Google Patentsopen access

Method for performing beam management in wireless communication system and … — Lg Electronics Inc. (US20240292232A1)

ABSTRACT

Abstract

A method of performing a beam management in a wireless communication system and a device therefor are disclosed. The method performed by a user equipment (UE) comprises receiving, from a base station (BS), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, receiving, from the BS, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and transmitting, to the BS, measurement information including at least one measurement value among the N reference signals.

Description

TECHNICAL FIELD

The present disclosure relates to a wireless communication system, and more particularly to a method of performing a beam management based on machine learning and a device supporting the same.

BACKGROUND ART

Wireless communication systems have been widely deployed to provide various types of communication services such as voice or data, and attempts to integrate artificial intelligence (AI) into communication systems are rapidly increasing.

AI integration methods that are being attempted may be roughly divided into C4AI (communications for AI) which develops communication technology to support AI, and AI4C (AI for communications) which uses AI to improve communication performance.

In the AI4C area, there are attempts to increase design efficiency by replacing a channel encoder/decoder with an end-to-end autoencoder.

In the C4AI area, there is a method of updating a common prediction model while protecting personal information by sharing only weight or gradient of a model with a server without sharing raw data between devices based on federated learning which is a technique of distributed learning. Further, there are methods to distribute the load on devices, network edges, and cloud servers based on split inference.

DISCLOSURE

Technical Problem

The present disclosure provides a method of performing a beam management by measuring only optimal candidate beams without separate signaling using machine learning, and a device therefor.

The technical objects to be achieved by the present disclosure are not limited to those that have been described hereinabove merely by way of example, and other technical objects that are not mentioned can be clearly understood by those skilled in the art, to which the present disclosure pertains, from the following descriptions.

Technical Solution

The present disclosure provides a method of performing a beam management in a wireless communication system. The method performed by a user equipment (UE) may comprise receiving, from a base station (BS), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, receiving, from the BS, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and transmitting, to the BS, measurement information including at least one measurement value among the N reference signals.

Based on the at least one serving beam being changed, new N candidate beams may be determined based on the candidate beam determination algorithm information.

Based on there being no reference signal whose a measurement value exceeds a threshold among the measured N reference signals, a number of candidate beams for measurement may be increased by +1.

The configuration information may further include at least one of information on N contention free random access (CFRA) resources and/or information on a number of candidate beams.

The method may further comprise, based on a pre-configured method, mapping the N CFRA resources to the N reference signals, determining one beam related to one reference signal of the N reference signals based on the resource information on the plurality of reference signals, and performing a beam failure recovery operation based on a CFRA resource related to the one beam.

The method may further comprise, based on a beam failure of the at least one serving beam being detected, measuring the N reference signals based on the resource information on the plurality of reference signals, and transmitting, to the BS, beam failure recovery (BFR) medium access control (MAC)-control element (CE) information. The BFR MAC-CE information may include a bitmap representing a reference signal, whose a measurement value exceeds a threshold among the N reference signals, for at least one serving cell.

A user equipment (UE) configured to perform a beam management in a wireless communication system according to the present disclosure may comprise at least one transceiver, at least one processor operatively connected to the at least one transceiver, and at least one memory operatively connected to the at least one processor and configured to store instruction that allow the at least one processor to perform operations, wherein the operations may comprise receiving, from a base station (BS), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, receiving, from the BS, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and transmitting, to the BS, measurement information including at least one measurement value among the N reference signals.

The present disclosure provides a method of performing a beam management in a wireless communication system. The method performed by a base station (BS) may comprise transmitting, to a user equipment (UE), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, transmitting, to the UE, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and receiving, from the UE, measurement information including at least one measurement value among the N reference signals.

Based on the at least one serving beam being changed, new N candidate beams may be determined based on the candidate beam determination algorithm information.

Based on there being no reference signal whose a measurement value exceeds a threshold among the measured N reference signals, a number of candidate beams for measurement may be increased by +1.

The configuration information may further include at least one of information on N contention free random access (CFRA) resources and/or information on a number of candidate beams.

The method may further comprise, based on a pre-configured method, mapping the N CFRA resources to the N reference signals, and performing a beam failure recovery operation based on a CFRA resource related to one reference signal of the N reference signals.

Based on a beam failure of the at least one serving beam being detected, the N reference signals may be measured based on the resource information on the plurality of reference signals. The method may further comprise receiving, from the UE, beam failure recovery (BFR) medium access control (MAC)-control element (CE) information, and the BFR MAC-CE information may include a bitmap representing a reference signal, whose a measurement value exceeds a threshold among the N reference signals, for at least one serving cell.

A base station (BS) configured to perform a beam management in a wireless communication system according to the present disclosure may comprise at least one transceiver, at least one processor operatively connected to the at least one transceiver, and at least one memory operatively connected to the at least one processor and configured to store instruction that allow the at least one processor to perform operations.

The operations may comprise transmitting, to a user equipment (UE), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, transmitting, to the UE, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and receiving, from the UE, measurement information including at least one measurement value among the N reference signals.

A processor device configured to control a user equipment (UE) to perform a beam management in a wireless communication system according to the present disclosure may comprise at least one processor, and at least one memory operatively connected to the at least one processor and configured to store instruction that allow the at least one processor to perform operations, wherein the operations may comprise receiving, from a base station (BS), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, receiving, from the BS, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and transmitting, to the BS, measurement information including at least one measurement value among the N reference signals.

In a non-transitory computer readable medium (CRM) according to the present disclosure storing instruction that allow at least one processor to perform operations, the operations may comprise receiving, from a base station (BS), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, receiving, from the BS, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and transmitting, to the BS, measurement information including at least one measurement value among the N reference signals.

ADVANTAGEOUS EFFECTS

The present disclosure has an effect of performing a beam management by measuring only optimal candidate beams without separate signaling using machine learning.

The present disclosure also has an effect of reducing a beam tracking latency due to reconfiguration by measuring a beam with larger coverage without radio resource control reconfiguration.

FIG. 11 ( a ) illustrates an example of configuring a UE-specific CSI-RS.

FIG. 11 ( b ) illustrates an example of configuring a UE group-specific CSI-RS.

FIG. 12 illustrates an example of a conventional beam management procedure.

FIG. 13 illustrates an example of a beam management method described in the present disclosure.

FIG. 14 illustrates an example of SSB reception and measurement of an idle mode UE according to a prior art.

FIG. 15 illustrates an example of SSB reception and measurement of an idle mode UE described in the present disclosure.

FIG. 16 illustrates an example of a BFR procedure described in the present disclosure.

