KR20240043133A - Apparatus and method for performing handover in a wireless communication system - Google Patents

Abstract

The present disclosure is intended to perform handover in a wireless communication system, and includes a method of operating a user equipment (UE), including receiving configuration information related to measurement from a first base station and performing measurement based on the configuration information. , transmitting a measurement report including the results of the measurement to the first base station, receiving a handover command message from the first base station, in response to the handover command message, 2 It may include the step of performing handover to the base station.

Description

Apparatus and method for performing handover in a wireless communication system

The following description is about a wireless communication system and relates to an apparatus and method 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 multiple access systems include code division multiple access (CDMA) systems, frequency division multiple access (FDMA) systems, time division multiple access (TDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, and single carrier frequency (SC-FDMA) systems. division multiple access) systems, etc.

In particular, as many communication devices require large communication capacity, enhanced mobile broadband (eMBB) communication technology is being proposed compared to the existing radio access technology (RAT). In addition, a communication system that takes into account reliability and latency-sensitive services/UE (user equipment) as well as mMTC (massive machine type communications), which connects multiple devices and objects to provide a variety of services anytime and anywhere, is being proposed. . Various technological configurations are being proposed for this purpose.

The present disclosure can provide an apparatus and method for effectively performing handover in a wireless communication system.

The present disclosure can provide an apparatus and method for reducing energy consumption for handover in a wireless communication system.

The present disclosure can provide an apparatus and method for reducing the number of measurements for handover in a wireless communication system.

The present disclosure can provide an apparatus and method for reducing the number of measurement reports (MR) for handover in a wireless communication system.

The present disclosure can provide an apparatus and method for performing handover using artificial intelligence (AI) technology in a wireless communication system.

The present disclosure can provide an apparatus and method for performing handover using a probability-based prediction model in a wireless communication system.

The present disclosure can provide an apparatus and method for performing handover in a wireless communication system using a prediction model designed based on probability-based past handover history.

The present disclosure can provide an apparatus and method for selecting a target cell for handover by considering both exploration and exploitation in a wireless communication system.

The present disclosure can provide an apparatus and method for applying an upper confidence bound (UCB) algorithm to select a target cell for handover in a wireless communication system.

The present disclosure can provide an apparatus and method for applying a Tompson sampling (TS) algorithm to select a target cell for handover in a wireless communication system.

The technical objectives sought to be achieved by the present disclosure are not limited to the matters mentioned above, and other technical tasks not mentioned are subject to common knowledge in the technical field to which the technical configuration of the present disclosure is applied from the embodiments of the present disclosure described below. Can be considered by those who have.

As an example of the present disclosure, a method of operating a user equipment (UE) in a wireless communication system includes receiving configuration information related to measurement from a first base station, performing measurement based on the configuration information, and transmitting a measurement report including results to the first base station, receiving a handover command message from the first base station, and in response to the handover command message, sending a measurement report to the second base station. It may include performing handover. The measurement report may include measurement information about the second base station as a target cell selected using a prediction model.

As an example of the present disclosure, a method of operating a first base station in a wireless communication system includes receiving configuration information related to measurement from a user equipment (UE), a measurement including a result of a measurement performed based on the configuration information. Receiving a measurement report from the UE, transmitting a handover request message to a second base station in response to the measurement report, and transmitting a handover command message to the UE. You can. The measurement report may include measurement information about the second base station as a target cell selected using a prediction model.

As an example of the present disclosure, a user equipment (UE) in a wireless communication system includes a transceiver and a processor connected to the transceiver, wherein the processor receives configuration information related to measurement from a first base station, and configures the configuration. Perform measurement based on the information, transmit a measurement report including a result of the measurement to the first base station, receive a handover command message from the first base station, and perform the handover. In response to the command message, control is performed to perform handover to the second base station, and the measurement report may include measurement information about the second base station as a target cell selected using a prediction model.

As an example of the present disclosure, a first base station in a wireless communication system includes a transceiver and a processor connected to the transceiver, wherein the processor receives configuration information related to measurement from a user equipment (UE), and configures the configuration. A measurement report containing the results of measurements performed based on the information is received from the UE, and in response to the measurement report, a handover request message is transmitted to the second base station, and a handover command (handover command) is sent to the UE. command) message, and the measurement report may include measurement information about the second base station as a target cell selected using a prediction model.

As an example of the present disclosure, a device includes at least one processor, at least one computer memory coupled to the at least one processor and storing instructions that direct operations as executed by the at least one processor; , the operations include, by the device, receiving configuration information related to measurement from a first base station, performing measurement based on the configuration information, and generating a measurement report including the results of the measurement. It may include transmitting to a first base station, receiving a handover command message from the first base station, and performing handover to a second base station in response to the handover command message. The measurement report may include measurement information about the second base station as a target cell selected using a prediction model.

As an example of the present disclosure, in a non-transitory computer-readable medium storing at least one instruction, the at least one executable by a processor Includes a command, wherein the at least one command causes the device to receive setting information related to measurement from a first base station, perform measurement based on the setting information, and perform a measurement report including a result of the measurement. report) to the first base station, receive a handover command message from the first base station, control to perform handover to the second base station in response to the handover command message, and measure the The report may include measurement information about the second base station as a target cell selected using a prediction model.

The above-described aspects of the present disclosure are only some of the preferred embodiments of the present disclosure, and various embodiments reflecting the technical features of the present disclosure will be described in detail by those skilled in the art. It can be derived and understood based on.

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

According to the present disclosure, energy consumption for handover can be reduced.

The effects that can be obtained from the embodiments of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned can be found in the technical field 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 with ordinary knowledge. That is, unintended effects resulting from implementing the configuration described in this disclosure may also be derived by a person skilled in the art from the embodiments of this disclosure.

The drawings attached below are intended to aid understanding of the present disclosure and may provide embodiments of the present disclosure along with a detailed description. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined to form a new embodiment. Reference numerals in each drawing may refer to structural elements.
1 shows an example of a communication system applicable to the present disclosure.
Figure 2 shows an example of a wireless device applicable to the present disclosure.
Figure 3 shows another example of a wireless device applicable to the present disclosure.
Figure 4 shows an example of a portable device applicable to the present disclosure.
5 shows an example of a vehicle or autonomous vehicle applicable to the present disclosure.
Figure 6 shows an example of AI (Artificial Intelligence) applicable to the present disclosure.
Figure 7 shows a method of processing a transmission signal applicable to the present disclosure.
Figure 8 shows an example of a communication structure that can be provided in a 6G (6th generation) system applicable to the present disclosure.
9 shows an electromagnetic spectrum applicable to the present disclosure.
Figure 10 shows a THz communication method applicable to the present disclosure.
11 shows examples of probability density functions of beta distributions applicable to the present disclosure.
12A to 12D show examples of updating a probability distribution model applicable to the present disclosure.
FIGS. 13A and 13B show examples of locations and timings of handover in a wireless communication system according to an embodiment of the present disclosure.
Figure 14 illustrates the concept of handover in a wireless communication system according to an embodiment of the present disclosure.
Figure 15 shows examples of probability density functions of beta distributions usable in a wireless communication system according to an embodiment of the present disclosure.
Figure 16 shows an example of a procedure for performing handover in a wireless communication system according to an embodiment of the present disclosure.
Figure 17 shows an example of a procedure for controlling handover in a wireless communication system according to an embodiment of the present disclosure.
FIG. 18 illustrates an example of signal exchange for handover based on an event-triggered measurement report and prediction operation of a terminal in a wireless communication system according to an embodiment of the present disclosure.
FIG. 19 illustrates an example of a procedure for handover based on an event-triggered measurement report and prediction operation of a terminal in a wireless communication system according to an embodiment of the present disclosure.
FIG. 20 illustrates an example of signal exchange for handover based on an event-triggered measurement report and prediction operation of a base station in a wireless communication system according to an embodiment of the present disclosure.
FIG. 21 illustrates an example of a procedure for handover based on an event-triggered measurement report and a prediction operation of a base station in a wireless communication system according to an embodiment of the present disclosure.
FIG. 22 illustrates an example of signal exchange for handover based on periodic measurement reporting and prediction operation of a terminal in a wireless communication system according to an embodiment of the present disclosure.
FIG. 23 illustrates an example of a procedure for handover based on periodic measurement reporting and prediction calculation of a terminal in a wireless communication system according to an embodiment of the present disclosure.
FIG. 24 illustrates an example of signal exchange for handover based on periodic measurement reporting and prediction operation of a base station in a wireless communication system according to an embodiment of the present disclosure.
FIG. 25 illustrates an example of a procedure for handover based on periodic measurement reporting and prediction operation of a base station in a wireless communication system according to an embodiment of the present disclosure.
Figure 26 shows a reward structure based on Thompson sampling in a wireless communication system according to an embodiment of the present disclosure.

The following embodiments combine the 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. Additionally, 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 features or features of one embodiment may be included in another embodiment or may be replaced with corresponding features or features of another embodiment.

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

Throughout the specification, when a part is said to “comprise or include” a certain element, this means that it does not exclude other elements but may further include other elements, unless specifically stated to the contrary. do. In addition, terms such as "... unit", "... unit", and "module" used in the specification refer to a unit that processes at least one function or operation, which refers to hardware, software, or a combination of hardware and software. It can be implemented as: Additionally, the terms “a or an,” “one,” “the,” and similar related terms may be used differently herein in the context of describing the present disclosure (particularly in the context of the claims below). It may be used in both singular and plural terms, unless indicated otherwise or clearly contradicted by context.

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

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

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

Additionally, the transmitting end refers to a fixed and/or mobile node that provides a data service or a voice service, and the receiving end refers to a fixed and/or mobile node that receives a data service or a voice service. Therefore, in the case of uplink, the mobile station can be the transmitting end and the base station can be the receiving end. Likewise, in the case of downlink, the mobile station can be the receiving end and the base station can be the transmitting end.

