ABSTRACT
Abstract
The present disclosure relates to a method for operating a terminal and a base station in a wireless communication system and an apparatus for supporting the same. In an embodiment of the present disclosure, a method for operating a terminal in a wireless communication system may include: transmitting a first message including information related to learning; receiving a second message including configuration information for learning; transmitting an uplink reference signal; and transmitting channel information related to a downlink channel measured based on a downlink reference signal.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is the National Stage filing under 35 U.S.C. 371 of International Application No. PCT/KR2020/008996, filed on Jul. 9, 2020, which claims the benefit of U.S. Provisional Application No. 62/942,191 filed on Dec. 1, 2019, the contents of which are all incorporated by reference herein in their entirety.
BACKGROUND OF THE INVENTION
Field of the Invention
The present disclosure relates to a wireless communication system and, more particularly, to a method and apparatus for estimating a channel in a wireless communication system.
Description of the Related Art
Radio access systems have come into widespread in order to provide various types of communication services such as voice or data. In general, a radio access system is a multiple access system capable of supporting communication with multiple users by sharing available system resources (bandwidth, transmit power, etc.). Examples of the multiple access system include a code division multiple access (CDMA) system, a frequency division multiple access (FDMA) system, a time division multiple access (TDMA) system, a single carrier-frequency division multiple access (SC-FDMA) system, etc.
In particular, as many communication apparatuses require a large communication capacity, an enhanced mobile broadband (eMBB) communication technology has been proposed compared to radio access technology (RAT). In addition, not only massive machine type communications (MTC) for providing various services anytime anywhere by connecting a plurality of apparatuses and things but also communication systems considering services/user equipments (UEs) sensitive to reliability and latency have been proposed. To this end, various technical configurations have been proposed.
SUMMARY
The present disclosure relates to a method and apparatus for estimating a channel more efficiently in a wireless communication system.
The present disclosure relates to a method and apparatus for estimating a channel based on machine learning in a wireless communication system.
The present disclosure relates to a method and apparatus for supporting machine learning for channel estimation in a wireless communication system.
The technical objects to be achieved in the present disclosure are not limited to the above-mentioned technical objects, and other technical objects that are not mentioned may be considered by those skilled in the art through the embodiments described below.
Technical Solution
In an embodiment of the present disclosure, a method for operating a terminal in a wireless communication system may include: transmitting a first message including information related to learning; receiving a second message including configuration information for learning; transmitting an uplink reference signal; and transmitting channel information related to a downlink channel measured based on a downlink reference signal.
In an embodiment of the present disclosure, a method for operating a base station in a wireless communication system may include: receiving, from a terminal, a first message including information related to learning; transmitting, to the terminal, a second message including configuration information for learning; receiving, from the terminal, an uplink reference signal; receiving, from the terminal, first channel information related to a downlink channel measured based on a downlink reference signal; and performing learning for a machine learning model for estimating a downlink channel using second channel information which is measured based on the first channel information and an uplink reference signal.
In an embodiment of the present disclosure, a terminal in a wireless communication system may include a transceiver and a processor coupled with the transceiver. The processor may be configured to: transmit a first message including information related to learning, receive a second message including configuration information for learning, transmit an uplink reference signal, and transmit channel information related to a downlink channel measured based on a downlink reference signal.
In an embodiment of the present disclosure, a base station in a wireless communication system may include a transceiver and a processor coupled with the transceiver. The processor may be configured to: receive, from a terminal, a first message including information related to learning, transmit, to the terminal, a second message including configuration information for learning, receive, from the terminal, an uplink reference signal, receive, from the terminal, first channel information related to a downlink channel measured based on a downlink reference signal, and perform learning for a machine learning model for estimating a downlink channel using second channel information measured based on an uplink reference signal.
The above-described aspects of the present disclosure are only a part of the preferred embodiments of the present disclosure, and various embodiments reflecting technical features of the present disclosure may be derived and understood by those skilled in the art on the basis of the detailed description of the present disclosure provided below.
Advantageous Effects
The following effects may be produced by embodiments based on the present disclosure.
According to the present disclosure, the learning of a machine learning model for estimating a channel during operation in a wireless communication system may be effectively implemented.
Effects obtained in the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned above may be clearly derived and understood by those skilled in the art, to which a technical configuration of the present disclosure is applied, from the following description of embodiments of the present disclosure. That is, effects, which are not intended when implementing a configuration described in the present disclosure, may also be derived by those skilled in the art from the embodiments of the present disclosure.
DESCRIPTION OF DRAWINGS
The accompanying drawings are provided to help understanding of the present disclosure, and may provide embodiments of the present disclosure together with a detailed description. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with each other to constitute a new embodiment. Reference numerals in each drawing may refer to structural elements.
FIG. 1 is a view showing an example of a communication system applicable to the present disclosure.
FIG. 2 is a view showing an example of a wireless apparatus applicable to the present disclosure.
FIG. 3 is a view showing another example of a wireless device applicable to the present disclosure.
FIG. 4 is a view showing an example of a hand-held device applicable to the present disclosure.
FIG. 5 is a view showing an example of a car or an autonomous driving car applicable to the present disclosure.
FIG. 6 is a view showing an example of a mobility applicable to the present disclosure.
FIG. 7 is a view showing an example of an XR device applicable to the present disclosure.
FIG. 8 is a view showing an example of a robot applicable to the present disclosure.
FIG. 9 is a view showing an example of artificial intelligence (AI) device applicable to the present disclosure.
FIG. 10 is a view showing physical channels applicable to the present disclosure and a signal transmission method using the same.
FIG. 11 is a view showing the structure of a control plane and a user plane of a radio interface protocol applicable to the present disclosure.
FIG. 12 is a view showing a method of processing a transmitted signal applicable to the present disclosure.
FIG. 13 is a view showing the structure of a radio frame applicable to the present disclosure.
FIG. 14 is a view showing a slot structure applicable to the present disclosure.