FIG. 17 illustrates BFR and Truncated BFR MAC CE in which the highest ServCellIndex of MAC entity's SCell configured with beam failure detection (BFD) is less than 8.

FIG. 18 illustrates BFR and Truncated BFR MAC CE in which the highest ServCellIndex of MAC entity's SCell configured with BFD is equal to or greater than 8.

FIG. 19 illustrates an example of BFR MAC CE described in the present disclosure.

FIG. 20 is a flow chart illustrating an operation method of a UE described in the present disclosure.

FIG. 21 is a flow chart illustrating an operation method of a base station described in the present disclosure.

FIG. 22 illustrates a communication system 10 applied to the present disclosure.

FIG. 23 illustrates a wireless device applicable to the present disclosure.

FIG. 24 illustrates a signal processing circuit for a transmission signal.

FIG. 25 illustrates another example of a wireless device applied to the present disclosure.

FIG. 26 illustrates a hand-held device applied to the present disclosure.

<div id="p-0045" n

TECHNICAL FIELD

The present disclosure relates to a wireless communication system, and more particularly to a method of performing a beam management based on machine learning and a device supporting the same.

BACKGROUND ART

Wireless communication systems have been widely deployed to provide various types of communication services such as voice or data, and attempts to integrate artificial intelligence (AI) into communication systems are rapidly increasing.

AI integration methods that are being attempted may be roughly divided into C4AI (communications for AI) which develops communication technology to support AI, and AI4C (AI for communications) which uses AI to improve communication performance.

In the AI4C area, there are attempts to increase design efficiency by replacing a channel encoder/decoder with an end-to-end autoencoder.

In the C4AI area, there is a method of updating a common prediction model while protecting personal information by sharing only weight or gradient of a model with a server without sharing raw data between devices based on federated learning which is a technique of distributed learning. Further, there are methods to distribute the load on devices, network edges, and cloud servers based on split inference.

DISCLOSURE

Technical Problem

The present disclosure provides a method of performing a beam management by measuring only optimal candidate beams without separate signaling using machine learning, and a device therefor.

The technical objects to be achieved by the present disclosure are not limited to those that have been described hereinabove merely by way of example, and other technical objects that are not mentioned can be clearly understood by those skilled in the art, to which the present disclosure pertains, from the following descriptions.

Technical Solution

The present disclosure provides a method of performing a beam management in a wireless communication system. The method performed by a user equipment (UE) may comprise receiving, from a base station (BS), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, receiving, from the BS, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and transmitting, to the BS, measurement information including at least one measurement value among the N reference signals.

Based on the at least one serving beam being changed, new N candidate beams may be determined based on the candidate beam determination algorithm information.

Based on there being no reference signal whose a measurement value exceeds a threshold among the measured N reference signals, a number of candidate beams for measurement may be increased by +1.

The configuration information may further include at least one of information on N contention free random access (CFRA) resources and/or information on a number of candidate beams.

The method may further comprise, based on a pre-configured method, mapping the N CFRA resources to the N reference signals, determining one beam related to one reference signal of the N reference signals based on the resource information on the plurality of reference signals, and performing a beam failure recovery operation based on a CFRA resource related to the one beam.

The method may further comprise, based on a beam failure of the at least one serving beam being detected, measuring the N reference signals based on the resource information on the plurality of reference signals, and transmitting, to the BS, beam failure recovery (BFR) medium access control (MAC)-control element (CE) information. The BFR MAC-CE information may include a bitmap representing a reference signal, whose a measurement value exceeds a threshold among the N reference signals, for at least one serving cell.

A user equipment (UE) configured to perform a beam management in a wireless communication system according to the present disclosure may comprise at least one transceiver, at least one processor operatively connected to the at least one transceiver, and at least one memory operatively connected to the at least one processor and configured to store instruction that allow the at least one processor to perform operations, wherein the operations may comprise receiving, from a base station (BS), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, receiving, from the BS, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and transmitting, to the BS, measurement information including at least one measurement value among the N reference signals.

The present disclosure provides a method of performing a beam management in a wireless communication system. The method performed by a base station (BS) may comprise transmitting, to a user equipment (UE), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, transmitting, to the UE, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and receiving, from the UE, measurement information including at least one measurement value among the N reference signals.

Based on the at least one serving beam being changed, new N candidate beams may be determined based on the candidate beam determination algorithm information.

Based on there being no reference signal whose a measurement value exceeds a threshold among the measured N reference signals, a number of candidate beams for measurement may be increased by +1.

The configuration information may further include at least one of information on N contention free random access (CFRA) resources and/or information on a number of candidate beams.

The method may further comprise, based on a pre-configured method, mapping the N CFRA resources to the N reference signals, and performing a beam failure recovery operation based on a CFRA resource related to one reference signal of the N reference signals.

Based on a beam failure of the at least one serving beam being detected, the N reference signals may be measured based on the resource information on the plurality of reference signals. The method may further comprise receiving, from the UE, beam failure recovery (BFR) medium access control (MAC)-control element (CE) information, and the BFR MAC-CE information may include a bitmap representing a reference signal, whose a measurement value exceeds a threshold among the N reference signals, for at least one serving cell.

A base station (BS) configured to perform a beam management in a wireless communication system according to the present disclosure may comprise at least one transceiver, at least one processor operatively connected to the at least one transceiver, and at least one memory operatively connected to the at least one processor and configured to store instruction that allow the at least one processor to perform operations.

The operations may comprise transmitting, to a user equipment (UE), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, transmitting, to the UE, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and receiving, from the UE, measurement information including at least one measurement value among the N reference signals.

A processor device configured to control a user equipment (UE) to perform a beam management in a wireless communication system according to the present disclosure may comprise at least one processor, and at least one memory operatively connected to the at least one processor and configured to store instruction that allow the at least one processor to perform operations, wherein the operations may comprise receiving, from a base station (BS), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, receiving, from the BS, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and transmitting, to the BS, measurement information including at least one measurement value among the N reference signals.

In a non-transitory computer readable medium (CRM) according to the present disclosure storing instruction that allow at least one processor to perform operations, the operations may comprise receiving, from a base station (BS), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information, determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals, receiving, from the BS, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals, and transmitting, to the BS, measurement information including at least one measurement value among the N reference signals.

ADVANTAGEOUS EFFECTS

The present disclosure has an effect of performing a beam management by measuring only optimal candidate beams without separate signaling using machine learning.

The present disclosure also has an effect of reducing a beam tracking latency due to reconfiguration by measuring a beam with larger coverage without radio resource control reconfiguration.

