WO2022050434A1 - Method and apparatus for performing handover in wireless communication system - Google Patents

Abstract

Disclosed are a method of operating a terminal and a base station in a wireless communication system, and an apparatus supporting same. As an example of the present disclosure, a method of operating a terminal in a wireless communication system may comprise the steps of: receiving, from a serving base station, respective handover model parameters of handover candidate base stations; determining a target base station on the basis of the handover model parameters; transmitting, to the serving base station, a message including information about the target base station; and receiving, from the serving base station, a handover command to the target base station.

Description

Method and apparatus for performing handover in a wireless communication system

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

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

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

The present disclosure relates to a method and apparatus for more efficiently performing handover in a wireless communication system.

The present disclosure relates to a method and apparatus for determining a target base station to be a handover target based on an existing handover history in a wireless communication system.

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

As an example of the present disclosure, a method of operating a terminal in a wireless communication system includes: receiving a handover model parameter of each of handover candidate base stations from a serving base station; determining a target base station based on the handover model parameter; transmitting a message including information on the target base station to the serving base station; and receiving a handover command from the serving base station to the target base station.

As an example of the present disclosure, measuring the channel quality of each of the handover candidate base stations; The method may further include calibrating a channel quality of the target base station among the channel qualities of each of the handover candidate base stations, wherein the information on the target base station may be corrected channel quality information.

As an example of the present disclosure, the corrected channel quality of the target base station may be corrected to exceed a largest value among channel quality values of the handover candidate base stations.

As an example of the present disclosure, the handover model parameter may include: a first coefficient indicating the number of successful handover to each of the handover candidate base stations; and a second coefficient indicating the number of failures of handover to each of the handover candidate base stations.

As an example of the present disclosure, the determining of the target base station may include generating a beta distribution of each of the handover candidate base stations based on the first coefficient and the second coefficient of each of the handover candidate base stations. ; and randomly sampling a random variable of each of the handover candidate base stations based on a beta distribution of each of the handover candidate base stations, wherein the target base station determines the random variables of each of the handover candidate base stations. The random variable value having the maximum value may be a sampled base station.

As an example of the present disclosure, after receiving a handover command, measuring a channel quality of the target base station; updating the handover model parameter based on a comparison result between the channel quality of the serving base station and the channel quality of the target base station; and transmitting a handover completion message including the updated handover model parameter to the target base station.

As an example of the present disclosure, the updating of the handover model parameter includes updating the first coefficient when an increase in the channel quality of the target base station compared to the channel quality of the serving base station is equal to or greater than a preset value, When the increase in the channel quality of the target base station compared to the channel quality of the serving base station is less than a preset value, the second coefficient may be updated.

As an example of the present disclosure, a method of operating a serving base station (BS) in a wireless communication system includes: transmitting handover model parameters of each of handover candidate base stations to a terminal; receiving, from the terminal, a message including information on a target base station determined based on the handover model parameter; transmitting a handover request message to the target base station; and transmitting a handover command to the target base station to the terminal.

As an example of the present disclosure, the channel quality information of the target base station may be corrected to have the largest value among the channel quality information of each of the terminal and the handover candidate base stations.

As an example of the present disclosure, the handover model parameter may include: a first coefficient indicating the number of successful handover to each of the handover candidate base stations; and a second coefficient indicating the number of failures of handover to each of the handover candidate base stations.

As an example of the present disclosure, the target base station may be determined based on a random variable value randomly sampled from a beta distribution generated based on the first coefficient and the second coefficient of each of the handover candidate base stations.

As an example of the present disclosure, the method may further include receiving a handover completion message from the target base station, wherein the handover completion message may include an updated handover model parameter.

As an example of the present disclosure, the updated handover model parameter is based on a comparison result of the channel quality of the serving base station measured by the terminal and the channel quality of the target base station measured after handover by the terminal As such, the first coefficient or the second coefficient may be updated information.

As an example of the present disclosure, the handover completion message may be transmitted by the target base station in which the handover of the terminal is maintained for a preset time.

Aspects of the present disclosure described above are only some of the preferred embodiments of the present disclosure, and various embodiments in which the technical features of the present disclosure are reflected are detailed descriptions of the present disclosure that will be described below by those of ordinary skill in the art can be derived and understood based on

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

According to the present disclosure, a terminal in a wireless communication system can efficiently perform handover.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

30 is a diagram illustrating a situation in which a plurality of handovers are performed in a communication system including a plurality of base stations applicable to the present disclosure.

31 is a diagram illustrating a handover operation of a terminal in a communication system applicable to the present disclosure.

32 is a diagram illustrating a handover operation of a serving base station in a communication system applicable to the present disclosure.

33 is a diagram illustrating signal exchange for handover in a communication system applicable to the present disclosure.

34 is a diagram illustrating a learning algorithm for handover of a terminal in a communication system applicable to the present disclosure.

35 is a diagram illustrating operations of a learning algorithm for handover of a terminal in a communication system applicable to the present disclosure.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Communication system applicable to the present disclosure

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

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

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

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

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

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

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

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

Here, the wireless communication technology implemented in the wireless devices 200a and 200b of the present specification may include a 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, and is limited to the above-mentioned names. not. Additionally or alternatively, the wireless communication technology implemented in the wireless devices 200a and 200b of the present specification may perform communication based on the LTE-M technology. In this case, as an example, the LTE-M technology may be an example of an LPWAN technology, and may be called various names such as enhanced machine type communication (eMTC). For example, LTE-M technology is 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) may be implemented in at least one of various standards such as LTE M, and is not limited to the above-described name. Additionally or alternatively, the wireless communication technology implemented in the wireless devices 200a and 200b of the present specification is at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) in consideration of low power communication. It may include any one, and is not limited to the above-mentioned names. For example, the ZigBee technology can create PAN (personal area networks) related to small/low-power digital communication based on various standards such as IEEE 802.15.4, and can be called by various names.

