KR20240038652A - Apparatus and method for selecting wireless access technology in consideration of battery efficiency in a wireless communication system - Google Patents

Abstract

The present disclosure is intended to perform handover in consideration of battery efficiency in a wireless communication system, and the operation method of the terminal includes measurement configuration including information related to the probability distribution model for candidate RATs from the first base station. Receiving information, transmitting a measurement report including signal quality values for the candidate RATs to the first base station, receiving a handover command message from the first base station, the hand After accessing the second base station indicated by the over command message, it may include transmitting information related to updating the probability distribution information to the second base station.

Description

Apparatus and method for selecting wireless access technology in consideration of battery efficiency in a wireless communication system

The following description relates to a wireless communication system and to an apparatus and method for selecting a radio access technology (RAT) 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 increasing battery efficiency in a wireless communication system.

The present disclosure may provide an apparatus and method for selecting a radio access technology (RAT) that can increase battery efficiency 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 terminal in a wireless communication system includes receiving measurement configuration information including information related to a probability distribution model for candidate RATs from a first base station, the candidate RAT transmitting to the first base station a measurement report including signal quality values for After accessing the base station, it may include transmitting information related to updating the probability distribution information to the second base station. The measurement report includes a signal quality value for the second base station, and the signal quality value for the second base station may be determined based on the probability distribution model.

As an example of the present disclosure, a method of operating a base station in a wireless communication system includes transmitting measurement configuration information including information related to a probability distribution model for candidate RATs to a terminal, and sending measurement configuration information to the candidate RATs. receiving a measurement report including signal quality values for the terminal, transmitting a handover command message indicating a target base station determined based on the measurement report, and the probability distribution information from the target base station. It may include receiving information related to the update of . The measurement report includes a signal quality value for the target base station, and the signal quality value for the target base station may be determined based on the probability distribution model.

As an example of the present disclosure, a terminal in a wireless communication system includes a transceiver and a processor connected to the transceiver. The processor receives measurement configuration information including information related to a probability distribution model for candidate RATs from a first base station, and a measurement report including signal quality values for the candidate RATs. ) to the first base station, receive a handover command message from the first base station, connect to the second base station indicated by the handover command message, and then send information related to updating the probability distribution information. It can be controlled to transmit to the second base station. The measurement report includes a signal quality value for the second base station, and the signal quality value for the second base station may be determined based on the probability distribution model.

As an example of the present disclosure, a base station in a wireless communication system includes a transceiver and a processor connected to the transceiver. The processor transmits measurement configuration information including information related to probability distribution models for candidate RATs to the terminal, and sends a measurement report including signal quality values for the candidate RATs. It can be controlled to receive from the terminal, transmit a handover command message indicating a target base station determined based on the measurement report, and receive information related to the update of the probability distribution information from the target base station. The measurement report includes a signal quality value for the target base station, and the signal quality value for the target base station may be determined based on the probability distribution 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 directing operations as executed by the at least one processor; , the operations comprising: the device receiving, from a first base station, measurement configuration information including information related to a probability distribution model for candidate RATs, including signal quality values for the candidate RATs; transmitting a measurement report to the first base station, receiving a handover command message from the first base station, and connecting to the second base station indicated by the handover command message, the probability It may include transmitting information related to the update of distribution information to the second base station. The measurement report includes a signal quality value for the second base station, and the signal quality value for the second base station may be determined based on the probability distribution model.

As an example of the present disclosure, a non-transitory computer-readable medium storing at least one instruction includes the at least one instruction executable by a processor. Includes, wherein the at least one command is such that the device receives measurement configuration information including information related to a probability distribution model for candidate RATs from a first base station, and sends signals for the candidate RATs. After transmitting a measurement report including quality values to the first base station, receiving a handover command message from the first base station, and connecting to a second base station indicated by the handover command message, Control to transmit information related to the update of the probability distribution information to a second base station, wherein the measurement report includes a signal quality value for the second base station, and the signal quality value for the second base station includes the probability It may be determined based on a distribution 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, battery efficiency can be increased in a situation where a plurality of radio access technologies (RATs) are available.

