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CN-122029868-A - User equipment, network entity, network client and method for automatic resource allocation and reporting conditioned on performance/constraint requirements

CN122029868ACN 122029868 ACN122029868 ACN 122029868ACN-122029868-A

Abstract

A wireless communication system comprising a network entity (200) and a user equipment (100) according to an embodiment is provided. The network entity (200) is configured for generating two or more configurations for another device of the wireless communication system, wherein the two or more configurations differ in at least one parameter. Furthermore, the network entity (200) is configured to communicate the two or more configurations to another entity of the wireless communication system, e.g. to the user equipment (100) or e.g. to the base station. The two or more configurations are adapted for different environments of the other device and/or for different states of the other device and/or for different signal propagation conditions and/or for different properties of the other device. The user equipment (100) is configured for receiving two or more configurations from the network entity (200) and selecting one of the two or more configurations depending on a current environment of the user equipment (100) and/or depending on a current state of the user equipment (100) and/or depending on current signal propagation conditions and/or depending on properties of the user equipment (100). Furthermore, the user equipment (100) is configured for applying said one of two or more configurations at the user equipment (100), e.g. for transmitting or receiving reference signals and/or for measuring and/or reporting reference signals.

Inventors

  • Georges Kantes
  • Mohammed Alavikh
  • Birendra Guimir
  • Ernst Eberling
  • Christopher mutschler

Assignees

  • 弗劳恩霍夫应用研究促进协会

Dates

Publication Date
20260512
Application Date
20240809
Priority Date
20230810

Claims (20)

  1. 1. A network entity (200) of a wireless communication system, wherein the network entity (200) is configured to: Generating two or more configurations for another device of the wireless communication system, wherein the two or more configurations differ in at least one parameter, and Transmitting the two or more configurations to another entity of the wireless communication system, e.g., to a user equipment (100) or, e.g., to a base station, Wherein the two or more configurations are adapted for different environments of the further device and/or for different states of the further device and/or for different signal propagation conditions and/or for different properties of the further device.
  2. 2. The network entity (200) of claim 1, Wherein the two or more configurations are suitable for machine learning data collection and/or for machine learning model monitoring.
  3. 3. The network entity (200) according to claim 1 or 2, Wherein the network entity (200) is configured to generate the two or more configurations by, for example, enabling or activating an artificial intelligence/machine learning model in accordance with a lifecycle management process.
  4. 4. The network entity (200) of claim 3, Wherein the network entity (200) is configured to generate the two or more configurations by, for example, enabling or activating the artificial intelligence/machine learning model in accordance with a lifecycle management process, Wherein the network entity (200) is configured to feed input information into the artificial intelligence/machine learning model to obtain an output of the artificial intelligence/machine learning model, Wherein, in response to receiving the input information, the artificial intelligence/machine learning model is configured to output at least one of the two or more configurations as an output of the artificial intelligence/machine learning model, or Wherein, in response to receiving the input information, the artificial intelligence/machine learning model is configured to output an intermediate output as an output of the artificial intelligence/machine learning model, and the network entity (200) is configured to generate at least one of the two or more configurations using the intermediate output.
  5. 5. The network entity (200) according to claim 3 or 4, Wherein the network entity (200) is configured to receive data for the artificial intelligence/machine learning model from the other entity.
  6. 6. A network entity (200) of a wireless communication system, wherein the network entity (200) is configured to: the receiving objective (e.g., performance objective), Requesting and/or receiving information and/or identifying configurations for necessary resources and/or measurements and/or reporting in accordance with the objective, Determining at least one resource and/or measurement and/or reporting configuration to achieve the objective, Using the configured resources and using the received reports to enable or activate an artificial intelligence/machine learning model to perform tasks (e.g., beam management or positioning); And/or Receive constraint limits on one or more available resources, Predicting the maximum performance achievable (e.g., positioning accuracy), Select and configure resources and/or select reporting complexity to achieve the maximum performance, The configured resources are used and the received reports are used to enable or activate an artificial intelligence/machine learning model to perform tasks (e.g., predict a location of a UE).
  7. 7. The network entity (200) of claim 6, Wherein the network entity (200) is configured to provide information about the at least one resource and/or about the at least one measurement and/or about the at least one reporting configuration to a user equipment (100) and/or TRP, which information has been determined by the network entity (200) to achieve the objective.
  8. 8. The network entity (200) according to claim 6 or 7, Wherein the network entity (200) is configured to receive data for the artificial intelligence/machine learning model from another entity of the wireless communication system.
  9. 9. The network entity (200) according to claim 5 or 8, Wherein the data for the artificial intelligence/machine learning model is measurement data or reporting data.
  10. 10. The network entity (200) according to any of the claims 3 to 9, Wherein the network entity (200) comprises the artificial intelligence/machine learning model.
  11. 11. The network entity (200) according to any of the claims 3 to 9, Wherein the artificial intelligence/machine learning model is distributed across two or more devices.
  12. 12. The network entity (200) according to any of claims 3 to 11, Wherein the artificial intelligence/machine learning model is a neural network.
  13. 13. The network entity (200) according to any of the claims 3 to 12, Wherein the network entity (200) is configured to receive training or validation data and a truth tag for the artificial intelligence/machine learning model, and wherein the network entity (200) is configured to train and/or validate the artificial intelligence/machine learning model using the training or validation data and the truth tag.
  14. 14. The network entity (200) according to any of claims 3 to 13, Wherein the network entity (200) is configured to perform data enhancement using two or more algorithms on the same data set to train the artificial intelligence/machine learning model.
  15. 15. The network entity (200) according to any of claims 3 to 14, Wherein the network entity (200) is configured to train the artificial intelligence/machine learning model in dependence of performance conditions and/or in dependence of constraints.
  16. 16. The network entity (200) according to any of claims 3 to 15, Wherein the network entity (200) is configured to request further training or verification data from the further device for training or verifying the artificial intelligence/machine learning model, Wherein the network entity (200) is configured to receive the further training or verification data from the further device, and Wherein the network entity (200) is configured to train or validate the artificial intelligence/machine learning model using the training or validation data.
  17. 17. The network entity (200) according to any of claims 3 to 16, Wherein the network entity (200) comprises a monitoring entity for evaluating the performance of the artificial intelligence/machine learning model.
  18. 18. The network entity (200) according to any of claims 3 to 17, Wherein the artificial intelligence/machine learning model is trained using supervised learning.
  19. 19. The network entity (200) according to any of claims 3 to 18, Wherein the artificial intelligence/machine learning model is compiled and/or compressed for the target device.
  20. 20. The network entity (200) of claim 19, Wherein the artificial intelligence/machine learning model that is compiled and/or compressed is transmitted to the target device.

