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WO2024140578A1 - Procédé de rétroaction de csi basé sur un modèle d'ia, terminal et dispositif côté réseau - Google Patents

Procédé de rétroaction de csi basé sur un modèle d'ia, terminal et dispositif côté réseau Download PDF

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Publication number
WO2024140578A1
WO2024140578A1 PCT/CN2023/141568 CN2023141568W WO2024140578A1 WO 2024140578 A1 WO2024140578 A1 WO 2024140578A1 CN 2023141568 W CN2023141568 W CN 2023141568W WO 2024140578 A1 WO2024140578 A1 WO 2024140578A1
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WO
WIPO (PCT)
Prior art keywords
groups
characteristic information
channel characteristic
information
priority
Prior art date
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Ceased
Application number
PCT/CN2023/141568
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English (en)
Chinese (zh)
Inventor
任千尧
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Vivo Mobile Communication Co Ltd
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Vivo Mobile Communication Co Ltd
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Filing date
Publication date
Application filed by Vivo Mobile Communication Co Ltd filed Critical Vivo Mobile Communication Co Ltd
Publication of WO2024140578A1 publication Critical patent/WO2024140578A1/fr
Anticipated expiration legal-status Critical
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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
    • H04W28/065—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information using assembly or disassembly of packets

Definitions

  • the transmitter can optimize the signal transmission based on CSI to make it more compatible with the channel status.
  • the Channel Quality Indicator CQI
  • MCS Modulation And Coding Scheme
  • PMI Precoding Matrix Indicator
  • Each coefficient has its own meaning, and the priority of the coefficient can be determined according to the corresponding port, subband and other information. Further, in order to better compress channel information, neural network or machine learning methods can be used. However, since all the information in the CSI compressed by the artificial intelligence (AI) model is compressed together, it is impossible to determine the priority according to the corresponding physical port or sub-band information. In the case of insufficient resources, it is impossible to discard based on priority. Therefore, how to better implement CSI feedback based on the AI model is a problem that needs to be solved.
  • AI artificial intelligence
  • Each of the groups is obtained by grouping based on target information;
  • the target information includes: the length of the channel characteristic information of each layer included in N-1 of the groups; or, the ratio of the channel characteristic information of each layer included in N-1 of the groups;
  • Each of the groups is obtained by grouping based on the priority sorting of the channel characteristic information
  • Each of the groups is obtained by sorting the priority of the channel characteristic information and grouping based on the target information;
  • Each of the groups is obtained by grouping based on layers
  • Each of the groups is obtained by grouping based on the segmentation of the channel characteristic information in each layer.
  • a CSI feedback device based on an AI model comprising:
  • the N groups satisfy at least one of the following conditions:
  • a network side device which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the second aspect are implemented.
  • FIG7 is a schematic diagram of the structure of a terminal provided in an embodiment of the present application.
  • Step 101 The terminal groups the channel characteristic information of at least one layer compressed by the AI model according to the priority to obtain N groups; the priority of the channel characteristic information in each group is the same; N is an integer greater than 0;
  • the above priority is for the segment, that is, the segment is grouped, each segment is discarded together, and the segments with the same priority are taken as a group;
  • part2 includes groups group0, group1, group2, and the like.
  • the length or ratio of the channel characteristic information of each layer included in each group is different.
  • the number of coefficients of layer1 and layer2 is 80 respectively, the corresponding length is 40, the number of coefficients of layer3 and layer4 is 40 and the corresponding length is 20, then the first 40 coefficients of layer1 and layer2 and the first 20 coefficients of layer3 and layer4 are group0, and the others are group1;
  • the priority is for the segments, that is, the segments are grouped as units, each segment is discarded together, and the segments with the same priority are taken as a group to ensure that the segments in each group are complete.
  • the terminal treats the coefficients of the channel characteristic information with the same priority as a group according to the priority.
  • the information is discarded in groups according to the priority, that is, reported. A higher priority group.
  • the second part is directly discarded and only the first part is transmitted.
  • FIG3 is a second flow chart of the CSI feedback method based on the AI model provided in an embodiment of the present application. As shown in FIG3 , the method provided in this embodiment includes:
  • Step 201 The network side device receives channel state information reported by the terminal based on N groups; the N groups are obtained by grouping the channel characteristic information of at least one layer after the AI model is compressed according to the priority; the priority of the channel characteristic information in each group is the same; N is an integer greater than 0;
  • Each of the groups is obtained by grouping based on target information;
  • the target information includes: the length of the channel characteristic information of each layer included in N-1 of the groups; or, the ratio of the channel characteristic information of each layer included in N-1 of the groups;
  • Each of the groups is obtained by sorting the priority of the channel characteristic information and grouping based on the target information;
  • Each of the groups is obtained by grouping based on layers
  • Each of the groups is obtained by grouping based on the segmentation of the channel characteristic information in each layer.
  • the network side device can also use the AI model to decode, decompress and other operations on the received channel state information to obtain channel information.
  • the target information is configured by the network side device alone, or configured together with other CSI parameters.
  • the channel characteristic information is segmented scalar quantized or vector quantized channel characteristic information.
  • each of the groups is obtained by grouping based on the segmentation of the channel characteristic information in each layer.