FIG. 15 is a view showing an example of a communication structure providable in a 6G system applicable to the present disclosure.
FIG. 16 is a view showing an electromagnetic spectrum applicable to the present disclosure.
FIG. 17 is a view showing a THz communication method applicable to the present disclosure.
FIG. 18 is a view showing a THz wireless communication transceiver applicable to the present disclosure.
FIG. 19 is a view showing a THz signal generation method applicable to the present disclosure.
FIG. 20 is a view showing a wireless communication transceiver applicable to the present disclosure.
FIG. 21 is a view showing a transmitter structure applicable to the present disclosure.
FIG. 22 is a view showing a modulator structure applicable to the present disclosure.
FIG. 23 is a view showing a perceptron architecture in an artificial neural network applicable to the present disclosure.
FIG. 24 is a view showing an artificial neural network architecture applicable to the present disclosure.
FIG. 25 is a view showing a deep neural network applicable to the present disclosure.
FIG. 26 is a view showing a convolutional neural network applicable to the present disclosure.
FIG. 27 is a view showing a filter operation of a convolutional neural network applicable to the present disclosure.
FIG. 28 is a view showing a neural network architecture with a recurrent loop applicable to the present disclosure.
FIG. 29 is a view showing an operational structure of a recurrent neural network applicable to the present disclosure.
FIG. 30 is a view showing a concept of a system that performs channel estimation based on machine learning applicable to the present disclosure.
FIG. 31 is a view showing learning and inference for a machine learning model applicable to the present disclosure.
FIG. 32 is a view showing a learning procedure of a machine learning model applicable to the present disclosure.
FIG. 33 is a view showing an embodiment of a procedure for supporting learning in a terminal applicable to the present disclosure.
FIG. 34 is a view showing an embodiment of a procedure for performing learning in a base station applicable to the present disclosure.
FIG. 35 is a view showing an embodiment of signal exchange for performing learning applicable to the present disclosure.
FIG. 36 is a view showing an embodiment of a procedure for controlling a learning mode applicable to the present disclosure.
FIG. 37 is a view showing another embodiment of a procedure for controlling a learning mode applicable to the present disclosure.
FIG. 38 is a view showing an embodiment of a procedure for controlling the progress of learning in a base station applicable to the present disclosure.
FIG. 39 is a view showing another embodiment of signal exchange for learning between a base station and a terminal, which is applicable to the present disclosure.
FIG. 40 is a view showing an example classification of a plurality of machine learning models applicable to the present disclosure.
MODE FOR INVENTION
The embodiments of the present disclosure described below are combinations of elements and features of the present disclosure in specific forms. The elements or features may be considered selective unless otherwise mentioned. Each element or feature may be practiced without being combined with other elements or features. Further, an embodiment of the present disclosure may be constructed by combining parts of the elements and/or features. Operation orders described in embodiments of the present disclosure may be rearranged. Some constructions or elements of any one embodiment may be included in another embodiment and may be replaced with corresponding constructions or features of another embodiment.
In the description of the drawings, procedures or steps which render the scope of the present disclosure unnecessarily ambiguous will be omitted and procedures or steps which can be understood by those skilled in the art will be omitted.
Throughout the specification, when a certain portion âincludesâ or âcomprisesâ a certain component, this indicates that other components are not excluded and may be further included unless otherwise noted. The terms âunitâ, â-or/erâ and âmoduleâ described in the specification indicate a unit for processing at least one function or operation, which may be implemented by hardware, software or a combination thereof. In addition, the terms âa or anâ, âoneâ, âtheâ etc. may include a singular representation and a plural representation in the context of the present disclosure (more particularly, in the context of the following claims) unless indicated othe
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is the National Stage filing under 35 U.S.C. 371 of International Application No. PCT/KR2020/008996, filed on Jul. 9, 2020, which claims the benefit of U.S. Provisional Application No. 62/942,191 filed on Dec. 1, 2019, the contents of which are all incorporated by reference herein in their entirety.
BACKGROUND OF THE INVENTION
Field of the Invention
The present disclosure relates to a wireless communication system and, more particularly, to a method and apparatus for estimating a channel in a wireless communication system.
Description of the Related Art
Radio access systems have come into widespread in order to provide various types of communication services such as voice or data. In general, a radio access system is a multiple access system capable of supporting communication with multiple users by sharing available system resources (bandwidth, transmit power, etc.). Examples of the multiple access system include a code division multiple access (CDMA) system, a frequency division multiple access (FDMA) system, a time division multiple access (TDMA) system, a single carrier-frequency division multiple access (SC-FDMA) system, etc.
In particular, as many communication apparatuses require a large communication capacity, an enhanced mobile broadband (eMBB) communication technology has been proposed compared to radio access technology (RAT). In addition, not only massive machine type communications (MTC) for providing various services anytime anywhere by connecting a plurality of apparatuses and things but also communication systems considering services/user equipments (UEs) sensitive to reliability and latency have been proposed. To this end, various technical configurations have been proposed.
SUMMARY
The present disclosure relates to a method and apparatus for estimating a channel more efficiently in a wireless communication system.
The present disclosure relates to a method and apparatus for estimating a channel based on machine learning in a wireless communication system.
The present disclosure relates to a method and apparatus for supporting machine learning for channel estimation in a wireless communication system.
The technical objects to be achieved in the present disclosure are not limited to the above-mentioned technical objects, and other technical objects that are not mentioned may be considered by those skilled in the art through the embodiments described below.
Technical Solution
In an embodiment of the present disclosure, a method for operating a terminal in a wireless communication system may include: transmitting a first message including information related to learning; receiving a second message including configuration information for learning; transmitting an uplink reference signal; and transmitting channel information related to a downlink channel measured based on a downlink reference signal.