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.
Figure 13 illustrates the concept of handover in a wireless communication system according to an embodiment of the present disclosure.
Figure 14 illustrates the concept of target RAT selection based on Thompson sampling (TS) 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 probability distribution sets defined according to battery status 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.
Figure 18 shows an example of a procedure for performing handover in a wireless communication system according to an embodiment of the present disclosure.
Figure 19 shows an example of a procedure for generating a measurement report in a wireless communication system according to an embodiment of the present disclosure.
Figure 20 shows an example of a procedure for handover in a wireless communication system according to an embodiment of the present disclosure.
Figure 21 shows an example of signal flow for handover in a wireless communication system according to an embodiment of the present disclosure.
Figure 22 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 operation 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 composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, and a memory control processor. 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 image 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 can 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. 10 is a diagram illustrating an example of a communication structure that can be provided in a 6G system applicable to the present disclosure.

Referring to Figure 10, 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 in 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) and thompson sampling (TS)

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.

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.

Specific embodiments of the present disclosure

This disclosure relates to RAT (radio access technology) selection in a wireless communication system, and relates to a technology for a terminal supporting multiple RATs to select a RAT in consideration of battery efficiency.

As communication systems continue to be generational from 4G to 5G and from 5G to 6G, the frequencies used for cellular communications will increase and the cell coverage area will become smaller. Therefore, during the transition period when the generation of the system is replaced, it is expected that base stations supporting the latest RAT will not be able to cover all areas. In this case, the handover frequency of the wireless device/terminal and the frequency of RAT selection during handover may increase.

Typically, the RAT is selected based on measurements of signal quality (e.g., received signal strength (RSS), signal to interference noise ratio (SINR), reference signal received power (RSRP), reference signal received quality (RSRQ), etc.) do. When selecting the RAT with the best signal quality after measuring the signal quality of cells supporting each RAT, the terminal can select the RAT that does not consider unnecessary handover and energy efficiency. In this case, energy waste may occur, which can be a serious issue for devices with low battery power. Accordingly, this disclosure proposes a technology for selecting a RAT considering battery efficiency. Through various embodiments described later, the terminal can select a RAT considering battery efficiency and optimize communication performance and power efficiency.

Handover in a conventional cellular communication system is performed based on the quality of signals received from base stations. Specifically, the terminal measures the received signal quality of the serving cell, and continuously camps on the serving cell if the measured signal quality value is greater than a threshold. On the other hand, if the specified signal quality value is below the threshold, handover is triggered. Accordingly, the terminal measures the signal quality of the serving cell and the neighboring cell based on the measurement configuration provided from the serving cell, and measures the signal quality of the serving cell, the signal quality of the neighboring cell, etc. Send a measurement report. The base station of the serving cell, that is, the serving base station, determines whether to proceed with handover, target RAT, target cell, etc. based on signal quality values, then transmits a handover request to the target base station supporting the target RAT and receives a response. . Afterwards, the serving base station transmits a handover command to the terminal. When the handover is completed, the terminal delivers a confirmation message.

In the same procedure described above, battery efficiency is not considered in the selection of RAT. To optimize battery efficiency, it is possible to set the criteria for selecting a RAT differently during the handover procedure depending on the status of the terminal. For example, when the remaining battery capacity is sufficient, the terminal may prefer a RAT that can provide a better channel environment under the condition that the energy consumed for communication does not exceed a certain value. On the other hand, when the remaining battery power is insufficient, the terminal may prefer a RAT that consumes less energy for communication under the condition that quality of service (QoS) is maintained. Therefore, according to various embodiments described later, if the RAT is selected based on an AI algorithm that considers the battery efficiency of the terminal, maintenance of QoS and battery efficiency can be harmonized.

The concept of handover according to various embodiments is shown in FIG. 13 below. Figure 13 illustrates the concept of handover in a wireless communication system according to an embodiment of the present disclosure. In Figure 13, base station 1320a-1 and base station 1320a-2 belong to the first system based on the first RAT, and base station 1320b-1 and base station 1320b-2 belong to the first system based on the second RAT. 2 Belongs to the system. Referring to FIG. 13, while the base station 1320a-1 is the serving base station of the terminal 1310, the terminal 1310 moves. Accordingly, the terminal 1310 is connected to the cell of the base station 1320a-1, the cell of the base station 1320b-1, the cell of the base station 1320a-1, the cell of the base station 1320a-2, and the cell of the base station 1320b-2. It stays in the cells sequentially. In this case, if handover is performed based only on signal quality, the terminal 1310 will perform a total of 4 handovers, of which 3 handovers between RATs will be performed. Specifically, the terminal 1310 will perform two handovers from the first RAT to the second RAT and one handover from the second RAT to the first RAT.