FIG. 11 ( a ) illustrates an example of configuring a UE-specific CSI-RS.

FIG. 11 ( b ) illustrates an example of configuring a UE group-specific CSI-RS.

FIG. 12 illustrates an example of a conventional beam management procedure.

FIG. 13 illustrates an example of a beam management method described in the present disclosure.

FIG. 14 illustrates an example of SSB reception and measurement of an idle mode UE according to a prior art.

FIG. 15 illustrates an example of SSB reception and measurement of an idle mode UE described in the present disclosure.

FIG. 16 illustrates an example of a BFR procedure described in the present disclosure.

FIG. 17 illustrates BFR and Truncated BFR MAC CE in which the highest ServCellIndex of MAC entity&#39;s SCell configured with beam failure detection (BFD) is less than 8.

FIG. 18 illustrates BFR and Truncated BFR MAC CE in which the highest ServCellIndex of MAC entity&#39;s SCell configured with BFD is equal to or greater than 8.

FIG. 19 illustrates an example of BFR MAC CE described in the present disclosure.

FIG. 20 is a flow chart illustrating an operation method of a UE described in the present disclosure.

FIG. 21 is a flow chart illustrating an operation method of a base station described in the present disclosure.

FIG. 22 illustrates a communication system 10 applied to the present disclosure.

FIG. 23 illustrates a wireless device applicable to the present disclosure.

FIG. 24 illustrates a signal processing circuit for a transmission signal.

FIG. 25 illustrates another example of a wireless device applied to the present disclosure.

FIG. 26 illustrates a hand-held device applied to the present disclosure.

FIG. 27 illustrates a vehicle or an autonomous vehicle applied to the present disclosure.

FIG. 28 illustrates a vehicle applied to the present disclosure.

FIG. 29 illustrates an XR device applied to the present disclosure.

FIG. 30 illustrates a robot applied to the present disclosure.

FIG. 31 illustrates an AI device applied to the present disclosure.

MODE FOR INVENTION

Reference will now be made in detail to embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. A detailed description to be disclosed below together with the accompanying drawing is to describe exemplary embodiments of the present disclosure and not to describe a unique embodiment for carrying out the present disclosure. The detailed description below includes details to provide a complete understanding of the present disclosure. However, those skilled in the art know that the present disclosure may be carried out without the details.

In some cases, in order to prevent a concept of the present disclosure from being ambiguous, known structures and devices may be omitted or illustrated in a block diagram format based on core functions of each structure and device.

In the present disclosure, a base station means a terminal node of a network directly performing communication with a terminal. In the present disclosure, specific operations described to be performed by the base station may be performed by an upper node of the base station in some cases. That is, it is apparent that in a network constituted by multiple network nodes including the base station, various operations performed for communication with the terminal may be performed by the base station or other network nodes other than the base station. The ‘base station (BS)’ may be substituted with terms such as a fixed station, Node B, evolved-NodeB (eNB), a base transceiver system (BTS), an access point (AP), gNB (general NB, generation NB), and the like. Further, the ‘terminal’ may be fixed or movable and be substituted with terms such as a user equipment (UE), a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS), a wireless terminal (WT), a Machine-Type Communication (MTC) device, a Machine-to-Machine (M2M) device, a Device-to-Device (D2D) device, and the like.

Hereinafter, downlink (DL) means communication from the base station to the terminal and uplink (UL) means communication from the terminal to the base station. In downlink, a transmitter may be part of the base station, and a receiver may be part of the terminal. In uplink, the transmitter may be part of the terminal and the receiver may be part of the base station.

Specific terms used in the following description are provided to help the understanding of the present disclosure and the use of the specific terms may be modified into other forms within the scope without departing from the technical spirit of the present disclosure.

The following technology may be used in various radio access system including CDMA, FDMA, TDMA, OFDMA, SC-FDMA, and the like. The CDMA may be implemented as radio technology such as Universal Terrestrial Radio Access (UTRA) or CDMA2000. The TDMA may be implemented as radio technology such as a global system for mobile communications (GSM)/general packet radio service (GPRS)/enhanced data rates for GSM evolution (EDGE). The OFDMA may be implemented as radio technology such as Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Evolved UTRA (E-UTRA), or the like. The UTRA is a part of Universal Mobile Telecommunications System (UMTS). 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) is a part of Evolved UMTS (E-UMTS) using the E-UTRA and LTE-Advanced (A)/LTE-A pro is an evolved version of the 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an evolved version of the 3GPP LTE/LTE-A/LTE-A pro. 3GPP 6G may be an evolved version of 3GPP NR.

Embodiments of the present disclosure may be supported by standard documents disclosed in at least one of IEEE 802, 3GPP, and 3GPP2 that are wireless access systems. Thai is, steps or portions of the embodiments of the present disclosure which are not described in order to clearly illustrate the technical spirit of the present disclosure may be supported by the standard documents. Further, all terms disclosed in the present disclosure may be described by the standard documents.

For clarity in the description, the following description will mostly focus on 3GPP communication system (e.g. LTE-A or 5G NR). However, technical features according to an embodiment of the present disclosure will not be limited only to this. LTE means technology after 3GPP TS 36.xxx Release 8. In detail, LTE technology after 3GPP TS 36.xxx Release 10 is referred to as the LTE-A and LTE technology after 3GPP TS 36.xxx Release 13 is referred to as the LTE-A pro. The 3GPP NR means technology after TS 38.xxx Release 15. The LTE/NR may be referred to as a 3GPP system. “xxx” means a detailed standard document number. The LTE/NR/6G may be collectively referred to as the 3GPP system. For terms and techniques not specifically described among terms and techniques used in the present disclosure, reference may be made to a wireless communication standard document published before the present disclosure is filed. For example, the following document may be referred to.

3GPP LTE

36.211: Physical channels and modulation

36.212: Multiplexing and channel coding

36.213: Physical layer procedures

36.300: Overall description

36.331: Radio Resource Control (RRC)

3GPP NR

38.211: Physical channels and modulation

38.212: Multiplexing and channel coding

38.213: Physical layer procedures for control

38.214: Physical layer procedures for data

38.300: NR and NG-RAN Overall Description

38.331: Radio Resource Control (RRC) protocol specification

Physical Channel and Frame Structure

Physical Channel and General Signal Transmission

FIG. 1 illustrates physical channels and general signal transmission used in a 3GPP system. In a wireless communication system, the UE receives information from the eNB through Downlink (DL) and the UE transmits information from the eNB through Uplink (UL). The information which the eNB and the UE transmit and receive includes data and various control information and there are various physical channels according to a type/use of the information which the eNB and the UE transmit and receive.