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

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

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

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

In addition, specific terms used in the embodiments of the present disclosure are provided to aid understanding of the present disclosure, and the use of such 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), and single carrier frequency division multiple access (SC-FDMA). It can be applied to various wireless access systems.

In the following, for clarity of explanation, the description is based on the 3GPP communication system (e.g., LTE, NR, etc.), but the technical idea of the present invention is not limited thereto. LTE is 3GPP TS 36.xxx Release 8 and later. 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” refers to the standard document detail number. LTE/NR/6G can be collectively referred to as a 3GPP system.

Regarding background technology, terms, abbreviations, etc. used in the present disclosure, reference may be made to matters described in standard documents published prior to the present invention. As an example, you can refer to the 36.xxx and 38.xxx standard documents.

Communication systems applicable to this disclosure

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

Hereinafter, a more detailed example will be provided with reference to the drawings. In the following drawings/descriptions, identical reference numerals may illustrate identical or corresponding hardware blocks, software blocks, or functional blocks, unless otherwise noted.

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

Referring to FIG. 1, the communication system 100 applied to the present disclosure includes a wireless device, a base station, and a network. Here, a wireless device refers to a device that performs communication using wireless access technology (e.g., 5G NR, LTE) and may be referred to as a communication/wireless/5G device. Although not limited thereto, wireless devices include robots (100a), vehicles (100b-1, 100b-2), extended reality (XR) devices (100c), hand-held devices (100d), and home appliances (100d). appliance) (100e), IoT (Internet of Thing) device (100f), and AI (artificial intelligence) device/server (100g). For example, vehicles may include vehicles equipped with wireless communication functions, autonomous vehicles, vehicles capable of inter-vehicle communication, etc. 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, including a head-mounted device (HMD), a head-up display (HUD) installed in a vehicle, a television, It can be implemented in the form of smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, etc. The mobile device 100d may include a smartphone, smart pad, wearable device (eg, smart watch, smart glasses), computer (eg, laptop, etc.), etc. Home appliances 100e may include a TV, refrigerator, washing machine, etc. IoT device 100f may include sensors, smart meters, etc. For example, the base station 120 and the network 130 may also be implemented as wireless devices, and a specific wireless device 120a may operate as a base station/network node for other wireless devices.

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, 4G (eg, LTE) network, or 5G (eg, NR) network. Wireless devices 100a to 100f may communicate with each other through the base station 120/network 130, but communicate directly (e.g., sidelink communication) without going through the base station 120/network 130. You may. For example, vehicles 100b-1 and 100b-2 may communicate directly (eg, vehicle to vehicle (V2V)/vehicle to everything (V2X) communication). Additionally, the IoT device 100f (eg, sensor) may communicate directly with other IoT devices (eg, sensor) or other wireless devices 100a to 100f.

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

Communication systems applicable to this disclosure

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

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

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. Processor 202a controls memory 204a and/or 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. Additionally, the processor 202a may receive a wireless signal including the second information/signal through the transceiver 206a and then store information obtained from signal processing of the second information/signal in the memory 204a. The memory 204a may be connected to the processor 202a and may store various information related to the operation of the processor 202a. For example, memory 204a may perform some or all of the processes controlled by processor 202a or instructions for performing the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed herein. Software code containing them can be stored. Here, the processor 202a and the memory 204a may be part of a communication modem/circuit/chip designed to implement wireless communication technology (eg, LTE, NR). Transceiver 206a may be coupled to processor 202a and may transmit and/or receive wireless signals via one or more antennas 208a. Transceiver 206a may include a transmitter and/or receiver. The transceiver 206a may be used interchangeably with a radio frequency (RF) unit. In this disclosure, a wireless device may mean a communication modem/circuit/chip.

The second wireless device 200b includes one or more processors 202b, one or more memories 204b, and may further include one or more transceivers 206b and/or one or more antennas 208b. Processor 202b controls memory 204b and/or transceiver 206b and may be configured to implement the descriptions, functions, procedures, suggestions, 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. Additionally, the processor 202b may receive a wireless 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, memory 204b may perform some or all of the processes controlled by processor 202b or instructions for performing the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed herein. Software code containing them can be stored. Here, the processor 202b and the memory 204b may be part of a communication modem/circuit/chip designed to implement wireless communication technology (eg, LTE, NR). Transceiver 206b may be coupled to processor 202b and may transmit and/or receive wireless signals via one or more antennas 208b. The transceiver 206b may include a transmitter and/or a receiver. The transceiver 206b may be used interchangeably with an RF unit. In this disclosure, a wireless device may mean a communication modem/circuit/chip.

Hereinafter, the 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 and 202b. For example, one or more processors 202a and 202b may operate on one or more layers (e.g., physical (PHY), media access control (MAC), radio link control (RLC), packet data convergence protocol (PDCP), and radio resource (RRC). control) and functional layers such as SDAP (service data adaptation protocol) can be implemented. One or more processors 202a, 202b may generate one or more Protocol Data Units (PDUs) and/or one or more service data units (SDUs) according to the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed in this document. can be created. One or more processors 202a and 202b may generate messages, control information, data or information according to the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in this document. One or more processors 202a, 202b generate signals (e.g., baseband signals) containing PDUs, SDUs, messages, control information, data, or information according to the functions, procedures, proposals, and/or methods disclosed herein. , can be provided to one or more transceivers (206a, 206b). One or more processors 202a, 202b may receive signals (e.g., baseband signals) from one or more transceivers 206a, 206b, and the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. Depending on the device, PDU, SDU, message, control information, data or information can be obtained.

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

One or more memories 204a and 204b may be connected to one or more processors 202a and 202b and may store various types of data, signals, messages, information, programs, codes, instructions and/or commands. 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 and 204b may be located internal to and/or external to one or more processors 202a and 202b. Additionally, one or more memories 204a and 204b may be connected to one or more processors 202a and 202b through various technologies, such as wired or wireless connections.

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

Wireless device structure applicable to this disclosure

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

Referring to FIG. 3, the wireless device 300 corresponds to the wireless devices 200a and 200b of FIG. 2 and includes various elements, components, units/units, and/or modules. ) can be composed of. For example, the wireless device 300 may include a communication unit 310, a control unit 320, a memory unit 330, and an additional element 340. The communication unit may include communication circuitry 312 and transceiver(s) 314. For example, communication circuitry 312 may include one or more processors 202a and 202b and/or one or more memories 204a and 204b of FIG. 2 . For example, 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 overall operations of the wireless device. For example, the control unit 320 may control the electrical/mechanical operation of the wireless device based on the program/code/command/information stored in the memory unit 330. In addition, the control unit 320 transmits the information stored in the memory unit 330 to the outside (e.g., another communication device) through the communication unit 310 through a wireless/wired interface, or to the outside (e.g., to another communication device) through the communication unit 310. Information received through a wireless/wired interface from another communication device can be stored in the memory unit 330.

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

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

Mobile devices to which this disclosure is applicable

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

Figure 4 illustrates a portable device to which the present disclosure is applied. Portable devices may include smartphones, smart pads, wearable devices (e.g., smart watches, smart glasses), and portable computers (e.g., laptops, etc.). A mobile device may be referred to as a mobile station (MS), user terminal (UT), mobile subscriber station (MSS), subscriber station (SS), advanced mobile station (AMS), or wireless terminal (WT).

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

The communication unit 410 can transmit and receive signals (eg, data, control signals, etc.) with other wireless devices and base stations. The control unit 420 can control the components of the portable device 400 to perform various operations. The control unit 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. Additionally, the memory unit 430 can store input/output data/information, etc. The power supply unit 440a supplies power to the portable device 400 and may include a wired/wireless charging circuit, a battery, etc. The interface unit 440b may support connection between the mobile device 400 and other external devices. The interface unit 440b may include various ports (eg, audio input/output ports, video input/output ports) for connection to external devices. The input/output unit 440c may input or output video information/signals, audio information/signals, data, and/or information input from the user. The input/output unit 440c may include a camera, a microphone, a user input unit, a display unit 440d, a speaker, and/or a haptic module.

For example, in the case of data communication, the input/output unit 440c acquires information/signals (e.g., touch, text, voice, image, video) input from the user, and the obtained information/signals are stored in the memory unit 430. It can be saved. The communication unit 410 can 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. Additionally, the communication unit 410 may receive a wireless signal from another wireless device or a base station and then restore the received wireless signal to the original information/signal. The restored information/signal may be stored in the memory unit 430 and then output in various forms (eg, text, voice, image, video, haptic) through the input/output unit 440c.

Types of wireless devices to which this disclosure is applicable

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

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

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

The communication unit 510 may transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, road side units, etc.), and servers. The control unit 520 may control elements of the vehicle or autonomous vehicle 500 to perform various operations. The control unit 520 may include an electronic control unit (ECU).

Figure 6 is a diagram showing an example of an AI device applied to the present disclosure. As an example, AI devices include fixed devices such as 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 can be implemented as a device or a movable device.

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

The communication unit 610 uses wired and wireless communication technology to communicate with wired and wireless signals (e.g., sensor information, user Input, learning model, control signal, etc.) can be transmitted and received. To this end, the communication unit 610 may transmit information in the memory unit 630 to an external device or transmit a signal received from an external device to the memory unit 630.

The control unit 620 may determine at least one executable operation of the AI device 600 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. And, the control unit 620 can control the components of the AI device 600 to perform the determined operation. For example, the control unit 620 may request, search, receive, or utilize data from the learning processor unit 640c or the memory unit 630, and may select at least one operation that is predicted or determined to be desirable among the executable operations. Components of the AI device 600 can be controlled to execute operations. In addition, the control unit 620 collects history information including the operation content of the AI device 600 or user feedback on the operation, and stores it in the memory unit 630 or the learning processor unit 640c, or the AI server ( It can be transmitted to an external device such as Figure 1, 140). The collected historical information can be used to update the learning model.

