KR20230068411A - Method and apparatus for recommending products in offline stores based on federated learning - Google Patents

Abstract

The present disclosure discloses a method of operating a client and a server in a store management system and an apparatus supporting the same. As an example of the present disclosure, receiving information of a global model of a product recommendation system from a server; generating a local model of the client based on the global model; determining placement of a plurality of products among products handled in the offline store in the offline store based on the local model; updating the local model based on preferences and non-preferences of each of the plurality of products; and transmitting updated information of the local model to the server.

Description

Method and apparatus for recommending products in offline stores based on federated learning

The following description relates to a method and apparatus for recommending a product in an offline store based on federated learning.

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

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

The present disclosure relates to a method and apparatus for recommending a product in an offline store based on federated learning.

The present disclosure relates to a method and apparatus for recommending products in each offline store based on a model obtained through federated learning using product preferences.

The technical objects to be achieved in the present disclosure are not limited to the above-mentioned matters, and other technical problems not mentioned above are common knowledge in the art 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 who have

As an example of the present disclosure, a method of operating a client in a store management system includes receiving information of a global model of a product recommendation system from a server; generating a local model of the client based on the global model; determining placement of a plurality of products among products handled in the offline store in the offline store based on the local model; updating the local model based on preferences and non-preferences of each of the plurality of products; and transmitting updated information of the local model to the server.

As an example of the present disclosure, the information of the global model may include a first coefficient indicating a preference of each of the handled products; and a second coefficient indicating a non-preference of each of the products to be handled.

As an example of the present disclosure, the step of determining the arrangement of the plurality of products in the offline store generates a beta distribution of each of the handled products based on the first coefficient and the second coefficient of each of the handled products. doing; randomly sampling a random variable based on a beta distribution of each of the handled products; and determining the plurality of products according to a size order of the random variable value of each of the handled products.

As an example of the present disclosure, determining the placement of the plurality of products in the offline store may include, prior to generating a beta distribution of each of the handled products, a second coefficient for a first coefficient among the handled products. It may include excluding at least some products whose ratio of coefficients exceeds a preset value.

As an example of the present disclosure, before generating a beta distribution of each of the handled products, excluding at least some products that do not exist in the offline store among the handled products may be included.

As an example of the present disclosure, preference and non-preference of each of the plurality of products may be measured using at least one sensor connected to the client.

As an example of the present disclosure, a method of operating a server in a store management system includes transmitting information of a global model of a product recommendation system to clients of each of offline stores; receiving updated information of a local model determined from the global model from a client of each of the offline stores; and updating the global model based on the information of the updated local model, wherein the global model may be a model based on a customer's preference for products handled in the offline stores.

As an example of the present disclosure, the information of the global model may include a first coefficient indicating a preference of each of the handled products; and a second coefficient indicating a non-preference of each of the products to be handled.

As an example of the present disclosure, the information related to the result of learning the local model includes preference and non-preference information of each of a plurality of products disposed in each of the offline stores, among products handled in the offline stores. can do.

As an example of the present disclosure, the plurality of products may be determined based on randomly sampled random variable values in a beta distribution generated based on first coefficients and second coefficients of each of the handled products.

As an example of the present disclosure, preference and non-preference information of each of the plurality of products may be measured by at least one sensor connected to the client.

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

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

According to the present disclosure, a product recommendation system for an offline store may provide effects of promoting sales and improving inventory management efficiency in the offline store.

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

The accompanying drawings are provided to aid understanding of the present disclosure, and may provide embodiments of the present disclosure together with a detailed description. However, the technical features of the present disclosure are not limited to specific drawings, and features disclosed in each drawing may be combined with each other to form a new embodiment. Reference numerals in each drawing may mean structural elements.
1 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 portable device applied to the present disclosure.
5 is a diagram illustrating an example of a vehicle or autonomous vehicle to which the present disclosure applies.
6 is a diagram showing an example of a moving body applied to the present disclosure.
7 is a diagram showing an example of an XR device applied to the present disclosure.
8 is a diagram showing 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 them.
11 is a diagram illustrating structures of a control plane and a user plane 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 showing 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 THz signal generation method 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 transmitter structure applicable to the present disclosure.
22 is a diagram illustrating a modulator structure applicable to the present disclosure.
23 is a diagram showing the structure of a perceptron included in an artificial neural network applicable to the present disclosure.
24 is a diagram showing 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 circular loop applicable to the present disclosure exists.
29 is a diagram illustrating an operating 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 federated learning applicable to the present disclosure.
32 is a diagram illustrating an embodiment of a product recommendation system of an offline store applicable to the present disclosure.
33 is a diagram illustrating an embodiment of signal exchange for associative learning applicable to the present disclosure. 34 is a diagram illustrating an embodiment of a method of updating a product recommendation model of a server applicable to the present disclosure.
35 is a diagram illustrating an embodiment of a method of updating a local model based on a product recommendation result of a client applicable to the present disclosure.
36 is a diagram illustrating an embodiment of an operation method of a server and a client of a product recommendation system for an offline store applicable to the present disclosure.
37 is a diagram illustrating an embodiment of a product recommendation method of a product recommendation system of an offline store applicable to the present disclosure.
38 is a diagram illustrating a product recommendation model learning algorithm of a client in a product recommendation system of an offline store applicable to the present disclosure.
39 is a diagram illustrating operations of a product recommendation model learning algorithm of a client in a product recommendation system of an offline store applicable to the present disclosure.

The following embodiments are those that 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 not combined with other components or features. In addition, an embodiment of the present disclosure may be configured by combining some elements and/or features. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Communication systems applicable to the present disclosure

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

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

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

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

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

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

Wireless devices applicable to the present disclosure

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

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

The first wireless device 200a includes one or more processors 202a and one or more memories 204a, and may further include one or more transceivers 206a and/or one or more antennas 208a. The processor 202a controls the memory 204a and/or the transceiver 206a and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flow diagrams disclosed herein. For example, the processor 202a may process information in the memory 204a to generate first information/signal, and transmit a radio signal including the first information/signal through the transceiver 206a. In addition, the processor 202a may receive a radio signal including the second information/signal through the transceiver 206a and store information obtained from signal processing of the second information/signal in the memory 204a. The memory 204a may be connected to the processor 202a and may store various information related to the operation of the processor 202a. For example, memory 204a may perform some or all of the processes controlled by processor 202a, or instructions for performing the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. It may store software codes including them. Here, the processor 202a and the memory 204a may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR). The transceiver 206a may be coupled to the processor 202a and may transmit and/or receive wireless signals through one or more antennas 208a. The transceiver 206a may include a transmitter and/or a receiver. The transceiver 206a may be used interchangeably with a radio frequency (RF) unit. In the present disclosure, a wireless device may mean a communication modem/circuit/chip.

The second wireless device 200b includes one or more processors 202b, one or more memories 204b, and may further include one or more transceivers 206b and/or one or more antennas 208b. The processor 202b controls the memory 204b and/or the transceiver 206b and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flow diagrams disclosed herein. For example, the processor 202b may process information in the memory 204b to generate third information/signal, and transmit a radio signal including the third information/signal through the transceiver 206b. In addition, the processor 202b may receive a radio signal including the fourth information/signal through the transceiver 206b and store information obtained from signal processing of the fourth information/signal in the memory 204b. The memory 204b may be connected to the processor 202b and may store various information related to the operation of the processor 202b. For example, the memory 204b may perform some or all of the processes controlled by the processor 202b, or instructions for performing the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. It may store software codes including them. Here, the processor 202b and the memory 204b may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR). The transceiver 206b may be coupled to the processor 202b and may transmit and/or receive wireless signals through one or more antennas 208b. The transceiver 206b may include a transmitter and/or a receiver. The transceiver 206b may be used interchangeably with an RF unit. In the present disclosure, a wireless device may mean a communication modem/circuit/chip.

Here, the wireless communication technology implemented in the wireless devices 200a and 200b of the present specification may include Narrowband Internet of Things for low power communication as well as LTE, NR, and 6G. At this time, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology, and may be implemented in standards such as LTE Cat NB1 and / or LTE Cat NB2. no. Additionally or alternatively, the wireless communication technology implemented in the wireless devices 200a and 200b of the present specification may perform communication based on LTE-M technology. At this time, as an example, LTE-M technology may be an example of LPWAN technology, and may be called various names such as eMTC (enhanced machine type communication). For example, LTE-M technologies are 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and/or 7) It may be implemented in at least one of various standards such as LTE M, and is not limited to the above-mentioned names. Additionally or alternatively, the wireless communication technology implemented in the wireless devices 200a and 200b of the present specification includes at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) considering low power communication. It may include any one, and is not limited to the above-mentioned names. For example, ZigBee technology can generate personal area networks (PANs) related to small/low-power digital communication based on various standards such as IEEE 802.15.4, and can be called various names.

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

One or more processors 202a, 202b may be referred to as a controller, microcontroller, microprocessor or microcomputer. One or more processors 202a, 202b may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more application specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more digital signal processing devices (DSPDs), one or more programmable logic devices (PLDs), or one or more field programmable gate arrays (FPGAs). may be included in one or more processors 202a and 202b. The descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, and the like. Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods and/or operational flow charts disclosed in this document may be included in one or more processors 202a or 202b or stored in one or more memories 204a or 204b. It can be driven by the above processors 202a and 202b. The descriptions, functions, procedures, suggestions, methods and/or operational flow charts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and/or sets of instructions.