When the UE is powered on or newly enters a cell, the UE performs an initial cell search operation such as synchronizing with the eNB (S 701 ). To this end, the UE may receive a Primary Synchronization Signal (PSS) and a (Secondary Synchronization Signal (SSS) from the eNB and synchronize with the eNB and acquire information such as a cell ID or the like. Thereafter, the UE may receive a Physical Broadcast Channel (PBCH) from the eNB and acquire in-cell broadcast information. Meanwhile, the UE receives a Downlink Reference Signal (DL RS) in an initial cell search step to check a downlink channel status.

A UE that completes the initial cell search receives a Physical Downlink Control Channel (PDCCH) and a Physical Downlink Control Channel (PDSCH) according to information loaded on the PDCCH to acquire more specific system information (S 12 ).

Meanwhile, when there is no radio resource first accessing the eNB or for signal transmission, the UE may perform a Random Access Procedure (RACH) to the eNB (S 13 to S 16 ). To this end, the UE may transmit a specific sequence to a preamble through a Physical Random Access Channel (PRACH) (S 13 and S 15 ) and receive a response message (Random Access Response (RAR) message) for the preamble through the PDCCH and a corresponding PDSCH. In the case of a contention based RACH, a Contention Resolution Procedure may be additionally performed (S 16 ).

The UE that performs the above procedure may then perform PDCCH/PDSCH reception (S 17 ) and Physical Uplink Shared Channel (PUSCH)/Physical Uplink Control Channel (PUCCH) transmission (S 18 ) as a general uplink/downlink signal transmission procedure. In particular, the UE may receive Downlink Control Information (DCI) through the PDCCH. Here, the DCI may include control information such as resource allocation information for the UE and formats may be differently applied according to a use purpose.

Meanwhile, the control information which the UE transmits to the eNB through the uplink or the UE receives from the eNB may include a downlink/uplink ACK/NACK signal, a Channel Quality Indicator (CQI), a Precoding Matrix Index (PMI), a Rank Indicator (RI), and the like. The UE may transmit the control information such as the CQI/PMI/RI, etc., via the PUSCH and/or PUCCH.

Structure of Uplink and Downlink Channels

Downlink Channel Structure

A base station transmits a related signal to a UE via a downlink channel to be described later, and the UE receives the related signal from the base station via the downlink channel to be described later.

(1) Physical Downlink Shared Channel (PDSCH)

A PDSCH carries downlink data (e.g., DL-shared channel transport block, DL-SCH TB) and is applied with a modulation method such as quadrature phase shift keying (QPSK), 16 quadrature amplitude modulation (QAM), 64 QAM, and 256 QAM. A codeword is generated by encoding TB. The PDSCH may carry multiple codewords. Scrambling and modulation mapping are performed for each codeword, and modulation symbols generated from each codeword are mapped to one or more layers (layer mapping). Each layer is mapped to a resource together with a demodulation reference signal (DMRS) to generate an OFDM symbol signal, and is transmitted through a corresponding antenna port.

(2) Physical Downlink Control Channel (PDCCH)

A PDCCH carries downlink control information (DCI) and is applied with a QPSK modulation method, etc. One PDCCH consists of 1, 2, 4, 8, or 16 control channel elements (CCEs) based on an aggregation level (AL). One CCE consists of 6 resource element groups (REGs). One REG is defined by one OFDM symbol and one (P)RB.

The UE performs decoding (aka, blind decoding) on a set of PDCCH candidates to acquire DCI transmitted via the PDCCH. The set of PDCCH candidates decoded by the UE is defined as a PDCCH search space set. The search space set may be a common search space or a UE-specific search space. The UE may acquire DCI by monitoring PDCCH candidates in one or more search space sets configured by MIB or higher layer signaling.

Uplink Channel Structure

A UE transmits a related signal to a base station via an uplink channel to be described later, and the base station receives the related signal from the UE via the uplink channel to be described later.

(1) Physical Uplink Shared Channel (PUSCH)

A PUSCH carries uplink data (e.g., UL-shared channel transport block, UL-SCH TB) and/or uplink control information (UCI) and is transmitted based on a CP-OFDM (Cyclic Prefix-Orthogonal Frequency Division Multiplexing) waveform, DFT-s-OFDM (Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing) waveform, or the like. When the PUSCH is transmitted based on the DFT-s-OFDM waveform, the UE transmits the PUSCH by applying a transform precoding. For example, if the transform precoding is not possible (e.g., transform precoding is disabled), the UE may transmit the PUSCH based on the CP-OFDM waveform, and if the transform precoding is possible (e.g., transform precoding is enabled), the UE may transmit the PUSCH based on the CP-OFDM waveform or the DFT-s-OFDM waveform. The PUSCH transmission may be dynamically scheduled by an UL grant within DCI, or may be semi-statically scheduled based on high layer (e.g., RRC) signaling (and/or layer 1 (L1) signaling (e.g., PDCCH)) (configured grant). The PUSCH transmission may be performed based on a codebook or a non-codebook.

(2) Physical Uplink Control Channel (PUCCH)

A PUCCH carries uplink control information, HARQ-ACK, and/or scheduling request (SR), and may be divided into multiple PUCCHs based on a PUCCH transmission length.

6G System General

A 6G (wireless communication) system has purposes such as (i) a very high data rate per device, (ii) a very large number of connected devices. (iii) global connectivity, (iv) a very low latency, (v) a reduction in energy consumption of battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capability. The vision of the 6G system may include four aspects such as intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system may satisfy the requirements shown in Table 1 below. That is, Table 1 shows an example of the requirements of the 6G system.

TABLE 1

Per device peak data rate

1

Tbps

E2E latency

1

ms

Maximum spectral efficiency

100

bps/Hz

Mobility support

Up to 1000 km/hr

Satellite integration

Fully

AI

Fully

Autonomous vehicle

Fully

XR

Fully

Haptic Communication

Fully

The 6G system may have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine type communications (mMTC), AI integrated communication, tactile Internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security. FIG. 2 illustrates an example of a communication structure providable in a 6G system.

The 6G system is expected to have 50 times greater simultaneous wireless communication connectivity than a 5G wireless communication system. URLLC, which is the key feature of 5G, will become more important technology by providing an end-to-end latency less than 1 ms in 6G communication. The 6G system may have much better volumetric spectrum efficiency unlike frequently used domain spectrum efficiency. The 6G system can provide advanced battery technology for energy harvesting and very long battery life, and thus mobile devices may not need to be separately charged in the 6G system. In 6G, new network characteristics may be as follows.