In an embodiment of the present disclosure, a method for operating a base station in a wireless communication system may include: receiving, from a terminal, a first message including information related to learning; transmitting, to the terminal, a second message including configuration information for learning; receiving, from the terminal, an uplink reference signal; receiving, from the terminal, first channel information related to a downlink channel measured based on a downlink reference signal; and performing learning for a machine learning model for estimating a downlink channel using second channel information which is measured based on the first channel information and an uplink reference signal.
In an embodiment of the present disclosure, a terminal in a wireless communication system may include a transceiver and a processor coupled with the transceiver. The processor may be configured to: transmit a first message including information related to learning, receive a second message including configuration information for learning, transmit an uplink reference signal, and transmit channel information related to a downlink channel measured based on a downlink reference signal.
In an embodiment of the present disclosure, a base station in a wireless communication system may include a transceiver and a processor coupled with the transceiver. The processor may be configured to: receive, from a terminal, a first message including information related to learning, transmit, to the terminal, a second message including configuration information for learning, receive, from the terminal, an uplink reference signal, receive, from the terminal, first channel information related to a downlink channel measured based on a downlink reference signal, and perform learning for a machine learning model for estimating a downlink channel using second channel information measured based on an uplink reference signal.
The above-described aspects of the present disclosure are only a part of the preferred embodiments of the present disclosure, and various embodiments reflecting technical features of the present disclosure may be derived and understood by those skilled in the art on the basis of the detailed description of the present disclosure provided below.
Advantageous Effects
The following effects may be produced by embodiments based on the present disclosure.
According to the present disclosure, the learning of a machine learning model for estimating a channel during operation in a wireless communication system may be effectively implemented.
Effects obtained in the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned above may be clearly derived and understood by those skilled in the art, to which a technical configuration of the present disclosure is applied, from the following description of embodiments of the present disclosure. That is, effects, which are not intended when implementing a configuration described in the present disclosure, may also be derived by those skilled in the art from the embodiments of the present disclosure.
DESCRIPTION OF DRAWINGS
The accompanying drawings are provided to help understanding of the present disclosure, and may provide embodiments of the present disclosure together with a detailed description. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with each other to constitute a new embodiment. Reference numerals in each drawing may refer to structural elements.
FIG. 1 is a view showing an example of a communication system applicable to the present disclosure.
FIG. 2 is a view showing an example of a wireless apparatus applicable to the present disclosure.
FIG. 3 is a view showing another example of a wireless device applicable to the present disclosure.
FIG. 4 is a view showing an example of a hand-held device applicable to the present disclosure.
FIG. 5 is a view showing an example of a car or an autonomous driving car applicable to the present disclosure.
FIG. 6 is a view showing an example of a mobility applicable to the present disclosure.
FIG. 7 is a view showing an example of an XR device applicable to the present disclosure.
FIG. 8 is a view showing an example of a robot applicable to the present disclosure.
FIG. 9 is a view showing an example of artificial intelligence (AI) device applicable to the present disclosure.
FIG. 10 is a view showing physical channels applicable to the present disclosure and a signal transmission method using the same.
FIG. 11 is a view showing the structure of a control plane and a user plane of a radio interface protocol applicable to the present disclosure.
FIG. 12 is a view showing a method of processing a transmitted signal applicable to the present disclosure.
FIG. 13 is a view showing the structure of a radio frame applicable to the present disclosure.
FIG. 14 is a view showing a slot structure applicable to the present disclosure.
FIG. 15 is a view showing an example of a communication structure providable in a 6G system applicable to the present disclosure.
FIG. 16 is a view showing an electromagnetic spectrum applicable to the present disclosure.
FIG. 17 is a view showing a THz communication method applicable to the present disclosure.
FIG. 18 is a view showing a THz wireless communication transceiver applicable to the present disclosure.
FIG. 19 is a view showing a THz signal generation method applicable to the present disclosure.
FIG. 20 is a view showing a wireless communication transceiver applicable to the present disclosure.
FIG. 21 is a view showing a transmitter structure applicable to the present disclosure.
FIG. 22 is a view showing a modulator structure applicable to the present disclosure.
FIG. 23 is a view showing a perceptron architecture in an artificial neural network applicable to the present disclosure.
FIG. 24 is a view showing an artificial neural network architecture applicable to the present disclosure.
FIG. 25 is a view showing a deep neural network applicable to the present disclosure.
FIG. 26 is a view showing a convolutional neural network applicable to the present disclosure.
FIG. 27 is a view showing a filter operation of a convolutional neural network applicable to the present disclosure.
FIG. 28 is a view showing a neural network architecture with a recurrent loop applicable to the present disclosure.
FIG. 29 is a view showing an operational structure of a recurrent neural network applicable to the present disclosure.
FIG. 30 is a view showing a concept of a system that performs channel estimation based on machine learning applicable to the present disclosure.
FIG. 31 is a view showing learning and inference for a machine learning model applicable to the present disclosure.
FIG. 32 is a view showing a learning procedure of a machine learning model applicable to the present disclosure.
FIG. 33 is a view showing an embodiment of a procedure for supporting learning in a terminal applicable to the present disclosure.
FIG. 34 is a view showing an embodiment of a procedure for performing learning in a base station applicable to the present disclosure.
FIG. 35 is a view showing an embodiment of signal exchange for performing learning applicable to the present disclosure.
FIG. 36 is a view showing an embodiment of a procedure for controlling a learning mode applicable to the present disclosure.
FIG. 37 is a view showing another embodiment of a procedure for controlling a learning mode applicable to the present disclosure.
FIG. 38 is a view showing an embodiment of a procedure for controlling the progress of learning in a base station applicable to the present disclosure.
FIG. 39 is a view showing another embodiment of signal exchange for learning between a base station and a terminal, which is applicable to the present disclosure.
FIG. 40 is a view showing an example classification of a plurality of machine learning models applicable to the present disclosure.
