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WO2025228705A1 - Système et appareil de prédiction d'une mesure dans un réseau et procédé associé - Google Patents

Système et appareil de prédiction d'une mesure dans un réseau et procédé associé

Info

Publication number
WO2025228705A1
WO2025228705A1 PCT/EP2025/060605 EP2025060605W WO2025228705A1 WO 2025228705 A1 WO2025228705 A1 WO 2025228705A1 EP 2025060605 W EP2025060605 W EP 2025060605W WO 2025228705 A1 WO2025228705 A1 WO 2025228705A1
Authority
WO
WIPO (PCT)
Prior art keywords
measurement
module
control signal
user device
cell
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/EP2025/060605
Other languages
English (en)
Inventor
Rajat PUSHKARNA
Rikin SHAH
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Aumovio Germany GmbH
Original Assignee
Aumovio Germany GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Aumovio Germany GmbH filed Critical Aumovio Germany GmbH
Publication of WO2025228705A1 publication Critical patent/WO2025228705A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

Definitions

  • the present disclosure generally relates to one or both of a system and a device for predicting measurement in a network in association with, for example, a User Equipment (UE) usable for communication.
  • UE User Equipment
  • the present disclosure further relates a method which can be associated with the system and/or the device.
  • Current techniques may not address the issue of enabling a User Equipment (UE) to select an Artificial Intelligence/Machine Learning (AIML) model for radio resource management (RRM) measurement prediction.
  • UE User Equipment
  • AIML Artificial Intelligence/Machine Learning
  • RRM radio resource management
  • enabling both cell level RRM Measurement and Beam level RRM Measurement prediction models can be difficult for the UE to know when to use beam cell level RRM Measurement and when to use Beam level RRM Measurement.
  • the current techniques may not facilitate energy efficiency and power saving in an optimal manner.
  • the present disclosure contemplates that it would be helpful to address or at least mitigate one or more issues in relation to conventional techniques for facilitating energy efficiency and power saving when predicting measurement (for example RRM measurement) in a network.
  • a method for predicting measurement in a network comprising: determining a measurement technique for a user device; generating a control signal based on the measurement technique; and communicating the control signal to the user device for predicting measurement.
  • the method as described herein can allow a user equipment (UE) or a user device to know which radio resource management (RRM) measurement is to be performed for the best prediction of RRM Measurement result.
  • RRM radio resource management
  • fundamental mechanisms of interworking and data information flow in radio access network collaboration for AIML support can also be realized.
  • the measurement technique comprises a cell level radio resource management (RRM) measurement and a beam level RRM measurement.
  • RRM radio resource management
  • each of the cell level radio resource management (RRM) measurement and the beam level RRM measurement comprises an Artificial Intelligence/Machine Learning (AIML) model.
  • AIML Artificial Intelligence/Machine Learning
  • determining a measurement technique comprises determining the AIML model performance of the cell level RRM measurement and the AIML model performance of the beam level RRM measurement.
  • communicating the control signal to the user device comprises communicating via system information block (SIB).
  • SIB system information block
  • communicating the control signal to the user device comprises determining a signal granularity; and communicating the control signal via at least of: Layer 1 signaling, Layer 2 signaling and/or Layer 3 signaling based on the signal granularity.
  • communicating the control signal to the user device comprises communicating via User Equipment (UE) specific signaling.
  • UE User Equipment
  • the method includes performing the measurement technique to determine a cell quality based on the control signal.
  • a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of the first aspect.
  • a computer readable storage medium having data stored therein representing software executable by a computer, the software including instructions, when executed by the computer, to carry out the method of the first aspect.
  • a device for predicting measurement in a network comprising: a first module configured to receive at least one signal associated with determining a cell quality; a second module configured to at least one of process and facilitate the method of the first aspect to generate at least one output signal; and a third module configured to communicate at least one output signal, wherein the output signal corresponds to a control signal for predicting measurement by the user device to determine a cell quality.
  • the device corresponds to a base station communicable with an apparatus corresponding to a User Equipment (UE), and wherein the base station corresponds to a Next generation Node B (gNB) configured to receive the at least one input signal from the UE.
  • UE User Equipment
  • gNB Next generation Node B
  • a system comprising: at least one apparatus(es); and at least one device(s), wherein the apparatus(es) and the device(s) are capable of being coupled via at least one of wired coupling and wireless coupling.