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

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

Wireless device structure applicable to the present disclosure

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

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

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

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

Mobile device to which the present disclosure is applicable

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

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

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

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

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

Types of wireless devices to which this disclosure is applicable

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

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

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

The communication unit 510 may transmit/receive signals (eg, data, control signals, etc.) with external devices such as other vehicles, base stations (eg, base stations, roadside base units, etc.), servers, and the like. The controller 520 may perform various operations by controlling elements of the vehicle or autonomous vehicle 500 . The controller 520 may include an electronic control unit (ECU). The driving unit 540a may drive the vehicle or autonomous vehicle 500 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 autonomous vehicle 500, and may include a wired/wireless charging circuit, a battery, and the like. The sensor unit 540c may obtain vehicle conditions, surrounding environment information, and user information. 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 detection sensor, a heading sensor, a position module, and a vehicle forward. /Can include a reverse sensor, battery sensor, fuel sensor, tire sensor, steering sensor, temperature sensor, humidity sensor, ultrasonic sensor, illuminance sensor, pedal position sensor, and the like. The autonomous driving unit 540d includes a technology for maintaining the driving lane, a technology for automatically adjusting the 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 and driving. 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 control unit 520 may control the driving unit 540a so that the vehicle or autonomous vehicle 500 moves along the autonomous driving route according to the driving plan (eg, speed/direction adjustment). During autonomous driving, the communication unit 510 may non/periodically obtain the latest traffic information data from an external server and obtain surrounding traffic information data from surrounding vehicles. In addition, during autonomous driving, the sensor unit 540c may obtain vehicle state and surrounding environment information. The autonomous driving unit 540d may update an autonomous driving route and a driving plan based on 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 based on information collected from the vehicle or self-driving vehicles, and may provide the predicted traffic information data to the vehicle or self-driving vehicles.

6 is a diagram showing an example of a moving body applied to the present disclosure.

Referring to FIG. 6 , a mobile body applied to the present disclosure may be implemented as at least one of a vehicle, a train, an air vehicle, and a ship. In addition, the mobile 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 body 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 respectively correspond to blocks 310 to 330/340 of FIG. 3 .

The communication unit 610 may transmit/receive signals (eg, data, control signals, etc.) with other mobile bodies or external devices such as base stations. The controller 620 may perform various operations by controlling components of the moving body 600 . The memory unit 630 may store data/parameters/programs/codes/commands supporting various functions of the moving object 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 location measurement unit 640b may obtain location 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 surrounding vehicles. The location measurement unit 640b may include GPS and various sensors.

For example, the communication unit 610 of the moving object 600 may receive map information, traffic information, and the like from an external server and store them in the memory unit 630 . The location measuring unit 640b may acquire moving object location information 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, location information of the moving object, and the like, and the input/output unit 640a may display the created virtual object on a window in the moving object (651, 652). In addition, the controller 620 may determine whether the moving object 600 is normally operated within the traveling line based on the moving object location information. When the moving object 600 abnormally departs from the driving line, the control unit 620 may display a warning on a window in the moving object through the input/output unit 640a. In addition, the control unit 620 may broadcast a warning message about driving abnormality to nearby moving objects through the communication unit 610 . Depending on the circumstances, the controller 620 may transmit location information of the moving object and information about driving/moving object abnormality to related organizations through the communication unit 610 .

7 is a diagram showing 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 smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like.

Referring to FIG. 7 , an 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 respectively correspond to blocks 310 to 330/340 of FIG. 3 .

The communication unit 710 may transmit/receive signals (eg, media data, control signals, etc.) with external devices such as other wireless devices, portable devices, or media servers. Media data may include video, image, sound, and the like. The controller 720 may perform various operations by controlling 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 output the created 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 XR device status, 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 a / or radar, etc. may be included. 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 a user, and the controller 720 may drive the XR device 700a according to the user's driving command. For example, when a user tries to watch a movie, news, etc. through the XR device 700a, the controller 720 transmits 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 content such as movies and news from other devices (eg, the mobile device 700b) or a media server to the memory unit 730 . The control unit 720 controls and/or performs procedures such as video/image acquisition, (video/image) encoding, metadata generation/processing, etc. for the content, and acquires the content through the input/output unit 740a/sensor unit 740b. An XR object may be created/output based on information about a surrounding space or a real object.

In addition, the XR device 700a is wirelessly connected to the mobile device 700b through the communication unit 710, and the operation of the XR device 700a may be controlled by the mobile device 700b. For example, the mobile device 700b may act 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 showing an example of a robot applied to the present disclosure. For example, robots may be classified into industrial, medical, household, military, and the like according to the purpose or field of use. At this time, 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 respectively correspond to blocks 310 to 330/340 of FIG. 3 .

The communication unit 810 may transmit/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 perform various operations by controlling components of the robot 800 . 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 output the 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 of the robot 800, surrounding environment information, user information, and the like. The sensor unit 840b may include a proximity sensor, an illuminance 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. In addition, the driving unit 840c may make the robot 800 drive on the ground or fly in the air. The driving unit 840c may include actuators, motors, wheels, brakes, propellers, and the like.

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

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

The communication unit 910 communicates wired and wireless signals (eg, sensor information, user data) with external devices such as other AI devices (eg, FIG. 1, 100x, 120, and 140) or AI servers (Fig. input, learning model, control signal, etc.) can be transmitted and received. To this end, the communication unit 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. And, the controller 920 may perform the determined operation by controlling components of the AI device 900 . For example, the controller 920 may request, retrieve, receive, or utilize data from the learning processor unit 940c or the memory unit 930, and may perform a predicted operation among at least one feasible operation or an operation determined to be desirable. Components of the AI device 900 may be controlled to execute an 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 running processor unit 940c, or the AI server ( 1, 140) can be transmitted to an external device. The collected history information can be used to update the learning model.

The memory unit 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 from 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 required for operation/execution of the controller 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 learning data for model learning and input data to which the learning model is to be applied. 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 illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and/or a radar. there is.

The learning processor unit 940c may learn a model composed of an artificial neural network using learning data. The running processor unit 940c may perform AI processing together with the running processor unit of the AI server (FIG. 1, 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 . In addition, 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.

Physical channels and general signal transmission

In a wireless access system, a terminal may receive information from a base station through downlink (DL) and transmit information to the 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 there are various physical channels according to the type/use of the information transmitted and received by the base station and the terminal.

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

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

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

Thereafter, the terminal may perform a random access procedure such as steps S1013 to S1016 in order 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 physical downlink shared channel corresponding thereto (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 performs a contention resolution procedure such as receiving a physical downlink control channel signal and a corresponding physical downlink shared channel signal (S1015). ) 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 as a general uplink/downlink signal transmission procedure (S1017) and physical uplink shared channel (physical uplink shared channel). channel, PUSCH) signal and/or physical uplink control channel (PUCCH) signal may be transmitted (S1018).

Control information transmitted from 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, UCI is generally transmitted periodically through PUCCH, but may be transmitted through PUSCH according to an embodiment (eg, when control information and traffic data are to be simultaneously transmitted). In addition, the UE may aperiodically transmit UCI through the PUSCH according to a request/instruction of the network.

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

Referring to FIG. 11 , Entity 1 may be a user equipment (UE). In this case, the terminal may be at least one of a wireless device, a portable device, a vehicle, a mobile device, an XR device, a robot, and an AI to which the present disclosure is applied in FIGS. 1 to 9 described above. In addition, a 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 eNB, gNB, and ng-eNB. Also, a base station may refer to a device that transmits a downlink signal to a terminal, and may not be limited to a specific type or device. 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 that performs 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. Also, the network function may refer to a function implemented to perform the network function, and entity 3 may be a device to which the function is applied. That is, 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. Also, the user plane may refer to 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 an upper layer using a physical channel. The physical layer is connected to the upper medium access control layer through a transport channel. At this time, data may move between the medium access control layer and the physical layer through the transport channel. Data may move between physical layers of a transmitting side and a receiving side through a physical channel. At this time, the physical channel uses time and frequency as radio resources.

A medium access control (MAC) layer of the second layer provides services 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. A 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 control of logical channels, transport channels, and physical channels in relation to configuration, re-configuration, and release of radio bearers (RBs). An RB may refer to a service provided by the second layer for data transmission between a terminal and a network. To this end, the RRC layer of the terminal and 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 several terminals. Different cells may be configured to provide different bandwidths. Downlink transport channels for transmitting data from the network to the terminal include a broadcast channel (BCH) for transmitting system information, a paging channel (PCH) for transmitting paging messages, and a shared channel (SCH) for transmitting user traffic or control messages. there is. Traffic or control messages of a downlink multicast or broadcast service may be transmitted through a downlink SCH or may be transmitted through a separate downlink multicast channel (MCH). Meanwhile, uplink transport channels for transmitting data from a terminal to a network include a random access channel (RACH) for transmitting an initial control message and an uplink shared channel (SCH) for transmitting user traffic or control messages. Logical channels located above the transport channel and mapped to the transport channel include a broadcast control channel (BCCH), a paging control channel (PCCH), a common control channel (CCCH), a multicast control channel (MCCH), and a multicast control channel (MTCH). traffic channels), etc.

12 is a diagram illustrating a method of processing a transmission signal applied to the present disclosure. For example, the transmitted signal may be processed by a signal processing circuit. In this case, the signal processing circuit 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. At this time, 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 . Also, 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 are not limited to the above-described embodiment.

The codeword may be converted into a radio signal through the signal processing circuit 1200 of FIG. 12 . Here, a codeword is an encoded bit sequence of an information block. Information blocks may include transport blocks (eg, UL-SCH transport blocks, DL-SCH transport blocks). 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. The scrambled bit sequence may be modulated into a modulation symbol sequence by modulator 1220. The modulation method may include pi/2-binary phase shift keying (pi/2-BPSK), m-phase shift keying (m-PSK), m-quadrature amplitude modulation (m-QAM), and the like.

The complex modulation symbol sequence may be mapped to one or more transport layers by the layer mapper 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 can be obtained by multiplying the output y of the layer mapper 1230 by the N*M precoding matrix W. Here, N is the number of antenna ports and M is the number of transport layers. Here, the precoder 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 time-frequency resources. The time-frequency resource may include a plurality of symbols (eg, CP-OFDMA symbols and DFT-s-OFDMA symbols) in the time domain and a plurality of subcarriers in the frequency domain. The signal generator 1260 generates a radio signal from the mapped modulation symbols, and the generated radio signal can be transmitted to other devices through each antenna. To this end, the signal generator 1260 may include an inverse fast fourier transform (IFFT) module, a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, and the like. .

The signal processing process for the received signal in the wireless device may be configured in reverse to the signal processing process 1210 to 1260 of FIG. 12 . For example, a wireless device (eg, 200a and 200b of FIG. 2 ) may receive a wireless signal from the outside through an antenna port/transceiver. The received radio signal may be converted into a baseband signal through a signal restorer. To this end, the signal restorer may include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast fourier transform (FFT) module. Thereafter, the baseband signal may be restored to a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a de-scramble process. The codeword may be restored to an original information block through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource demapper, a postcoder, a demodulator, a descrambler, and a decoder.