The memory unit 630 can store data supporting various functions of the AI device 600. For example, the memory unit 630 may store data obtained from the input unit 640a, data obtained from the communication unit 610, output data from the learning processor unit 640c, and data obtained from the sensing unit 640. Additionally, the memory unit 630 may store control information and/or software codes necessary for operation/execution of the control unit 620.

The input unit 640a can obtain various types of data from outside the AI device 600. For example, the input unit 620 may obtain training data for model training and input data to which the learning model will be applied. The input unit 640a may include a camera, microphone, and/or a user input unit. The output unit 640b may generate output related to vision, hearing, or tactile sensation. The output unit 640b may include a display unit, a speaker, and/or a haptic module. The sensing unit 640 may obtain at least one of internal information of the AI device 600, surrounding environment information of the AI device 600, and user information using various sensors. The sensing unit 640 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 640c can train a model composed of an artificial neural network using training data. The learning processor unit 640c may perform AI processing together with the learning processor unit of the AI server (FIG. 1, 140). The learning processor unit 640c may process information received from an external device through the communication unit 610 and/or information stored in the memory unit 630. Additionally, the output value of the learning processor unit 640c may be transmitted to an external device through the communication unit 610 and/or stored in the memory unit 630.

Figure 7 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. At this time, the signal processing circuit 700 may include a scrambler 710, a modulator 720, a layer mapper 730, a precoder 740, a resource mapper 750, and a signal generator 760. At this time, as an example, the operation/function of FIG. 7 may be performed in the processors 202a and 202b and/or transceivers 206a and 206b of FIG. 2. Additionally, as an example, the hardware elements of FIG. 7 may be implemented in the processors 202a and 202b and/or transceivers 206a and 206b of FIG. 2. As an example, blocks 710 to 760 may be implemented in processors 202a and 202b of FIG. 2. Additionally, blocks 710 to 750 may be implemented in the processors 202a and 202b of FIG. 2, and block 760 may be implemented in the transceivers 206a and 206b of FIG. 2, and are not limited to the above-described embodiment.

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

The complex modulation symbol sequence may be mapped to one or more transport layers by the layer mapper 730. The modulation symbols of each transport layer may be mapped to corresponding antenna port(s) by the precoder 740 (precoding). The output z of the precoder 740 can be obtained by multiplying the output y of the layer mapper 730 with 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 740 may perform precoding after performing transform precoding (eg, discrete Fourier transform (DFT) transform) on complex modulation symbols. Additionally, the precoder 740 may perform precoding without performing transform precoding.

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

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

6G communication system

6G (wireless communications) systems require (i) very high data rates per device, (ii) very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) battery- The goal is to reduce the energy consumption of battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be four aspects such as “intelligent connectivity”, “deep connectivity”, “holographic connectivity”, and “ubiquitous connectivity”, and the 6G system can satisfy the requirements as shown in Table 1 below. In other words, Table 1 is a table showing the requirements of the 6G system.

Per device peak data rate 1 Tbps E2E latency 1ms Maximum spectral efficiency 100bps/Hz Mobility support up to 1000 km/hr Satellite integration Fully A.I. 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 communication. tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and improved data security. It can have key factors such as enhanced data security.

FIG. 8 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 Figure 8, the 6G system is expected to have simultaneous wireless communication connectivity 50 times higher than that of the 5G wireless communication system. URLLC, a key feature of 5G, is expected to become an even more mainstream technology in 6G communications by providing end-to-end delays of less than 1ms. At this time, the 6G system will have much better volume spectrum efficiency, unlike the frequently used area spectrum efficiency. 6G systems can provide very long battery life and advanced battery technologies for energy harvesting, so mobile devices in 6G systems may not need to be separately charged.

Core implementation technology of 6G system

- Artificial Intelligence (AI)

The most important and newly introduced technology in the 6G system is AI. AI was not involved in the 4G system. 5G systems will support partial or very limited AI. However, 6G systems will be AI-enabled for full automation. Advances in machine learning will create more intelligent networks for real-time communications in 6G. Introducing AI in communications can simplify and improve 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 handover, network selection, and resource scheduling can be performed instantly by using AI. AI can also play an important role in M2M, machine-to-human and human-to-machine communications. Additionally, AI can enable rapid communication in 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 focused on the application layer and network layer, and in particular, deep learning is focused on wireless resource management and allocation. come. However, this research is gradually advancing to the MAC layer and 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 signal processing and communication mechanisms based on AI drivers, rather than traditional communication frameworks, in terms of fundamental signal processing and communication mechanisms. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO (multiple input multiple output) mechanism, It may include AI-based resource scheduling and allocation.

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

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

Deep learning-based AI algorithms require a large amount of training data to optimize training parameters. However, due to limitations in acquiring data from a specific channel environment as training data, a lot of training data is used offline. This means that static training on training data in a specific channel environment may result in a contradiction between the dynamic characteristics and diversity of the wireless channel.

Additionally, 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 wireless communication signals, more research is needed on neural networks that detect complex domain signals.

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

Machine learning refers to a series of operations that train machines to create machines that can perform tasks that are difficult or difficult for humans to perform. Machine learning requires data and a learning model. In machine learning, data learning methods can be broadly divided into three types: supervised learning, unsupervised learning, and reinforcement learning.

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

Supervised learning uses training data in which the correct answer is labeled, while unsupervised learning may not have the correct answer labeled in the training data. That is, for example, in the case of supervised learning on data classification, the learning data may be data in which each training data is labeled with a category. Labeled learning data is input to a neural network, and error can be calculated by comparing the output (category) of the neural network with the label of the learning data. The calculated error is backpropagated in the reverse direction (i.e., from the output layer to the input layer) in the neural network, and the connection weight of each node in each layer of the neural network can be updated according to backpropagation. The amount of change in the connection weight of each updated node may be determined according to the learning rate. The neural network's calculation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stages of neural network training, a high learning rate can be used to ensure that the neural network quickly achieves a certain level of performance to increase efficiency, and in the later stages of training, a low learning rate can be used to increase accuracy.

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

The learning model corresponds to the human brain, and can be considered the most basic linear model. However, deep learning is a machine learning paradigm that uses a highly complex neural network structure, such as artificial neural networks, as a learning model. ).

Neural network cores used as learning methods are broadly divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent neural networks (recurrent boltzmann machine). And this learning model can be applied.

Terahertz (THz) communications

THz communication can be applied in the 6G system. As an example, the data transfer rate can be increased by increasing the bandwidth. This can be accomplished by using sub-THz communications with wide bandwidth and applying advanced massive MIMO technology.

Figure 9 is a diagram showing an electromagnetic spectrum applicable to the present disclosure. As an example, referring to Figure 9, THz waves, also known as submillimeter radiation, typically represent 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 the main part of the THz band for cellular communications. Adding the Sub-THz band to the 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 wideband, but it is at the border of the wideband and immediately behind the RF band. Therefore, this 300 GHz-3 THz band shows similarities to RF.

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

Terahertz (THz) wireless communication

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

Referring to Figure 10, THz wireless communication uses wireless communication using THz waves with a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and is a terahertz (THz) band wireless communication that uses a very high carrier frequency of 100 GHz or more. It can mean communication. THz waves are located between RF (Radio Frequency)/millimeter (mm) and infrared bands. (i) Compared to visible light/infrared, they penetrate non-metal/non-polarized materials better and have a shorter wavelength than RF/millimeter waves, so they have high straightness. Beam focusing may be possible.

Multi-arm bandits (MAB)

MAB refers to a system in which one candidate can be selected at a time in an environment where there are multiple selectable candidates, and the level of compensation provided in response to selection is different for each candidate. Here, the selectable candidate may be referred to as an arm. At this time, given a limited number of N choices, the MAB problem is to find the answer to how to choose to maximize the sum of rewards.

MAB problems can be solved through exploration and exploitation. Exploitation is a method of selecting the best candidate based on existing observations, and exploration is a method of selecting a new candidate to obtain more observation results. If there is too little accumulated exploration, choices may be made based on incorrect information, and conversely, if there is too much exploration, unnecessary opportunity costs may be incurred to obtain more information despite having sufficient information. In this way, use and exploration are in a trade-off relationship, and optimizing them is the key to solving the MAB problem.

thompson sampling (TS)

As one approach to solving the MAB problem, Thompson sampling can be used. Thompson sampling expresses the probability of receiving a positive reward when selecting each arm as a beta distribution. Here, the beta distribution is a probability distribution model expressed by two parameters α and β. According to Thompson sampling, candidate selection is achieved by randomly sampling values on the x-axis for each of the beta distributions and identifying the candidate corresponding to the largest value. The reward value according to the selection of the corresponding candidate is used to update α and β, which constitute the beta distribution of the corresponding sign. For example, a positive result increases α by 1, and a negative result increases β by 1.

The beta distribution used to express the probability distribution of each candidate in Thompson sampling is defined as [Equation 1].

Referring to [Equation 1], the beta distribution is a continuous probability distribution defined in the [0, 1] interval by two parameters α and β. The beta distribution is visualized as a graph as shown in Figure 11 below. 11 shows examples of probability density functions of beta distributions applicable to the present disclosure. Figure 11 shows that (α, β) is (1/3,1), (10,30), (20,20), (1,3), (2,6), (4,4), (2/ Examples of beta distributions are 3,2/3), (2,1), and (1,1). Referring to FIG. 11, the larger the α/(α+β) value, the closer the center of the beta distribution is to 1, and the larger the β/(α+β) value, the closer the center of the beta distribution is to 0. The larger the (α+β) value, the narrower the width of the beta distribution, and all values become closer to the center. Additionally, the smaller the (α+β) value, the wider the values of the beta distribution are distributed.

According to Thompson sampling, the reward distribution of each candidate is estimated using existing data, and the candidate who will give the highest reward is selected according to the estimated distribution. Specifically, one candidate is stochastically selected by random sampling based on a beta distribution. α or β of the selected candidate is updated based on the results of performing the action according to the selected candidate. As the number of times a candidate is selected increases, the beta distribution will change to become more concentrated in the central position. The larger the proportion of α, the higher the probability of being selected again next time, and the larger the proportion of β, the higher the probability of being selected again next time. It gets lower. If the number of candidate selections is small, the beta distribution will change to a widely distributed form, increasing the possibility of being selected in the future.