Hereinafter, hardware elements of the wireless devices 200a and 200b will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors 202a, 202b. For example, one or more processors 202a, 202b may include one or more layers (eg, PHY (physical), MAC (media access control), RLC (radio link control), PDCP (packet data convergence protocol), RRC (radio resource) control) and a functional layer such as service data adaptation protocol (SDAP)). The one or more processors 202a, 202b may be configured to process one or more protocol data units (PDUs) and/or one or more service data units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed herein. can create The one or more processors 202a, 202b may generate messages, control information, data, or information according to the description, function, procedure, proposal, method, and/or flow charts disclosed herein. The one or more processors 202a, 202b generate a signal (eg, a baseband signal) including a PDU, SDU, message, control information, data or information according to the functions, procedures, proposals and/or methods disclosed herein. , may be provided to one or more transceivers 206a and 206b. One or more processors 202a, 202b may receive signals (eg, baseband signals) from one or more transceivers 206a, 206b, and the descriptions, functions, procedures, proposals, methods, and/or flowcharts of operation disclosed herein. PDUs, SDUs, messages, control information, data, or information may be acquired according to the fields.

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

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

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

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. ) may consist of For example, the wireless device 300 may include a communication unit 310 , a control unit 320 , a memory unit 330 , and an additional element 340 . The communication unit may include communication circuitry 312 and transceiver(s) 314 . For example, communication circuitry 312 may include one or more processors 202a, 202b and/or one or more memories 204a, 204b of FIG. 2 . For example, the transceiver(s) 314 may include one or more transceivers 206a , 206b and/or one or more antennas 208a , 208b of FIG. 2 . The control unit 320 is electrically connected to the communication unit 310 , the memory unit 330 , and the additional element 340 and controls general operations of the wireless device. For example, the controller 320 may control the electrical/mechanical operation of the wireless device based on the program/code/command/information stored in the memory unit 330 . In addition, the control unit 320 transmits the information stored in the memory unit 330 to the outside (eg, another communication device) through the communication unit 310 through a wireless/wired interface, or externally (eg, through the communication unit 310) Information received through a wireless/wired interface from another communication device) may be stored in the memory unit 330 .

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

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

Mobile device to which the present disclosure is applicable

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

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

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

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

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

Types of wireless devices to which the present disclosure is applicable

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

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

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

The communication unit 510 may transmit/receive signals (eg, data, control signals, etc.) to and from external devices such as other vehicles, base stations (eg, base stations, roadside units, etc.), and servers. The controller 520 may control elements of the vehicle or the autonomous driving vehicle 500 to perform various operations. The controller 520 may include an electronic control unit (ECU). The driving unit 540a may cause the vehicle or the autonomous driving vehicle 500 to run on the ground. The driving unit 540a may include an engine, a motor, a power train, a wheel, a brake, a steering device, and the like. The power supply unit 540b supplies power to the vehicle or the autonomous driving vehicle 500 , and may include a wired/wireless charging circuit, a battery, and the like. The sensor unit 540c may obtain vehicle state, surrounding environment information, user information, and the like. The sensor unit 540c includes an inertial measurement unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight sensor, a heading sensor, a position module, and a vehicle forward movement. / may include a reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, and the like. The autonomous driving unit 540d includes a technology for maintaining a driving lane, a technology for automatically adjusting speed such as adaptive cruise control, a technology for automatically driving along a predetermined route, and a technology for automatically setting a route when a destination is set. technology can be implemented.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Physical channels and general signal transmission

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

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

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

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

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

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

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

11 is a diagram illustrating a control plane and a user plane structure 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 term "terminal" may be at least one of a wireless device, a portable device, a vehicle, a mobile body, an XR device, a robot, and an AI to which the present disclosure is applied in FIGS. 1 to 9 described above. In addition, the terminal refers to a device to which the present disclosure can be applied and may not be limited to a specific device or device.

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

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

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

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

12 is a diagram illustrating a method of processing a transmission signal applied to the present disclosure. As an example, the transmission signal may be processed by a signal processing circuit. In this case, the signal processing circuit 1200 may include a scrambler 1210 , a modulator 1220 , a layer mapper 1230 , a precoder 1240 , a resource mapper 1250 , and a signal generator 1260 . In this case, as an example, the operation/function of FIG. 12 may be performed by the processors 202a and 202b and/or the transceivers 206a and 206b of FIG. 2 . Also, as an example, the hardware elements of FIG. 12 may be implemented in the processors 202a and 202b and/or the transceivers 206a and 206b of FIG. 2 . As an example, blocks 1010 to 1060 may be implemented in the processors 202a and 202b of FIG. 2 . In addition, blocks 1210 to 1250 may be implemented in the processors 202a and 202b of FIG. 2 , and block 1260 may be implemented in the transceivers 206a and 206b of FIG. 2 , and the embodiment is not limited thereto.

The codeword may be converted into a wireless signal through the signal processing circuit 1200 of FIG. 12 . Here, the codeword is a coded bit sequence of an information block. The information block may include a transport block (eg, a UL-SCH transport block, a DL-SCH transport block). The radio signal may be transmitted through various physical channels (eg, PUSCH, PDSCH) of FIG. 10 . Specifically, the codeword may be converted into a scrambled bit sequence by the scrambler 1210 . A scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of a wireless device, and the like. The scrambled bit sequence may be modulated by a modulator 1220 into a modulation symbol sequence. The modulation method may include pi/2-binary phase shift keying (pi/2-BPSK), m-phase shift keying (m-PSK), m-quadrature amplitude modulation (m-QAM), and the like.

The complex modulation symbol sequence may be mapped to one or more transport layers by a layer mapper 1230 . Modulation symbols of each transport layer may be mapped to corresponding antenna port(s) by the precoder 1240 (precoding). The output z of the precoder 1240 may be obtained by multiplying the output y of the layer mapper 1230 by the precoding matrix W of N*M. Here, N is the number of antenna ports, and M is the number of transport layers. Here, the precoder 1240 may perform precoding after performing transform precoding (eg, discrete fourier transform (DFT) transform) on the complex modulation symbols. Also, the precoder 1240 may perform precoding without performing transform precoding.