However, according to various embodiments, when the battery efficiency of the terminal 1310 is taken into consideration, handover between RATs may be at least partially excluded. According to various embodiments, in the inter-RAT handover procedure, the battery efficiency of the terminal 1310 is considered when selecting a target RAT. That is, according to various embodiments, a RAT considering the battery efficiency of the terminal 1310 may be selected according to an artificial intelligence algorithm based on whether QoS is maintained after handover and changes in battery consumption rate. For example, when the terminal 1310 moves from the cell of the base station 1320a-1 to the cell of the base station 1320b-1, the terminal 1310 or the base station 1320a-1 provides not only the signal quality measurement result, but also the signal quality measurement result, It is possible to determine whether to perform inter-RAT handover by considering at least one of whether QoS is maintained after handover from the past base station 1320a-1 to base station 1320b-1 and a change in battery consumption rate.

The technology for determining the RAT to handover according to various embodiments is a RAT selection algorithm that takes into account the battery efficiency of the terminal during handover between RATs based on the Thompson sampling method, which guarantees excellent performance among MAB problem solving methods. MAB is a technology that balances exploitation and exploration in recommendations. Exploitation recommends the best RAT to the terminal, while discovery recommends new RATs in a balanced manner. By applying the MAB technique, new RATs are appropriately recommended through discovery, and feedback on selection can be efficiently reflected to the terminal and all base stations. There is a trade-off between use and discovery, and adjusting use and discovery may temporarily appear to be detrimental to the terminal, but because multiple RATs are identified through discovery, it is more efficient overall.

Figure 14 illustrates the concept of Thompson sampling-based target RAT selection in a wireless communication system according to an embodiment of the present disclosure. Referring to FIG. 14, when the use and search-based MAB-Thomson sampling technique 1420 is applied based on data on a plurality of candidate RAT cells 1410-1 to 1410-N, a plurality of candidate RAT cells ( The optimal RAT cell 1430 may be selected from 1410-1 to 1410-N. According to various embodiments, the data used in the MAB-Thomson sampling technique 1420 is a probability distribution for each RAT.

Thompson sampling is an algorithm that estimates the reward distribution of candidate RATs for handover based on data observed in the past, and selects with a high probability the candidate who will give the highest reward in the future based on the estimated distribution. . 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 by the base station. .

Figure 16 shows an example of probability distribution sets defined according to battery status in a wireless communication system according to an embodiment of the present disclosure. Referring to FIG. 16, probability distributions for each RAT can be defined for each battery state. Here, the classification of battery states 1610-1 to 1610-3 may vary according to various purposes. For example, battery states may be classified based on at least one of the remaining battery capacity, battery usage period, and battery average power consumption rate. That is, the system manages probability distributions for a plurality of RATs for each battery state. Accordingly, when handover is necessary, the system can diagnose the battery state of the terminal for which handover is to be performed and control the target RAT to be selected using a probability distribution set corresponding to the diagnosed state.

According to one embodiment, the beta distribution corresponding to each of the candidate RATs may be updated based on evaluation after handover. To this end, the terminal can collect information to update the beta distribution after performing a handover. The collected information is used to create evaluation indicators of interest and includes matters to be considered in determining whether the handover is successful. Here, the collected information and the evaluation indicators generated based on the collected information may be collectively referred to as 'evaluation information.' For example, when considering battery efficiency, the information collected may be related to battery consumption. Through evaluation of the collected information, that is, through evaluation indicators generated based on the collected information, the reward value (e.g. 0 or 1) is determined, and the beta distribution of the target RAT is updated based on the determined reward value. It can be. For example, if the compensation value is 1, α may be increased by 1, and if the compensation value is 0, β may be increased by 1. The information collected to determine the compensation value and the rules for determining the compensation value from the collected information can be designed in various ways depending on the matters to be reflected in the performance of handover.

According to one embodiment, the compensation value may be determined based on whether QoS is maintained and the change in battery consumption rate. In other words, whether QoS is maintained and changes in battery consumption speed can be used as evaluation indicators. For this purpose, the information collected may include service quality indicators (e.g. lowest transmission rate, latency, throughput, etc.), battery consumption rate, etc. In this case, after completion of handover, the terminal can observe whether QoS is maintained and changes in battery consumption rate, and based on the observation results, determine a compensation value to be reflected in the beta distribution corresponding to the selected candidate RAT. The criteria for determining specific compensation values can be defined in various ways. If QoS is not maintained after handover, or handover is requested again within a certain time, the compensation value may be determined as 0. On the other hand, if the quality of service is maintained after handover, and handover is requested again within a certain time If not, the compensation value may be determined according to changes in the battery consumption rate. For example, the compensation value may be determined as shown in [Equation 2] below.