MODE FOR INVENTION
The embodiments of the present disclosure described below are combinations of elements and features of the present disclosure in specific forms. The elements or features may be considered selective unless otherwise mentioned. Each element or feature may be practiced without being combined with other elements or features. Further, an embodiment of the present disclosure may be constructed by combining parts of the elements and/or features. Operation orders described in embodiments of the present disclosure may be rearranged. Some constructions or elements of any one embodiment may be included in another embodiment and may be replaced with corresponding constructions or features of another embodiment.
In the description of the drawings, procedures or steps which render the scope of the present disclosure unnecessarily ambiguous will be omitted and procedures or steps which can be understood by those skilled in the art will be omitted.
Throughout the specification, when a certain portion âincludesâ or âcomprisesâ a certain component, this indicates that other components are not excluded and may be further included unless otherwise noted. The terms âunitâ, â-or/erâ and âmoduleâ described in the specification indicate a unit for processing at least one function or operation, which may be implemented by hardware, software or a combination thereof. In addition, the terms âa or anâ, âoneâ, âtheâ etc. may include a singular representation and a plural representation in the context of the present disclosure (more particularly, in the context of the following claims) unless indicated otherwise in the specification or unless context clearly indicates otherwise.
In the embodiments of the present disclosure, a description is mainly made of a data transmission and reception relationship between a Base Station (BS) and a mobile station. A BS refers to a terminal node of a network, which directly communicates with a mobile station. A specific operation described as being performed by the BS may be performed by an upper node of the BS.
Namely, it is apparent that, in a network comprised of a plurality of network nodes including a BS, various operations performed for communication with a mobile station may be performed by the BS, or network nodes other than the BS. The term âBSâ may be replaced with a fixed station, a Node B, an evolved Node B (eNode B or eNB), an Advanced Base Station (ABS), an access point, etc.
In the embodiments of the present disclosure, the term terminal may be replaced with a UE, a Mobile Station (MS), a Subscriber Station (SS), a Mobile Subscriber Station (MSS), a mobile terminal, an Advanced Mobile Station (AMS), etc.
A transmitter is a fixed and/or mobile node that provides a data service or a voice service and a receiver is a fixed and/or mobile node that receives a data service or a voice service. Therefore, a mobile station may serve as a transmitter and a BS may serve as a receiver, on an UpLink (UL). Likewise, the mobile station may serve as a receiver and the BS may serve as a transmitter, on a DownLink (DL).
The embodiments of the present disclosure may be supported by standard specifications disclosed for at least one of wireless access systems including an Institute of Electrical and Electronics Engineers (IEEE) 802.xx system, a 3 rd Generation Partnership Project (3GPP) system, a 3GPP Long Term Evolution (LTE) system, 3GPP 5 th generation (5G) new radio (NR) system, and a 3GPP2 system. In particular, the embodiments of the present disclosure may be supported by the standard specifications, 3GPP TS 36.211, 3GPP TS 36.212, 3GPP TS 36.213, 3GPP TS 36.321 and 3GPP TS 36.331.
In addition, the embodiments of the present disclosure are applicable to other radio access systems and are not limited to the above-described system. For example, the embodiments of the present disclosure are applicable to systems applied after a 3GPP 5G NR system and are not limited to a specific system.
That is, steps or parts that are not described to clarify the technical features of the present disclosure may be supported by those documents. Further, all terms as set forth herein may be explained by the standard documents.
Reference will now be made in detail to the embodiments of the present disclosure with reference to the accompanying drawings. The detailed description, which will be given below with reference to the accompanying drawings, is intended to explain exemplary embodiments of the present disclosure, rather than to show the only embodiments that can be implemented according to the disclosure.
The following detailed description includes specific terms in order to provide a thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that the specific terms may be replaced with other terms without departing the technical spirit and scope of the present disclosure.
The embodiments of the present disclosure can be applied to various radio access systems such as Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-FDMA), etc.
Hereinafter, in order to clarify the following description, a description is made based on a 3GPP communication system (e.g., LTE, NR, etc.), but the technical spirit of the present disclosure is not limited thereto. LTE may refer to technology after 3GPP TS 36.xxx Release 8. In detail, LTE technology after 3GPP TS 36.xxx Release 10 may be referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 may be referred to as LTE-A pro. 3GPP NR may refer to technology after TS 38.xxx Release 15. 3GPP 6G may refer to technology TS Release 17 and/or Release 18. âxxxâ may refer to a detailed number of a standard document. LTE/NR/6G may be collectively referred to as a 3GPP system.
For background arts, terms, abbreviations, etc. used in the present disclosure, refer to matters described in the standard documents published prior to the present disclosure. For example, reference may be made to the standard documents 36.xxx and 38.xxx.
Communication System Applicable to the Present Disclosure
Without being limited thereto, various descriptions, functions, procedures, proposals, methods and/or operational flowcharts of the present disclosure disclosed herein are applicable to various fields requiring wireless communication/connection (e.g., 5G).
Hereinafter, a more detailed description will be given with reference to the drawings. In the following drawings/description, the same reference numerals may exemplify the same or corresponding hardware blocks, software blocks or functional blocks unless indicated otherwise.
FIG. 1 is a view showing an example of a communication system applicable to the present disclosure.
Referring to FIG. 1 , the communication system 100 applicable to the present disclosure includes a wireless device, a base station and a network. The wireless device refers to a device for performing communication using radio access technology (e.g., 5G NR or LTE) and may be referred to as a communication/wireless/5G device. Without being limited thereto, the wireless device may include a robot 100 a , vehicles 100 b - 1 and 100 b - 2 , an extended reality (XR) device 100 c , a hand-held device 100 d , a home appliance 100 e , an Internet of Thing (IoT) device 100 f , and an artificial intelligence (AI) device/server 100 g . For example, the vehicles may include a vehicle having a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc. The vehicles 100 b - 1 and 100 b - 2 may include a unmanned aerial vehicle (UAV) (e.g., a drone). The XR device 100 c includes an augmented reality (AR)/virtual reality (VR)/mixed reality (MR) device and may be implemented in the form of a head-mounted device (HMD), a head-up display (HUD) provided in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle or a robot. The hand-held device 100 d may include a smartphone, a smart pad, a wearable device (e.g., a smart watch or smart glasses), a computer (e.g., a laptop), etc. The home appliance 100 e may include a TV, a refrigerator, a washing machine, etc. The IoT device 100 f may include a sensor, a smart meter, etc. For example, the base station 120 and the network 130 may be implemented by a wireless device, and a specific wireless device 120 a may operate as a base station/network node for another wireless device.