The resource mapper 1250 may map modulation symbols of each antenna port to a time-frequency resource. The time-frequency resource may include a plurality of symbols (eg, a CP-OFDMA symbol, a DFT-s-OFDMA symbol) in the time domain and a plurality of subcarriers in the frequency domain. The signal generator 1260 generates a radio signal from the mapped modulation symbols, and the generated radio signal may be transmitted to another device through each antenna. To this end, the signal generator 1260 may include an inverse fast fourier transform (IFFT) module and a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, and the like. .

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

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

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

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 may indicate the number of symbols in a slot, N ^{frame, μ} _slot may indicate the number of slots in a frame, and N ^{subframe, μ} _slot may indicate the number of slots in a subframe. .

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

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

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

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

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

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

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

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

6G communication system

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Core implementation technology of 6G system

- Artificial Intelligence (AI)

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

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

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

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

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

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

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

Hereinafter, machine learning will be described in more detail.

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

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

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

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

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

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

THz (Terahertz) communication

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

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

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

optical wireless technology

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

FSO backhaul network

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

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, large-scale MIMO technology will be important in 6G systems. Since the MIMO technology uses multiple paths, a multiplexing technique and a beam generation and operation technique suitable for the THz band should also be considered important so that a data signal can be transmitted through one or more paths.

blockchain

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

3D Networking

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

quantum communication

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

drone

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

Cell-free Communication

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

Wireless information and energy transfer (WIET)

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

Integration of sensing and communication

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

Consolidation of access backhaul networks

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

holographic beamforming

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

Big Data Analytics

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

LIS (large intelligent surface)

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

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

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

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

Transceivers 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 may be classified based on a method for generating and receiving THz. The THz generation method can be classified into an optical device or an electronic device-based technology.

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

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

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

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

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

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

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

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

Artificial Intelligence System

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

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

Meanwhile, the perceptron structure shown in FIG. 23 may be described as being composed of a total of three layers based on an input value and an output value. An artificial neural network in which H (d+1)-dimensional perceptrons exist between the ^1st layer and the ^2nd layer and K (H+1)-dimensional perceptrons exist between the ^2nd layer and the ^3rd layer can be expressed as shown in FIG. can

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Specific embodiments of the present disclosure

The present disclosure relates to a method and apparatus for performing handover in a wireless communication system. Specifically, the present disclosure describes a technique for an efficient handover operation method to which search type artificial intelligence is applied.

Various embodiments to be described below can be easily applied to a case in which a terminal intends to perform handover in an environment in which various communication nodes coexist in a wireless communication system.

When the terminal performs handover in a wireless communication system, it is preferable that the terminal consumes as low power as possible and accesses the base station as quickly as possible. And when the terminal performs handover, it is preferable that the base station efficiently manage radio resources and data capacity. However, due to a myriad of wireless devices and obstacles such as high-rise buildings existing in a wireless communication environment, the terminal may not directly access the target base station during handover, and may perform handover several times or frequently. Therefore, in managing resources and maintaining the network, the burden increases.

In general, the terminal performing handover continuously measures changes in the environment and radio channel state (eg, CSI (channel state information), CQI (channel quality indicator), etc.), and a measurement report indicating the measurement result ( measurement report). However, due to frequent changes in the environment of the wireless communication system, the terminal may not be able to quickly deliver the measurement report to the base station. As a result, the terminal may perform handover in a state that does not reflect the current radio channel state, and thus frequent handover may occur. Measures are needed to solve the information, environment and situation of such a wireless communication system.

Accordingly, the present disclosure proposes embodiments for resolving the above-described limitations, efficient handover operation, and efficient radio resource management. For example, the embodiments described below may be understood as a handover method to which search type artificial intelligence is applied.

30 is a diagram illustrating a situation in which a plurality of handovers are performed in a communication system including a plurality of base stations applicable to the present disclosure. In FIG. 30 , one serving base station 3020 and two handover candidate base stations 3031 and 3032 are exemplified. However, even when the wireless communication system includes three or more handover candidate base stations, the embodiments described below will be applied. can

Referring to FIG. 30 , the terminal 3010 measures the reception level of the serving base station 3020 . When the reception level is equal to or greater than the threshold value, the terminal 3010 may continue to camp on the serving base station 3020 . In addition, when the terminal 3010 moves away from the serving base station 3020 , the reception level for the serving base station 3020 may gradually decrease. As a result, when the reception level of the serving base station 3020 is less than the threshold, base station reselection of the terminal 3010 may be triggered.

When triggered to reselect the base station, the terminal 3010 measures the received signal strength (RSS) from the serving base station 3020 and the candidate base stations 3031 and 3032 adjacent to the serving base station 3020 and , the handover may be performed to the base station having the maximum RSS value.

According to the handover method based on the above-described RSS value, the serving base station 3020 determines the maximum RSS value in the current situation and environment based on the RSS values of the handover candidate base stations 3031 and 3032 measured by the terminal. A base station having a base station is determined as a target base station. However, when the channel changes quickly and the rank of RSS values is frequently changed, the handover method based on the RSS value may cause unnecessary handover. That is, the handover method based on the RSS value has a limitation in not reflecting the rapid change of the radio channel between the terminal 3010 and the base stations.

Accordingly, according to the present disclosure, the terminal 3010 determines a base station expected to have an optimal RSS value as a target base station by reflecting the existing handover history information, rather than simply performing handover based on the measurement result of the RSS value. suggest techniques to In terms of reflecting changes in radio channel conditions, it can be expected that handover performance will be secured when the terminal performs handover to the most selected base station based on the existing handover history. According to the proposed handover method, the terminal 3010 may recommend a target base station expected to have optimal performance after a change in a radio channel state based on handover details of other existing terminals. Accordingly, the terminal can perform handover to a target base station quickly with low power during handover, and the base station can efficiently manage resources and maximize data capacity.

In order to predict a selection capable of deriving an optimal result based on the existing details, the terminal 3010 may apply a multi-armed bandits (MAB) algorithm.

MAB is an algorithm that predicts the optimal behavior of the user based on the reward according to the existing behavior. MAB is an algorithm that proposes optimal behavior to users by adjusting the exploitation method, which is a method of selecting the optimal behavior with current information, and the exploration method, which reflects the existing information to predict the optimal behavior am.