Satellites integrated network: To provide a global mobile group, 6G will be integrated with satellite. Integration of terrestrial, satellite and public networks into one wireless communication system is critical for 6G. Connected intelligence: Unlike the wireless communication systems of previous generations, 6G is innovative and may update wireless evolution from “connected things” to “connected intelligence”. AI may be applied in each step (or each signal processing procedure to be described later) of a communication procedure. Seamless integration of wireless information and energy transfer: A 6G wireless network may transfer power to charge batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated. Ubiquitous super 3D connectivity: Access to networks and core network functions of drone and very low earth orbit satellite will establish super 3D connectivity in 6G ubiquitous.

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

Small cell networks: The idea of a small cell network has been introduced to improve received signal quality as a result of throughput, energy efficiency, and spectrum efficiency improvement in a cellular system. As a result, the small cell network is an essential feature for 5G and beyond 5G (5 GB) communication systems. Accordingly, the 6G communication system also employs the characteristics of the small cell network. Ultra-dense heterogeneous network: Ultra-dense heterogeneous networks will be another important characteristic of the 6G communication system. A multi-tier network consisting of heterogeneous networks improves overall QoS and reduces costs. High-capacity backhaul: Backhaul connectivity is characterized by a high-capacity backhaul network in order to support high-capacity traffic. A high-speed optical fiber and free space optical (FSO) system may be a possible solution for this problem. Radar technology integrated with mobile technology: High-precision localization (or location-based service) through communication is one of the functions of the 6G wireless communication system. Accordingly, the radar system will be integrated with the 6G network. Softwarization and virtualization: Softwarization and virtualization are two important functions which are the bases of a design process in a 5 GB network in order to ensure flexibility, reconfigurability and programmability. Further, billions of devices can be shared on a shared physical infrastructure.

Core Implementation Technology of 6G System

Artificial Intelligence (AI)

Technology which is most important in the 6G system and will be newly introduced is AI. AI was not involved in the 4G system. The 5G system will support partial or very limited AI. However, the 6G system will support AI for full automation. Advance in machine learning will create a more intelligent network for real-time communication in 6G. When AI is introduced to communication, real-time data transmission can be simplified and improved. AI may determine a method of performing complicated target tasks using countless analysis. That is, AI can increase efficiency and reduce processing delay.

Time-consuming tasks such as handover, network selection or resource scheduling may be immediately performed by using AI. AI may play an important role even in M2M, machine-to-human and human-to-machine communication. In addition, AI may be rapid communication in a brain computer interface (BCI). An AI based communication system may be supported by meta materials, intelligent structures, intelligent networks, intelligent devices, intelligent recognition radios, self-maintaining wireless networks and machine learning.

Recently, attempts have been made to integrate AI with a wireless communication system in the application layer or the network layer, and in particular, deep learning has been focused on the wireless resource management and allocation field. However, such studies have been gradually developed to the MAC layer and the physical layer, and in particular, attempts to combine deep learning in the physical layer with wireless transmission are emerging. AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in a fundamental signal processing and communication mechanism. For example, channel coding and decoding based on deep learning, signal estimation and detection based on deep learning, multiple input multiple output (MIMO) mechanisms based on deep learning, resource scheduling and allocation based on AI, etc. may be included.

Machine learning may be used for channel estimation and channel tracking and may be used for power allocation, interference cancellation, etc. in the physical layer of DL. The machine learning may also be used for antenna selection, power control, symbol detection, etc. in the MIMO system.

However, application of a deep neutral network (DNN) for transmission in the physical layer may have the following problems.

A deep learning based AI algorithm requires a lot of training data in order to optimize training parameters. However, due to limitations in acquiring data in a specific channel environment as the training data, a lot of training data is used offline. Static training for the training data in the specific channel environment may cause a contradiction between the diversity and dynamic characteristics of a radio channel.

Currently, the deep learning mainly targets real signals. However, signals of the physical layer of wireless communication are complex signals. For matching of the characteristics of a wireless communication signal, studies on a neural network for detecting a complex domain signal are further required.

Hereinafter, machine learning is described in more detail.

Machine learning refers to a series of operations to train a machine in order to create a machine capable of doing tasks that people cannot do or are difficult for people to do. Machine learning requires data and learning models. In the machine learning, a data learning method may be roughly divided into three methods, that is, supervised learning, unsupervised learning and reinforcement learning.

Neural network learning is to minimize an output error. The neural network learning refers to a process of repeatedly inputting training data to a neural network, calculating an error of an output and a target of the neural network for the training data, backpropagating the error of the neural network from an output layer to an input layer of the neural network for the purpose of reducing the error, and updating a weight of each node of the neural network.

The supervised learning may use training data labeled with a correct answer, and the unsupervised learning may use training data which is not labeled with a correct answer. That is, for example, in supervised learning for data classification, training data may be data in which each training data is labeled with a category. The labeled training data may be input to the neural network, and the error may be calculated by comparing the output (category) of the neural network with the label of the training data. The calculated error is backpropagated in the neural network in the reverse direction (i.e., from the output layer to the input layer), and a connection weight of respective nodes of each layer of the neural network may be updated based on the backpropagation. Change in the updated connection weight of each node may be determined depending on a learning rate. The calculation of the neural network for input data and the backpropagation of the error may construct a learning cycle (epoch). The learning rate may be differently applied based on the number of repetitions of the learning cycle of the neural network. For example, in the early stage of learning of the neural network, efficiency can be increased by allowing the neural network to rapidly ensure a certain level of performance using a high learning rate, and in the late of learning, accuracy can be increased using a low learning rate.

The learning method may vary depending on the feature of data. For example, in order for a reception end to accurately predict data transmitted from a transmission end on a communication system, it is preferable that learning is performed using the supervised learning rather than the unsupervised learning or the reinforcement learning.

The learning model corresponds to the human brain and may be regarded as the most basic linear model. However, a paradigm of machine learning using, as the learning model, a neural network structure with high complexity, such as artificial neural networks, is referred to as deep learning.

Neural network cores used as the learning method may roughly include a deep neural network (DNN) method, a convolutional deep neural network (CNN) method, and a recurrent Boltzmann machine (RNN) method.

The artificial neural network is an example of connecting several perceptrons.

FIG. 3 illustrates an example of a structure of a perceptron.