In [Equation 2], R is the compensation value, P _A is the battery consumption rate before handover, P _B is the battery consumption rate after handover, and P _T is the threshold for the change in battery consumption rate. The standard illustrated in [Equation 2] is based on the perspective that if the decrease in battery consumption rate is greater than the standard value, selecting to proceed with handover can save more battery.

According to one embodiment, when a standard such as [Equation 2] is used, the threshold for the change in battery consumption rate may be set differently depending on the state of the terminal. For example, if the remaining battery capacity of the terminal is above a certain value, a relatively large RSS threshold and a small threshold for the change in battery consumption rate are used, and further under the condition that the energy consumed for communication does not exceed a certain value. A RAT that can secure a good channel environment can be selected. On the other hand, if the remaining battery power of the terminal is below a certain value, a relatively small RSS threshold and a threshold for a large change in battery consumption rate are used, and accordingly, a RAT that consumes less energy for communication is selected under the condition that QoS is maintained. It can be.

Hereinafter, various embodiments for determining whether to handover between RATs based on Thompson sampling are described. According to various embodiments, one RAT among accessible RATs is selected, and measures are taken to increase the likelihood that handover to the selected RAT will be performed. In the following description, selection of a RAT and selection of a cell are described as separate operations, but in some cases, selection of a RAT may include selection of a cell. This is because if neighboring cells capable of handover from one base station support different RATs, selection of one of the neighboring cells includes selection of a RAT. Therefore, in the following description, 'select a RAT' can be understood as 'select a cell that supports RAT'. However, if there are multiple cells supporting the same RAT among neighboring cells, RAT selection and cell selection may be separated.

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 1320a-1 in FIG. 13) that controls handover.

Referring to FIG. 17, in step S1701, the base station transmits measurement setting information including information related to the probability distribution model for candidate RATs. Here, the probability distribution model may be a probability distribution model based on a beta distribution and may include probability distributions for each candidate RAT. Specifically, the information related to the probability distribution model is information informing each of the probability distributions for each candidate RAT, and may include two parameters (e.g., α, β) constituting the beta distribution for each candidate RAT.

In step S1703, the base station receives a measurement report containing signal quality values for candidate RATs. Here, candidate RATs include RATs supported by the base station and other RATs. According to one embodiment, the measurement report may include signal quality values representing each of the RATs (e.g., a signal quality value for the first RAT, a signal quality value for the second RAT, etc.). According to another embodiment, the signal quality values are signal quality values representing each of the cells (e.g., a signal quality value for a first cell supporting the first RAT, a signal quality value for a second cell supporting the first RAT) , signal quality value for the third cell supporting the second RAT, etc.).

In step S1705, the base station performs a handover procedure. Specifically, the base station may transmit a handover command message to the terminal requesting handover to a target cell that supports the target RAT determined based on the measurement report, and receive a message notifying completion of the handover from the base station of the target cell. there is.

In step S1707, the base station obtains information related to updating the probability distribution model. Here, information related to updating the probability distribution model may include information indicating an updated probability distribution model or evaluation information necessary to update the probability distribution model. Additionally, the evaluation information is information related to the compensation value for updating the beta distribution and may include the compensation value or information used to determine the compensation value. For example, the information used to determine the compensation value includes QoS value, evaluation of QoS (e.g., information on whether to maintain QoS), remaining battery capacity, battery consumption rate, amount of change in battery consumption rate, and information related to change in battery consumption rate. It may include at least one of: According to one embodiment, information related to updating the probability distribution model may be received through the base station of the target cell during a handover procedure. For example, information related to the update of the probability distribution model may be included in a message notifying the completion of handover. According to another embodiment, information related to the update of the probability distribution model may be received through a separate message after the handover procedure is completed.