  • the channel state information includes a first part and a second part, the first part is used to determine the length of the second part, and the second part is obtained by mapping the N groups.
  • the priority of the target packet included in the channel state information is greater than the priority of the remaining packets in the N packets except the target packet; or, the channel state information only includes the first part.
  • the channel state information only includes the first part.
  • the first part includes at least one of the following: rank RI, channel quality indication CQI, total number of coefficients, quantization segment information, and quantization parameter of each segment.
  • a processing module 210 is used to group the channel feature information of at least one layer compressed by the AI model according to the priority to obtain N groups;
  • the sending module 220 is used to report the channel state information to the network side device based on the N groups; the priority of the channel characteristic information in each group is the same; N is an integer greater than 0;
  • the N groups satisfy at least one of the following conditions:
  • Each of the groups is obtained by grouping based on target information;
  • the target information includes: the length of the channel characteristic information of each layer included in N-1 of the groups; or, the ratio of the channel characteristic information of each layer included in N-1 of the groups;
  • Each of the groups is obtained by grouping based on the priority sorting of the channel characteristic information
  • Each of the groups is obtained by sorting the priority of the channel characteristic information and grouping based on the target information;
  • each of the groups is obtained by grouping based on layers.
  • the channel state information includes a first part and a second part, the first part is used to determine the length of the second part, and the second part is obtained by mapping the N groups.
  • the first part includes at least one of the following: rank RI, channel quality indication CQI, total number of coefficients, quantization segment information, and quantization parameter of each segment.
  • the device of this embodiment can be used to execute the method of any of the embodiments in the aforementioned terminal side method embodiments. Its specific implementation process and technical effects are the same as those in the terminal side method embodiments. For details, please refer to the detailed introduction in the terminal side method embodiments, which will not be repeated here.
  • the receiving module 310 is used to receive the channel state information reported by the terminal based on N groups; the N groups are obtained by grouping the channel characteristic information of at least one layer after the AI model is compressed according to the priority; the priority of the channel characteristic information in each group is the same; N is an integer greater than 0;
  • Each of the groups is obtained by grouping based on target information;
  • the target information includes: the length of the channel characteristic information of each layer included in N-1 of the groups; or, the ratio of the channel characteristic information of each layer included in N-1 of the groups;
  • the channel state information includes a first part and a second part, the first part is used to determine the length of the second part, and the second part is obtained by mapping the N groups.
  • the embodiment of the present application further provides a communication device 600, including a processor 601 and a memory 602, wherein the memory 602 stores a program or instruction that can be run on the processor 601.
  • the communication device 600 is a terminal
  • the program or instruction is executed by the processor 601 to implement the various steps of the above-mentioned CSI feedback method embodiment based on the AI model, and can achieve the same technical effect.
  • the communication device 600 is a network side device
  • the program or instruction is executed by the processor 601 to implement the various steps of the above-mentioned CSI feedback method embodiment based on the AI model, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • Each of the groups is obtained by sorting the priority of the channel characteristic information and grouping based on the target information;
  • Each of the groups is obtained by grouping based on layers
  • Each of the groups is obtained by grouping based on the segmentation of the channel characteristic information in each layer.
  • FIG. 7 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application.
  • the terminal 1000 includes but is not limited to: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, a user input unit 1007, an interface unit 1008, a memory 1009, and at least some of the components in the processor 1010.
  • the terminal 1000 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 1010 through a power management system, so as to manage charging, discharging, and power consumption management through the power management system.
  • a power source such as a battery
  • the terminal structure shown in FIG7 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently, which will not be described in detail here.
  • the input unit 1004 may include a graphics processing unit (GPU) 10041 and a microphone 10042, and the graphics processor 10041 processes the image data of the static picture or video obtained by the image capture device (such as a camera) in the video capture mode or the image capture mode.
  • the display unit 1006 may include a display panel 10061, which may be configured in the form of a liquid crystal display, an organic light emitting diode, etc.
  • the user input unit 1007 includes a touch panel 10071 and at least one of other input devices 10072. Touch panel 10071 is also called a touch screen. Touch panel 10071 may include two parts: a touch detection device and a touch controller.
  • Other input devices 10072 may include but are not limited to a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.
  • the radio frequency unit 1001 is used to report the channel state information to the network side device based on the N groups; the priority of the channel characteristic information in each group is the same; N is an integer greater than 0;
  • Each of the groups is obtained by grouping based on target information;
  • the target information includes: the length of the channel characteristic information of each layer included in N-1 of the groups; or, the ratio of the channel characteristic information of each layer included in N-1 of the groups;
  • the target information is configured by the network side device alone, or configured together with other CSI parameters.
  • the channel state information only includes the first part.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