By applying the MAB algorithm, the UE can predict and determine an optimal target base station among handover candidate base stations. A method in which the terminal determines a target base station based on RSS values of handover candidate base stations corresponds to a method of using the MAB algorithm. And, the method in which the terminal determines the target base station based on the existing handover history information corresponds to the search method of the MAB algorithm.

In performing handover, when the utilization method is applied, the terminal determines the optimal base station at the current time as the target base station. On the other hand, when the search method is applied, it is possible to recommend an optimal base station at a time point after a change in the radio channel environment although it is uncertain in terms of performance at the current time point.

The utilization method and the search method may have a trade-off relationship. In performing handover, if the discovery method is applied, it may be a loss for the current terminal 3010, but through the discovery method, the terminal 3010 predicts an optimal base station after a change in the radio channel environment, and You can request over. In addition, the terminal 3010 may perform handover by checking the feedback on the handover request to which the discovery method is applied from the base station and reflecting the feedback information.

Accordingly, since the terminal performs handover to a stable base station despite a change in the radio channel environment, it is possible to perform an efficient handover as a whole. In addition, the serving base station 3020 can be efficiently operated as a whole without users flocking to a specific target base station, a specific frequency, or a specific radio access tech (RAT).

In predicting the optimal behavior based on the MAB, the terminal 3010 may predict the optimal behavior by applying a Thompson sampling algorithm. The Thompson sampling algorithm is an algorithm for estimating a distribution of rewards based on past data, and selecting an object (eg, a base station) that will give the highest reward based on the estimated distribution with high probability. For example, according to the most basic Bernoulli-Thomson sampling algorithm, the compensation of each base station has a value of 0 or 1 with a probability of p by a Bernoulli trial, and the probability p is determined in the form of a beta distribution. The beta distribution has two parameters (

coefficient and

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

If the value is large, p can be close to 1,

If the value is large, p can be close to 0. Beta distribution parameters

coefficient and

The coefficient may be determined based on the number of times that the compensation in each base station is 1 and 0, and the compensation in the base station may indicate whether handover to the base station succeeds.

The serving base station 3020 may select a target base station by using probability matching for the estimated beta distribution. The serving base station 3020 may select a target base station having the highest probability p when instructing the handover to the terminal 3010, respectively, and transmit the handover command.

Based on the MAB of the Thompson sampling method, the handover method to which the search type artificial intelligence is applied may be as described below.

31 is a diagram illustrating a handover operation of a terminal in a communication system applicable to the present disclosure. 31 illustrates a method performed by a terminal.

Referring to FIG. 31 , in step S3101, the terminal receives a handover model parameter from a serving base station. In other words, the terminal receives a message including the handover model parameter from the serving base station. The terminal may obtain handover model parameters based on the existing handover history information from the message of the serving base station. The handover model parameter is calculated based on the number of successful handovers of each of the handover candidate base stations.

Calculated based on the count and the number of handover failures

It may include coefficients. In addition, the message may include an indicator instructing to perform artificial intelligence-based handover.

In step S3103, the terminal determines a target base station based on the handover model parameter. For example, the terminal may search for a target base station based on the handover model parameter. As a result of the search, the terminal may determine a target base station to request a handover from among the handover candidate base stations.

In step S3105, the terminal measures the channel quality of the handover candidate base stations. For example, the terminal may obtain channel quality values for each of the handover candidate base stations by measuring the channel quality using reference signals transmitted from each of the handover candidate base stations including the target base station.

In step S3107, the terminal transmits information on the target base station to the serving base station. For example, the information on the target base station may include a channel quality information value of the target base station. In addition, the terminal may transmit the channel quality information values of each of the handover target candidate base stations together. In this case, the channel quality information value of the target base station may be transmitted after being corrected to have the largest value among the channel quality information values of the handover candidate base stations.

In step S3109, the terminal performs handover to the target base station. Specifically, the terminal may receive a handover command from the serving base station and perform handover to the target base station. The handover command may include information on the target base station. According to this embodiment, the base station indicated by the information on the target base station may be a base station determined by the terminal.

32 is a diagram illustrating a handover operation of a serving base station in a communication system applicable to the present disclosure. 32 illustrates a method performed by a serving base station.

Referring to FIG. 32 , in step S3201, the serving base station may transmit handover model parameters to the terminal. That is, the serving base station may transmit a message including the handover model parameter to the terminal. The handover model parameter may be a parameter based on handover details of each of the candidate base stations to be handover. Here, the message may include an AI-based handover performance indicator instructing the terminal to perform AI-based handover.

In step S3203, the serving base station may receive information on the target base station from the terminal. The target base station may be a base station determined by the terminal based on the handover model parameter. The message received by the serving base station in S3203 may include RSS information of each of the handover target base stations, and the value of the RSS information of the target base station may be the largest value among the RSS information values of the handover target candidate base stations. The value of the RSS information of the target base station may be a value corrected by the terminal.

In step S3205, the serving base station may control the handover of the terminal. Specifically, the serving base station may transmit a handover request message that is a handover request message to the target base station, and may receive a handover request acknowledgment (ACK) that is a response message to the handover request message from the target base station. Then, the serving base station may instruct the terminal to handover. The handover command may include information on the target base station. Here, the target base station indicated in the information included in the handover command may be a base station determined by the terminal based on the handover model parameter.

Although not shown in FIG. 32 , the serving base station may receive the updated handover model parameter from the target base station. When the handover model parameter is received, the serving base station may update the handover model parameter stored in the database of the serving base station based on the received handover model parameter.

33 is a diagram illustrating signal exchange for handover in a communication system applicable to the present disclosure. 33 illustrates signal exchange between the terminal 3310 and the base stations 3320 and 3331. In FIG. 33, a wireless communication system including one serving base station 3320 and three handover candidate base stations 3331, 3332, 3333 is exemplified, but if the wireless communication system includes a plurality of candidate base stations, Embodiments to be described later may be applied.