Description

User equipment, network entity, network client and method for automatic resource allocation and reporting conditioned on performance/constraint requirements Technical Field The present invention relates to wireless communication systems, and more particularly, to a method for automatic resource allocation and reporting for user equipment, network entities, network clients, and performance/constraint requirements. Background Fig. 7 is a schematic diagram of an example of a terrestrial wireless network 500, as shown in fig. 7 (a), the terrestrial wireless network 500 including a core network and one or more radio access networks RANs 1、RAN2、…RANN (ran=radio access network). Fig. 7 (B) is a schematic diagram of an example of a radio access network RAN n, which may include one or more base stations gNB 1 to gNB 5 (gnb=next generation node B), each serving a particular area around the base station schematically represented by respective cells 106 1 to 106 5. The base station is provided for users within the serving cell. One or more base stations may serve users in licensed and/or unlicensed frequency bands. The term Base Station (BS) refers to an eNB in UMTS/LTE-a Pro, or a BS in other mobile communication standards, in a gNB in 5G network. The user may be a fixed device or a mobile device. Mobile or fixed internet of things (IoT) devices may also access the wireless communication system, with these devices connected to base stations or users. Mobile devices or IoT devices may include physical devices, ground-based vehicles (e.g., robots or automobiles), aircraft (e.g., human or Unmanned Aerial Vehicles (UAVs), the latter also referred to as drones), buildings, and other items or devices with electronics, software, sensors, actuators, or the like embedded therein, as well as network connectivity that allows the devices to collect and exchange data across existing network infrastructure. Fig. 7 (b) shows an example view of five cells, however, RAN n may include more or fewer such cells, and RAN n may also include only one base station. Fig. 7 (b) shows two user UEs 1 and 2 (ue=user equipment), also referred to as User Equipment (UE), located in the cell 106 2 and served by the base station gNB 2. another user UE 3 is shown in cell 106 4 as being served by base station gNB 4. Arrows 108 1、1082 and 108 3 schematically represent uplink/downlink connections for transmitting data from user UEs 1、UE2 and 3 to base station gNB 2、gNB4 or for transmitting data from base station gNB 2、gNB4 to user UE 1、UE2、UE3. This may be done on licensed or unlicensed frequency bands. Further, fig. 7 (b) shows two IoT devices 110 1 and 110 2 in cell 106 4, which may be fixed or mobile devices. IoT device 110 1 accesses the wireless communication system via base station gNB 4 to receive and transmit data, as schematically represented by arrow 112 1. IoT device 110 2 accesses a wireless communication system via user UE 3, as schematically represented by arrow 112 2. Each base station gNB 1 to gNB 5 may be connected to the core network 102, e.g., via an S1 interface, via respective backhaul links 114 1 to 114 5, which are schematically represented in fig. 7 (b) by arrows pointing to the "core". The core network 102 may be connected to one or more external networks. The external network may be the internet or a private network such as an intranet or any other type of campus network, for example, a private WiFi or 4G or 5G mobile communication system. Furthermore, some or all of the individual base stations gNB 1 to gNB 5 may be connected to each other via respective backhaul links 116 1 to 116 5, for example via an S1 or X2 interface or an XN interface in NR (new radio), which are schematically represented in fig. 7 (b) by arrows pointing to "gNB". The side-uplink channel allows direct communication between UEs, also referred to as D2D (device-to-device) communication. The side-link interface in 3GPP (third generation partnership project) is named PC5 (proximity-based communication 5). For data transmission, a physical resource grid may be used. The physical resource grid may include a set of resource elements to which various physical channels and physical signals are mapped. For example, the physical channels may include physical downlink, uplink, and sidelink shared channels carrying user specific data (also referred to as downlink, uplink, and sidelink payload data), PDSCH (physical downlink shared channel), PUSCH (physical uplink shared channel), PSSCH (physical sidelink shared channel), physical Broadcast Channels (PBCH) carrying, for example, a Master Information Block (MIB) and one or more System Information Blocks (SIBs), one or more sidelink information blocks (SLIBs, if supported), physical downlink, uplink, and sidelink control channels (SCI) carrying Downlink Control Information (DCI), uplink Control Information (UCI), and Sidelink Control Information (SCI), PDCCH (physical downlink control channel), PUC