La présente demande se rapporte au domaine technique des communications et concerne un procédé de rétroaction d'informations d'état de canal (CSI) basé sur un modèle d'IA, un terminal et un dispositif côté réseau. Selon les modes de réalisation de la présente demande, le procédé de rétroaction de CSI basé sur le modèle d'IA comprend les étapes suivantes : selon une priorité, un terminal regroupe les informations de caractéristiques de canal d'au moins une couche après compression du modèle d'IA afin d'obtenir N groupes ; et le terminal rapporte les CSI à un dispositif côté réseau d'après les N groupes. La priorité des informations de caractéristiques de canal dans chaque groupe est identique. Les N groupes satisfont au moins l'un des éléments suivants : chaque groupe est obtenu en effectuant un regroupement d'après les informations cibles, les informations cibles comprenant la longueur ou la proportion des informations de caractéristiques de canal de chaque couche comprise dans N-1 groupes ; chaque groupe est obtenu en effectuant un regroupement d'après le classement de priorité des informations de caractéristiques de canal ; chaque groupe est obtenu en effectuant un regroupement d'après les couches ; et chaque groupe est obtenu en effectuant un regroupement d'après la segmentation des informations de caractéristiques de canal dans chaque couche.
PCT/CN2023/141568 2022-12-27 2023-12-25 Procédé de rétroaction de csi basé sur un modèle d'ia, terminal et dispositif côté réseau Ceased WO2024140578A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202211690213.2A CN118264289A (zh) 2022-12-27 2022-12-27 基于ai模型的csi反馈方法、终端及网络侧设备
CN202211690213.2 2022-12-27

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111835459A (zh) * 2019-08-23 2020-10-27 维沃移动通信有限公司 信道状态信息csi报告的传输方法、终端及网络侧设备
CN113228532A (zh) * 2019-03-11 2021-08-06 三星电子株式会社 用于复用和省略信道状态信息的方法和设备
WO2022040046A1 (fr) * 2020-08-18 2022-02-24 Qualcomm Incorporated Rapport de configurations de traitement sur la base d'un réseau neuronal au niveau d'un ue

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113228532A (zh) * 2019-03-11 2021-08-06 三星电子株式会社 用于复用和省略信道状态信息的方法和设备
CN111835459A (zh) * 2019-08-23 2020-10-27 维沃移动通信有限公司 信道状态信息csi报告的传输方法、终端及网络侧设备
WO2022040046A1 (fr) * 2020-08-18 2022-02-24 Qualcomm Incorporated Rapport de configurations de traitement sur la base d'un réseau neuronal au niveau d'un ue

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
VIVO: "Other aspects on AI/ML for CSI feedback enhancement", 3GPP DRAFT; R1-2203551, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. RAN WG1, no. e-Meeting; 20220509 - 20220520, 29 April 2022 (2022-04-29), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France, XP052153026 *

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