Although not shown in FIG. 33 , the serving base station 3320 may store past handover detail information related to the handover candidate base stations 3331 , 3332 , and 3333 and a handover model parameter based on the handover detail information. For example, the serving base station 3320 may store handover history information including the number of handover successes and the number of handover failures of each of the handover candidate base stations 3331 , 3332 , and 3333 . And the serving base station 3320 is calculated based on the number of successful handover of each of the handover candidate base stations 3331, 3332, 3333.

Calculated based on the count and the number of handover failures

Handover model parameters including coefficients may be stored.

Referring to FIG. 33 , in step S3301 , the serving base station 3320 may transmit a measurement configuration message to the terminal 3310 . When the serving base station 3320 intends to instruct the terminal 3310 to perform a search type AI-based handover, the measurement configuration message may include an AI-based handover execution indicator. In addition, the measurement setup message may include handover model parameters of each of the handover candidate base stations 3331 , 3332 , and 3333 that are data necessary to perform AI-based handover. The handover model parameter is the handover candidate base station (3331, 3332, 3333) of each

coefficient and

It may include coefficients.

Accordingly, the terminal 3310 receives each of the handover candidate base stations 3331, 3332, and 3333 from the measurement setup message.

coefficient and

A handover model parameter including a coefficient may be obtained. When the measurement configuration message includes an AI-based handover performance indicator, the terminal 3310 selects one target base station to request a handover from among the handover candidate base stations 3331, 3332, and 3333 based on the handover model parameter. can be explored.

In step S3303, the terminal 3310 may perform a random sampling operation to search for one target base station to request a handover. For example, the terminal 3310 is each of the handover candidate base stations 3331 , 3332 , and 3333 .

coefficient and

A beta distribution of each of the handover candidate base stations 3331 , 3332 , and 3333 may be generated based on the coefficient, and a random variable may be selected based on the beta distributions.

According to an embodiment, the terminal 3310 may randomly sample a random variable based on a beta distribution of each of the handover candidate base stations 3331 , 3332 , and 3333 . Specifically, the UE may determine the random variable by arbitrarily selecting one sample from the sample space of the beta distribution of each of the handover candidate base stations 3331 , 3332 , and 3333 .

According to another embodiment, the terminal 3310 may correct the beta distribution of each of the handover candidate base stations 3331 , 3332 , and 3333 . For example, the terminal 3310 obtains a higher random variable value from a beta distribution having an average value greater than or equal to a preset value among beta distributions of each of the handover candidate base stations 3331 , 3332 , and 3333 , the beta distribution can be corrected. For example, the terminal 3310 compares the average value of the beta distribution of each of the handover candidate base stations 3331 , 3332 , and 3333 with a preset value, and obtains at least some beta distributions having an average value greater than or equal to the preset value. By correcting, it is possible to correct to sample only values greater than or equal to the average value of each of the beta distributions. That is, the terminal may correct to exclude a range less than the average value from at least a portion of the beta distribution. Alternatively, the terminal 3310 compares the average value of the beta distribution of each of the handover candidate base stations 3331, 3332, and 3333 with a preset value, and corrects at least some beta distributions having an average value less than the preset value. , can be corrected to sample only values less than the mean value of each of the beta distributions. That is, the terminal may correct to exclude a range greater than or equal to the average value from at least a portion of the beta distribution. The preset value may be set according to a channel quality state between the serving base station 3320 and the terminal 3310 .

The terminal 3310 may sample a random variable based on the beta distribution or the corrected beta distribution of the handover candidate base stations 3331 , 3332 , and 3333 . That is, the terminal 3310 may randomly sample a random variable of each of the handover candidate base stations based on the beta distribution of each of the handover candidate base stations 3331 , 3332 , and 3333 including the corrected beta distributions of at least some. there is.

The preset value may be a fixed value for securing handover performance between the terminal and the base station. Alternatively, the preset value may be a value adjusted according to a current communication environment state, such as an RSS value for the serving base station. For example, when the RSS value for the serving base station is low and the handover performance is to be secured, the terminal 3310 may adjust the preset value to be high and correct the beta distribution based on the adjusted value.

After sampling the random variable from the beta distribution or the corrected beta distribution, the terminal 3310 compares the random variable values sampled from the corrected beta distribution of each of the handover candidate base stations 3331 , 3332 , and 3333 , and performs handover One base station having the largest random variable value among the candidate base stations 3331 , 3332 , and 3333 may be selected as the target base station. In the example of FIG. 33 , when the value of the random variable of the first candidate base station 3331 is the largest as a result of sampling for searching for the target base station, the terminal 3310 selects the first candidate base station 3331 as the target base station as a result of the search .

In step S3305, the terminal 3310 may measure the channel quality of each of the handover candidate base stations 3331, 3332, and 3333. Channel quality includes received signal strength (RSS), reference signal received quality (RSRQ), reference signal received power (RSRP), signal to interference noise ratio (SINR), signal to noise ratio (SNR), and carrier to interference noise ratio (CINR). ) may include at least one of. In addition, the terminal 3310 may compare the channel quality measurement results of the handover candidate base stations 3331 , 3332 , and 3333 . For example, the terminal may measure the RSS for each of the handover candidate base stations 3331 , 3332 , and 3333 . In the example of FIG. 33 , as a result of the RSS measurement for each of the handover candidate base stations 3331 , 3332 , and 3333 , it is assumed that the RSS value of the second candidate base station 3332 is the largest.

In calculating a channel quality value (eg, an RSS value, etc.) of each of the handover candidate base stations 3331 , 3332 , and 3333 , the terminal 3310 may reflect context characteristic information of the terminal 3310 . For example, the context characteristic information of the terminal 3310 includes the location information of the terminal 3310, the ID of the cell in which the terminal 3310 is located, and the information of the terminal 3310 and the handover candidate base stations 3331, 3332, and 3333. It may include beam characteristic information. The terminal 3310 may determine a weight to be given to the channel quality measurement value of each of the handover candidate base stations 3331 , 3332 , and 3333 based on the context characteristic information of the terminal 3310 . Specifically, the terminal 3310 performs a handover candidate base station 3331 , 3332 , 3333 from the context characteristic information of the terminal 3310 based on a pre-trained (eg, deep-learning, reinforcement learning, etc.) neural network. It is possible to determine a weight to be given to the channel quality measurement value. The terminal 3310 may assign the determined weight to the channel quality measurement value of each of the handover candidate base stations 3331 , 3332 , and 3333 , and calculate the weighted channel quality value.