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

Uplink and downlink transmission based on the NR system may be based on the frame 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 (half-frame, HF). One half-frame may be defined as five 1ms subframes (subframes, SFs). 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 is used, each slot may include 14 symbols. When an extended CP is used, each slot may include 12 symbols. Here, the symbol may include an OFDM symbol (or CP-OFDM symbol) and an SC-FDMA symbol (or 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 SCS when a normal CP is used, and Table 2 shows the number of slots according to SCS when an extended CSP is used. Indicates the number of symbols, the number of slots per frame, and the number of slots per subframe.

0 14 10 One One 14 20 2 2 14 40 4 3 14 80 8 4 14 160 16 5 14 320 32

2 12 40 4

In Tables 1 and 2, N ^slot _symb represents the number of symbols in a slot, N ^{frame, μ} _slot represents the number of slots in a frame, and N ^{subframe, μ} _slot represents 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 among a plurality of cells merged into one UE. Accordingly, (absolute time) intervals of time resources (e.g., SFs, slots, or TTIs) (for convenience, collectively referred to as time units (TUs)) composed of the same number of symbols may be set differently between merged cells.

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

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

Frequency range designation Corresponding frequency range Subcarrier Spacing FR1 410MHz - 7125MHz 15, 30, 60 kHz FR2 24250MHz - 52600MHz 60, 120, 240 kHz

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

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

One slot includes a plurality of symbols in the time domain. For example, in the case of a normal CP, one slot includes 7 symbols, but in the case of an extended CP, one slot may include 6 symbols. A carrier includes a plurality of subcarriers 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 up to N (eg, 5) BWPs. Data communication is performed through an 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 communication system

6G (radio communications) systems are characterized by (i) very high data rates per device, (ii) very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) battery- It aims to lower energy consumption of battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be four aspects such as "intelligent connectivity", "deep connectivity", "holographic connectivity", and "ubiquitous connectivity", and the 6G system can satisfy the requirements shown in Table 4 below. That is, Table 4 is a table showing the requirements of the 6G system.

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

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

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

Referring to FIG. 15 , a 6G system is expected to have 50 times higher simultaneous wireless communication connectivity than a 5G wireless communication system. URLLC, a key feature of 5G, is expected to become a more mainstream technology by providing end-to-end latency of less than 1 ms in 6G communications. At this time, the 6G system will have much better volume spectral efficiency, unlike the frequently used area spectral efficiency. 6G systems can provide very long battery life and advanced battery technology for energy harvesting, so mobile devices in 6G systems may not need to be charged separately. In addition, new network characteristics in 6G may be as follows.

- Satellites integrated network: 6G is expected to be integrated with satellites to serve the global mobile population. Integration of terrestrial, satellite and public networks into one wireless communications system could be critical for 6G.

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

- Seamless integration wireless information and energy transfer: 6G wireless networks will transfer 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 capabilities of drones and very low Earth orbit satellites will make super 3-D connectivity in 6G ubiquitous.

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

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

- Ultra-dense heterogeneous networks: Ultra-dense heterogeneous networks will be another important feature of 6G communication systems. Multi-tier networks composed of heterogeneous networks improve overall QoS and reduce costs.

- High-capacity backhaul: A backhaul connection is characterized by a high-capacity backhaul network to support high-capacity traffic. High-speed fiber and free space optical (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 features of 6G wireless communication systems. Thus, radar systems will be integrated with 6G networks.

- Softwarization and virtualization: Softwarization and virtualization are two important features fundamental to 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.

Core implementation technology of 6G system

- Artificial Intelligence (AI)

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

Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly by using AI. AI can also play an important role in M2M, machine-to-human and human-to-machine communications. In addition, AI can be a rapid communication in the brain computer interface (BCI). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

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

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

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

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

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

Hereinafter, machine learning will be described in more detail.

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

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

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

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

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

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

Terahertz (THz) communication

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

16 is a diagram showing an electromagnetic spectrum applicable to the present disclosure. As an example, referring to FIG. 16 , THz waves, also known as sub-millimeter radiation, generally represent a frequency band between 0.1 THz and 10 THz with corresponding wavelengths in the range of 0.03 mm-3 mm. The 100 GHz-300 GHz band range (sub THz band) is considered a major part of the THz band for cellular communications. Adding to the sub-THz band mmWave band will increase 6G cellular communications capacity. Among the defined THz bands, 300 GHz-3 THz is in the far infrared (IR) frequency band. The 300 GHz-3 THz band is part of the broad band, but is at the border of the wide band, just behind the RF band. Thus, this 300 GHz-3 THz band exhibits similarities to RF.

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

optical wireless technology

Optical wireless communication (OWC) technology is envisioned for 6G communications in addition to RF-based communications for all possible device-to-access networks. These networks access network-to-backhaul/fronthaul network connections. OWC technology is already in use after the 4G communication system, but will be more widely used 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 wide band are already well-known technologies. Communications based on optical wireless technology can provide very high data rates, low latency and secure communications. Light detection and ranging (LiDAR) can also be used for super-resolution 3D mapping in 6G communications based on broadband.

FSO backhaul network

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

Massive 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, massive MIMO technology will be important in 6G systems. Since MIMO technology uses multiple paths, multiplexing technology and beam generation and operation technology suitable for the THz band must be considered as important so that data signals can be transmitted through more than one path.

block chain

Blockchain will be an important technology for managing large amounts of data in future communication systems. Blockchain is a form of distributed ledger technology, where a distributed ledger is a database that is distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger. Blockchain is managed as a peer to peer (P2P) network. It can exist without being managed by a centralized authority or server. Data on a blockchain is collected together and organized into blocks. Blocks are linked together and protected using cryptography. Blockchain is the perfect complement to the IoT at scale with inherently improved interoperability, security, privacy, reliability and scalability. Thus, blockchain technology provides multiple capabilities such as interoperability between devices, traceability of large amounts of data, autonomous interaction of other IoT systems, and large-scale connection reliability in 6G communication systems.

3D Networking

The 6G system integrates terrestrial and air 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 height and related degrees of freedom makes 3D connections quite different from traditional 2D networks.

quantum communication

In the context of 6G networks, unsupervised reinforcement learning of networks is promising. Supervised learning approaches cannot label the vast amount of data generated by 6G. Labeling is not required in unsupervised learning. 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.

drone

Unmanned aerial vehicles (UAVs) or drones will be an important element in 6G wireless communications. In most cases, high-speed data wireless connectivity is provided using UAV technology. Base station entities are installed on UAVs 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, deployment of terrestrial communications infrastructure is not economically feasible and cannot provide services in sometimes volatile environments. UAVs can easily handle this situation. UAV will become a new paradigm in the field of wireless communication. This technology facilitates three basic requirements of a wireless network: eMBB, URLLC and mMTC. UAVs can also support multiple purposes, such as improving network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, accident monitoring, and more. Therefore, UAV technology is recognized as one of the most important technologies for 6G communication.

Cell-free Communication

The tight integration of multiple frequencies and heterogeneous communication technologies is critical for 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 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, resulting in 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.

Integration of wireless information and energy transfer (WIET)

WIET uses the same fields and waves as wireless communication systems. In particular, sensors and smartphones will be charged using wireless power transfer during communication. WIET is a promising technology for extending the lifetime of battery charging wireless systems. Thus, battery-less devices will be supported in 6G communications.

Integration of sensing and communication

Autonomous radio networks are capable of continuously sensing dynamically changing environmental conditions and exchanging information between different nodes. In 6G, sensing will be tightly integrated with communications to support autonomous systems.

Integration of access backhaul networks

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

Holographic Beamforming

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

big data analytics

Big data analysis 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 preferences. Big data is collected from various sources such as videos, social networks, images and sensors. This technology is widely used to process massive data in 6G systems.

large intelligent surface (LIS)

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

Terahertz (THz) wireless communication

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

Referring to FIG. 17, THz wireless communication uses wireless communication using THz waves having a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and a terahertz (THz) band radio using a very high carrier frequency of 100 GHz or more. can mean communication. THz waves are located between RF (Radio Frequency)/millimeter (mm) and infrared bands, and (i) transmit non-metal/non-polarizable materials better than visible light/infrared rays, and have a shorter wavelength than RF/millimeter waves and have high straightness. Beam focusing may be possible.

In addition, since the photon energy of the THz wave is only a few meV, it is harmless to the human body. A 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 molecular absorption in the air. Standardization discussions on THz wireless communication are being discussed centering on the IEEE 802.15 THz working group (WG) in addition to 3GPP, and standard documents issued by the IEEE 802.15 TG (task group) (e.g., TG3d, TG3e) are described in this specification. It can be specified or supplemented. THz wireless communication may be applied to wireless cognition, sensing, imaging, wireless communication, THz navigation, and the like.

Specifically, referring to FIG. 17 , a THz wireless communication scenario can be classified into a macro network, a micro network, and a nanoscale network. In macro networks, THz wireless communication can be applied to V2V (vehicle-to-vehicle) connections and backhaul/fronthaul connections. In micro networks, THz wireless communication 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. It can be. Table 5 below is a table showing an example of a technique that can be used in THz waves.

Transceivers Device Available immature: UTC-PD, RTD and SBD Modulation and coding Low order modulation techniques (OOK, QPSK), LDPC, Reed Soloman, Hamming, Polar, Turbo Antenna Omni and Directional, phased array with low number of antenna elements Bandwidth 69 GHz (or 23 GHz) at 300 GHz Channel models Partially Data rate 100 Gbps outdoor deployment No Fee space loss High Coverage Low Radio Measurements 300 GHz indoor Device size Few micrometers

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

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

At this time, 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 method using a compound semiconductor HEMT (high electron mobility transistor) based There are a monolithic microwave integrated circuits (MMIC) method using an integrated circuit, a method using an integrated circuit based on Si-CMOS, and the like. In the case of FIG. 18, a doubler, tripler, or multiplier is applied to increase the frequency, and the radiation is emitted by the antenna after passing through the subharmonic mixer. Since the THz band forms high frequencies, a multiplier is essential. Here, the multiplier is a circuit that makes the output frequency N times greater than the input, matches the desired harmonic frequency, and filters out all other frequencies. In addition, beamforming may be implemented by applying an array antenna or the like to the antenna of FIG. 18 . In FIG. 18, IF denotes an intermediate frequency, a tripler and a 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 THz signal generation method applicable to the present disclosure. 20 is a diagram illustrating a wireless communication transceiver applicable to the present disclosure.