The wireless devices 100 a to 100 f may be connected to the network 130 through the base station 120 . AI technology is applicable to the wireless devices 100 a to 100 f , and the wireless devices 100 a to 100 f may be connected to the AI server 100 g through the network 130 . The network 130 may be configured using a 3G network, a 4G (e.g., LTE) network or a 5G (e.g., NR) network, etc. The wireless devices 100 a to 100 f may communicate with each other through the base station 120 /the network 130 or perform direct communication (e.g., sidelink communication) without through the base station 120 /the network 130 . For example, the vehicles 100 b - 1 and 100 b - 2 may perform direct communication (e.g., vehicle to vehicle (V2V)/vehicle to everything (V2X) communication). In addition, the IoT device 100 f (e.g., a sensor) may perform direct communication with another IoT device (e.g., a sensor) or the other wireless devices 100 a to 100 f.
Wireless communications/connections 150 a , 150 b and 150 c may be established between the wireless devices 100 a to 100 f /the base station 120 and the base station 120 /the base station 120 . Here, wireless communication/connection may be established through various radio access technologies (e.g., 5G NR) such as uplink/downlink communication 150 a , sidelink communication 150 b (or D2D communication) or communication 150 c between base stations (e.g., relay, integrated access backhaul (JAB). The wireless device and the base station/wireless device or the base station and the base station may transmit/receive radio signals to/from each other through wireless communication/connection 150 a , 150 b and 150 c . For example, wireless communication/connection 150 a , 150 b and 150 c may enable signal transmission/reception through various physical channels. To this end, based on the various proposals of the present disclosure, at least some of various configuration information setting processes for transmission/reception of radio signals, various signal processing procedures (e.g., channel encoding/decoding, modulation/demodulation, resource mapping/demapping, etc.), resource allocation processes, etc. may be performed.
Wireless Device Applicable to the Present Disclosure
FIG. 2 is a view showing an example of a wireless device applicable to the present disclosure.
Referring to FIG. 2 , a first wireless device 200 a and a second wireless device 200 b may transmit and receive radio signals through various radio access technologies (e.g., LTE or NR). Here, {the first wireless device 200 a , the second wireless device 200 b } may correspond to {the wireless device 100 x , the base station 120 } and/or {the wireless device 100 x , the wireless device 100 x } of FIG. 1 .
The first wireless device 200 a may include one or more processors 202 a and one or more memories 204 a and may further include one or more transceivers 206 a and/or one or more antennas 208 a . The processor 202 a may be configured to control the memory 204 a and/or the transceiver 206 a and to implement descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed herein. For example, the processor 202 a may process information in the memory 204 a to generate first information/signal and then transmit a radio signal including the first information/signal through the transceiver 206 a . In addition, the processor 202 a may receive a radio signal including second information/signal through the transceiver 206 a and then store information obtained from signal processing of the second information/signal in the memory 204 a . The memory 204 a may be connected with the processor 202 a , and store a variety of information related to operation of the processor 202 a . For example, the memory 204 a may store software code including instructions for performing all or some of the processes controlled by the processor 202 a or performing the descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed herein. Here, the processor 202 a and the memory 204 a may be part of a communication modem/circuit/chip designed to implement wireless communication technology (e.g., LTE or NR). The transceiver 206 a may be connected with the processor 202 a to transmit and/or receive radio signals through one or more antennas 208 a . The transceiver 206 a may include a transmitter and/or a receiver. The transceiver 206 a may be used interchangeably with a radio frequency (RF) unit. In the present disclosure, the wireless device may refer to a communication modem/circuit/chip.
The second wireless device 200 b may include one or more processors 202 b and one or more memories 204 b and may further include one or more transceivers 206 b and/or one or more antennas 208 b . The processor 202 b may be configured to control the memory 204 b and/or the transceiver 206 b and to implement the descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed herein. For example, the processor 202 b may process information in the memory 204 b to generate third information/signal and then transmit the third information/signal through the transceiver 206 b . In addition, the processor 202 b may receive a radio signal including fourth information/signal through the transceiver 206 b and then store information obtained from signal processing of the fourth information/signal in the memory 204 b . The memory 204 b may be connected with the processor 202 b to store a variety of information related to operation of the processor 202 b . For example, the memory 204 b may store software code including instructions for performing all or some of the processes controlled by the processor 202 b or performing the descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed herein. Herein, the processor 202 b and the memory 204 b may be part of a communication modem/circuit/chip designed to implement wireless communication technology (e.g., LTE or NR). The transceiver 206 b may be connected with the processor 202 b to transmit and/or receive radio signals through one or more antennas 208 b . The transceiver 206 b may include a transmitter and/or a receiver. The transceiver 206 b may be used interchangeably with a radio frequency (RF) unit. In the present disclosure, the wireless device may refer to a communication modem/circuit/chip.
Hereinafter, hardware elements of the wireless devices 200 a and 200 b will be described in greater detail. Without being limited thereto, one or more protocol layers may be implemented by one or more processors 202 a and 202 b . For example, one or more processors 202 a and 202 b may implement one or more layers (e.g., functional layers such as PHY (physical), MAC (media access control), RLC (radio link control), PDCP (packet data convergence protocol), RRC (radio resource control), SDAP (service data adaptation protocol)). One or more processors 202 a and 202 b may generate one or more protocol data units (PDUs) and/or one or more service data unit (SDU) according to the descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed herein. One or more processors 202 a and 202 b may generate messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed herein. One or more processors 202 a and 202 b may generate PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and/or methods disclosed herein and provide the PDUs, SDUs, messages, control information, data or information to one or more transceivers 206 a and 206 b . One or more processors 202 a and 202 b may receive signals (e.g., baseband signals) from one or more transceivers 206 a and 206 b and acquire PDUs, SDUs, messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed herein.