Referring to FIG. 3 , when an input vector x=(x1, x2, . . . , xd) is input, each component is multiplied by a weight (W1, W2, . . . , Wd), and all the results are summed. After that, the entire process of applying an activation function σ(•) is called a perceptron. The huge artificial neural network structure may extend the simplified perceptron structure illustrated in FIG. 3 to apply the input vector to different multidimensional perceptrons. For convenience of explanation, an input value or an output value is referred to as a node.

The perceptron structure illustrated in FIG. 3 may be described as consisting of a total of three layers based on the input value and the output value. FIG. 4 illustrates an artificial neural network in which the number of (d+1) dimensional perceptrons between a first layer and a second layer is H. and the number of (H+1) dimensional perceptrons between the second layer and a third layer is K, by way of example. FIG. 4 illustrates an example of a structure of a multilayer perceptron.

A layer where the input vector is located is called an input layer, a layer where a final output value is located is called an output layer, and all layers located between the input layer and the output layer are called a hidden layer. FIG. 4 illustrates three layers, by way of example. However, since the number of layers of the artificial neural network is counted excluding the input layer, it can be seen as a total of two layers. The artificial neural network is constructed by connecting the perceptrons of a basic block in two dimensions.

The above-described input layer, hidden layer, and output layer can be jointly applied in various artificial neural network structures, such as CNN and RNN to be described later, as well as the multilayer perceptron. The greater the number of hidden layers, the deeper the artificial neural network is, and a machine learning paradigm that uses the sufficiently deep artificial neural network as a learning model is called deep learning. In addition, the artificial neural network used for deep learning is called a deep neural network (DNN).

The deep neural network illustrated in FIG. 5 is a multilayer perceptron consisting of eight hidden layers+eight output layers. The multilayer perceptron structure is expressed as a fully connected neural network. In the fully connected neural network, a connection relationship does not exist between nodes located at the same layer, and a connection relationship exists only between nodes located at adjacent layers. The DNN has a fully connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, so it can be usefully applied to understand correlation characteristics between input and output. The correlation characteristic may mean a joint probability of input and output.

Based on how the plurality of perceptrons are connected to each other, various artificial neural network structures different from the above-described DNN can be formed.

In the DNN, nodes located inside one layer are arranged in a one-dimensional longitudinal direction. However, in FIG. 6 , it may be assumed that w nodes horizontally and h nodes vertically are arranged in two dimensions (convolutional neural network structure of FIG. 6 ). In this case, since in a connection process leading from one input node to the hidden layer, a weight is given for each connection, a total of h×w weights needs to be considered. Since there are h×w nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.

The convolutional neural network of FIG. 6 has a problem in that the number of weights increases exponentially depending on the number of connections. Therefore, instead of considering the connections of all the nodes between adjacent layers, it is assumed that a small-sized filter exists, and a weighted sum and an activation function calculation are performed on an overlap portion of the filters as illustrated in FIG. 7 .

One filter has a weight corresponding to the number as much as its size, and learning of the weight may be performed so that a certain feature on an image can be extracted and output as a factor. In FIG. 7 , a filter having a size of 3×3 is applied to the upper leftmost 3-3 area of the input layer, and an output value obtained by performing a weighted sum and an activation function calculation for

CLAIMS

Claims ( 14 )

1 . A method of performing, by a user equipment (UE), a beam management in a wireless communication system, the method comprising:

receiving, from a base station (BS), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information; determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals; receiving, from the BS, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals; and transmitting, to the BS, measurement information including at least one measurement value among the N reference signals.

2 . The method of claim 1 , wherein, based on the at least one serving beam being changed, new N candidate beams are determined based on the candidate beam determination algorithm information.

3 . The method of claim 1 , wherein, based on there being no reference signal whose a measurement value exceeds a threshold among the measured N reference signals, a number of candidate beams for measurement is increased by +1.

4 . The method of claim 1 , wherein the configuration information further includes at least one of information on N contention free random access (CFRA) resources and/or information on a number of candidate beams.

5 . The method of claim 4 , further comprising:

based on a pre-configured method, mapping the N CFRA resources to the N reference signals; determining one beam related to one reference signal of the N reference signals based on the resource information on the plurality of reference signals; and performing a beam failure recovery operation based on a CFRA resource related to the one beam.

6 . The method of claim 1 , further comprising:

based on a beam failure of the at least one serving beam being detected, measuring the N reference signals based on the resource information on the plurality of reference signals; and transmitting, to the BS, beam failure recovery (BFR) medium access control (MAC)-control element (CE) information, wherein the BFR MAC-CE information includes a bitmap representing a reference signal, whose a measurement value exceeds a threshold among the N reference signals, for at least one serving cell.

7 . A user equipment (UE) configured to perform a beam management in a wireless communication system, the UE comprising:

at least one transceiver; at least one processor operatively connected to the at least one transceiver; and at least one memory operatively connected to the at least one processor and configured to store instruction that allow the at least one processor to perform operations, wherein the operations comprise: receiving, from a base station (BS), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information; determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals; receiving, from the BS, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals; and transmitting, to the BS, measurement information including at least one measurement value among the N reference signals.

8 . A method of performing, by a base station (BS), a beam management in a wireless communication system, the method comprising:

transmitting, to a user equipment (UE), configuration information including i) resource information on a plurality of reference signals and ii) candidate beam determination algorithm information; determining N candidate beams for at least one serving beam based on the candidate beam determination algorithm information, the N candidate beams being related to N reference signals of the plurality of reference signals; transmitting, to the UE, the N reference signals of the plurality of reference signals based on the resource information on the plurality of reference signals; and receiving, from the UE, measurement information including at least one measurement value among the N reference signals.

9 . The method of claim 8 , wherein, based on the at least one serving beam being changed, new N candidate beams are determined based on the candidate beam determination algorithm information.

10 . The method of claim 8 , wherein, based on there being no reference signal whose a measurement value exceeds a threshold among the measured N reference signals, a number of candidate beams for measurement is increased by +1.

11 . The method of claim 8 , wherein the configuration information further includes at least one of information on N contention free random access (CFRA) resources and/or information on a number of candidate beams.

12 . The method of claim 11 , further comprising:

based on a pre-configured method, mapping the N CFRA resources to the N reference signals; and performing a beam failure recovery operation based on a CFRA resource related to one reference signal of the N reference signals.

13 . The method of claim 8 , wherein, based on a beam failure of the at least one serving beam being detected, the N reference signals are measured based on the resource information on the plurality of reference signals,

wherein the method further comprises receiving, from the UE, beam failure recovery (BFR) medium access control (MAC)-control element (CE) information, and wherein the BFR MAC-CE information includes a bitmap representing a reference signal, whose a measurement value exceeds a threshold among the N reference signals, for at least one serving cell.