Referring to FIGS. 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. An optical element-based THz signal generation technology is a technology that generates an ultra-high speed optical signal using a laser and an optical modulator and converts it into a THz signal using an ultra-high speed photodetector. Compared to a technique using only an electronic device, this technique can easily increase the frequency, generate a high-power signal, and obtain a flat response characteristic in a wide frequency band. As shown in FIG. 19, a laser diode, a broadband light modulator, and a high-speed photodetector are required to generate a THz signal based on an optical device. In the case of FIG. 19 , a THz signal corresponding to a wavelength difference between the lasers is generated by multiplexing light signals of two lasers having different wavelengths. In FIG. 19, an optical coupler refers to a semiconductor device that transmits an electrical signal using light waves in order to provide electrical isolation and coupling between circuits or systems, and UTC-PD (uni-travelling carrier photo- The detector is one of the photodetectors, and is a device that uses electrons as active carriers and reduces the movement time of electrons through bandgap grading. UTC-PD is capable of photodetection above 150 GHz. In FIG. 20, an erbium-doped fiber amplifier (EDFA) represents an optical fiber amplifier to which erbium is added, a photo detector (PD) represents a semiconductor device capable of converting an optical signal into an electrical signal, and an OSA represents various optical communication functions (e.g. , photoelectric conversion, electrical optical conversion, etc.) represents an optical module (optical sub assembly) that is modularized into one component, and DSO represents a digital storage oscilloscope.

21 is a diagram illustrating a transmitter structure applicable to the present disclosure. 22 is a diagram showing 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 a microwave contact or the like. Thus, the optical modulator output is formed into a modulated waveform. A photoelectric modulator (O/E converter) is an optical rectification operation by a nonlinear crystal, an O/E conversion by a photoconductive antenna, and a bundle of electrons in light flux. THz pulses can be generated according to emission from relativistic electrons, etc. A THz pulse generated in the above manner may have a unit length of femto second to pico second. An O/E converter uses non-linearity of a device to perform down conversion.

Considering the THz spectrum usage, it is necessary to use several contiguous GHz bands for fixed or mobile service for terahertz systems. more likely to use According to outdoor scenario criteria, 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 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 to utilize the nonlinearity of the O/E converter. That is, in order to down-convert to the desired terahertz band (THz band), the photoelectric converter (O / E converter) having the most ideal non-linearity to move to the corresponding terahertz band (THz band) 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 will 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. Depending on the channel environment, as many photoelectric converters as the number of carriers may be required in a multi-carrier system. In particular, in the case of a multi-carrier system using several broadbands according to a plan related to the above-mentioned spectrum use, the phenomenon will be conspicuous. In this regard, a frame structure for the multi-carrier system may be considered. A signal down-frequency converted based on the photoelectric converter may be transmitted in a specific resource region (eg, a specific frame). The frequency domain of the specific resource domain may include a plurality of chunks. Each chunk may consist of at least one component carrier (CC).

Artificial Intelligence System

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

As described above, an artificial intelligence system may be applied in a 6G system. At this time, 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 using a neural network structure having a high complexity such as an artificial neural network as a learning model may be referred to as deep learning. In addition, the neural network cord used in the 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. At this time, as an example, referring to FIG. 23, the artificial neural network may be composed of several perceptrons. At this time, the input vector x={x ₁ , x ₂ , … , x _d } are input, the weight {W ₁ , W ₂ , . . . , W _d }, summing up all the results, and then applying the activation function σ(·) can be called a perceptron. The huge artificial neural network structure extends the simplified perceptron structure shown in FIG. 23, and the input vector can be applied to different multi-dimensional 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 can be described as being composed of a total of three layers based on input values and output values. An artificial neural network in which there are H number of (d + 1) dimensional perceptrons between the 1 ^st layer and the 2 ^nd layer and K number of (H + 1) dimensional perceptrons between the 2 ^nd layer and the 3 ^rd layer is represented as shown in FIG. can

At this time, the layer where the input vector is located is called the input layer, the layer where the final output value is located is called the output layer, and all layers located between the input layer and the output layer are called hidden layers. For example, although three layers are disclosed in FIG. 24 , the artificial neural network illustrated in FIG. 24 can be understood as a total of two layers, since the number of actual artificial neural network layers is counted excluding input layers. The artificial neural network is composed of two-dimensionally connected perceptrons of basic blocks.

The above-described input layer, hidden layer, and output layer can be jointly applied to various artificial neural network structures such as CNN and RNN, which will be described later, as well as multi-layer perceptrons. As the number of hidden layers increases, the artificial neural network becomes deeper, and a machine learning paradigm in which a sufficiently deep artificial neural network is used as a learning model may be referred to as deep learning. In addition, an artificial neural network used for deep learning may be referred to as a deep neural network (DNN).

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

Referring to FIG. 25 , the deep neural network may be a multi-layer perceptron composed of 8 hidden layers + 8 output layers. At this time, the multilayer perceptron structure can be expressed as a fully-connected neural network. In a fully-connected neural network, there is no connection relationship between nodes located on the same layer, and a connection relationship may exist only between nodes located on adjacent layers. DNN has a fully-connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, so it can be usefully applied to identify the correlation characteristics between inputs and outputs. Here, the correlation characteristic may mean a joint probability of input and 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, various artificial neural network structures different from the aforementioned DNN can be formed depending on how a plurality of perceptrons are connected to each other. At this time, in the DNN, nodes located inside one layer are arranged in a one-dimensional vertical direction. However, referring to FIG. 26, it can be assumed that the nodes are two-dimensionally arranged with w nodes horizontally and h nodes vertically. (convolutional neural network structure in FIG. 26). In this case, since a weight is added for each connection in the connection process from one input node to the hidden layer, a total of h×w weights should 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, since the convolutional neural network of FIG. 26 has a problem in that the number of weights increases exponentially according to the number of connections, it can be assumed that there is a filter with a small size instead of considering all mode connections between adjacent layers. can For example, as shown in FIG. 27, a weighted sum and an activation function operation may be performed on a portion where filters overlap.

At this time, one filter has weights corresponding to the number of filters, and learning of weights can be performed so that a specific feature on an image can be extracted as a factor and output. In FIG. 27 , a 3×3 filter is applied to a 3×3 area at the top left of the input layer, and an output value obtained by performing a weighted sum and an activation function operation on a corresponding node may be stored in z ₂₂ .

At this time, the above-described filter is moved by a certain distance horizontally and vertically while scanning the input layer, and the weighted sum and activation function calculations are performed, and the output value can be placed at the position of the current filter. Since this operation method is similar to the convolution operation for images in the field of computer vision, the deep neural network of this structure is called a convolutional neural network (CNN), and the result of the convolution operation The hidden layer may be called a convolutional layer. In addition, a neural network including a plurality of convolutional layers may be referred to as a deep convolutional neural network (DCNN).

Also, in the convolution layer, the number of weights may be reduced by calculating a weighted sum including only nodes located in a region covered by the filter in the node where the current filter is located. This allows one filter to be used to focus on features for a local area. Accordingly, CNN can be effectively applied to image data processing in which a physical distance in a 2D area is an important criterion. Meanwhile, in the CNN, a plurality of filters may be applied immediately before the convolution layer, and a plurality of output results may be generated through a convolution operation of each filter.

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

28 is a diagram illustrating a neural network structure in which a circular loop applicable to the present disclosure exists. 29 is a diagram illustrating an operating 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) } into the fully connected neural network, the immediately preceding point in time t-1 is the hidden vector {z ₁ ^(t-1) , z ₂ ^(t-1) , . . . , z _H ^(t-1) } together to apply a weighted sum and an activation function. The reason why the hidden vector is transmitted to the next time point in this way is that information in the input vector at previous time points is regarded as being accumulated in the hidden vector of the current time point.

Also, referring to FIG. 29 , the recurrent neural network may operate in a predetermined sequence of views with respect to an input data sequence. At this time, the input vector at time point 1 {x ₁ ^(t) , x ₂ ^(t) , . . . , x _d ^(t) } is input to the recurrent neural network {z ₁ ⁽¹⁾ , z ₂ ⁽¹⁾ , . . . , z _H ⁽¹⁾ } is the input vector {x ₁ ⁽²⁾ , x ₂ ⁽²⁾ , . , x _d ⁽²⁾ }, the vector {z ₁ ⁽²⁾ , z ₂ ⁽²⁾ , . . . of the hidden layer through the weighted sum and activation function , z _H ⁽²⁾ } is determined. This process is at time point 2, time point 3, . . . , iteratively performed until time point T.

Meanwhile, when a plurality of hidden layers are arranged in a recurrent neural network, it is referred to as a deep recurrent neural network (DRNN). Recurrent neural networks are 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), and deep Q-Network It includes various deep learning techniques such as computer vision, voice recognition, natural language processing, and voice/signal processing.

Recently, attempts have been made to integrate AI with wireless communication systems, but these are application layer and network layer, especially in the case of deep learning, wireless resource management and allocation has been focused on the field. However, such research is gradually developing into a MAC layer and a physical layer, and in particular, attempts to combine deep learning with wireless transmission are appearing in the physical layer. AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver, not a traditional communication framework, in fundamental signal processing and communication mechanisms. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanism, AI-based resource scheduling ( scheduling) and allocation.

Specific embodiments of the present disclosure

The present disclosure relates to artificial intelligence-based product recommendation, in which a global model is updated by performing federated learning based on a product placement result in each offline store, and offline stores are based on the updated model. Each of them describes a technique for recommending a product.

According to the autonomous recommendation operation method and system currently being considered, products in offline stores can be displayed through existing advertisements and publicity rather than customer needs. According to this operating method, the following problems may occur.

First, offline stores may not be able to effectively display new products due to a situation in which many products are handled or space limitations of offline stores. Therefore, offline stores do not provide customers with an opportunity to select new products.

It is desirable for offline store employees to respond to customers with in-depth knowledge of many products. However, because of the rapid development of new products, store employees may not be informed about new products. Accordingly, response service and display of new products by store staff may be performed inefficiently.