Specific examples of updating beta distributions are shown below in FIGS. 12A to 12D. 12A to 12D show examples of updating a probability distribution model applicable to the present disclosure. Figures 12A to 12D illustrate changes in three beta distributions (eg, Arm 1, Arm 2, and Arm 3) when approximately 1500 selections are made.

Referring to Figure 12a, the (α,β) of the first arm 1, arm 2, and arm 3 are the same as (1,1), (1,1), and (1,1). Since (α,β) is (1,1), the beta distribution has a uniform distribution with the same probability (e.g. 1) for all values of x. Since all three arms have the same probability distribution, the search begins with the same probability.

Referring to Figure 12b, after about 8 searches are performed, the (α,β) of arm 1, arm 2, and arm 3 are (3,2), (2,3), and (2,2). As the beta distribution is updated, the probability of each cancer being selected is also updated. No clear differences between cancers have yet been identified. For each of Arm 1, Arm 2, and Arm 3, one value is sampled from the x-axis based on probability. In the case of Figure 12b, since the selected value in the beta distribution of arm 3 is the largest, arm 3 will be selected. The selection of the value follows the corresponding beta distribution and is performed by random sampling considering probability. For example, in the case of a (2,2) beta distribution such as Arm3, if random sampling is performed considering probability, 0.5, which has the highest probability, will be selected as the highest frequency, but values other than 0.5 also have lower frequencies. can be selected. Specifically, in the case of a (2,2) beta distribution such as Arm3, the y-axis value of 0.5 is about 1.5, and the y-axis value of 0.2 is about 1, so the frequency with which 0.5 is selected through random sampling is 0.2. It can be understood that this is about 1.5 times the frequency.

Referring to Figure 12c, after 13 selections, the (α,β) of arm 1, arm 2, and arm 3 are (4,3), (2,3), and (5,2). In the case of Figure 12c, the probability of being selected in the order of arm 3, arm 1, and arm 2 tends to decrease. At this time, one value is sampled on the x-axis based on probability for each of Arm 1, Arm 2, and Arm 3, and since the value selected from the beta distribution of Arm 3 is the largest, Arm 3 will be selected. Referring to Figure 12d, after 1496 selections, the (α, β) of arm 1, arm 2, and arm 3 are (33,100), (100,223), and (436,611). Because about 1,500 sufficient searches have been performed, the probability that arm 3 will be selected is overwhelmingly high.

UCB (upper confidence bound)

UCB is another technique to solve the MAB problem. UCB is an algorithm that determines the best candidate by considering the uncertainty of data in a given situation and environment. According to UCB, the selector uses past observed data to estimate the average reward value of each candidate when selecting, and increases the probability of being selected by adding a certain weight to candidates with high uncertainty. Through this, UCB makes it possible to proceed with selection while conducting balanced exploration. The selection algorithm of the UCB model can be expressed as [Equation 2] below.

In [Equation 2], A _t is the index of the candidate to be currently selected, a is the index of the candidate, Q _t (a) is the average reward value when selecting candidate a in the tth selection, c is a parameter expressing the degree of search, t means the number of selections to date, and N _t (a) means the number of times candidate a has been selected so far.

Specific embodiments of the present disclosure

This disclosure relates to handover in a wireless communication system, and relates to technology for reducing the number of unnecessary measurement report (MR) transmissions without unnecessarily increasing the handover frequency. Specifically, this disclosure proposes a technology for efficient early handover based on artificial intelligence.

As mobile subscribers and data communication volume increase, mobile communication networks are developing in the form of ultra-small dense cells. Accordingly, the frequency of handovers that occur due to movement of the terminal may increase. In this situation, with respect to handover conditions, using a fixed threshold for the received signal strength (RSS) size and retention time of the target cell causes unnecessary measurement report transmission and handover failure (HOF). ) rate may increase, which will result in throughput and energy waste.

The current handover procedure is as follows. After the terminal connects to the cellular network, handover can be performed as follows. The handover procedure may vary depending on the transmission method of the measurement report. The measurement report transmission method can be divided into an event triggered measurement report transmission method and a periodic measurement report transmission method.

In the case of event-triggered measurement report transmission, the terminal measures the RSS value of the serving cell according to the received measurement configuration information, and if the RSS value is lower than the set threshold, event A2 is sent. In response to satisfaction, a measurement report is sent. Thereafter, in order to select a target cell, the serving cell base station transmits new measurement configuration information, and the terminal measures the RSS values of the serving and adjacent cells. At this time, if the handover margin (HM) and time-to-trigger (TTT) conditions are satisfied, the terminal transmits a measurement report. The serving cell base station determines handover based on the measured RSS value and requests handover from the target cell base station. When a response is received, the serving cell base station transmits a handover command to the terminal, and when the handover is completed, a confirmation message is received from the target cell base station.

In the case of periodic measurement report transmission, the terminal measures the RSS value of the serving cell according to the received measurement setting information and periodically transmits the measurement report. When the measured RSS value is lower than the set threshold, the serving cell base station provides new measurement setting information to select a target cell. If the size and retention time of the measured RSS value satisfy certain conditions, the serving cell base station determines handover and requests handover from the target cell base station. When a response is received, the serving cell base station transmits a handover command to the terminal, and when the handover is completed, a confirmation message is received from the target cell base station.

According to the above-described handover procedure, the following problems may occur. In the case of event triggered measurement report transmission, if the thresholds related to RSS size and retention time are set large, measurement report transmission is delayed, and the failure rate and retransmission probability of measurement report transmission increase. This results in throughput and energy waste, and may also increase the HOF rate. In the case of periodic measurement report transmission, if the thresholds related to the RSS size and retention time are set large, the handover decision may be delayed and the number of measurement report transmissions may increase. This results in throughput and energy waste, and may also increase the HOF rate.

Therefore, in order to improve communication performance and energy efficiency, a method is needed to minimize the impact of the corresponding threshold. Accordingly, the present disclosure proposes a technology that can minimize the impact of the target cell's RSS size and retention time on the threshold, improve communication performance, and increase energy efficiency without increasing the handover frequency. do.

FIGS. 13A and 13B show examples of locations and timings of handover in a wireless communication system according to an embodiment of the present disclosure.

Referring to FIG. 13A, base station 1310-1 is currently the serving base station of terminal 1320. The terminal 1320 moves toward the cell of the base station 1310-2, and accordingly, handover from the base station 1310-1 to the base station 1310-2 will be required. At this time, if handover is performed on the first border line 1302a according to the conventional handover procedure, handover according to various embodiments of the present disclosure may be performed on the second border line 1304a. That is, according to various embodiments of the present disclosure, handover can be performed earlier.

Figure 13b illustrates changes in RSS values for the base station 1310-1 and base station 1310-2 according to the movement of the terminal 1320. Referring to FIG. 13B, as the terminal 1320 moves, a handover from the base station 1310-1 to the base station 1310-2 will be required. At this time, if handover is performed at the first time point 1302b according to the conventional handover procedure, handover according to various embodiments of the present disclosure may be performed at the second time point 1304b. That is, according to various embodiments of the present disclosure, handover can be performed earlier.

For early handover as shown in FIGS. 13A and 13B, the RSS size and retention time measured for the target cell can be adjusted based on handover prediction information. In other words, unlike the conventional technology, the proposed technology adjusts the RSS value and retention time for cells selected by prediction based on artificial intelligence (e.g., cells whose reliability is above a certain standard in the prediction result), thereby ensuring that handover is relative. encourage it to proceed early. Through this, the number of unnecessary measurement report transmissions and HOF ratio can be reduced.

Figure 14 illustrates the concept of handover in a wireless communication system according to an embodiment of the present disclosure. Referring to FIG. 14, the terminal 1420 moves from the cell of the base station 1410-1 to the cell of the base station 1410-2. Accordingly, the A2 event may be triggered at the first point 1402, and handover may be performed at the second point 1404. On the movement path of the terminal 1420, the second point 1404 is a location reached before the handover performance point 1406 according to the prior art.

To this end, the base station 1410-1, which is a serving cell, can retain information related to handover prediction at all locations. When the RSS value for the serving cell measured by the terminal 1420 falls below a certain value, a handover prediction operation is performed by the terminal 1420 or the base station 1410-1. If the reliability of the cell selected by prediction is above a certain standard, a certain gain is added to the RSS value and the time counter value measured for the selected cell, and thus the handover can proceed early.

Depending on the needs or implementation of the system, the prediction operation may be performed by the base station 1410-1 or the terminal 1420. When the prediction operation is performed in the terminal 1420, the number of prediction operations can be minimized, and there is an advantage in that the computational burden of the base station 1410-1 is shared with the terminal 1420. Information (e.g., prediction model and input information) required to process the prediction operation in the terminal 1420 may be provided from the base station 1410-1. When the prediction operation is performed at the base station 1410-1, signaling overhead between the base station 1410-1 and the terminal 1420, the amount of calculation that the terminal 1420 must bear, and the energy of the terminal 1420 Consumption can be minimized. In this case, the base station 1410-1 delivers the prediction result (eg, selected cell information) to the terminal 1420.

For handover according to various embodiments, a prediction model is used. Selection of a target cell can be treated as a MAB problem, and accordingly, this disclosure proposes using the UCB technique or the Thompson sampling technique to solve the MAB problem.

Prediction algorithms such as the UCB technique and the Thompson sampling technique are techniques that balance exploitation and exploration. Here, utilization is a concept in which the terminal predicts and recommends the best cell, and exploration can be understood as a concept in which a cell with high uncertainty is recommended in order to collect more information. Since the conventional handover technology follows usage-based recommendation, which is its main focus, it has the limitation of recommending only good cells in the current situation and environment. In contrast, the proposed technology can appropriately recommend new cells through search and efficiently reflect the feedback from the cell to the terminal and all base stations. There is a trade-off between use and discovery, and adjusting use and discovery may temporarily be detrimental to the terminal. However, because the terminal checks several cells through search, overall efficiency can be increased.