One or more processors 202 a and 202 b may be referred to as controllers, microcontrollers, microprocessors or microcomputers. One or more processors 202 a and 202 b may be implemented by hardware, firmware, software or a combination thereof. For example, one or more application specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more digital signal processing devices (DSPDs), programmable logic devices (PLDs) or one or more field programmable gate arrays (FPGAs) may be included in one or more processors 202 a and 202 b . The descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed herein may be implemented using firmware or software, and firmware or software may be implemented to include modules, procedures, functions, etc. Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed herein may be included in one or more processors 202 a and 202 b or stored in one or more memories 204 a and 204 b to be driven by one or more processors 202 a and 202 b . The descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed herein implemented using firmware or software in the form of code, a command and/or a set of commands.
One or more memories 204 a and 204 b may be connected with one or more processors 202 a and 202 b to store various types of data, signals, messages, information, programs, code, instructions and/or commands. One or more memories 204 a and 204 b may be composed of read only memories (ROMs), random access memories (RAMs), erasable programmable read only memories (EPROMs), flash memories, hard drives, registers, cache memories, computer-readable storage mediums and/or combinations thereof. One or more memories 204 a and 204 b may be located inside and/or outside one or more processors 202 a and 202 b . In addition, one or more memories 204 a and 204 b may be connected with one or more processors 202 a and 202 b through various technologies such as wired or wireless connection.
One or more transceivers 206 a and 206 b may transmit user data, control information, radio signals/channels, etc. described in the methods and/or operational flowcharts of the present disclosure to one or more other apparatuses. One or more transceivers 206 a and 206 b may receive user data, control information, radio signals/channels, etc. described in the methods and/or operational flowcharts of the present disclosure from one or more other apparatuses. For example, one or more transceivers 206 a and 206 b may be connected with one or more processors 202 a and 202 b to transmit/receive radio signals. For example, one or more processors 202 a and 202 b may perform control such that one or more transceivers 206 a and 206 b transmit user data, control information or radio signals to one or more other apparatuses. In addition, one or more processors 202 a and 202 b may perform control such that one or more transceivers 206 a and 206 b receive user data, control information or radio signals from one or more other apparatuses. In addition, one or more transceivers 206 a and 206 b may be connected with one or more antennas 208 a and 208 b , and one or more transceivers 206 a and 206 b may be configured to transmit/receive user data, control information, radio signals/channels, etc. described in the descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed herein through one or more antennas 208 a and 208 b . In the present disclosure, one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports). One or more transceivers 206 a and 206 b may convert the received radio signals/channels, etc. from RF band signals to baseband signals, in order to process the received user data, control information, radio signals/channels, etc. using one or more processors 202 a and 202 b . One or more transceivers 206 a and 206 b may convert the user data, control information, radio signals/channels processed using one or more processors 202 a and 202 b from baseband signals into RF band signals. To this end, one or more transceivers 206 a and 206 b may include (analog) oscillator and/or filters.
Structure of Wireless Device Applicable to the Present Disclosure
FIG. 3 is a view showing another example of a wireless device applicable to the present disclosure.
Referring to FIG. 3 , a wireless device 300 may correspond to the wireless devices 200 a and 200 b of FIG. 2 and include various elements, components, units/portions and/or modules. For example, the wireless device 300 may include a communication unit 310 , a control unit (controller) 320 , a memory unit (memory) 330 and additional components 340 . The communication unit may include a communication circuit 312 and a transceiver(s) 314 . For example, the communication circuit 312 may include one or more processors 202 a and 202 b and/or one or more memories 204 a and 204 b of FIG. 2 . For example, the transceiver(s) 314 may include one or more transceivers 206 a and 206 b and/or one or more antennas 208 a and 208 b of FIG. 2 . The control unit 320 may be electrically connected with the communication unit 310 , the memory unit 330 and the additional components 340 to control overall operation of the wireless device. For example, the control unit 320 may control electrical/mechanical operation of the wireless device based on a program/code/instruction/information stored in the memory unit 330 . In addition, the control unit 320 may transmit the information stored in the memory unit 330 to the outside (e.g., another communication device) through the wireless/wired interface using the communication unit 310 over a wireless/wired interface or store information received from the outside (e.g., another communication device) through the wireless/wired interface using the communication unit 310 in the memory unit 330 .
The additional components 340 may be variously configured according to the types of the wireless devices. For example, the additional components 340 may include at least one of a power unit/battery, an input/output unit, a driving unit or a computing unit. Without being limited thereto, the wireless device 300 may be implemented in the form of the robot ( FIG. 1 , 100
a ), the vehicles ( FIG. 1 , 100
b - 1 and 100 b - 2 ), the XR device ( FIG. 1 , 100
c ), the hand-held device ( FIG. 1 , 100
d ), the home appliance ( FIG. 1 , 100
e ), the IoT device ( FIG. 1 , 100
f ), a digital broadcast terminal, a hologram apparatus, a public safety apparatus, an MTC apparatus, a medical apparatus, a Fintech device (financial device), a security device, a climate/environment device, an AI server/device ( FIG. 1 , 140 ), the base station ( FIG. 1 , 120 ), a network node, etc. The wireless device may be movable or may be used at a fixed place according to use example/service.