Off-line stores have limitations in displaying products due to space limitations, and therefore, need to display products efficiently. However, managers of each of the offline stores may not be able to sufficiently secure product display data, and thus may not efficiently arrange products in the offline stores.

In addition, consumption patterns of products may differ depending on regions and environments. Inventory management and product sales information can be individually managed in each offline store. In order to reflect different consumption patterns for each store, it is desirable for each offline store to elaborate a sales strategy. However, in a situation where sales information is managed by individual stores, it can be difficult to envision an organic sales strategy.

In order to overcome these limitations, the present disclosure proposes a method for effectively responding to customers, promoting sales, and increasing efficiency of resources through an organic operating method and system between a store and a server. For example, the following embodiments may be used for efficient federated learning of an artificial intelligence-based product recommendation system.

AI (artificial intelligence) algorithms currently used in offline or unmanned stores are being applied to analyzing customers' behavior, collecting product images, making payments, and managing inventory. In addition, many methods for increasing sales of offline stores or unmanned stores based on AI algorithms are being applied to offline stores and unmanned stores. Among them, as an efficient learning method for an AI-based product recommendation algorithm, a federated learning method can be used.

Federated learning is a type of "machine learning" technique that trains an algorithm on multiple devices or servers that have local "data samples" without exchanging data samples. Federated learning addresses important issues such as data privacy, data security, data access rights, and heterogeneous data access, as multiple devices can build a common, powerful machine learning model without sharing data. It is known as a technology that can be solved. The general principle of federated learning is to train a local model on local data samples, and pass the local model's parameters (eg, neural network weights, etc.) to the server to 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 clients participate in federated learning is exemplified, but embodiments described later may be applied even when two or less clients participate in federated learning.

Referring to FIG. 30 , a plurality of clients 3010a, 3010b, and 3010c and a base station 3020 may perform federated learning. The base station 3020 may perform and control federated learning by including a server, performing functions of a server, or interworking with a server. For federated learning, the plurality of clients 3010a, 3010b, and 3010c and the base station 3020 are connected through various communication means and operate organically.

For federated learning, global and local models are used. Local models 3012a, 3012b, and 3012c reside on clients 3010a, 3010b, and 3010c, and global model 3022 resides on base station 3020. Local models 3012a, 3012b, and 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 be different for each client by learning in each client. The base station 3020 provides information w ^BS representing the global model 3022 to clients 3010a, 3010b, and 3010c, and the clients 3010a, 3010b, and 3010c represent the local model 3012a, 3012b, and 3012c. Information w ₁ to w _u is transmitted to the base station 3020 .

A 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 federated learning applicable to the present disclosure. 31 illustrates how clients and servers operate.

Referring to FIG. 31 , in step S3101, the server may transmit a global model to clients. Here, the client may correspond to an offline store. In step S3103, the client updates the local model based on the global model and local data received from the server. Here, the local data means data generated or acquired by a client.

In step S3105, the client transmits the local model to the server. For example, the client may transmit information (eg, w _u ) related to weights of connections included in the local model to the server.

In step S3107, the server updates the global model based on the local model received from the clients. In step S3109, the server determines whether the global model has converged. If the global model has not converged, the procedure returns to step S3101. That is, the above-described steps may be repeated until the evaluation results converge.

32 is a diagram illustrating an embodiment of a product recommendation system of an offline store applicable to the present disclosure. 32 illustrates a system comprising a server 3210 and three or more clients 3231a, 3232a, 3233a exchanging signals over a network 3220.

Referring to FIG. 32 , a server 3210 may store information about products disposed in offline stores 3231, 3232, and 3233 of the product recommendation system. Information about products may include information about preferences of each product. Server 3210 may create a global model of the product recommendation system based on information about products. The server 3210 may transmit the global model to clients 3231a, 3232a, and 3233a connected through the network 3220. In addition, the server 3210 may receive the local model updated by the clients 3231a, 3232a, and 3233a. The server 3210 may update the global model based on the received updated local model.

The network 3220 may refer to a connection structure for exchanging information between the server 3210 and the clients 3231a, 3232a, and 3233a. The network 3220 includes the Internet, a local area network (LAN), a wireless local area network (WLAN), a wide area network (WAN), a personal area network (PAN), 3G, 4G, long term evolution (LTE), Voice over LTE (VoLTE), 5G, new radio (NR), 6G, Wi-Fi, Bluetooth, near field communication (NFC), radio frequency ID (RFID), internet of things (IoT), home network ( home network), etc.

The clients 3231a, 3232a, and 3233a may be connected to the server 3210 through the network 3220 and receive the global model of the entire product recommendation system from the server 3210. The clients 3231a, 3232a, and 3233a may generate a local model based on the received global model and information stored in a storage device. Also, the clients 3231a, 3232a, and 3233a may determine products to be placed in the offline stores 3231, 3232, and 3233 based on the generated local model.

The clients 3231a, 3232a, and 3233a may obtain sensing results in the offline stores 3231, 3232, and 3233 from the connected sensor devices 3231b, 3232b, and 3233b. The sensor devices 3231b, 3232b, and 3233b may include a camera that captures an image, a closed circuit television (CCTV), and the like, and may include ultrasonic waves for detecting movement of an object located in the offline stores 3231, 3232, and 3233. A motion detection sensor such as a sensor or an infrared sensor may be included. The sensor devices 3231b, 3232b, and 3233b may capture images in the offline stores 3231, 3232, and 3233, and transmit the captured images to the connected clients 3231a, 3232a, and 3233a. The clients 3231a, 3232a, and 3233a determine the location of the offline stores 3231, 3232, and 3233 based on the detection results of the offline stores 3231, 3232, and 3233 obtained from the sensor devices 3231b, 3232b, and 3233b. Customers' preferences for products can be measured. That is, the clients 3231a, 3232a, and 3233a analyze images in the offline stores 3231, 3232, and 3233 obtained from the sensor devices 3231b, 3232b, and 3233b, and the offline stores 3231, 3232, and 3233 ), and based on the analyzed consumer's motion information, the consumer's preference for products disposed in the offline stores 3231, 3232, and 3233 can be measured.

According to the method of recommending the arrangement of existing products, the client may recommend placing products that the customer has purchased a lot in the current situation and environment. However, the method of recommending existing product arrangements is a method of simply recommending popular products, and has a limitation in not reflecting changes in consumption trends. In order to reflect changing consumption trends, clients need to properly recommend new products with somewhat uncertain preferences.

Therefore, the present disclosure does not simply recommend placing products that existing customers have purchased a lot, but predicting products based on existing customers' preferences for each of the products and recommending products by clients who additionally recommend new products. suggest a technique In addition, the client recommending new products may perform learning by receiving user feedback on the recommended products. Therefore, the client is likely to recommend a product having a higher preference than the existing product.

The server may apply a multi-armed bandits (MAB) algorithm, one of reinforcement learning algorithms, in order to predict a selection capable of deriving an optimal product recommendation result based on product preferences of existing customers.

MAB is an algorithm that predicts an optimal action for a user based on a reward according to an existing action. MAB presents the optimal action to the user by adjusting the exploitation method, which selects the optimal action with current information, and the exploration method, which anticipates the optimal action by reflecting the existing information.

By applying the MAB algorithm, the client can recommend the optimal product to be placed in the offline store. In the situation and environment so far, the method of recommending the placement of products that customers have purchased a lot corresponds to the method of using the MAB algorithm. Also, the method of recommending the placement of an optimal product among products including new products based on existing customers' product preferences corresponds to the search method of the MAB algorithm.

In recommending a product, if a utilization method is applied, the client can recommend an optimal product at the current point in time. On the other hand, when the search method is applied, the client has an uncertain preference at the present time, but can recommend an optimal product according to a change in consumption trend.

The utilization method and the search method may be in a trade-off relationship. The server may recommend product placement by alternately applying the utilization method and the search method. In the case of recommending the arrangement of products by applying only the utilization method, the manager of the offline store may not be able to place a new product that may be of interest to the customer in the offline store. By further applying the search method, when recommending a product, the current server may recommend the customer's preferred product to be placed in the offline store with a rather low accuracy, but the placed product may attract the customer's interest. Thus, the offline store can maximize the overall cumulative profit.

In addition, the server may execute distributed federated learning based on product sales information in offline stores and unmanned stores in many places. The server may transmit the model generated as a learning result to offline stores and unmanned stores. Clients of offline stores and unmanned stores may improve sales volume of offline stores and unmanned stores by recommending product placement through models obtained from the server.

계수 및

계수)에 의해 [0, 1] 구간에서 정의되는 연속 확률 분포이다. 베타 분포에서,

값이 크면 p는 1에 가까워질 수 있고,

값이 크면 p는 0에 가까워질 수 있다. In recommending an optimal action based on the MAB, the client may recommend an optimal action by applying a Thompson sampling algorithm. The Thomson sampling algorithm uses past data to estimate the distribution of rewards, and based on the estimated distribution, select an object that will give the highest reward (for example, a product placed in an offline store) with a high probability. It is an algorithm that selects For example, according to the most basic Bernoulli-Thomson sampling algorithm, the reward of each product has a value of 0 or 1 with a probability of p by a Bernoulli trial, and the probability p can be determined in the form of a beta distribution. The beta distribution has two parameters (

coefficient and

is a continuous probability distribution defined in the interval [0, 1] by From the beta distribution,

When the value is large, p can approach 1,

For large values, p can approach zero.

계수 및

계수는 제품에 대한 보상 값이 1, 0이 나온 횟수를 기초로 결정될 수 있다. 여기서 보상 값은 제품들 각각에 대한 고객의 선호도일 수 있다. 고객이 제품에 대한 관심을 가질 경우, 보상 값은 1일 수 있으며, 고객이 제품에 대한 관심을 가지지 않을 경우, 보상 값은 0일 수 있다. 즉, 특정 제품의 보상 값 1 및 0이 각각 2회 및 1회 측정된 경우, 제품의 보상 값의 확률 p는 Beta(2, 1)의 분포를 갖는다고 추정할 수 있다. 서버는 추정한 베타 분포에 확률 매칭(probability matching)을 이용하여, 추천하고자 하는 제품 및 제품의 배치를 결정할 수 있다. parameters of the beta distribution.

coefficient and

The coefficient may be determined based on the number of times the compensation value for the product is 1 or 0. Here, the compensation value may be a customer's preference for each of the products. If the customer is interested in the product, the reward value may be 1, and if the customer is not interested in the product, the reward value may be 0. That is, when the reward values 1 and 0 of a specific product are measured twice and once, respectively, the probability p of the reward value of the product can be estimated to have a distribution of Beta(2, 1). The server may determine a product to be recommended and an arrangement of products by using probability matching on the estimated beta distribution.