Thereafter, although not shown in FIG. 17, after handover of the terminal, the base station may update the probability distribution information based on the acquired information. According to one embodiment, the base station may check the beta distribution information (e.g., new α value and β value) of the target base station updated by the terminal and apply it to the stored probability distribution information. According to another embodiment, the base station may confirm or determine a compensation value based on the received evaluation information and update the probability distribution of the target base station among the probability distribution information based on the determined compensation value. The compensation value can be determined as 0 or 1, with 1 indicating success of handover and 0 indicating failure of handover. At this time, the updated probability distribution information may be the beta distribution of the neighboring base station to which the terminal has handed over, that is, the target RAT. In this case, the base station may increase either α or β constituting the beta distribution by 1 depending on the compensation value.

Figure 18 shows an example of a procedure for performing handover in a wireless communication system according to an embodiment of the present disclosure. FIG. 18 illustrates an operation method of a terminal (e.g., terminal 1310 of FIG. 13) performing handover.

Referring to Figure 18, in step S1801, the terminal receives measurement setting information including information related to the probability distribution model for candidate RATs. Here, the probability distribution model may be a probability distribution model based on a beta distribution and may include probability distributions for each candidate RAT. Specifically, the information related to the probability distribution model is information informing each of the probability distributions for each candidate RAT, and may include two parameters (e.g., α, β) constituting the beta distribution for each candidate RAT.

In step S1803, the terminal transmits a measurement report including signal quality values for candidate RATs. Here, candidate RATs include RATs supported by the base station and other RATs. That is, the terminal may perform measurement on the serving cell and at least one neighboring cell according to the measurement settings, and transmit a measurement report including a plurality of signal quality values. According to one embodiment, the measurement report may include signal quality values representing each of the RATs (e.g., a signal quality value for the first RAT, a signal quality value for the second RAT, etc.). According to another embodiment, the signal quality values are signal quality values representing each of the cells (e.g., a signal quality value for a first cell supporting the first RAT, a signal quality value for a second cell supporting the first RAT) , signal quality value for the third cell supporting the second RAT, etc.).

In step S1805, the terminal performs a handover procedure. Specifically, the terminal receives a handover command message from the base station that indicates the target RAT and target cell, requests handover, and connects to the base station of the target cell. Additionally, the terminal may transmit a message notifying the completion of handover to the base station of the target cell.

In step S1807, the terminal transmits information related to updating the probability distribution model. Here, information related to updating the probability distribution model may include information indicating an updated probability distribution model or evaluation information necessary to update the probability distribution model. Additionally, the evaluation information is information related to the compensation value for updating the beta distribution and may include the compensation value or information used to determine the compensation value. For example, the information used to determine the compensation value includes QoS value, evaluation of QoS (e.g., information on whether to maintain QoS), remaining battery capacity, battery consumption rate, amount of change in battery consumption rate, and information related to change in battery consumption rate. It may include at least one of: According to one embodiment, evaluation information may be received from the base station of the target cell during a handover procedure. For example, evaluation information may be included in a message notifying completion of handover. According to another embodiment, the evaluation information may be received through a separate message after the handover procedure is completed.

In the embodiments described with reference to FIGS. 17 and 18, the base station transmits a probability distribution model to the terminal through measurement setting information. According to various embodiments, the probability distribution model includes probability distribution sets for each battery state (e.g., probability distribution sets 1610-1 to 1610-3 in FIG. 16). Therefore, according to one embodiment, the base station may transmit measurement setting information including information on all probability distribution sets. Alternatively, according to another embodiment, the base station may transmit through measurement setting information including information on at least one probability distribution set corresponding to the battery state of the terminal among all probability distribution sets. To this end, the base station can obtain information about the battery state of the terminal and select a probability distribution set corresponding to the battery state. For example, the terminal may request measurement settings as it detects deterioration in channel quality for the serving cell, and may transmit information indicating the battery status along with the request for measurement settings.

In the embodiment described with reference to FIG. 18, the terminal transmits a measurement report including measurement results. The measurement report includes signal quality values for a plurality of cells. At this time, among the signal quality values, the signal quality value related to the RAT selected based on Thompson sampling is reported with a weight applied. An embodiment of the generation of a measurement report is shown in FIG. 19 below.

Figure 19 shows an example of a procedure for generating a measurement report in a wireless communication system according to an embodiment of the present disclosure. FIG. 19 illustrates an operation method of a terminal (e.g., terminal 1310 of FIG. 13) performing handover.

Referring to Figure 19, in step S1901, the terminal checks a probability distribution set corresponding to the battery state. That is, the terminal checks the battery status (e.g., at least one of remaining capacity, usage period, and consumption rate) and checks one probability distribution set corresponding to the battery status among a plurality of probability distribution sets. One set of probability distributions includes probability distributions (e.g., beta distributions) for each candidate RAT.