In FIG. 3 , various elements, components, units/portions and/or modules in the wireless device 300 may be connected with each other through wired interfaces or at least some thereof may be wirelessly connected through the communication unit 310 . For example, in the wireless device 300 , the control unit 320 and the communication unit 310 may be connected by wire, and the control unit 320 and the first unit (e.g., 130 or 140 ) may be wirelessly connected through the communication unit 310 . In addition, each element, component, unit/portion and/or module of the wireless device 300 may further include one or more elements. For example, the control unit 320 may be composed of a set of one or more processors. 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 graphic processing processor, a memory control processor, etc. In another example, the memory unit 330 may be composed of a random access memory (RAM), a dynamic RAM (DRAM), a read only memory (ROM), a flash memory, a volatile memory, a non-volatile memory and/or a combination thereof.
Hand-Held Device Applicable to the Present Disclosure
FIG. 4 is a view showing an example of a hand-held device applicable to the present disclosure.
FIG. 4 shows a hand-held device applicable to the present disclosure. The hand-held device may include a smartphone, a smart pad, a wearable device (e.g., a smart watch or smart glasses), and a hand-held computer (e.g., a laptop, etc.). The hand-held device may be referred to as a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS) or a wireless terminal (WT).
Referring to FIG. 4 , the hand-held device 400 may include an antenna unit (antenna) 408 , a communication unit (transceiver) 410 , a control unit (controller) 420 , a memory unit (memory) 430 , a power supply unit (power supply) 440 a , an interface unit (interface) 440 b , and an input/output unit 440 c . An antenna unit (antenna) 408 may be part of the communication unit 410 . The blocks 410 to 430 / 440 a to 440 c may correspond to the blocks 310 to 330 / 340 of FIG. 3 , respectively.
The communication unit 410 may transmit and receive signals (e.g., data, control signals, etc.) to and from other wireless devices or base stations. The control unit 420 may control the components of the hand-held device 400 to perform various operations. The control unit 420 may include an application processor (AP). The memory unit 430 may store data/parameters/program/code/instructions necessary to drive the hand-held device 400 . In addition, the memory unit 430 may store input/output data/information, etc. The power supply unit 440 a may supply power to the hand-held device 400 and include a wired/wireless charging circuit, a battery, etc. The interface unit 440 b may support connection between the hand-held device 400 and another external device. The interface unit 440 b may include various ports (e.g., an audio input/output port and a video input/output port) for connection with the external device. The input/output unit 440 c may receive or output video information/signals, audio information/signals, data and/or user input information. The input/output unit 440 c may include a camera, a microphone, a user input unit, a display 440 d , a speaker and/or a haptic module.
For example, in case of data communication, the input/output unit 440 c may acquire user input information/signal (e.g., touch, text, voice, image or video) from the user and store the user input information/signal in the memory unit 430 . The communication unit 410 may convert the information/signal stored in the memory into a radio signal and transmit the converted radio signal to another wireless device directly or transmit the converted radio signal to a base station. In addition, the communication unit 410 may receive a radio signal from another wireless device or the base station and then restore the received radio signal into original information/signal. The restored information/signal may be stored in the memory unit 430 and then output through the input/output unit 440 c in various forms (e.g., text, voice, image, video and haptic).
Type of Wireless Device Applicable to the Present Disclosure
FIG. 5 is a view showing an example of a car or an autonomous driving car applicable to the present disclosure.
FIG. 5 shows a car or an autonomous driving vehicle applicable to the present disclosure. The car or the autonomous driving car may be implemented as a mobile robot, a vehicle, a train, a manned/unmanned aerial vehicle (AV), a ship, etc. and the type of the car is not limited.
Referring to FIG. 5 , the car or autonomous driving car 500 may include an antenna unit (antenna) 508 , a communication unit (transceiver) 510 , a control unit (controller) 520 , a driving unit 540 a , a power supply unit (power supply) 540 b , a sensor unit 540 c , and an autonomous driving unit 540 d . The antenna unit 550 may be configured as part of the communication unit 510 . The blocks 510 / 530 / 540 a to 540 d correspond to the blocks 410 / 430 / 440 of FIG. 4 .
The communication unit 510 may transmit and receive signals (e.g., data, control signals, etc.) to and from external devices such as another vehicle, a base station (e.g., a base station, a road side unit, etc.), and a server. The control unit 520 may control the elements of the car or autonomous driving car 500 to perform various operations. The control unit 520 may include an electronic control unit (ECU). The driving unit 540 a may drive the car or autonomous driving car 500 on the ground. The driving unit 540 a may include an engine, a motor, a power train, wheels, a brake, a steering device, etc. The power supply unit 540 b may supply power to the car or autonomous driving car 500 , and include a wired/wireless charging circuit, a battery, etc. The sensor unit 540 c may obtain a vehicle state, surrounding environment information, user information, etc. The sensor unit 540 c may include an inertial navigation unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight sensor, a heading sensor, a position module, a vehicle forward/reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illumination sensor, a brake pedal position sensor, and so on. The autonomous driving sensor 540 d may implement technology for maintaining a driving lane, technology for automatically controlling a speed such as adaptive cruise control, technology for automatically driving the car along a predetermined route, technology for automatically setting a route when a destination is set and driving the car, etc.
For example, the communication unit 510 may receive map data, traffic information data, etc. from an external server. The autonomous driving unit 540 d may generate an autonomous driving route and a driving plan based on the acquired data. The control unit 520 may control the driving unit 540 a (e.g., speed/direction control) such that the car or autonomous driving car 500 moves along the autonomous driving route according to the driving plane. During autonomous driving, the communication unit 510 may aperiodically/periodically acquire latest traffic information data from an external server and acquire surrounding traffic information data from neighboring cars. In addition, during autonomous driving, the sensor unit 540 c may acquire a vehicle state and surrounding environment information. The autonomous driving unit 540 d may update the autonomous driving route and the driving plan based on newly acquired data/information. The communication unit 510 may transmit information such as a vehicle location, an autonomous driving route, a driving plan, etc. to the external server. The external server may predict traffic information data using AI technology or the like based on the information collected from the cars or autonomous driving cars and provide the predicted traffic information data to the cars or autonomous driving cars.