33 is a diagram illustrating an embodiment of signal exchange for associative learning applicable to the present disclosure. 33 illustrates signal exchange between three or more clients 3310a, 3310b, and 3310c and a server 3320.

계수 및 오프라인 매장의 취급 제품들 각각의 비선호도를 기초로 결정된

계수를 전송할 수 있다. 클라이언트들(3310a, 3310b, 3310c) 각각은 글로벌 모델 및 로컬 데이터를 기초로 로컬 모델을 훈련하고 업데이트 한다. Although not shown in FIG. 33, the server 3320 transmits the global model to each of the clients 3310a, 3310b, and 3310c. That is, the server determines based on the preference of each of the products handled in the offline store to each of the clients 3310a, 3310b, and 3310c.

Determined based on the coefficient and non-preference of each of the products handled in the offline store

coefficients can be transmitted. Each of the clients 3310a, 3310b, and 3310c trains and updates a local model based on the global model and local data.

계수인

및 오프라인 매장의 취급 제품들 각각의 비선호도를 기초로 업데이트된

계수인

를 전송할 수 있다. Referring to FIG. 33 , in steps S3301-1, S3301-2, and S3301-3, each of the clients 3310a, 3310b, and 3310c transmits a local model to the server 3320. That is, each of the clients 3310a, 3310b, and 3310c sends the server 3320 updated information based on preferences of products handled in the offline store.

coefficient

and updated based on the non-preference of each of the products handled in the offline store.

coefficient

can transmit.

값들 및

값들을 평균화함으로써 갱신된 글로벌 모델의

계수인

및 글로벌 모델의

계수인

를 결정할 수 있다. 서버(3320)는 클라이언트들(3310a, 3310b, 3310c)로부터 제공된 로컬 모델들에 관련된 정보를 기초로 글로벌 모델을 갱신할 때, 클라이언트들(3310a, 3310b, 3310c) 각각의 특성에 따라서, 로컬 모델에 관련된 정보들을 다르게 취급할 수 있다. 예를 들어, 서버(3320)는 클라이언트를 구비하는 오프라인 매장의 특성에 따라서, 로컬 모델에 관련된 정보들에 서로 다른 가중치를 더 부여할 수 있다. In step S3303, the server 3320 updates the global model based on the received information. For example, server 3320 receives

values and

of the updated global model by averaging the values.

coefficient

and global model

coefficient

can decide When the server 3320 updates the global model based on information related to the local models provided from the clients 3310a, 3310b, and 3310c, the server 3320 updates the local model according to the characteristics of each of the clients 3310a, 3310b, and 3310c. Relevant information may be treated differently. For example, the server 3320 may further assign different weights to information related to the local model according to characteristics of an offline store having a client.

및 오프라인 매장의 취급 제품들 각각의 비선호도를 기초로 결정된

를 전송할 수 있다.In step S3305, the server 3320 transmits the updated global model to the clients 3310a, 3310b, and 3310c. That is, the server determines based on the preference of each of the products handled in the offline store to each of the clients 3310a, 3310b, and 3310c.

And determined based on the non-preference of each of the products handled in the offline store

can transmit.

A product recommendation model update method in an offline store through federated learning and a product recommendation method in an offline store based on the MAB of the Thomson sampling method are described below.

34 is a diagram illustrating an embodiment of a method of updating a product recommendation model of a server applicable to the present disclosure. 34 illustrates an operating method of a server of a product recommendation system of an offline store.

Referring to FIG. 34 , in step S3401, the server may transmit the global model to the client. That is, the server may transmit information related to the global model of the product recommendation system to clients of each of the offline stores. For example, the information related to the global model may include information on the structure of the global model and parameters constituting the global model (eg, preferences of each product).

In step S3403, the server may receive updated local models from clients of each of the offline stores. The local model may be a model generated by each of the clients based on the global model. The local model is initially the same as the global model, but can be updated later through client learning. That is, the server may receive information about the updated local model from clients of each of the offline stores. Information on the updated local model may include information on the structure of the local model changed from the global model and parameters constituting the local model (eg, preferences of each product).

In step S3405, the server may determine whether the generation cycle of the global model has elapsed. If the generation cycle of the global model has not elapsed, the procedure may return to S3403. That is, until the generation period of the global model elapses, the server may receive updated local models from clients.

When the generation period of the global model has elapsed, in step S3407, the server may generate a new global model using the received updated local model. That is, the server may create a global model based on a periodically updated local model.

The procedure described with reference to FIG. 34 may be understood as a global model update procedure for one period. According to another embodiment, the operations illustrated in FIG. 34 may be repeatedly performed. Therefore, after performing step S3407, the server may repeatedly perform steps S3401 to S3407 according to a preset period.

35 is a diagram illustrating an embodiment of a method of updating a local model based on a product recommendation result of a client applicable to the present disclosure. 35 illustrates an operating method of a client of a product recommendation system of an offline store.

Referring to FIG. 35 , in step S3501, a client may receive a global model from a server connected through a network. For example, the client may receive information on the structure of the global model of the product recommendation system and parameters constituting the global model (eg, preferences of each product) from the server.

In step S3503, the client may create a local model based on the global model. If the local model previously stored in the database of the client differs from the global model received from the server, the client may create a local model based on the global model. The initial local model generated by the client is the same as the global model, but can be updated through subsequent learning.

In step S3505, the client may determine product placement based on the local model. Specifically, the client may determine the placement of a plurality of products in the offline store among products handled in the offline store based on the local model. For example, the client may determine whether to arrange each of the products handled in the offline store based on the local model. Alternatively, the client may determine a product placement area of each of the plurality of products in the offline store based on the local model. The arrangement of the determined products may be notified in a manner recognizable by the manager of the offline store so that the manager of the offline store can utilize the arrangement.

In step S3507, the client may update the local model based on the customer's preference for the product. The client may measure the customer's preference for products placed in the offline store through a connected sensor (eg, camera, CCTV, etc.). Specifically, the client may obtain information such as an image of the inside of the offline store from a sensor, analyze the information such as the acquired image, and derive operation information of the consumer inside the offline store. The client may measure the customer's preference for products placed in the offline store based on the consumer's motion information. In other words, if the consumer's response to the product is confirmed, such as when the consumer is located near the product for more than a preset time or if the consumer touches the product, the client is placed in an offline store based on the confirmed consumer's response. You can measure customer preferences for the products you have purchased. Also, the client may update parameters of the local model (eg, preference of each product) based on the customer's preference for products.

In step S3509, the client may transmit the updated local model to the server. For example, the client may transmit information on the structure of the updated local model and parameters constituting the updated local model (eg, preferences of each product, etc.) to the server.

36 is a diagram illustrating an embodiment of an operation method of a server and a client of a product recommendation system for an offline store applicable to the present disclosure. 36 illustrates a combined learning operation method through linkage between a server and a client of a product recommendation system of an offline store.

계수 및 오프라인 매장의 취급 제품들 각각의 비선호도를 기초로 결정된

계수를 포함할 수 있다. 즉, 제품 추천 시스템의 글로벌 모델은 오프라인 매장의 취급 제품들 각각의

계수 및

계수 쌍을 포함할 수 있다. Referring to FIG. 36 , in step S3601, the server may transmit the global model to clients of each of the offline stores. For example, the server may transmit information on the structure of the global model of the product recommendation system and the parameters constituting the global model to the respective clients. The structure of the global model of the product recommendation system may include information about configurations of products handled in the offline store and parameters of each of the products handled in the offline store. And the parameters constituting the global model are determined based on the preferences of each product handled in the offline store.

Determined based on the coefficient and non-preference of each of the products handled in the offline store

coefficients may be included. In other words, the global model of the product recommendation system is

coefficient and

Can contain pairs of coefficients.

계수 및 비선호도를 기초로 결정된

계수 등의 정보에 기반하여 생성될 수 있다. 로컬 모델은 초기에 글로벌 모델과 동일하지만, 이후의 학습을 통해 갱신되는 모델일 수 있다. In step S3603, the client may generate a local model based on the received global model. The local model is determined based on the information of products handled in offline stores indicated by the global model and the preference of each product handled in offline stores.

determined based on coefficients and dislikes

It may be generated based on information such as coefficients. The local model is initially the same as the global model, but may be a model that is updated through subsequent learning.

계수 및

계수를 기초로 제품들 각각의 베타 분포를 생성할 수 있다. 그리고 클라이언트는 제품들 각각의 베타 분포를 기초로 확률 변수를 랜덤 샘플링할 수 있다. In step S3605, the client may randomly sample random variables of products based on the generated local model. For example, the client has each of the products handled in the offline store.

coefficient and

Based on the coefficients, a beta distribution of each of the products can be generated. And the client may randomly sample a random variable based on the beta distribution of each product.

In step S3607, the client may determine the arrangement of products handled in the offline store in the offline store based on the random sampling result. For example, the client may determine a product to be placed in the offline store by comparing randomly sampled random variables of each of the products handled in the offline store. Alternatively, the client may determine a product to be placed in a specific area of the offline store based on a comparison result of a random variable of each product handled in the offline store.

In step S3609, the client can confirm the customer's preference for the placed product. The client may detect movement and motion of the customer through a connected sensor (eg, camera, CCTV, etc.). Also, the client may measure the customer's preference for products placed in the offline store based on the customer's movement and motion detection results. Specifically, the client may obtain movement and motion information of the customer from the sensor, and measure the customer's preference for products placed in the offline store based on the acquired movement and motion information of the customer. For example, the client may measure the customer's preference for the product by analyzing the customer's movement and motion information obtained from the sensor, obtaining time information of the customer's location in front of the product.

In step S3611, the client may update the local model based on the customer's preferences for products. Specifically, the client may update parameters of the local model (eg, preferences for each of the products, etc.) based on the customer's preferences for the products. The client may update a coefficient indicating preference and non-preference of each product based on the customer's preference for each product placed in the offline store.