In step S1903, the terminal samples values from probability distributions for each candidate RAT. That is, the terminal samples values from the beta distributions of candidate RATs. Here, sampling refers to the operation of selecting one value from each beta distribution based on a random number, but taking into account the probability expressed by the beta distribution curve. For example, in the case of beta(1,1) in Figure 15, if sampling is performed considering probability, 0.5, which has the highest probability, will be selected with the highest frequency, but values other than 0.5 will also be selected with lower frequency. You can.

In step S1903, the terminal applies a weight to the measurement value for the RAT corresponding to the maximum value among the sampled values. Here, weights are applied to increase the probability that the selected RAT is selected as the target RAT. That is, the signal quality value for the selected RAT is reported to the base station as a value greater than the measured value. For example, the terminal may add a positive offset value (hereinafter 'Δ value') to the measurement value for the selected RAT or multiply it by a weight value of 1 or more. If there are a plurality of candidate cells supporting the selected RAT, the terminal can apply weights to all signal qualities of the plurality of candidate cells.

Figure 20 shows an example of a procedure for handover in a wireless communication system according to an embodiment of the present disclosure.

Referring to Figure 20, in step S2001, the terminal connects to the cellular network. Next, in step S2003, the base station transmits measurement setting information including beta distribution information of candidate RAT cells to the terminal. The base station holds TS models, that is, beta distribution information, of candidate RAT cells that can be selected for handover from its own cell for each terminal's status (e.g., remaining battery capacity). In other words, it holds beta distributions that reflect the compensation value determined based on whether or not QoS is maintained after the handover of the terminal that has handed over to each RAT cell and the change in battery consumption rate.

In step S2005, the terminal measures RSS for the serving cell and at least one candidate RAT cell. In step S2007, the terminal selects a RAT considering the battery efficiency of the terminal based on the TS model. And, although not shown in FIG. 20, the terminal adds Δ to the RSS value for the cell supporting the selected RAT and then transmits a measurement report including a plurality of RSS values to the base station of the serving cell. In step S2009, the base station determines whether to perform handover. The base station of the serving cell determines whether to proceed with the handover based on the measurement report and determines the target RAT and target cell for the handover. When it is decided to proceed with handover, the base station transmits a handover command.

In step S2011, the terminal receives a handover command from the base station of the serving cell and performs handover to the selected target cell. In step S2013, the terminal determines the compensation value and updates the TS model based on the compensation value. In step S2015, the terminal transmits information related to the updated TS model and a handover ACK to the previous serving base station. That is, the terminal transmits a handover confirmation message to the new serving base station, that is, the target base station before handover. Then, the new serving base station transmits a handover confirmation ACK message to the previous serving base station.

In the embodiment described with reference to FIG. 20, the TS model is updated by the terminal. However, according to another embodiment, the TS model may be updated by the base station. In this case, the terminal may evaluate whether QoS is maintained and changes in battery consumption rate, and determine a compensation value based on the evaluation results. Then, the terminal provides the compensation value to the previous serving base station through the new serving base station, and the previous serving base station can update the TS model. Alternatively, according to another embodiment, the terminal may provide information related to QoS and battery consumption rate, and the base station may determine a compensation value.

Figure 21 shows an example of signal flow for handover in a wireless communication system according to an embodiment of the present disclosure. Figure 21 illustrates a procedure in which the terminal 2110 hands over from the first base station 2120-1, which is a serving base station, to the second base station 2120-2, which is a neighboring base station.

Referring to FIG. 21, in step S2101, the first base station 2120-1 transmits measurement settings to the terminal 2110. The measurement setup contains the information needed to perform the measurement. For example, the measurement setting may include at least one of measurement object information, measurement report setting information, measurement identification information, measurement item (quantity) information, and measurement gap information. Additionally, according to one embodiment, the measurement setting includes probability distribution model information of candidate RAT cells, that is, beta distribution information.

In step S2103, the terminal 2110 measures RSS values for the serving cell and at least one candidate RAT cell. In step S2105, the terminal 2110 selects the RAT cell with the maximum value among values randomly sampled from the beta distribution of candidate RAT cells corresponding to the current state (e.g., remaining battery capacity) of the terminal 2110. In step S2107, the terminal 2110 adds Δ to the RSS measurement value of the selected RAT cell. In step S2109, the terminal 2110 transmits a measurement report to the first base station 2120-1. The measurement report includes RSS values for the serving cell and at least one candidate RAT cell. Here, the RSS value of the selected RAT cell among the RSS values is reported increased by Δ.