The UCB technique determines the best cell by considering the uncertainty of candidate data in the current situation and environment during handover. The UCB technique uses past observed data to estimate the average compensation value of a candidate cell during handover, and then estimates the average compensation value of the candidate cell when the uncertainty is high (e.g., the number of times the cell has been selected is small compared to the total number of handovers up to the time of operation). By assigning a certain weight to cells, the probability of being selected increases. Through this, the UCB model can proceed with selection while exploring in a balanced manner. When the UCB model is applied to cell selection for handover, each variable in [Equation 2] described above can be understood as follows. Q _t (a) is the average reward value when handover to cell a is performed up to the point at which prediction operation is performed. {Sum of rewards given when handover to cell a} is calculated as {the number of handovers to cell a. It can be determined by dividing by }. t is the total number of handovers up to the current point, and N _t (a) is the number of times cell a has been selected up to the current point.

In the initial section of application of the UCB technique, even if the average compensation value is small, the algorithm can be operated to perform a relatively large number of searches by increasing the probability of cells with high data uncertainty being selected due to a small number of selections. As the number of handovers increases, uncertainty decreases and the impact of the added weight also decreases. If a sufficient number of handovers are performed, the average compensation value has a greater influence because the numerator of the weight value of the uncertainty of the average compensation value is a log scale and the denominator is a linear scale. Accordingly, after sufficient search, that is, after the uncertainty of the candidate data is reduced, handover will be performed mainly on cells with a high average compensation value.

In the UCB model, an action is an operation in which the terminal measures the RSS values of the serving cell and adjacent cells, adjusts the RSS size and retention time of the selected cell, and transmits the measurement result to the serving cell base station. It can be understood. Compensation may be determined and the compensation value (e.g. Q) may be updated based on whether quality of service (QoS) is maintained after handover to the selected cell and the RSS value for the new serving cell. For example, if the quality of service is not maintained for a certain time, the compensation is 0, and if the quality of service is maintained for a certain time or more, the RSS value and threshold of the new serving cell (e.g., the previous Compensation may be determined based on comparison of the RSS value for the serving cell + a constant value RSS _Δ ).

In [Equation 3], R is the compensation value, RSS _T is the RSS for the cell after handover, RSS _S is the RSS value for the previous serving cell, and RSS _Δ means the threshold for determining whether or not quality of service is maintained. do.

The Thompson sampling technique is an algorithm that determines the best cell in the current situation and environment during handover. The Thompson sampling technique is an algorithm that estimates the reward distribution of candidate RATs for handover based on past observed data, and selects the candidate who will give the highest reward in the future based on the estimated distribution with a high probability. am. In the most basic Bernoulli Thompson sampling, the reward given to each candidate has the value of 0 or 1 with probability p by Bernoulli trial, and the prior probability of p can follow the Beta distribution. The beta distribution is a continuous probability distribution defined in the interval [0, 1] by two parameters α and β. The beta distribution is visualized as a graph as shown in Figure 15 below.

Figure 15 shows examples of probability density functions of beta distributions usable in a wireless communication system according to an embodiment of the present disclosure. Figure 15 illustrates beta distributions where (α,β) is (0.5,0.5),(5,1),(1,3),(2,2),(2,5). Referring to FIG. 15, the larger the α/(α+β) value, the closer the center of the beta distribution is to 1, and the larger the β/(α+β) value, the closer the center of the beta distribution is to 0. For example, if a reward of 1 and a reward of 0 occur 5 and 3 times, respectively, for a selected candidate, the reward probability p of that candidate can be estimated to have a distribution of Beta(5, 3).

When Thompson sampling is applied to the selection of RATs for handover according to various embodiments, selectable RATs correspond to the beta distributions illustrated in FIG. 15. In this case, the RAT is selected using probability matching based on the given exclusive distributions, that is, the estimated distribution, and this is a method that maximizes the probability of receiving a positive reward for the RAT selected at the base station. .

The Thompson sampling technique has a faster convergence speed and better performance than the UCB technique, but has a relatively large computational burden due to the calculation of the beta distribution. Therefore, when processing prediction calculations in a terminal that is sensitive to energy efficiency and computational burden, the UCB technique may be suitable. On the other hand, when processing prediction calculations in a base station with relatively abundant computing resources, the Thompson sampling technique may be more suitable. However, the present disclosure is not limited to this, and for various reasons, the terminal may process a prediction operation according to the Thompson sampling technique, or the base station may process a prediction operation according to the UCB technique.

Hereinafter, various embodiments for performing handover based on a prediction model will be described. Specifically, this disclosure describes various embodiments for performing handover to a target cell selected based on a prediction model. However, even if the target cell is selected based on the prediction model, it is not guaranteed that handover to the selected cell will be successfully completed. That is, some of the embodiments described below correspond to cases in which handover to a target cell selected based on a prediction model is successful, but in some cases, handover may fail or handover may be performed to another cell.

Figure 16 shows an example of a procedure for performing handover in a wireless communication system according to an embodiment of the present disclosure. FIG. 16 illustrates a method of operating a terminal (e.g., the terminal 1320 in FIG. 13A and the terminal 1320 in FIG. 14) performing a handover.

Referring to Figure 16, in step S1601, the terminal receives configuration information related to measurement. Here, the measurement is the signal quality for the serving cell or neighboring cells required to perform handover (e.g., RSS, signal to interference noise ratio (SINR), reference signal received power (RSRP), reference signal received quality (RSRQ) etc.), and the setting information includes setting information related to measurement performance, measurement reporting, etc. According to one embodiment, additionally, the setting information may include at least one of information indicating a target cell selected using an artificial intelligence-based prediction model or information necessary to perform a prediction operation using a prediction model. For example, the information required to perform a prediction operation is information related to a prediction model for each location, information indicating the number of handovers performed by the prediction model and the number of times selected for each cell, or information included in the prediction model. It may include at least one piece of information indicating the probability distribution for each candidate cell.

In step S1603, the terminal transmits a measurement report containing measurement information for the cell selected based on the prediction. That is, the measurement report includes measurement values for target cells selected according to predictions made using a prediction model. Here, the prediction operation using the prediction model may be performed by the terminal or the base station. At this time, the signal quality value included in the measurement information is increased by a certain gain value and then transmitted. At this time, the increased amount may vary depending on the reliability of the prediction using the prediction model. Here, reliability may be proportional to a metric calculated or selected for selection using a prediction model.

In step S1605, the terminal receives a handover command message. After transmitting the measurement report, the terminal may receive a handover command message from the base station commanding to perform handover. The handover command message may be an RRC reconfiguration message containing information about the target cell. At this time, the handover command message may indicate a cell selected based on prediction as the target cell.

In step S1607, the terminal performs handover. The terminal may perform random access to the base station corresponding to the indicated target cell and transmit a handover confirmation message. At this time, according to one embodiment, the terminal may generate information for updating the prediction model according to the result of the handover and transmit the generated information through a handover confirmation message. Information for updating the prediction model includes information indicating whether service quality is maintained after handover, information indicating signal quality for the serving cell after handover, compensation value used in the prediction model, and probability for each cell included in the prediction model. It may include at least one piece of information indicating distribution.

According to the embodiment described with reference to FIG. 16, handover using a prediction model may be performed. In the above-described operations, the measurement value for the selected target cell is adjusted using the prediction model. At this time, the amount of increase or range of increase (e.g., upper limit value or lower limit value) of the adjusted measured value may be set by the base station. In this case, information related to the increase amount may be included in setting information related to measurement. That is, the setting information may further include information (e.g., gain value, gain value range, etc.) necessary to adjust the measurement value of the selected target cell using the prediction model.

In the embodiment described with reference to FIG. 16, the measurement value for the selected target cell is adjusted using a prediction model. Additionally, according to one embodiment, the time counter value for the selected target cell may be adjusted using a prediction model. Here, the time counter value is a value that counts the elapsed time from the start of satisfying the condition in order to determine whether the state in which the measured value satisfies a specific condition is maintained for a given timer. Since handover begins when the time counter value reaches the timer value, the time counter value may be adjusted to be larger than actual for early handover. However, the counter value can be adjusted in the terminal or the base station. For example, if the measurement report is transmitted based on event triggering, the terminal can adjust the time counter value.

Figure 17 shows an example of a procedure for controlling handover in a wireless communication system according to an embodiment of the present disclosure. FIG. 17 illustrates an operation method of a base station (e.g., base station 1310-1 in FIG. 13A and base station 1410-1 in FIG. 14) that controls handover.

Referring to FIG. 17, in step S1701, the base station transmits configuration information related to measurement. Setting information includes setting information related to measurement performance, measurement reporting, etc. According to one embodiment, additionally, the setting information may include at least one of information indicating a target cell selected using an artificial intelligence-based prediction model or information necessary to perform a prediction operation using a prediction model. For example, the information required to perform a prediction operation is information related to a prediction model for each location, information indicating the number of handovers performed by the prediction model and the number of times selected for each cell, or information included in the prediction model. It may include at least one piece of information indicating the probability distribution for each candidate cell.

In step S1703, the base station receives a measurement report containing measurement information for the cell selected based on the prediction. That is, the measurement report includes measurement values for target cells selected according to predictions made using a prediction model. Here, the prediction operation using the prediction model may be performed by the terminal or the base station. At this time, the signal quality value included in the measurement information is increased by a certain gain value and then transmitted. At this time, the increased amount may vary depending on the reliability of the prediction using the prediction model. Here, the reliability may be proportional to the indicator calculated or selected for selection using the prediction model.

In step S1705, the base station transmits a handover command message. After receiving the measurement report, the base station may determine handover of the terminal based on the measurement value included in the measurement report. Accordingly, the base station can transmit a handover command message to the terminal indicating the cell selected based on prediction as the target cell.