계수 및 비선호도를 기초로 업데이트된

계수를 포함할 수 있다. In step S3613, the client may transmit the updated local model to the server. For example, the client may transmit the updated structure of the local model and the updated parameters of the local model to the server. The updated parameters of the local model are updated based on the preferences of each product handled in the offline store.

updated based on coefficients and disliking

coefficients may be included.

In step S3615, the server may determine whether or not the global model creation cycle has elapsed. When the global model generation cycle has not elapsed, the server may suspend global model generation until the global model generation cycle has elapsed. That is, until the generation period of the global model passes, the client may transmit the updated local model to the server, and the server may receive the updated local model from the client.

When the generation period of the global model has elapsed, in step S3617, the server may generate a new global model using the received local model. That is, the server may create a new global model based on the updated local model. The server that created the new global model ends the cycle.

The procedure described with reference to FIG. 36 can be understood as a global model update procedure for one period. According to another embodiment, the operations illustrated in FIG. 36 may be repeatedly performed. Therefore, after performing step S3617, the server and the client may repeatedly perform steps S3601 to S3617 according to a preset cycle.

37 is a diagram illustrating an embodiment of a product recommendation method of a product recommendation system of an offline store applicable to the present disclosure. 37 illustrates signal exchange between a server and a client of a product recommendation system of an offline store. Also, for ease of description, the operation of client-1 (hereinafter, referred to as client-1 (3710a)) among the clients 3710a, 3710b, and 3710c is mainly described, but is not limited thereto.

Referring to FIG. 37 , in step S3701, the server 3720 may transmit the global model of the product recommendation system to client-1 (3710a) of each offline store. For example, the server 3720 sends the client-1 3710a the structure of the global model (eg, information on products handled at each offline store, etc.), parameters constituting the global model (eg, Preference of each product, etc.) can be transmitted.

계수와 취급 제품들 각각의 비선호도를 기초로 결정되는

계수를 포함할 수 있다. 로컬 모델은 초기에 글로벌 모델과 동일하지만, 이후의 학습을 통해 갱신되는 모델일 수 있다. In step S3703, client-1 (3710a) may create a local model based on the received global model. The local model may include information about preference and non-preference of each product handled in the offline store. And the local model is determined based on the preference of each product handled in the offline store.

Determined based on the coefficient and the non-preference of each of the products handled

coefficients may be included. The local model is initially the same as the global model, but may be a model that is updated through subsequent learning.

계수와 취급 제품들 각각의

계수를 기초로 제품들 각각의 베타 분포를 생성할 수 있다. 그리고 클라이언트-1(3710a)은 제품들 각각의 베타 분포를 기반으로 임의의 확률 변수를 선택할 수 있다. 구체적으로 클라이언트-1(3710a)은 오프라인 매장의 취급 제품들 각각의 베타 분포의 표본 공간에서 하나의 표본을 임의로 선택함으로써, 오프라인 매장의 취급 제품들 각각의 확률 변수를 결정할 수 있다. In step S3705, client-1 (3710a) may randomly sample a random variable of each product based on the generated local model. For example, Client-1 (3710a) is each of the products handled in the offline store.

Each of the counting and handling products

Based on the coefficients, a beta distribution of each of the products can be generated. In addition, Client-1 3710a may select a random variable based on the beta distribution of each of the products. In detail, client-1 3710a may determine a random variable of each of the products handled in the offline store by randomly selecting one sample from the sample space of the beta distribution of each of the products handled in the offline store.

In step S3707, client-1 (3710a) may determine the arrangement of the products handled in the offline store based on the random sampling result of the random variable of each product. Here, the arrangement determination may include a determination of whether to arrange each product and a determination of the arrangement order of the products to be placed. For example, client-1 3710a may determine a product to be placed in an offline store from among products handled in the offline store by comparing the values of the random variables of the products handled in the offline store. Client-1 3710a may determine to place a plurality of products having the highest probability variable value among products handled in the offline store in the offline store.

In step S3707, client-1 (3710a) may determine a product to be placed in a specific area of the offline store based on a comparison result of random variable values of products handled in the offline store. That is, client-1 (3710a) may further determine the location of each of the products to be placed in the offline store.

For example, different priorities may be assigned to each of the zones included in the offline store. Client-1 (3710a) may determine a product placement area by comparing random variable values of products handled in an offline store. For example, client-1 3710a may determine to place a product having the largest random variable value among products determined to be placed in an offline store in a zone having the highest priority in the offline store. Also, client-1 3710a may determine to place a product having a second largest random variable value among products determined to be placed in an offline store in a zone having a second highest priority in the offline store.

However, products indicated by the global model and products handled in actual offline stores may differ in part. Accordingly, Client-1 3710a needs to perform calibration for at least some of the products indicated by the global model.

Client-1 (3710a) may exclude at least some products that do not currently exist in the offline store from among products handled in the offline store. For example, client-1 3710a may exclude at least some products that do not currently exist in the offline store prior to generating a beta distribution of each of the products handled in the offline store. Accordingly, client-1 3710a may determine to place a plurality of products having the highest probability variable value among products that are not excluded among products handled in the offline store in the offline store.

In addition, as a result of random sampling, the random variable value of a specific product may be randomly sampled relatively higher than that of other products even though existing customers have low preference for the product. Based on the sampling results, Client-1 3710a may decide to place a particular product in the offline store, but considering existing customers' preferences for the product, it may be reasonable not to place the particular product. Therefore, it is necessary to perform calibration on at least some of the products handled in the offline store.

계수에 대한

계수의 비가 미리 설정된 값을 초과하는 적어도 일부의 제품을 제외할 수 있다. 예를 들어, 클라이언트-1(3710a)은 오프라인 매장의 취급 제품들 각각의 베타 분포를 생성하기에 앞서,

계수에 대한

계수의 비가 미리 설정된 값을 초과하는 적어도 일부의 제품을 제외할 수 있다. 따라서, 클라이언트-1(3710a)은 오프라인 매장의 취급 제품들 중 제외되지 않은 제품들 중에서 가장 높은 확률 변수 값을 갖는 복수의 제품들을 오프라인 매장에 배치하는 것으로 결정할 수 있다. Client-1 (3710a) among the products handled in the offline store,

for coefficient

At least some products whose coefficient ratio exceeds a preset value may be excluded. For example, prior to client-1 3710a generating a beta distribution of each of the offline store's handling products,

for coefficient

At least some products whose coefficient ratio exceeds a preset value may be excluded. Accordingly, client-1 3710a may determine to place a plurality of products having the highest probability variable value among products that are not excluded among products handled in the offline store in the offline store.

As a result of random sampling, client-1 3710a may exclude at least some products for which the size of a random variable value is less than or equal to a preset value. Client-1 3710a may determine to place new products in an offline store by replacing at least some products having a random variable value less than or equal to a preset value.

In step S3707, client-1 (3710a) may determine whether the products handled in the offline store are disposed based on the random sampling result of the random variable of each of the products, and transmit information about whether the products handled in the offline store are disposed offline. You can print it to the manager of the store. A manager of the offline store may place products in the offline store based on information about whether the products are placed.

In step S3709, client-1 (3710a) may measure the customer's preference for the placed product. Specifically, client-1 (3710a) may measure the customer's preference for products placed in the offline store through a connected sensor (eg, camera, CCTV, etc.). For example, Client-1 3710a may measure a customer's preference for a product based on the amount of time the customer is in front of the product.

계수를 업데이트할 수 있고, 고객이 제품 앞에 위치하는 시간이 미리 설정된 값 미만인 경우, 클라이언트-1(3710a)은 제품의

계수를 업데이트할 수 있다. In step S3711, client-1 3710a may update the local model based on the customer's preferences for products. Specifically, client-1 3710a may update parameters (eg, preference and non-preference of each product) of the local model based on the customer's preference for products. For example, if the customer is in front of the product for more than a preset value, Client-1 (3710a)

If the coefficient can be updated, and the time the customer is in front of the product is less than the preset value, Client-1 3710a returns the product's

Coefficients can be updated.

계수,

계수 등)을 서버(3720)에게 전송할 수 있다. In step S3713, client-1 3710a may transmit the updated local model to the server 3720. For example, Client-1 3710a may determine the structure of the updated local model, the parameters of the updated local model (e.g., each of the products

Coefficient,

coefficient, etc.) may be transmitted to the server 3720.

In step S3715, the server 3720 may update the global model based on the updated local model information. For example, the server 3720 configures the structure of the global model and the global model based on information of the updated structure of the local model and parameters constituting the local model (eg, preferences of each product). Parameters (eg, preferences for each of the products, etc.) may be updated. That is, the server 3720 may update the global model by reflecting the received local model to the global model.

38 is a diagram illustrating a product recommendation model learning algorithm of a client in a product recommendation system of an offline store applicable to the present disclosure. 38 illustrates an algorithm executed by a client.

계수 및 비선호도를 기초로 산출되는

계수 등의 정보를 서버로부터 수신할 수 있다. Referring to FIG. 38 , in step S3810, the client may execute a decision algorithm for determining a product to be placed in the offline store among products handled in the offline store. Specifically, to run the decision algorithm, the client may receive a global model from the server. The client calculates based on the products handled in the offline store and the preferences of each product from the server.

Calculated based on coefficients and non-preferences

Information such as coefficients may be received from the server.

)을 랜덤 샘플링할 수 있다. In step S3811, the client may generate a beta distribution of each of the products handled in the offline store based on the local model. The client selects a random variable value based on the beta distribution of each of the products handled in the offline store (

) can be randomly sampled.

)들 중 최대값을 결정(

)함으로써, 오프라인 매장에서의 제품 배치를 최적화할 수 있다. 즉, 클라이언트는 오프라인 매장의 취급 제품들 각각의 확률 변수 값을 기초로 오프라인 매장에 배치될 제품들 및 제품들의 배치 구역을 결정할 수 있다. In step S3813, the client randomly sampled random variable values of each of the products handled in the offline store (

) Determine the maximum value of (

), product placement in offline stores can be optimized. That is, the client may determine products to be placed in the offline store and a disposition area of the products based on a random variable value of each of the products handled in the offline store.