In step S3307 , the terminal 3310 may correct a channel quality measurement value of at least one of the handover candidate base stations 3331 , 3332 , and 3333 . In other words, since the channel quality measurement value of the selected base station is not the maximum value among the channel quality measurement values of the handover candidate base stations as a result of applying the search method, the terminal 3310 determines the channel of the first candidate base station 3331 selected as the target base station. Quality measurements can be calibrated. For example, the terminal 3310 may correct the channel quality measurement value of the first candidate base station 3331 to have the largest value among the channel quality measurement values of the handover candidate base stations 3331 , 3332 , and 3333 . That is, as a result of the correction, the channel quality measurement value of the first candidate base station 3331 determined as the target base station may be corrected to be greater than the channel quality measurement value of the second candidate base station 3332 .

In step S3309 , the terminal 3310 may transmit a measurement report message that is a response message to the measurement configuration message to the serving base station 3320 . The measurement report message may include a channel quality measurement value of each of the handover candidate base stations 3331 , 3332 , and 3333 . In addition, when the terminal 3310 calibrates the channel quality measurement value of the target base station 3331 , the measurement report message may include the calibrated channel quality measurement value of the target base station 3331 .

In step S3311, the serving base station 3320 is a handover candidate base station (3331, 3332, 3333) on the basis of the comparison result of the channel quality measurement value of each of the target base station 3331 having the maximum channel quality measurement value. Over can be decided. The largest value among the channel quality measurement values of each of the handover candidate base stations 3331 , 3332 , and 3333 included in the measurement report message may be the channel quality measurement value of the target base station 3331 corrected by the terminal 3310 . As a result, the serving base station 3320 determines the target base station 3331 determined by the terminal 3310 as the handover target base station.

In step S3313 , the serving base station 3320 may request a handover from the target base station 3331 . That is, the serving base station 3320 may transmit a handover request message to the determined target base station 3331 . In step S3313 , the target base station 3331 may receive a handover request message from the serving base station 3320 .

Upon receiving the handover request message, the target base station 3331 may transmit a handover request ACK message for the handover request message to the serving base station 3320 in step S3315 . In step S3315 , the serving base station 3320 may check whether the handover request message is received from the target base station 3331 by receiving the handover request ACK message from the target base station 3331 .

In step S3317 , the serving base station 3320 may instruct the terminal 3310 to handover. That is, the serving base station 3320 may transmit a handover command to the terminal 3310 . In step S3317 , the terminal 3310 may receive a handover command from the serving base station 3320 . Upon receiving the handover command, the terminal 3310 may perform handover from the serving base station 3320 to the target base station 3331 .

In step S3319 , the terminal 3310 updates the handover model parameter for the target base station 3331 . To this end, the terminal 3310 may measure the channel quality for the target base station 3331 and determine a reward parameter for the target base station 3331 based on the channel quality measurement result. In addition, the terminal 3310 may update the handover model parameter based on the reward parameter. For example, the terminal 3310 sets the handover model parameter based on the reward parameter determined according to the RSS value for the target base station 3331 .

coefficient and

Coefficients can be updated.

For example, when the RSS value for the target base station 3331 increases by more than a preset threshold value compared to the RSS value for the serving base station 3320 before handover, the reward parameter is determined to be 1, and the terminal 3310 is of the target base station 3331

The coefficient can be increased by 1. On the other hand, when the RSS value for the target base station 3331 increases by less than a preset threshold value compared to the RSS value for the serving base station 3320 before handover, the reward parameter is determined to be 0, and the terminal 3310 is the target of the base station 3331

The coefficient can be increased by 1. Alternatively, if the RSS value for the target base station 3331 is reduced than the RSS value for the serving base station 3320 before handover, the reward parameter is determined to be 0, and the terminal 3310 is the target base station 3331.

The coefficient can be increased by 1.

In step S3321 , the terminal 3310 may transmit a handover confirm message to the target base station 3331 to the target base station 3331 . When the terminal 3310 performs a search type AI-based handover, the handover completion message is the updated handover model parameter (eg, the target base station 3331 ).

coefficient and

coefficient) may be included. In step S3321 , the target base station 3331 may receive a handover completion message from the terminal 3310 . In step S3321 , the target base station 3331 receives the updated handover model parameters of the target base station 3331 from the handover completion message (eg, the

coefficient and

coefficient) can be obtained.

In step S3323 , the target base station 3331 may transmit a handover confirm ACK (ACK) message to the serving base station 3320 . Here, the handover completion ACK message may not be transmitted depending on the duration of the handover with the terminal 3310 . When the handover duration with the terminal 3310 is equal to or greater than a preset threshold, the target base station 3331 may transmit a handover completion ACK message indicating handover success to the serving base station 3320 . The handover completion ACK message is the updated handover model parameter.

coefficient and

It may include coefficients. On the other hand, when the duration of handover with the terminal 3310 is less than a preset threshold, the target base station 3331 may not transmit a handover completion ACK message indicating handover success.

Although not shown in FIG. 33 , the serving base station 3320 receiving the handover completion ACK message is the target base station 3331 included in the handover completion ACK message.

coefficient and

Handover model parameters may be updated based on the coefficients. The serving base station 3320 may support efficient and effective handover during handover of other terminals 3310 by utilizing the updated handover model parameter.

In performing the handover according to the above-described embodiment, the serving base station 3320 may be in a state in which a sufficient level of handover details cannot be secured. If a sufficient degree of handover details cannot be obtained from the serving base station 3320, the terminal 3310 may determine a target base station based on insufficient handover details, and thus the probability of determining an optimal target base station is lowered. can Accordingly, in a state in which the handover details are not secured, the handover operation of the communication system based on the handover details may be as follows.