In step S1707, the base station receives a handover confirmation message. The handover confirmation message is received from the base station of the target cell after the terminal's handover is completed. At this time, according to one embodiment, the handover confirmation message may include information for updating the prediction model. there is. Information for updating the prediction model includes information indicating whether service quality is maintained after handover, information indicating signal quality for the serving cell after handover, compensation value used in the prediction model, and probability for each cell included in the prediction model. It may include at least one piece of information indicating distribution.

In step S1709, the base station updates the prediction model. That is, the base station can update the prediction model based on the information included in the handover confirmation message. If the information included in the handover confirmation message includes observation information, the base station determines a compensation value based on the observation information and updates the prediction model using the determined compensation value. If the information included in the handover confirmation message includes an updated prediction model, the base station may replace at least part of the existing prediction model with the updated prediction model.

As described above, handover can be performed using a prediction model. The handover procedure according to various embodiments can minimize the number of measurement reports by using prediction information to select a target cell and performing early handover. At this time, depending on the transmission method of the measurement report and the processing entity of the prediction calculation using the artificial intelligence model, specific procedures can be divided as follows.

Scenario 1) Apply event triggering measurement reporting method and perform prediction calculation on the terminal

Scenario 2) Apply event triggering MR method and perform prediction calculation at base station

Scenario 3) Apply periodic MR method and perform prediction calculation in terminal

Scenario 4) Apply periodic MR method and perform prediction calculation at base station

Each of the scenarios listed above can have advantages and disadvantages. For example, in scenario 2, since the measurement report is transmitted only under the condition that satisfies the measurement report event, signaling overhead is small and energy efficiency can be high compared to the periodic measurement report method. Additionally, in scenario 2, since the prediction operation is processed at the base station, the signaling overhead for prediction is small and the energy consumption of the terminal can be low. In addition, by processing the prediction calculation at a base station with relatively ample computing resources, there is less burden on using a prediction algorithm with a relatively high calculation amount but better performance.

Below, the handover procedure for each scenario is described in detail.

FIG. 18 illustrates an example of signal exchange for handover based on an event-triggered measurement report and prediction operation of a terminal in a wireless communication system according to an embodiment of the present disclosure. In Figure 18, the first base station 1810-1 is a serving base station, and the second base station 1810-2 is a target base station.

Referring to FIG. 18, in step S1801, the first base station 1810-1 transmits measurement setting information to the UE 1820. That is, after the UE 1820 is connected to the cellular network, the first base station 1810-1 transmits measurement settings to the UE 1820. In step S1803, the UE 1820 transmits a measurement report to the first base station 1810-1. That is, the UE 1820 determines that the A2 event is satisfied when the RSS value of the serving cell measured according to the measurement setting information becomes less than a certain value. Accordingly, the UE 1820 transmits a measurement report to the first base station 1810-1.

In step S1805, the first base station 1810-1 transmits measurement configuration information to the UE 1820. Here, according to one embodiment, the measurement setting information may include information necessary for prediction operation. That is, the first base station 1810-1 transmits information necessary for prediction of the location of the UE 1820 (e.g., use model, input information of the model) to the UE 1820 through measurement settings. For example, in the case of the UCB model, information required for prediction may include the total number of handovers at each location within a certain range and the selected number of serving cells and candidate cells. As another example, in the case of a TS model, information required for prediction may include beta distribution information of serving cells and candidate cells at each location within a certain range.

In step S1807, the UE 1820 performs measurements on cells, selects a target cell based on a prediction operation, and then adjusts the RSS value and/or counter value of the selected cell. In other words, the UE 1820 measures the RSS of the serving cell and at least one candidate cell. Then, the UE 1820 selects the cell with the highest reliability by performing a prediction operation. For example, when the UCB model is used, the cell with the maximum value among the final values that reflect the uncertainty of the data in the average compensation value of the candidate cells is selected. As another example, when the TS model is used, the cell with the maximum value among the values randomly sampled from the beta distribution of candidate cells is selected. When the prediction reliability is above a certain standard, the UE 1820 adds a certain gain Δ to each of the RSS value and time counter of the selected cell. That is, the UE 1820 performs the calculation of RSS value = RSS value + Δ ₁ and time counter = time counter + Δ ₂ . At this time, the higher the reliability, the greater the benefit can be applied.

According to one embodiment, the range of gain Δ is set by the base station and may be included in the measurement settings transmitted in step S1805. Here, the method of adding gain may follow one of the two modes below. For the first mode, which allows the predicted cell to be selected as the target cell, the range of gain Δ is such that the final RSS value of the predicted target cell is greater than or equal to the maximum value of the RSS values of all neighboring cells, and a measurement report can be triggered. It can be set to be. In the case of the second mode, which increases the probability that a predicted cell is selected as a target cell by the base station, the range of gain Δ may be set based on a value or ratio defined for each step according to the sampling value for the predicted cell.

In step S1809, the UE 1820 transmits a measurement report to the first base station 1810-1. In other words, the UE 1820 transmits a measurement report to the serving cell. Here, the measurement report may include at least one of an RSS value summed with the gain and a time counter value summed with the gain. In step S1811, the first base station 1810-1 determines handover of the UE 1820. The first base station 1810-1 determines handover based on the measurement report. In this example procedure, the second base station 1810-2 is selected as the target cell. In step S1813, the first base station 1810-1 transmits a handover request message to the second base station 1810-2. Accordingly, the second base station 1810-2 determines whether to accept the UE 1820. In this example procedure, the second base station 1810-2 allows acceptance by the UE 1820. In step S1815, the second base station 1810-2 transmits a handover request ACK (acknowledge) message to the first base station 1810-1. In step S1817, the first base station 1810-1 transmits a handover command message to the UE 1820. Accordingly, the UE 1820 performs handover according to the command of the first base station 1810-1. Here, the handover command message indicates the second base station 1810-2 as the target cell, and the UE 1820 can perform handover to the second base station 1810-2.

In step S1819, the UE 1820 determines prediction model information to update. That is, after performing a handover to the target cell, the UE 1820 checks the RSS value of the new serving cell and whether the UE 1820 maintains service quality, and provides information for updating the prediction model based on the confirmed information (e.g. Determine the reward or Q value for the UCB model and the reward or beta distribution information for the TS model. In step S1821, the UE 1820 transmits a handover confirmation message to the second base station 1810-2. Here, the handover confirmation message includes information for updating the prediction model (hereinafter referred to as 'model update information'). That is, the UE 1820 transmits model update information together with ACK information. In step S1823, the second base station 1810-2 transmits a handover confirmation ACK message to the first base station 1810-1. Here, the handover confirmation ACK message includes model update information received from the UE (1820). In step S1825, the first base station 1810-1 updates the prediction model. That is, the first base station 1810-1 can update the prediction model based on the model update information included in the handover confirmation ACK message.

FIG. 19 illustrates an example of a procedure for handover based on an event-triggered measurement report and prediction operation of a terminal in a wireless communication system according to an embodiment of the present disclosure. Figure 19 illustrates an operation method of a terminal, a first base station operating as a serving base station, and a second base station operating as a target base station.

Referring to Figure 19, in step S1901, the terminal connects to the cellular network. In step S1903, the first base station transmits measurement settings to the terminal. In step S1905, the terminal detects the A2 event and transmits a measurement report to the first base station. In step S1907, the first base station transmits a measurement setting containing information necessary for prediction progress of the terminal. In step S1909, the terminal measures the RSS values of the serving cell and candidate cells, adjusts the RSS size and retention time measurement values of the cell selected by prediction, and transmits a measurement report.

In step S1911, the first base station determines whether to proceed with handover. When it is decided to proceed with the handover, in step S1913, the terminal receives a command from the first base station, which is the serving cell base station, and proceeds with handover to the second base station, which is the selected target cell. In step S1915, the terminal calculates prediction model information to be updated. In step S1917, the terminal transmits a handover confirmation message to the second base station, which is the target cell. The handover confirmation message includes prediction model information to be updated. In step S1919, the second base station transmits a handover ACK message to the first base station, which is the previous serving cell. The handover ACK message includes prediction model information to be updated. In step S1921, the first base station updates the prediction model.

FIG. 20 illustrates an example of signal exchange for handover based on an event-triggered measurement report and prediction operation of a base station in a wireless communication system according to an embodiment of the present disclosure. In Figure 20, the first base station 2010-1 is a serving base station, and the second base station 2010-2 is a target base station.

Referring to FIG. 20, in step S2001, the first base station 2010-1 transmits measurement setting information to the UE 2020. That is, after the UE (2020) is connected to the cellular network, the first base station (2010-1) transmits measurement settings to the UE (2020). In step S2003, the UE (2020) transmits a measurement report to the first base station (2010-1). That is, the UE (2020) determines that the A2 event is satisfied when the RSS value of the serving cell measured according to the measurement setting information is smaller than a certain value. Accordingly, the UE (2020) transmits a measurement report to the first base station (2010-1).

In step S2005, the first base station 2010-1 selects a target cell based on a prediction operation. That is, the first base station 2010-1 selects a cell with the highest reliability by performing prediction using a model corresponding to the terminal location.

In step S2007, the first base station 2010-1 transmits measurement setting information to the UE 2020. Here, according to one embodiment, the measurement setting information may include information about the target cell selected in step S2005. That is, if the reliability of the cell selected according to the prediction is above a certain standard, the first base station 2010-1 adds information related to the prediction result to the measurement settings and transmits the measurement settings to the UE 2020.

In step S2009, the UE (2020) performs measurements on cells and adjusts the RSS value and/or counter value of the cell selected by prediction. In other words, the UE 2020 measures the RSS of the serving cell and at least one candidate cell. And, the UE (2020) adds a certain gain Δ to each of the RSS value and time counter of the selected cell, which are indicated through measurement information. That is, the UE (2020) performs the calculation of RSS value = RSS value + Δ ₁ and time counter = time counter + Δ ₂ . At this time, the higher the reliability, the greater the benefit can be applied.