로 표현될 수 있으며, 최적화된 제품들을 오프라인 매장에 배치하도록 추천하는 동작을 의미할 수 있다. 액션 결과 클라이언트는 오프라인 매장의 관리자에게 최적화된 제품들을 오프라인 매장에 배치하도록 추천하고, 오프라인 매장 관리자는 클라이언트의 추천 결과에 따라 오프라인 매장에 최적화된 제품들을 배치할 수 있다.As a result of executing the decision algorithm, the client can execute an action. action is

It may be expressed as , and may mean an operation of recommending to place optimized products in an offline store. As a result of the action, the client recommends optimized products to be placed in the offline store to the manager of the offline store, and the offline store manager may arrange products optimized for the offline store according to the client's recommendation result.

)를 결정할 수 있다. 예를 들어, 고객이 제품 앞에 위치하는 시간이 미리 설정된 값 이상인 경우, 리워드 파라미터(

)는 1로 결정될 수 있고, 고객이 제품 앞에 위치하는 시간이 미리 설정된 값 미만인 경우, 리워드 파라미터(

)는 0으로 결정될 수 있다. In step S3820, the client may measure preferences of products placed in the offline store. Specifically, the client may obtain information about the movement of customers from a sensor of an offline store. And the client measures the preference of customers for each of the products placed in the offline store based on the information obtained from the sensor, and the reward parameter (

) can be determined. For example, if the time the customer is in front of the product is more than a preset value, the reward parameter (

) may be determined to be 1, and if the time the customer is in front of the product is less than a preset value, the reward parameter (

) may be determined to be 0.

계수 및

계수를 업데이트할 수 있다. 리워드 파라미터(

)가 1인 경우, 클라이언트는

계수에 1을 더해,

계수를 업데이트(

)할 수 있고, 리워드 파라미터(

)가 0인 경우, 클라이언트는

계수에 1을 더해,

계수를 업데이트(

)할 수 있다. 클라이언트의

계수 및

계수 업데이트 동작을 관찰(observation) 동작이라고 지칭할 수 있다. And the client, according to the reward parameters of each of the products, the local model parameters of each of the products

coefficient and

Coefficients can be updated. Reward parameter (

) is 1, the client

Add 1 to the coefficient,

update the coefficients (

), and the reward parameter (

) is 0, the client

Add 1 to the coefficient,

update the coefficients (

)can do. client's

coefficient and

The coefficient update operation may be referred to as an observation operation.

)을 포함하는 관찰 결과를 산출할 수 있다. 클라이언트는 서버에게 리워드 파라미터(

) 및 업데이트된 로컬 모델의 파라미터들(

)을 전송할 수 있다. The client receives the product placement result reward parameters of the offline store and the parameters of the updated local model (

) can be calculated. The client sends the server a reward parameter (

) and parameters of the updated local model (

) can be transmitted.

) 및 업데이트된 로컬 모델의 파라미터들(

)을 기초로 글로벌 모델의 파라미터들을 업데이트할 수 있으며, 업데이트된 글로벌 모델을 클라이언트에게 전송할 수 있다. 클라이언트는 서버로부터 획득한 글로벌 모델의 파라미터들을 기초로 오프라인 매장의 제품 배치 결정 동작을 수행할 수 있다. The server has a reward parameter (

) and parameters of the updated local model (

), parameters of the global model may be updated, and the updated global model may be transmitted to the client. The client may perform an operation of determining product placement in an offline store based on the parameters of the global model acquired from the server.

39 is a diagram illustrating operations of a product recommendation model learning algorithm of a client in a product recommendation system of an offline store applicable to the present disclosure. 39 illustrates a method performed by a client.

Referring to FIG. 39, in step S3910, the client executes a decision algorithm for determining a product to be placed in the offline store among products handled in the offline store, so that the offline store among the products handled in the offline store You can decide which products to place on. The client may receive the global model, which is information necessary for executing the decision algorithm, from the server.

Specifically, in step S3911, the client may update the local model based on the global model received from the server. The client may receive parameters of the global model (preference and non-preference of each product handled in an offline store) from the server, and may create a local model based on the received parameters of the global model.

계수 및

계수를 기초로 오프라인 매장의 취급 제품들 각각의 베타 분포를 생성할 수 있다. In step S3913, the client may generate a beta distribution of each of the products handled in the offline store based on preference and non-preference information of each of the products handled in the offline store. That is, the client has each of the products handled in the offline store.

coefficient and

Based on the coefficient, a beta distribution of each of the products handled in the offline store may be generated.

)를 랜덤 샘플링할 수 있다. 구체적으로 클라이언트는 오프라인 매장의 취급 제품들 각각의 베타 분포의 표본 공간에서 하나의 표본을 샘플링하고, 샘플링된 표본을 기초로 제품들 각각의 확률 변수를 결정할 수 있다. In step S3915, the client determines a random variable based on the beta distribution of each of the products handled in the offline store (

) can be randomly sampled. Specifically, the client may sample one sample from the sample space of the beta distribution of each product handled in the offline store, and determine a random variable of each product based on the sampled sample.

)들 중 최대값을 결정(

)하고, 오프라인 매장에 최적화된 제품의 배치를 결정할 수 있다. 클라이언트는 가장 큰 확률 변수 값을 갖는 제품들을 오프라인 매장에 배치할 제품들로 결정할 수 있다. In step S3917, the client may determine the arrangement of products optimized for the offline store based on the random variable values of each of the products handled in the offline store. The client is a random variable of each of the products (

) Determine the maximum value of (

), and the placement of products optimized for offline stores can be determined. The client may determine products having the largest random variable values as products to be placed in an offline store.

를 실행할 수 있다. 액션

는 최적화된 제품들을 오프라인 매장에 배치하도록 추천하는 동작을 의미할 수 있다. 액션 결과 클라이언트는 오프라인 매장의 관리자에게 최적화된 제품들을 오프라인 매장에 배치하도록 추천하고, 오프라인 매장 관리자는 클라이언트의 추천 결과에 따라 오프라인 매장에 최적화된 제품들을 배치할 수 있다.The client takes action based on the product placement decision in the offline store.

can run action

may refer to an operation of recommending optimized products to be placed in an offline store. As a result of the action, the client recommends optimized products to be placed in the offline store to the manager of the offline store, and the offline store manager may arrange products optimized for the offline store according to the client's recommendation result.

)를 결정할 수 있다. 클라이언트는 제품들 각각의 리워드 파라미터(

)에 따라서, 제품들 각각의 로컬 모델 파라미터의

계수 및

계수들을 각각

로 업데이트할 수 있다. In step S3920, the client may determine whether to prefer each of the products placed in the offline store. Specifically, the client may obtain information about the movement of customers from a sensor of an offline store. And the client calculates the customer's preference for each of the products placed in the offline store based on the information about the customer's movement, and the reward parameter (

) can be determined. The client has a reward parameter of each of the products (

), the local model parameters of each of the products

coefficient and

the coefficients respectively

can be updated with

)는 1이고, 클라이언트는

계수에 1을 더함으로써,

계수를 업데이트(

)할 수 있다. 반면, 고객들이 제품 앞에 위치하는 시간이 미리 설정된 시간 미만인 경우, S3921-2 단계에서, 리워드 파라미터(

)는 0이고, 클라이언트는

계수에 1을 더함으로써,

계수를 업데이트(

)할 수 있다. 클라이언트는 오프라인 매장에서의 제품 배치에 따른 리워드 파라미터 및 관찰(observation) 결과를 출력할 수 있다. For example, if the time that customers are located in front of the product is more than a preset time, in step S3921-1, the reward parameter (

) is 1, and the client

By adding 1 to the coefficient,

update the coefficients (

)can do. On the other hand, if the time that customers are located in front of the product is less than the preset time, in step S3921-2, the reward parameter (

) is 0, and the client

By adding 1 to the coefficient,

update the coefficients (

)can do. The client may output reward parameters and observation results according to product placement in the offline store.

In step S3923, the client may update the local model based on the reward parameters and observation results. The client may update each of the parameters of the local model based on the preference and non-preference of each of the updated products as a result of the observation.

) 및 업데이트된 로컬 모델의 파라미터들(예를 들어,

)을 서버에게 전송할 수 있다. In step S3925, the client may transmit the updated local model to the server. The client is a reward parameter (calculated as a result of product placement in an offline store)

) and parameters of the updated local model (e.g.

) to the server.

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

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

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

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

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

Claims (11)

In the operation method of the client in the store management system,
Receiving information of a global model of a product recommendation system from a server;
generating a local model of the client based on the global model;
determining placement of a plurality of products among products handled in the offline store in the offline store based on the local model;
updating the local model based on preferences and non-preferences of each of the plurality of products; and
And transmitting the updated information of the local model to the server. The method of claim 1,
The information of the global model,
a first coefficient indicating a preference of each of the handling products; and
and a second coefficient indicative of a non-preference of each of the handled products. The method of claim 2,
Determining the placement of the plurality of products in the offline store,
generating a beta distribution of each of the handled products based on the first coefficient and the second coefficient of each of the handled products;
randomly sampling a random variable based on a beta distribution of each of the handled products; and
determining the plurality of products according to a magnitude ranking of the value of the random variable of each of the handled products. The method of claim 3,
Determining the placement of the plurality of products in the offline store,
Excluding at least some of the products in which the ratio of the second coefficient to the first coefficient exceeds a preset value, before generating a beta distribution of each of the handled products. , method. The method of claim 3,
prior to generating the beta distribution of each of the handled products, excluding at least some of the handled products that do not exist in the brick-and-mortar store. The method of claim 1,
The preference and non-preference of each of the plurality of products,
measured using at least one sensor coupled to the client. In the operating method of the server in the store management system,
Transmitting information of the global model of the product recommendation system to clients of each of the offline stores;
receiving updated information of a local model determined from the global model from a client of each of the offline stores; and
Updating the global model based on information of the updated local model;
The global model is
A method based on a customer's preference for products handled by the offline stores. The method of claim 7,
The information of the global model,
a first coefficient indicating a preference of each of the handling products; and
and a second coefficient indicative of a non-preference of each of the handled products. The method of claim 7,
Information related to the learning result for the local model,
Among the products handled by the offline stores, including preference and non-preference information of each of a plurality of products disposed in each of the offline stores. The method of claim 9,
The plurality of products,
and determined based on randomly sampled random variable values from a beta distribution generated based on first coefficients and second coefficients of each of the handled products. The method of claim 9,
Preference and non-preference information of each of the plurality of products,
measured by at least one sensor coupled to the client.

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