According to an embodiment of the present disclosure, the serving base station 3320 may perform the handover operation according to the present embodiment even in a state in which handover details are not secured. For example, in step S3301 , the serving base station 3320 that does not secure the handover history is any of the handover candidate base stations 3331 , 3332 , and 3333 , respectively.

coefficient and

A handover model parameter including a coefficient may be transmitted to the terminal 3310 .

According to another embodiment of the present disclosure, the serving base station 3320 may instruct the terminal to perform handover according to the conventional method in a state in which handover details are not secured. The serving base station 3320 may instruct the terminal to perform handover according to the conventional method in order to secure handover details for a preset period. And when the preset period has elapsed, the serving base station may instruct the terminal to perform handover according to the present disclosure. Alternatively, the serving base station 3320 may instruct the terminal to perform a handover according to the conventional method before securing the handover details of a preset number of times. And when the handover history of the preset number of times is secured, the serving base station may instruct the terminal to perform handover according to the present disclosure.

In performing the handover according to the above-described embodiment, in step S3307, the terminal 3310 corrects the channel quality measurement value of the selected base station as a result of applying the search method to obtain the maximum value among the channel quality measurement values of the handover candidate base stations. can be corrected to have However, the operation of the terminal 3310 in step S3307 is not limited thereto, and may be replaced with the operation described below. Of course, it will be apparent that the operation of the terminal 3310 in step S3307 is not limited to the embodiment described further below.

According to another embodiment of the present invention, in step S3307, the terminal 3310 excludes the channel quality measurement value of the first candidate base station 3331 among the channel quality measurement values of the handover candidate base stations 3331 , 3332 , and 3333 . You can delete values.

In step S3309 , the terminal 3310 may transmit a measurement report message including only information about the channel quality measurement value of the first candidate base station 3331 to the serving base station 3320 . That is, in step S3309, the serving base station 3320 may receive a measurement report message including only the channel quality measurement value of the first candidate base station 3331, and thus, the serving base station 3320 is the first candidate base station 3331 ) may be determined as the target base station.

According to another embodiment of the present invention, in step S3309, the measurement report message may include a separate indicator instructing handover to the target base station 3331 among the handover candidate base stations 3331, 3332, and 3333. .

And in step S3311, the serving base station may determine handover to the target base station 3331 indicated by a separate indicator included in the measurement report message. To this end, the separate indicator is information for requesting handover to the indicated base station, and may be an indicator predefined in the wireless communication system.

34 is a diagram illustrating a learning algorithm for handover of a terminal in a communication system applicable to the present disclosure. 34 illustrates an algorithm executed by a terminal.

Referring to FIG. 34 , in step S3410, the terminal may execute a decision algorithm, which is an algorithm for determining a target base station among handover candidate base stations. Specifically, in order to execute the decision algorithm, the terminal may receive and update the handover model parameter from the serving base station. The handover model parameter is calculated based on the number of successful handovers of handover candidate base stations.

Calculated based on the count and the number of handover failures

It may include coefficients.

Specifically, in step S3411, the terminal based on the handover model parameter, the beta distribution of each of the handover candidate base stations (Beta (

)) is created. The terminal determines the random variable value (

) can be randomly sampled.

In step S3413, the terminal determines the random variable value (

) to determine the maximum value (

), it is possible to search for an optimized target base station. That is, as a result of the search, the terminal determines the random variable values (

), the handover candidate base station having the largest random variable value may be determined as the target base station.

As a result of executing the decision algorithm, the terminal may perform an action. action is

It can be expressed as, and corrects the RSS value of the optimized target base station (

) and may mean an operation of transmitting to the serving base station. As a result of the action, the terminal may receive a handover command from the serving base station to the target base station. Upon receiving the handover command from the serving base station, the terminal may perform handover to the target base station and measure the RSS value of the target base station.

In step S3420, the terminal may check whether the RSS value for the target base station is equal to or greater than a threshold value. The terminal compares the RSS value and the threshold value for the target base station, and as a result of the comparison, the reward parameter (

) can be determined. If the RSS value for the target base station is greater than or equal to the threshold, the reward parameter (

) may be determined to be 1, and when the RSS value for the target base station is less than the threshold value, the reward parameter (

) may be determined to be 0.

The terminal determines the handover model parameter of the target base station based on the reward parameter.

coefficient and

Coefficients can be updated. reward parameters (

) is 1, the terminal

add 1 to the coefficient

update the coefficients (

), and the reward parameter (

) is 0, the terminal

add 1 to the coefficient

update the coefficients (

)can do. terminal

coefficient and

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

The terminal receives the handover result reward parameter (

) and observation results can be calculated. The terminal sends a reward parameter (

) and the updated handover model parameters of the target base station (

) can be sent. And the target base station is a reward parameter (

) and the updated handover model parameters of the target base station (

) may be transmitted to the serving base station including a message.

The serving base station is a reward parameter (

) and handover model parameters (

), the handover model parameter may be updated based on and transmitted to the UE. The terminal may perform a handover operation based on the handover model parameter obtained from the serving base station.

35 is a diagram illustrating operations of a learning algorithm for handover of a terminal in a communication system applicable to the present disclosure. 35 illustrates a method performed by a terminal.

Referring to FIG. 35 , in step S3510, the terminal may determine a target base station among handover candidate base stations by executing a decision algorithm, which is an algorithm for determining a preset target base station.

Specifically, in step S3511, the terminal may obtain a handover model parameter from the serving base station. The handover model parameter is calculated based on the number of successful handovers of handover candidate base stations.

Calculated based on the count and the number of handover failures

It may include coefficients.

In step S3513, the terminal may generate a beta distribution of each of the handover candidate base stations based on the handover model parameter. The terminal receives each of the handover candidate base stations.

coefficient and

A beta distribution of each of the handover candidate base stations may be generated based on the coefficient.

In step S3515, the terminal determines a random variable (

) can be randomly sampled. Specifically, the UE may sample one sample in the sample space of the beta distribution of each of the handover candidate base stations, and determine a random variable of each of the handover candidate base stations based on the sampled sample.