According to one embodiment, the range of gain Δ is set by the base station and may be included in the measurement settings transmitted in step S2007. Here, the method of adding gain may follow one of the two modes below. For the first mode, which allows the predicted cell to be selected as the target cell, the range of gain Δ is such that the final RSS value of the predicted target cell is greater than or equal to the maximum value of the RSS values of all neighboring cells, and a measurement report can be triggered. It can be set to be. In the case of the second mode, which increases the probability that a predicted cell is selected as a target cell by the base station, the range of gain Δ may be set based on a value or ratio defined for each step according to the sampling value for the predicted cell.

In step S2011, the UE (2020) transmits a measurement report to the first base station (2010-1). In other words, the UE 2020 transmits a measurement report to the serving cell. Here, the measurement report may include at least one of an RSS value summed with the gain and a time counter value summed with the gain. In step S2013, the first base station 2010-1 determines handover of the UE 2020. The first base station (2010-1) determines handover based on the measurement report. In this example procedure, the second base station 2010-2 is selected as the target cell. In step S2015, the first base station 2010-1 transmits a handover request message to the second base station 2010-2. Accordingly, the second base station 2010-2 determines whether to accept the UE 2020. In this example procedure, the second base station 2010-2 allows acceptance by the UE 2020. In step S2017, the second base station 2010-2 transmits a handover request ACK message to the first base station 2010-1. In step S2019, the first base station 2010-1 transmits a handover command message to the UE 2020. Accordingly, the UE 2020 performs handover according to the command of the first base station 2010-1. Here, the handover command message indicates the second base station 2010-2 as the target cell, and the UE 2020 can perform handover to the second base station 2010-2.

In step S2021, the UE (2020) transmits a handover confirmation message to the second base station (2010-2). Here, the handover confirmation message includes model update information. That is, the UE (2020) transmits model update information together with ACK information. Here, the model update information may include an RSS value for the second base station 2010-2, which is a new serving cell, and information indicating whether service quality is maintained. In step S2023, the second base station 2010-2 transmits a handover confirmation ACK message to the first base station 2010-1. Here, the handover confirmation ACK message includes model update information received from the UE (2020). In step S2025, the first base station 2010-1 updates the prediction model. That is, the first base station 2010-1 may update the prediction model based on the model update information included in the handover confirmation ACK message.

FIG. 21 illustrates an example of a procedure for handover based on an event-triggered measurement report and a prediction operation of a base station in a wireless communication system according to an embodiment of the present disclosure. Figure 21 illustrates an operation method of a terminal, a first base station operating as a serving base station, and a second base station operating as a target base station.

Referring to Figure 21, in step S2101, the terminal connects to the cellular network. In step S2103, the first base station transmits measurement settings to the terminal. In step S2105, the terminal detects the A2 event and transmits a measurement report to the first base station. In step S2107, the first base station transmits a measurement setting including the result of prediction by the first base station. In step S2109, the terminal measures the RSS values of the serving cell and candidate cells, adjusts the RSS size and retention time measurement values of the cell selected by prediction, and transmits a measurement report.

In step S2111, the first base station determines whether to proceed with handover. When it is decided to proceed with handover, in step S2113, the terminal receives a command from the first base station, which is the serving cell base station, and proceeds with handover to the second base station, which is the selected target cell. In step S2115, the terminal transmits a handover confirmation message to the second base station, which is the target cell. The handover confirmation message is prediction model information to be updated and includes information indicating whether to maintain service and an RSS value for the new serving cell. In step S2117, the second base station transmits a handover ACK message to the first base station, which is the previous serving cell. The handover ACK message includes information indicating whether to maintain service and an RSS value for the new serving cell. In step S2119, the first base station updates the prediction model.

FIG. 22 illustrates an example of signal exchange for handover based on periodic measurement reporting and prediction operation of a terminal in a wireless communication system according to an embodiment of the present disclosure. In Figure 22, the first base station 2210-1 is a serving base station, and the second base station 2210-2 is a target base station.

Referring to FIG. 22, in step S2201, the first base station 2210-1 transmits measurement setting information to the UE 2220. That is, after the UE 2220 is connected to the cellular network, the first base station 2210-1 transmits measurement settings to the UE 2220. In step S2203, the UE 2220 transmits a measurement report to the first base station 2210-1. At this time, the UE 2220 transmits a measurement report according to a cycle configured by measurement configuration information.

In step S2205, the first base station 2210-1 transmits measurement configuration information to the UE 2220. At this time, if the RSS value of the serving cell measured by the UE 2220 is smaller than a certain value, the first base station 2210-1 will include information necessary for prediction operation at the location of the UE 2220 in the measurement settings. You can. That is, the first base station 2210-1 transmits information necessary for prediction at the location of the UE 2220 (e.g., use model, input information of the model) to the UE 2220 through measurement settings. For example, in the case of the UCB model, information required for prediction may include the total number of handovers at each location within a certain range and the selected number of serving cells and candidate cells. As another example, in the case of a TS model, information required for prediction may include beta distribution information of serving cells and candidate cells at each location within a certain range.

In step S2207, the UE 2220 performs measurements on cells, selects a target cell based on a prediction operation, and then adjusts the RSS value of the selected cell. In other words, the UE 2220 measures the RSS of the serving cell and at least one candidate cell. Then, the UE 2220 selects the cell with the highest reliability by performing a prediction operation. For example, when the UCB model is used, the cell with the maximum value among the final values that reflect the uncertainty of the data in the average compensation value of the candidate cells is selected. As another example, when the TS model is used, the cell with the maximum value among the values randomly sampled from the beta distribution of candidate cells is selected. When the prediction reliability is above a certain standard, the UE 2220 adds a certain gain Δ to the RSS value of the selected cell. That is, the UE 2220 performs the calculation of RSS value = RSS value + Δ ₁ . At this time, the higher the reliability, the greater the benefit can be applied.

According to one embodiment, the range of gain Δ is set by the base station and may be included in the measurement settings transmitted in step S2205. Here, the method of adding gain may follow one of the two modes below. For the first mode, which allows the predicted cell to be selected as the target cell, the range of gain Δ is such that the final RSS value of the predicted target cell is greater than or equal to the maximum value of the RSS values of all neighboring cells, and a measurement report can be triggered. It can be set to be. In the case of the second mode, which increases the probability that a predicted cell is selected as a target cell by the base station, the range of gain Δ may be set based on a value or ratio defined for each step according to the sampling value for the predicted cell.

In step S2209, the UE 2220 transmits a measurement report to the first base station 2210-1. In other words, as the periodic transmission time of the measurement report arrives, the UE 2220 transmits the measurement report to the serving cell. Here, the measurement report may include at least one of information indicating the selected cell, an RSS value summed with gain, and reliability information for the selected cell.

In step S2211, the first base station 2210-1 adjusts the counter value of the selected cell. That is, the first base station 2210-1 adds a certain gain Δ to the time counter of the cell selected according to the prediction by the UE 2220. That is, the first base station 2010-1 performs the calculation of time counter = time counter + Δ ₂ . At this time, the higher the reliability, the greater the benefit can be applied.

In step S2213, the first base station 2210-1 determines handover of the UE 2220. The first base station 2210-1 determines handover based on the measurement report. In this example procedure, the second base station 2210-2 is selected as the target cell. In step S2215, the first base station 2210-1 transmits a handover request message to the second base station 2210-2. Accordingly, the second base station 2210-2 determines whether to accept the UE 2220. In this example procedure, the second base station 2210-2 allows acceptance by the UE 2220. In step S2217, the second base station 2210-2 transmits a handover request ACK message to the first base station 2210-1. In step S2219, the first base station 2210-1 transmits a handover command message to the UE 2220. Accordingly, the UE 2220 performs handover according to the command of the first base station 2210-1. Here, the handover command message indicates the second base station 2210-2 as the target cell, and the UE 2220 can perform handover to the second base station 2210-2.

In step S2221, the UE 2220 determines prediction model information to be updated. That is, after performing a handover to the target cell, the UE 2220 checks the RSS value of the new serving cell and whether the UE 2220 maintains service quality, and provides model update information (e.g., in the case of the UCB model) based on the confirmed information. Determine compensation or Q value, compensation or beta distribution information in the case of TS model). In step S2223, the UE 2220 transmits a handover confirmation message to the second base station 2210-2. Here, the handover confirmation message includes model update information. In step S2225, the second base station 2210-2 transmits a handover confirmation ACK message to the first base station 2210-1. Here, the handover confirmation ACK message includes model update information received from the UE (2220). In step S2227, the first base station 2210-1 updates the prediction model. That is, the first base station 2210-1 can update the prediction model based on the model update information included in the handover confirmation ACK message.

FIG. 23 illustrates an example of a procedure for handover based on periodic measurement reporting and prediction calculation of a terminal in a wireless communication system according to an embodiment of the present disclosure. Figure 23 illustrates an operation method of a terminal, a first base station operating as a serving base station, and a second base station operating as a target base station.

Referring to Figure 23, in step S2301, the terminal connects to the cellular network. In step S2303, the first base station transmits measurement settings to the terminal. In step S2305, the terminal periodically transmits a measurement report to the first base station. In step S2307, the first base station transmits a measurement setting containing information necessary for prediction progress of the terminal. In step S2309, the terminal measures the RSS values of the serving cell and candidate cells, adjusts the RSS size and retention time measurement values of the cell selected by prediction, and transmits a measurement report.

In step S2311, the first base station determines whether to proceed with handover. When it is decided to proceed with the handover, in step S2313, the terminal receives a command from the first base station, which is the serving cell base station, and proceeds with handover to the second base station, which is the selected target cell. In step S2315, the terminal calculates prediction model information to be updated. In step S2317, the terminal transmits a handover confirmation message to the second base station, which is the target cell. The handover confirmation message includes prediction model information to be updated. In step S2319, the second base station transmits a handover ACK message to the first base station, which is the previous serving cell. The handover ACK message includes prediction model information to be updated. In step S2321, the first base station updates the prediction model.