14 - 16 . (canceled)

US18/570,514

2021-06-18

2021-06-18

Method for performing beam management in wireless communication system and device therefor

Pending

US20240292232A1

( en )

Applications Claiming Priority (1)

Application Number

Priority Date

Filing Date

Title

PCT/KR2021/007690

WO2022265141A1

( en )

2021-06-18

2021-06-18

Method for performing beam management in wireless communication system and device therefor

Publications (1)

Publication Number

Publication Date

US20240292232A1

true

US20240292232A1 ( en )

2024-08-29

Family

ID=84526559

Family Applications (1)

Application Number

Title

Priority Date

Filing Date

US18/570,514

Pending

US20240292232A1

( en )

2021-06-18

2021-06-18

Method for performing beam management in wireless communication system and device therefor

Country Status (3)

Country

Link

US

( 1 )

US20240292232A1

( en )

KR

( 1 )

KR20240021223A

( en )

WO

( 1 )

WO2022265141A1

( en )

Cited By (8)

* Cited by examiner, † Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20230123914A1

( en )

*

2021-10-18

2023-04-20

Samsung Electronics Co., Ltd.

Electronic device for performing beam switching and/or beamforming and operating method thereof

US20240022921A1

( en )

*

2022-07-15

2024-01-18

At&amp;T Intellectual Property I, L.P.

Apparatuses and methods for reducing paging overhead in relation to beam-sweeping in networks and systems

US20250007597A1

( en )

*

2021-11-22

2025-01-02

Nokia Technologies Oy

User equipment (ue) beam prediction with machine learning

US20250030473A1

( en )

*

2022-01-19

2025-01-23

Qualcomm Incorporated

Reference signal index and machine learning for beam prediction

US20250048151A1

( en )

*

2023-08-03

2025-02-06

Qualcomm Incorporated

Measurement procedures for training of predictive beam management models

US20250150848A1

( en )

*

2023-11-06

2025-05-08

Qualcomm Incorporated

Indication of quasi co-location relation for artificial intelligence - machine learning based positioning

WO2026032938A1

( en )

*

2024-08-08

2026-02-12

Aumovio Germany Gmbh

Communication method for beam failure recovery

WO2026054127A1

( en )

*

2024-09-06

2026-03-12

Sharp Kabushiki Kaisha

Communication apparatuses, communication servers, and communication methods

Families Citing this family (10)

* Cited by examiner, † Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US12069597B2

( en )

*

2021-11-17

2024-08-20

Qualcomm Incorporated

PDSCH rate-matching in NTN

CN118555654A

( en )

*

2023-02-27

2024-08-27

华为技术有限公司

A communication method and device

WO2024191604A1

( en )

*

2023-03-10

2024-09-19

Kyocera Corporation

Location-based candidate beam list

WO2024197841A1

( en )

*

2023-03-31

2024-10-03

Huawei Technologies Co., Ltd.

Beam prediction evaluation in a communication system

CN118921690A

( en )

*

2023-05-06

2024-11-08

大唐移动通信设备有限公司

Method, device and storage medium for sending and receiving measurement report

CN119233425A

( en )

*

2023-06-30

2024-12-31

中兴通讯股份有限公司

Radio resource management method, apparatus and computer readable storage medium

WO2025015510A1

( en )

*

2023-07-17

2025-01-23

Oppo广东移动通信有限公司

Processing method and apparatus, device, and storage medium used for measurement

US20250202564A1

( en )

*

2023-12-13

2025-06-19

Qualcomm Incorporated

Beamforming retro-reflective tag with beam selection

WO2025138155A1

( en )

*

2023-12-29

2025-07-03

富士通株式会社

Method and apparatus for beam management

WO2026014638A1

( en )

*

2024-07-11

2026-01-15

엘지전자 주식회사

Apparatus and method for adaptively changing waveform in wireless communication system

Citations (6)

* Cited by examiner, † Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20190007122A1

( en )

*

2017-06-29

2019-01-03

Telefonaktiebolaget Lm Ericsson (Publ)

A Network Node, A Wireless Device, and Methods Therein for Beam Selection

US20190037530A1

( en )

*

2017-07-28

2019-01-31

Samsung Electronics Co., Ltd.

Apparatus and method for controlling directivity in wireless communication system

US20210167875A1

( en )

*

2019-11-29

2021-06-03

Samsung Electronics Co., Ltd.

Method and user equipment for a signal reception

US20210336683A1

( en )

*

2020-04-24

2021-10-28

Qualcomm Incorporated

Reporting beam measurements for proposed beams and other beams for beam selection

US20240064661A1

( en )

*

2019-02-11

2024-02-22

Comcast Cable Communications, Llc

Power Control and Retransmission

US20240306057A1

( en )

*

2021-01-15

2024-09-12

Telefonaktiebolaget Lm Ericsson (Publ)

Determination of RLP Relaxation Factor and/or Signal Quality Threshold Based on RS Occasion Periodicity for Performing Relaxed RLP

Family Cites Families (5)

* Cited by examiner, † Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US10484076B2

( en )

*

2017-03-22

2019-11-19

Telefonaktiebolaget Lm Ericsson (Publ)

Methods, network node and wireless terminal for beam tracking when beamforming is employed

EP3665792A1

( en )

*

2017-08-09

2020-06-17

IDAC Holdings, Inc.

Methods and systems for beam recovery and management

EP3738221A1

( en )

*

2018-01-12

2020-11-18

Nokia Technologies Oy

Coreset and qcl association in beam recovery procedure

US11297554B2

( en )

*

2019-03-28

2022-04-05

At&amp;T Intellectual Property I, L.P.

Candidate beam selection and control for 5G or other next generation network

US12113600B2

( en )

*

2019-06-05

2024-10-08

Telefonaktiebolaget Lm Ericsson (Publ)

Determination of candidate set of beams for beam tracking

2021

2021-06-18

KR

KR1020247000765A

patent/KR20240021223A/en

active

Pending

2021-06-18

US

US18/570,514

patent/US20240292232A1/en

active

Pending

2021-06-18

WO

PCT/KR2021/007690

patent/WO2022265141A1/en

not_active

Ceased

Patent Citations (6)

* Cited by examiner, † Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20190007122A1

( en )

*

2017-06-29

2019-01-03

Telefonaktiebolaget Lm Ericsson (Publ)

A Network Node, A Wireless Device, and Methods Therein for Beam Selection

US20190037530A1

( en )

*

2017-07-28

2019-01-31

Samsung Electronics Co., Ltd.