In step S2111, the first base station 2120-1 determines handover to the second base station 2120-2. The first base station 2120-1 selects the second base station 2120-2 as a target cell for handover based on the measurement report received in step S2109, and performs handover to the second base station 2120-2. Decide. In step S2113, the first base station 2120-1 transmits a handover request message to the second base station 2120-2. The handover request message includes information about the terminal 2110. Accordingly, the second base station 2120-2 determines whether it can accept the handover of the terminal 2110. In this embodiment, handover of terminal 2110 is accepted.

In step S2115, the second base station 2120-2 transmits a handover request ACK message to the first base station 2120-1. That is, the second base station 2120-2 notifies that it can accept the handover of the terminal 2110. In step S2117, the first base station 2120-1 transmits a handover command message to the terminal 2110. The handover command message includes information about the target cell, that is, information about the second base station 2120-2.

In step S2119, the terminal 2110 transmits a handover confirmation message to the second base station 2120-2. Although not shown in FIG. 21, the terminal 2110 may perform a random access procedure for the second base station 2120-2 for handover and establish a connection. Afterwards, the terminal 2110 transmits a handover confirmation message through the established connection. Here, the handover confirmation message may include calculated compensation information, that is, a compensation value. That is, the terminal 2110 may determine a compensation value based on whether to maintain service after handover and the change in battery consumption rate, and provide the compensation value to the second base station 2120-2.

In step S2121, the second base station 2120-2 transmits a handover confirmation ACK message to the first base station 2120-1. The handover confirmation ACK message notifies the success of the handover. Additionally, the handover confirmation ACK message may include a compensation value received from the terminal 2110. That is, the second base station 2120-2 transmits the compensation value received from the terminal 2110 to the first base station 2120-1.

In step S2123, the first base station 2120-1 updates the beta distribution information of the TS model. Specifically, the first base station 2120-1 updates the beta distribution of the second base station 2120-2 based on the compensation value. In other words, the first base station 2120-1 may update parameter α or parameter β of the beta distribution of the second base station 2120-2.

As in the above-described embodiments, the terminal measures RSS values for the serving cell and candidate cells, applies a weight to the RSS value of the RAT cell selected in the TS model (e.g., RSS+Δ), and then sends the measurement report to the serving cell. send to Here, the Δ value may be predefined. Alternatively, the Δ value or the range of the Δ value is set by the base station, and information about the Δ value or the range of the Δ value may be included in the measurement settings including measurement gap information. According to one embodiment, when a Δ value is provided, the terminal may apply the provided Δ value. According to another embodiment, when a range of Δ values is provided, the terminal may randomly select a Δ value within the provided range.

According to various embodiments, the Δ value or the range of the Δ value may be determined based on at least one of a probability value sampled based on a TS model and RSS values reported in the past. For example, the Δ value or the range of Δ values can be determined based on one of the two modes listed in Table 2 below.

mode explanation mode 1 A mode that allows a predicted RAT to be selected as the target RAT, where the final RSS value of a cell supporting the predicted RAT is greater than the RSS values of all neighboring cells, and a measurement report is triggered (e.g., serving cell RSS value) The minimum value that can be achieved (must be greater than a certain value) is set in the range of Δ. mode 2 This is a mode that increases the probability that the RAT predicted by the base station will be selected as the target RAT, and sets a value or ratio determined by each stage in the range of Δ according to the TS value of the cell supporting the predicted RAT cell.

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 22 below. Figure 22 shows a reward structure based on Thompson sampling in a wireless communication system according to an embodiment of the present disclosure. Figure 22 shows a structure in which compensation is fed back based on evaluation indicators (e.g., whether QoS is maintained, battery consumption rate change information) after completion of handover. Referring to FIG. 22, sampling includes randomly sampling values from the beta distribution 2210 of candidate cells. Among the sampled values, the maximum value is selected by optimization 2220. The action includes RSS values for a plurality of candidate cells after applying a weight (e.g., RSS+Δ) to the RSS value for the candidate RAT corresponding to the selected maximum value, that is, the highest battery efficiency. Includes operations for transmitting measurement reports. Observation includes an operation of updating parameters of the beta distribution for compensation based on an evaluation (2230) that checks whether QoS is maintained and changes in battery consumption rate after completion of handover. The updated parameters are reflected in the beta distribution 2210.