FIG. 24 illustrates an example of signal exchange for handover based on periodic measurement reporting and prediction operation of a base station in a wireless communication system according to an embodiment of the present disclosure. In Figure 24, the first base station 2410-1 is a serving base station, and the second base station 2410-2 is a target base station.

Referring to FIG. 24, in step S2401, the first base station 2410-1 transmits measurement setting information to the UE 2420. That is, after the UE 2420 is connected to the cellular network, the first base station 2410-1 transmits measurement settings to the UE 2420. In step S2403, the UE 2420 transmits a measurement report to the first base station 2410-1. In step S2405, the first base station 2410-1 transmits measurement configuration information to the UE 2420. In step S2407, the UE 2420 transmits a measurement report to the first base station 2410-1. That is, the UE 2420 transmits a measurement report according to a period configured by measurement configuration information.

In step S2409, the first base station 2010-1 selects a target cell based on the prediction operation and adjusts the RSS value and/or counter value of the cell selected by prediction. That is, the first base station 2010-1 selects a cell with the highest reliability by performing prediction using a model corresponding to the terminal location. Then, the first base station 2010-1 adds a certain gain Δ to each of the RSS value and time counter of the selected cell. That is, the first base station (2010-1) performs the calculation of RSS value = RSS value + Δ ₁ and time counter = time counter + Δ ₂ . At this time, the higher the reliability, the greater the benefit can be applied.

According to one embodiment, the range of gain Δ may be set by the base station. Here, the method of adding gain may follow one of the two modes below. For the first mode, which allows the predicted cell to be selected as the target cell, the range of gain Δ is such that the final RSS value of the predicted target cell is greater than or equal to the maximum value of the RSS values of all neighboring cells, and a measurement report can be triggered. It can be set to be. In the case of the second mode, which increases the probability that a predicted cell is selected as a target cell by the base station, the range of gain Δ may be set based on a value or ratio defined for each step according to the sampling value for the predicted cell.

In step S2411, the first base station 2410-1 determines handover of the UE 2420. The first base station 2410-1 determines handover based on the measurement report. In this example procedure, the second base station 2410-2 is selected as the target cell. In step S2413, the first base station 2410-1 transmits a handover request message to the second base station 2410-2. Accordingly, the second base station 2410-2 determines whether to accept the UE 2420. In this example procedure, the second base station 2410-2 allows acceptance by the UE 2420. In step S2415, the second base station 2410-2 transmits a handover request ACK message to the first base station 2410-1. In step S2417, the first base station 2410-1 transmits a handover command message to the UE 2420. Accordingly, the UE 2420 performs handover according to the command of the first base station 2410-1. Here, the handover command message indicates the second base station 2410-2 as the target cell, and the UE 2420 can perform handover to the second base station 2410-2.

In step S2419, the UE 2420 transmits a handover confirmation message to the second base station 2410-2. Here, the handover confirmation message includes model update information. That is, the UE 2420 transmits model update information together with ACK information. Here, the model update information may include an RSS value for the second base station 2410-2, which is a new serving cell, and information indicating whether or not quality of service is maintained. In step S2421, the second base station 2410-2 transmits a handover confirmation ACK message to the first base station 2410-1. Here, the handover confirmation ACK message includes model update information received from the UE (2420). In step S2423, the first base station 2410-1 updates the prediction model. That is, the first base station 2410-1 can update the prediction model based on the model update information included in the handover confirmation ACK message.

FIG. 25 illustrates an example of a procedure for handover based on periodic measurement reporting and prediction operation of a base station in a wireless communication system according to an embodiment of the present disclosure. Figure 25 illustrates an operation method of a terminal, a first base station operating as a serving base station, and a second base station operating as a target base station.

Referring to Figure 25, in step S2501, the terminal connects to the cellular network. In step S2503, the first base station transmits measurement settings to the terminal. In step S2505, the terminal measures the RSS value for the first base station, which is the serving cell, and then periodically transmits a measurement report to the first base station. In step S2507, the first base station transmits measurement settings to the terminal. In step S2509, the terminal measures RSS values for the serving cell and at least one candidate cell and then periodically transmits a measurement report to the first base station.

In step S2511, the first base station determines whether to proceed with handover. When it is decided to proceed with the handover, in step S2513, the terminal receives a command from the first base station, which is the serving cell base station, and proceeds with handover to the second base station, which is the selected target cell. In step S2515, the terminal transmits a handover confirmation message to the second base station, which is the target cell. The handover confirmation message is prediction model information to be updated and includes information indicating whether to maintain service and an RSS value for the new serving cell. In step S2517, the second base station transmits a handover ACK message to the first base station, which is the previous serving cell. The handover ACK message includes information indicating whether to maintain service and an RSS value for the new serving cell. In step S2519, the first base station updates the prediction model.

As in the various embodiments described above, handover performance can be gradually improved by using the Thompson sampling model. The beta distribution update according to compensation in the Thompson sampling model is shown in Figure 26 below. Figure 26 shows a reward structure based on Thompson sampling in a wireless communication system according to an embodiment of the present disclosure. Figure 26 shows a structure in which compensation is fed back based on evaluation indicators (e.g., whether QoS is maintained) after completion of handover. Referring to FIG. 26, sampling includes randomly sampling values from the beta distribution 2610 of candidate cells. Among the sampled values, the maximum value is selected by optimization 2620. The action measures the RSS values for the serving cell and neighboring cells at the terminal, and determines the cell with the highest probability of handover selection (e.g., the cell with the highest value among the values randomly sampled from the beta distribution of neighboring cells). ) means adjusting the RSS and retention time and then transmitting the measurement results to the serving cell base station. Observation means updating the parameters of the beta function according to a reward determined based on an evaluation (2630) that checks whether QoS is maintained after completion of handover and the RSS value for the new serving cell. The updated parameters are reflected in the beta distribution 2610.

It is clear that examples of the proposed methods described above can also be included as one of the implementation methods of the present disclosure, and thus can be regarded as a type of proposed methods. Additionally, the proposed methods described above may be implemented independently, but may also be implemented in the form of a combination (or merge) of some of the proposed methods. A rule may be defined so that the base station informs the terminal of the application of the proposed methods (or information about the rules of the proposed methods) through a predefined signal (e.g., a physical layer signal or a higher layer signal). .

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

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

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

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

Claims (15)

In a method of operating a UE (user equipment) in a wireless communication system,
Receiving setup information related to measurement from a first base station;
performing measurement based on the setting information;
Transmitting a measurement report including the results of the measurement to the first base station;
Receiving a handover command message from the first base station;
In response to the handover command message, performing a handover to a second base station,
The measurement report includes measurement information about the second base station as a target cell selected using a prediction model. In claim 1,
A method wherein the measurement information for the second base station includes an additional gain and a summed signal quality value in response to selection using the prediction model. In claim 1,
wherein the measurement report is transmitted based on a time counter value summed with an additional gain in response to selection using the prediction model. In claim 1,
The setting information includes at least one of information indicating the second base station as a target cell selected using the prediction model or information necessary to perform a prediction operation using the prediction model. In claim 4,
The information required to perform a prediction operation using the prediction model includes information indicating the number of handovers performed by the prediction model and the number of selected cells for each cell, or information indicating the probability distribution for each candidate cell included in the prediction model. How to include at least one of the following information. In claim 1,
The setting information includes information related to at least one gain value added in response to selection using the prediction model. In claim 1,
After completing handover to the second base station, the method further includes transmitting information necessary to update the prediction model to the second base station. In claim 7,
Information required to update the prediction model includes information indicating whether to maintain service quality after handover to the second base station, information indicating signal quality for the second base station, and compensation value used in the prediction model. , A method including at least one of information indicating a probability distribution for each cell included in the prediction model. In claim 1,
The prediction model includes an upper confidence bound (UCB) model or a Tompson sampling (TS) model. In claim 1,
The target cell is selected by one of the UE or the first base station using the prediction model. In a method of operating a first base station in a wireless communication system,
Receiving setup information related to measurement from a user equipment (UE);
Receiving a measurement report including results of measurement performed based on the configuration information from the UE;
In response to the measurement report, transmitting a handover request message to a second base station; and
Including transmitting a handover command message to the UE,
The measurement report includes measurement information about the second base station as a target cell selected using a prediction model. In UE (user equipment) in a wireless communication system,
transceiver; and
Includes a processor connected to the transceiver,
The processor,
Receive setting information related to measurement from the first base station,
Perform measurements based on the setting information,
Transmitting a measurement report including the results of the measurement to the first base station,
Receiving a handover command message from the first base station,
In response to the handover command message, control to perform handover to a second base station,
The measurement report includes measurement information about the second base station as a target cell selected using a prediction model. In a first base station in a wireless communication system,
transceiver; and
Includes a processor connected to the transceiver,
The processor,
Receive measurement-related setting information from UE (user equipment),
Receiving a measurement report from the UE including the results of the measurement performed based on the configuration information,
In response to the measurement report, transmitting a handover request message to the second base station,
Controls to transmit a handover command message to the UE,
The measurement report includes measurement information about the second base station as a target cell selected using a prediction model. In the device,
at least one processor;
At least one computer memory connected to the at least one processor and storing instructions that direct operations as executed by the at least one processor,
The operations are such that the device,
Receiving setup information related to measurement from a first base station;
performing measurement based on the setting information;
Transmitting a measurement report including the results of the measurement to the first base station;
Receiving a handover command message from the first base station;
In response to the handover command message, performing a handover to a second base station,
The measurement report includes measurement information about the second base station as a target cell selected using a prediction model. A non-transitory computer-readable medium storing at least one instruction, comprising:
Contains the at least one instruction executable by a processor,
The at least one command may cause the device to:
Receive setting information related to measurement from the first base station,
Perform measurements based on the setting information,
Transmitting a measurement report including the results of the measurement to the first base station,
Receiving a handover command message from the first base station,
In response to the handover command message, control to perform handover to a second base station,
The measurement report includes measurement information for the second base station as a target cell selected using a prediction model.

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