Apparatus and method for controlling directivity in wireless communication system

US20240064661A1

( en )

*

2019-02-11

2024-02-22

Comcast Cable Communications, Llc

Power Control and Retransmission

US20210167875A1

( en )

*

2019-11-29

2021-06-03

Samsung Electronics Co., Ltd.

Method and user equipment for a signal reception

US20210336683A1

( en )

*

2020-04-24

2021-10-28

Qualcomm Incorporated

Reporting beam measurements for proposed beams and other beams for beam selection

US20240306057A1

( en )

*

2021-01-15

2024-09-12

Telefonaktiebolaget Lm Ericsson (Publ)

Determination of RLP Relaxation Factor and/or Signal Quality Threshold Based on RS Occasion Periodicity for Performing Relaxed RLP

Cited By (8)

* Cited by examiner, † Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20230123914A1

( en )

*

2021-10-18

2023-04-20

Samsung Electronics Co., Ltd.

Electronic device for performing beam switching and/or beamforming and operating method thereof

US20250007597A1

( en )

*

2021-11-22

2025-01-02

Nokia Technologies Oy

User equipment (ue) beam prediction with machine learning

US20250030473A1

( en )

*

2022-01-19

2025-01-23

Qualcomm Incorporated

Reference signal index and machine learning for beam prediction

US20240022921A1

( en )

*

2022-07-15

2024-01-18

At&amp;T Intellectual Property I, L.P.

Apparatuses and methods for reducing paging overhead in relation to beam-sweeping in networks and systems

US20250048151A1

( en )

*

2023-08-03

2025-02-06

Qualcomm Incorporated

Measurement procedures for training of predictive beam management models

US20250150848A1

( en )

*

2023-11-06

2025-05-08

Qualcomm Incorporated

Indication of quasi co-location relation for artificial intelligence - machine learning based positioning

WO2026032938A1

( en )

*

2024-08-08

2026-02-12

Aumovio Germany Gmbh

Communication method for beam failure recovery

WO2026054127A1

( en )

*

2024-09-06

2026-03-12

Sharp Kabushiki Kaisha

Communication apparatuses, communication servers, and communication methods

Also Published As

Publication number

Publication date

KR20240021223A

( en )

2024-02-16

WO2022265141A1

( en )

2022-12-22

Similar Documents

Publication

Publication Date

Title

KR20240021223A

( en )

2024-02-16

Method for performing beam management in a wireless communication system and apparatus therefor

KR102592653B1

( en )

2023-10-23

Xr device for providing ar mode and vr mode and method for controlling the same

US20220264619A1

( en )

2022-08-18

Operation method of ue associated with sci in wireless communication system

KR102622882B1

( en )

2024-01-09

Method for providing xr content and xr device

US20250150428A1

( en )

2025-05-08

Device and method for performing priority setting and processing on basis of semantic message type in semantic communication

US12556941B2

( en )

2026-02-17

Method for performing federated learning in wireless communication system, and apparatus therefor

US11997037B2

( en )

2024-05-28

UE operation method related to sidelink PTRS in wireless communication system

US20190384977A1

( en )

2019-12-19

Method for providing xr content and xr device

US20230289564A1

( en )

2023-09-14

Neural network-based communication method and device

US10950058B2

( en )

2021-03-16

Method for providing XR content and XR device for providing XR content

US20230318691A1

( en )

2023-10-05

Method for preprocessing downlink in wireless communication system and apparatus therefor

US12537645B2

( en )

2026-01-27

Communication method and communication system for reducing overhead of reference signal

US20250016619A1

( en )

2025-01-09

Method for performing federated learning in wireless communication system, and apparatus therefor

KR20250051607A

( en )

2025-04-17

Device and method for transmitting and receiving signals in a wireless communication system

US12273159B2

( en )

2025-04-08

Method for controlling calculations of deep neural network in wireless communication system, and apparatus therefor

US20240414746A1

( en )

2024-12-12

Device and method for performing, on basis of channel information, device grouping for federated learning-based aircomp of non-iid data environment in communication system

US20230325634A1

( en )

2023-10-12

Communication method and server for distributed learning by which server derives final learning results on basis of learning results of plurality of devices

US20240205775A1

( en )

2024-06-20

Device and method for performing handover in consideration of battery efficiency in wireless communication system

KR20230060505A

( en )

2023-05-04

Communication method for federated learning and device performing the same

US20240259131A1

( en )

2024-08-01

Method for transmitting/receiving signal in wireless communication system by using auto encoder, and apparatus therefor

EP4429344A1

( en )

2024-09-11

Method for transmitting and receiving signal in wireless communication system, and device supporting same

KR20240024907A

( en )

2024-02-26

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

KR20230049664A

( en )

2023-04-13

Communication method based on similarity of channel space information between frequency bands in wireless communication system and apparatus therefor

US12113567B2

( en )

2024-10-08

Wireless signal digital conversion device

EP4645935A1

( en )

2025-11-05

Method for transmitting/receiving signals in wireless communication system, and device for supporting same

Legal Events

Date

Code

Title

Description

2023-12-27

AS

Assignment

Owner name : LG ELECTRONICS INC., KOREA, REPUBLIC OF

Free format text : ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:LEE, EUNJONG;LEE, KYUNGHO;LEE, SANGRIM;REEL/FRAME:065962/0530

Effective date : 20231205

2024-05-30

STPP

Information on status: patent application and granting procedure in general

Free format text : DOCKETED NEW CASE - READY FOR EXAMINATION

2026-04-24

STPP

Information on status: patent application and granting procedure in general

Free format text : NON FINAL ACTION COUNTED, NOT YET MAILED

2026-04-28

STPP

Information on status: patent application and granting procedure in general

Free format text : NON FINAL ACTION MAILED

Related documents

• Generating augmented reality experiences utilizing physical objects to … — Meta Platforms Technologies, Llc (US20260037214A1)#653960
• Virtual reality system using multiple force arrays for a solver — DeepMotion, Inc. (US10657696B2)#653961
• Tracking position of device inside-out for virtual reality interactivity — Sony Interactive Entertainment America Llc (US10535153B2)#653962
• Augmented reality system with spatialized audio tied to user manipulated … — Magic Leap, Inc. (US11231770B2)#653963
• Big data-based autonomous flight drone system and autonomous flight method … — Rgblab Co., Ltd. (US20210390867A1)#653964