In step S3517, the terminal may search for an optimized target base station based on the random variable values of each of the handover candidate base stations. The terminal is a handover candidate base station each random variable (

) to determine the maximum value (

), it is possible to determine an optimized target base station. The terminal may determine a handover candidate base station in which the maximum random variable value is sampled as the target base station.

The terminal performs an action based on the target base station determination result

can be performed. action

may be an operation of correcting the determined channel quality value of the target base station. The terminal may transmit information on the determined target base station to the serving base station. Upon receiving the handover command from the serving base station, the terminal may perform handover to the target base station. The terminal may measure the RSS value for the target base station.

In step S3520, the terminal may compare the RSS value for the target base station and a threshold value. The terminal compares the RSS value with the threshold value for the target base station (

) of the handover model parameters for the target base station based on

coefficient and

Coefficients can be updated. For example, as in S3521a, when the RSS value of the target base station is greater than or equal to a threshold value (eg, a value obtained by adding an RSS value for the serving base station and a preset value), the reward parameter (

) is 1, and the terminal is

count

can be updated with On the other hand, as in S3521b, when the RSS value of the target base station is smaller than a threshold value (eg, a value obtained by adding the RSS value for the serving base station and a preset value), the reward parameter (

) is 0, and the terminal is

count

can be updated with The terminal may output a handover result reward parameter and an observation result.

In step S3530, the terminal may transmit a handover completion message to the target base station. The handover completion message is the reward parameter (

) and updated handover model parameters (eg,

) may be included.

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

The present disclosure may be embodied in other specific forms without departing from the technical ideas and essential characteristics described in the present disclosure. Accordingly, the above detailed description should not be construed as restrictive in all respects but as exemplary. The scope of the present disclosure should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present disclosure are included in the scope of the present disclosure. In addition, claims that are not explicitly cited in the claims may be combined to form an embodiment, or may be included as new claims by amendment after filing.

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

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

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

Claims (15)

1. A method of operating a terminal in a wireless communication system, the method comprising:

   Receiving handover model parameters of each of the handover candidate base stations from the serving base station;

   determining a target base station based on the handover model parameter;

   transmitting a message including information on the target base station to the serving base station; and

   Receiving a handover command from the serving base station to the target base station, the terminal operating method.
2. The method according to claim 1,

   measuring channel quality of each of the handover candidate base stations;

   The method further comprising correcting the channel quality of the target base station among the channel quality of each of the handover candidate base stations,

   The information on the target base station is calibrated channel quality information, the operating method of the terminal.
3. 3. The method according to claim 2,

   The corrected channel quality of the target base station is,

   Corrected to exceed the largest value among the respective channel quality values of the handover candidate base stations, the operating method of the terminal.
4. The method according to claim 1,

   The handover model parameter is

   a first coefficient indicating the number of successful handover to each of the handover candidate base stations; and

   and a second coefficient indicating the number of failures of handover to each of the handover candidate base stations.
5. 5. The method according to claim 4,

   The step of determining the target base station,

   generating a beta distribution of each of the handover candidate base stations based on the first coefficient and the second coefficient of each of the handover candidate base stations; and

   The method further comprises the step of randomly sampling a random variable of each of the handover candidate base stations based on the beta distribution of each of the handover candidate base stations,

   The target base station,

   The method of claim 1, wherein the value of a random variable having a maximum value among random variables of each of the handover candidate base stations is a sampled base station.
6. 6. The method of claim 5,

   The step of determining the target base station,

   Comprising the step of correcting at least some beta distributions of the generated beta distributions of each of the handover candidate base stations,

   The step of randomly sampling the random variables of each of the handover candidate base stations,

   A method of operating a terminal for randomly sampling a random variable of each of the handover candidate base stations based on a beta distribution of each of the handover candidate base stations including at least some of the corrected beta distributions.
7. 5. The method according to claim 4,

   after receiving the handover command, measuring the channel quality of the target base station;

   updating the handover model parameter based on a comparison result between the channel quality of the serving base station and the channel quality of the target base station; and

   The method of operating a terminal further comprising transmitting a handover completion message including the updated handover model parameter to the target base station.
8. 8. The method of claim 7,

   Updating the handover model parameter includes:

   When the increase in the channel quality of the target base station compared to the channel quality of the serving base station is equal to or greater than a preset value, updating the first coefficient;

   When the increase in the channel quality of the target base station compared to the channel quality of the serving base station is less than a preset value, updating the second coefficient.
9. In the operating method of a serving base station (serving base station) in a wireless communication system,

   Transmitting handover model parameters of each of the handover candidate base stations (candidate BS) to the terminal;

   receiving, from the terminal, a message including information on a target base station determined based on the handover model parameter;

   transmitting a handover request message to the target base station; and

   A method of operating a serving base station, comprising transmitting a handover command to the target base station to the terminal.
10. 10. The method of claim 9,

    The channel quality information of the target base station,

    Corrected to have the largest value among the channel quality information of each of the terminal and the handover candidate base stations, the method of operating a serving base station.
11. 10. The method of claim 9,

    The handover model parameter is

    a first coefficient indicating the number of successful handover to each of the handover candidate base stations; and

    A method of operating a serving base station, comprising a second coefficient indicating the number of failures of handover to each of the handover candidate base stations.
12. 12. The method of claim 11,

    The target base station,

    The method of operating a serving base station, which is determined based on a random variable value randomly sampled from a beta distribution generated based on the first coefficient and the second coefficient of each of the handover candidate base stations.
13. 12. The method of claim 11,

    Further comprising the step of receiving a handover completion message from the target base station,

    The handover completion message is

    A method of operating a serving base station, including updated handover model parameters.
14. 14. The method of claim 13,

    The updated handover model parameter is,

    Information in which the first coefficient or the second coefficient is updated based on a comparison result of the channel quality of the serving base station measured by the terminal and the channel quality of the target base station measured after handover by the terminal, A method of operation of a serving base station.
15. 14. The method of claim 13,

    The handover completion message is

    A method of operating a serving base station, transmitted by the target base station in which the handover of the terminal is maintained for a preset time.

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