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 (17)

In a method of operating a terminal in a wireless communication system,
Receiving measurement configuration information including information related to a probability distribution model for candidate RATs from a first base station;
transmitting a measurement report including signal quality values for the candidate RATs to the first base station;
Receiving a handover command message from the first base station;
After accessing the second base station indicated by the handover command message, transmitting information related to updating the probability distribution information to the second base station,
The measurement report includes a signal quality value for the second base station,
A method wherein the signal quality value for the second base station is determined based on the probability distribution model. In claim 1,
Confirming a probability distribution set corresponding to the battery state of the terminal from the probability distribution model;
selecting a RAT based on a probability distribution related to a handover success probability for each candidate RAT included in the probability distribution set;
The method further comprising applying weights to measurements of signal quality for neighboring base stations supporting the RAT. In claim 2,
The step of applying a weight to the measurement value of signal quality for a neighboring base station supporting the RAT is,
A method comprising adding an offset value to the measured value. In claim 3,
The method further includes receiving information indicating the offset value or the range of the offset value from the base station. In claim 1,
Information related to the update of the probability distribution model includes evaluation information for determining whether handover to the second base station is successful, a compensation value for the probability distribution of the second base station, and updated based on the compensation value. The method further comprising transmitting at least one of the probability distributions of the second base station. In claim 1,
A method in which information related to the update of the probability distribution model is transmitted through a handover confirmation message. In claim 1,
The probability distribution model includes a beta distribution of each of the candidate RATs. In a method of operating a base station in a wireless communication system,
Transmitting measurement configuration information including information related to probability distribution models for candidate RATs to the terminal;
Receiving a measurement report including signal quality values for the candidate RATs from the terminal;
Transmitting a handover command message indicating a target base station determined based on the measurement report;
Receiving information related to updating the probability distribution information from the target base station,
The measurement report includes signal quality values for the target base station,
A method in which the signal quality value for the target base station is determined based on the probability distribution model. In claim 8,
A method wherein the signal quality value for the target base station includes the sum of a measurement value of the signal quality for the target base station measured by the terminal and an offset value. In claim 9,
The method further includes transmitting information indicating the offset value or the range of the offset value to the terminal. In claim 8
Information related to the update of the probability distribution model includes evaluation information for determining whether handover to the second base station is successful, a compensation value for the probability distribution of the second base station, and updated based on the compensation value. The method further comprising transmitting at least one of the probability distributions of the second base station. In claim 8,
A method in which information related to the update of the probability distribution model is received through a handover confirmation ACK (acknowledge) message. In claim 8,
The probability distribution model includes a beta distribution of each of the candidate RATs. In a terminal in a wireless communication system,
transceiver; and
Includes a processor connected to the transceiver,
The processor,
receive information related to a threshold of signal strength for triggering a measurement report from a base station;
transmitting a measurement report to the base station based on information related to the threshold,
Controls handover from the base station to a neighboring base station,
The threshold is determined based on a probability distribution determined based on information observed by terminals that performed handover at the base station. In a base station in a wireless communication system,
transceiver; and
Includes a processor connected to the transceiver,
The processor,
Transmit information related to the threshold of signal strength for triggering a measurement report to the terminal,
Receiving a measurement report generated by the terminal based on information related to the threshold,
Controlling the terminal to perform a handover from the base station to a neighboring base station,
The threshold is determined based on a probability distribution determined based on information observed by terminals that performed handover at the base station. 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 measurement configuration information including information related to a probability distribution model for candidate RATs from a first base station;
transmitting a measurement report including signal quality values for the candidate RATs to the first base station;
Receiving a handover command message from the first base station;
After accessing the second base station indicated by the handover command message, transmitting information related to updating the probability distribution information to the second base station,
The measurement report includes a signal quality value for the second base station,
A signal quality value for the second base station is determined based on the probability distribution 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 measurement configuration information including information related to a probability distribution model for candidate RATs from a first base station, and send a measurement report including signal quality values for the candidate RATs to the first base station. 1 Transmit to a base station, receive a handover command message from the first base station, connect to the second base station indicated by the handover command message, and then send information related to updating the probability distribution information to the second base station. Controlled to transmit,
The measurement report includes a signal quality value for the second base station,
A computer-readable medium wherein the signal quality value for the second base station is determined based on the probability distribution model.

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