FIG. 6 is a view showing an example of a mobility applicable to the present disclosure.
Referring to FIG. 6 , the mobility applied to the present disclosure may be implemented as at least one of a transportation means, a train, an aerial vehicle or a ship. In addition, the mobility applied to the present disclosure may be implemented in the other forms and is not limited to the above-described embodiments.
At this time, referring to FIG. 6 , the mobility 600 may include a communication unit (transceiver) 610 , a control unit (controller) 620 , a memory unit (memory) 630 , an input/output unit 640 a and a positioning unit 640 b . Here, the blocks 610 to 630 / 640 a to 640 b may corresponding to the blocks 310 to 330 / 340 of FIG. 3 .
The communication unit 610 may transmit and receive signals (e.g., data, control signals, etc.) to and from external devices such as another mobility or a base station. The control unit 620 may control the components of the mobility 600 to perform various operations. The memory unit 630 may store data/parameters/programs/code/instructions supporting the various functions of the mobility 600 . The input/output unit 640 a may output AR/VR objects based on information in the memory unit 630 . The input/output unit 640 a may include a HUD. The positioning unit 640 b may acquire the position information of the mobility 600 . The position information may include absolute position information of the mobility 600 , position information in a driving line, acceleration information, position information of neighboring vehicles, etc. The positioning unit 640 b may include a global positioning system (GPS) and various sensors.
For example, the communication unit 610 of the mobility 600 may receive map information, traffic information, etc. from an external server and store the map information, the traffic information, etc. in the memory unit
CLAIMS
Claims ( 20 )
What is claimed is:
1 . A method performed by a user equipment (UE) in a wireless communication system, the method comprising:
receiving, from a base station, a first message including configuration information related to an uplink reference signal; transmitting the uplink reference signal based on the configuration information; receiving a downlink reference signal that is transmitted by the base station; and transmitting information related to a channel measured based on the downlink reference signal, wherein the first message includes information related to learning with respect to a machine learning model for measuring the channel, and wherein the learning is performed by the base station.
2 . The method of claim 1 , further comprising transmitting, to the base station, a second message related to a registration,
wherein the second message includes information related to the learning.
3 . The method of claim 2 , wherein the second message includes at least one of information related to learning support capability of the UE, information notifying a state related to the learning, and information requesting a change of the state related to the learning.
4 . The method of claim 2 , wherein the first message includes at least one of information related to progress of the learning, information indicating a resource for the downlink reference signal, information indicating a resource for the uplink reference signal, and information for reporting a measurement result of the downlink channel.
5 . The method of claim 2 , wherein the first message includes information related to a machine learning model for measuring the downlink channel, and
further comprising inferring the downlink channel by using the downlink reference signal and the machine learning model.
6 . The method of claim 2 , further comprising determining a learning mode based on at least one of a load and a mobility change rate of the UE,
wherein the second message includes information requesting change into the determined learning mode.
7 . The method of claim 6 , wherein the determining of the learning mode comprises:
selecting a mode for suspending learning based on the load or the mobility change rate exceeding a threshold; and selecting a mode for performing learning or of standing by to perform learning.
8 . The method of claim 2 , wherein the transmitting of the second message comprises transmitting, at an initial access, a capability information message which includes information related to learning support capability of the UE.
9 . The method of claim 2 , wherein the transmitting of the second message further comprises transmitting a message for requesting registration to a UE pool for learning based on a state change of the UE.
10 . The method of claim 2 , wherein the second message includes information indicating a learning period, and
further comprising ending learning after the learning period.
11 . A user equipment (UE) in a wireless communication system, the UE comprising:
a transceiver; and at least one processor coupled to the transceiver and configured to: receive, from a base station, a first message including configuration information related to an uplink reference signal; transmit the uplink reference signal based on the configuration information; receive a downlink reference signal that is transmitted by the base station; and transmit information related to a channel measured based on the downlink reference signal, wherein the first message includes information related to learning with respect to a machine learning model for measuring the channel, and wherein the learning is performed by the base station.
12 . The UE of claim 11 , wherein the at least one processor is further configured to transmit, to the base station, a second message related to a registration,
wherein the second message includes information related to the learning.
13 . The UE of claim 12 , wherein the second message comprises a capability information message which includes information related to learning support capability of the UE.
14 . The UE of claim 12 , wherein the second message includes at least one of information related to learning support capability of the UE, information notifying a state related to the learning, and information requesting a change of the state related to the learning.
15 . The UE of claim 12 , wherein the first message includes at least one of information related to progress of the learning, information indicating a resource for the downlink reference signal, information indicating a resource for the uplink reference signal, and information for reporting a measurement result of the downlink channel.
16 . The UE of claim 12 , wherein the first message includes information related to a machine learning model for measuring the downlink channel, and
further comprising inferring the downlink channel by using the downlink reference signal and the machine learning model.
17 . The UE of claim 12 , further comprising determining a learning mode based on at least one of a load and a mobility change rate of the UE,
wherein the second message includes information requesting change into the determined learning mode.
18 . The UE of claim 12 , wherein the transmitting of the second message further comprises transmitting a message for requesting registration to a UE pool for learning based on a state change of the UE.
19 . The UE of claim 12 , wherein the second message includes information indicating a learning period, and
further comprising ending learning after the learning period.
20 . A communication apparatus comprising:
at least one processor; and at least one computer memory operably connected to the at least one processor and storing instructions that, based on being executed by the at least one processor, perform operations comprising: receiving a first message including configuration information related to an uplink reference signal; transmitting the uplink reference signal based on the configuration information; receiving a downlink reference signal that is transmitted by a base station; and transmitting information related to a channel measured based on the downlink reference signal, wherein the first message includes information related to learning with respect to a machine learning model for measuring the channel, and wherein the learning is performed by the base station.
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