits information related to the processed global model. The information related to the processed global model includes information for building a global model in the device. For example, information related to the processed global model includes values of structures and variables (eg, weights of connections, biases of nodes, etc.) of the processed global model, or differences from previously provided global models (eg, information indicating an added connection, a deleted connection, a change in weight, etc.).

41 is a diagram illustrating another embodiment of signal exchange for associative learning applicable to the present disclosure. FIG. 41 exemplifies signal exchange between type 1

devices

4112a and 4112b and type 2

devices

4114a and 4114b having different specifications, and a server 4120 .

Referring to FIG. 41 , in steps S4101-1 and S4101-2, each of the

first type devices

4112a and 4112b transmits information related to a local model trained using local data. For example, in order to operate as an autonomous/unmanned device, the first-

class devices

4112a and 4112b acquire sensing data for environment identification, object identification around an object, driving detection and speed measurement, etc. through advanced sensors, and obtain It learns using the sensed data. The parameters (eg, weight values) of the local model updated by learning are transmitted to the server 4120 . Although not shown in FIG. 41 , prior to steps S4101-1 and S4101-2, the server 4120 may transmit information related to the global model to the first-

type devices

4112a and 4112b.

In step S4103, the server 3520 updates the global model using the received information. In this case, evaluation of the global model may be performed, and the update of the global model may be repeatedly performed until the results are converged. For example, the server 3520 may determine ^{the weight w BS} _priemium of the updated global model by averaging the _{weights w 1} to w _u received from the

first type devices

4112a and 4112b .

In steps S4105-1 and S4105-2, the server 4120 transmits information related to the updated global model to the

first type devices

4112a and 4112b. For example, the information related to the updated global model may include the updated weight w ^BS _priemium . The first-

type devices

4112a and 4112b may ^{build a global model using the weight w BS} _priemium and perform an inference operation using the built global model.

In step S4107, the server 4120 may process the updated global model. That is, the server 4120 may process the weight w ^BS _priemium ^{of the updated global model into a weight w BS} _{Low_tier} for devices of low grade. To this end, the server 4120 may remove at least one connection from the updated global model, quantize at least one weight, or encode at least one weight.

In steps S4109-1 and S4109-2, the server 4120 transmits information related to the processed global model to the

second type devices

4114a and 4114b. For example, information related to the processed global model may include a ^{weight w BS} _{Low_tier.} The

second type devices

4114a and 4114b may ^{build a global model using the weight w BS} _{Low_tier} and perform an inference operation using the built global model.

Since examples of the above-described proposed method may also be included as one of the implementation methods of the present disclosure, it is clear that they may be regarded as a kind of proposed method. In addition, the above-described proposed methods may be implemented independently, but may also be implemented in the form of a combination (or merge) of some of the proposed methods. Rules may be defined so that the base station informs the terminal of whether the proposed methods are applied or not (or information on the rules of the proposed methods) through a predefined signal (eg, a physical layer signal or a higher layer signal) to the terminal. .

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 restrictive in all respects but as exemplary. The scope of the present disclosure should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present disclosure are included in the scope of the present disclosure. In addition, claims that are not explicitly cited 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 radio access systems, there is a 3rd Generation Partnership Project (3GPP) or a 3GPP2 system.

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

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

Claims

A method of operating a server in a wireless communication system, the method comprising:

transmitting information related to the global model to at least one first-class device among the devices;

receiving information related to a result of learning on a local model determined from the global model from at least one of the devices of the first type;

updating the global model based on information related to the learning result; and

and transmitting information related to the updated global model to the at least one type 1 device and the at least one type 2 device.
The method according to claim 1,

Transmitting information related to the updated global model comprises:

processing the updated global model in consideration of hardware characteristics of the at least one second type device; and

and transmitting information on the processed global model to the at least one second type device.
3. The method according to claim 2,

The updated global model includes pruning of at least one connection among connections included in the updated global model, clustering of weights, quantizing weights using a codebook, encoding of weights ( encoding).
3. The method according to claim 2,

The processing of the updated global model comprises:

reducing the complexity of the updated global model or performing model compression.
The method according to claim 1,

The devices are devices that perform autonomous driving, and recognize a surrounding physical environment using at least one provided sensor and the updated global model.
The method according to claim 1,

The method in which the first-class device has the ability to collect data for the learning by using a sensor that is not used in the second-type device.
7. The method of claim 6,

The sensor not used in the second type device includes a light detection and ranging (LiDAR).
A method of operating a device in a wireless communication system, the method comprising:

Receiving information related to the global model from the server;

performing learning on the local model determined from the global using data collected through a plurality of sensors;

transmitting information related to the learning result to the server;

Receiving information related to the global model updated based on the information related to the result of the learning from the server,

The information related to the updated global model is provided to the other type of device for an inference operation in the other type of device that does not use at least one of the plurality of sensors.
9. The method of claim 8,

The device is a device that performs autonomous driving, and recognizes a surrounding physical environment using the plurality of sensors and the updated global model.
9. The method of claim 8,

wherein the device has the ability to collect data for the learning using a sensor that is not used in the other type of device.
11. The method of claim 10,

The sensor not used in the other type of device includes a light detection and ranging (LiDAR).
9. The method of claim 8,

The step of performing learning on the local model is,

acquiring first sensing data through at least one first sensor used in the other type of device among the plurality of sensors;

acquiring second sensing data through at least one second sensor that is not used in the other type of device among the plurality of sensors; and

and performing learning by using the first sensed data and the second sensed data as inputs.
9. The method of claim 8,

The step of performing learning on the local model is,

acquiring first sensing data through at least one first sensor used in the other type of device among the plurality of sensors;

acquiring second sensing data through at least one second sensor that is not used in the other type of device among the plurality of sensors;

composing labeled data by pairing an estimated result using the second sensed data and the first sensed data; and

and performing learning using the labeled data.
9. The method of claim 8,

The step of performing learning on the local model is,

acquiring first sensing data through at least one first sensor used in the other type of device among the plurality of sensors;

performing primary learning using the first sensing data;

acquiring second sensing data through at least one second sensor that is not used in the other type of device among the plurality of sensors;

obtaining data for secondary learning by comparing a result inferred using the primary learned local model and a result estimated using the second sensing data; and

and performing secondary learning on the local model using data for secondary learning.
9. The method of claim 8,

The information on the updated global model includes information indicating a difference from the previously received global model before the update.