  • the system as described herein can provide the UE (or user device) to perform the appropriate RRM measurement so as to obtain a best prediction of the RRM measurement result. Accordingly, the cell (or base station or gNB) quality can be accurately determined.
  • the proposed gNB-UE collaboration operation for AIML support can improve the AIML performance for wireless communication.
  • FIG. 1A shows a schematic diagram illustrating a system for predicting measurement in a network which can include at least one device, according to an embodiment of the invention.
  • Figs. 1 B shows an example scenario in association with the system of Fig. 1 A, according to an embodiment of the invention.
  • FIG. 2 shows a schematic diagram illustrating the device of Fig. 1A in further detail, according to an embodiment of the invention.
  • FIG. 3 shows a method in association with the system of Fig. 1A, according to an embodiment of the invention.
  • FIG. 4A to Fig. 4B show schematic diagrams illustrating the flow of information in association with the method of Fig. 3, according to an embodiment of the invention.
  • the present specification discloses apparatus and/or device for performing the operations of the methods.
  • Such apparatus and/or device may be specially constructed for the required purposes, or may comprise a computer or other device selectively activated or reconfigured by a computer program stored in the computer.
  • the algorithms and displays presented herein are not inherently related to any particular computer or other apparatus.
  • Various machines may be used with programs in accordance with the teachings herein.
  • the construction of more specialized apparatus to perform the required method steps may be appropriate.
  • the structure of a computer will appear from the description below.
  • the present specification also implicitly discloses a computer program, in that it would be apparent to the person skilled in the art that the individual steps of the method described herein may be put into effect by computer code.
  • the computer program is not intended to be limited to any particular programming language and implementation thereof. It will be appreciated that a variety of programming languages and coding thereof may be used to implement the teachings of the disclosure contained herein.
  • the computer program is not intended to be limited to any particular control flow. There are many other variants of the computer program, which can use different control flows without departing from the spirit or scope of the disclosure.
  • the computer readable medium may include storage devices such as magnetic or optical disks, memory chips, or other storage devices suitable for interfacing with a computer.
  • the computer readable medium may also include a hard-wired medium such as exemplified in the Internet system, or wireless medium such as exemplified in the mobile telephone system.
  • the computer program when loaded and executed on such a computer effectively results in an apparatus and/or a device that implements the steps of the preferred method.
  • the present disclosure generally contemplates the facilitation and optimization of a network (for example in association with 3GPP based standard/specification etc.) and/or user equipment (UE) efficiency (for example energy efficiency or power saving), in accordance with an embodiment of the invention.
  • UE user equipment
  • the present disclosure contemplates the possibility of predicting measurement by a user device (or UE) to determine a cell (or base station or gNB) quality in connection with 3GPP standard(s).
  • the present disclosure generally contemplates that Artificial Intelligence/Machine Learning (AIML) can be an important technology in 3GPP, specifically Rel-19 in RAN2, and AIML can facilitate network optimization and energy efficient networks.
  • AIML Artificial Intelligence/Machine Learning
  • a cell or a base station e.g. a Next generation Node B gNB
  • RRM radio resource management
  • Beam level RRM Measurement are to be used for RRM Measurement prediction.
  • the present disclosure thus contemplates that by enabling both cell level RRM Measurement and Beam level RRM Measurement prediction models, it can be difficult for the UE (or user device) to know when to use beam cell level RRM Measurement and when to use Beam level RRM Measurement.
  • the present disclosure also contemplates that the cell (or base station or gNB) can indicate to the UE whether cell level RRM Measurement or Beam level RRM Measurement are to be used for RRM Measurement prediction.
  • the UE can effectively select a model (e.g. (AIML) model) for RRM Measurement prediction as the UE (or user device) may know which RRM measurement is to be performed for the best prediction of the RRM Measurement result. Power saving and energy consumption efficiency can possibly be facilitated in the network, in accordance with an embodiment of the invention.
  • a model e.g. (AIML) model
  • FIG. 1A a schematic diagram illustrating a system 100 for predicting measurement in a network is shown, according to an embodiment of the invention.
  • the system 100 can, for example, be suitable for facilitating energy and improve power efficiency, in accordance with an embodiment of the invention.
  • the system 100 can include one or more apparatuses 102, at least one device 104 and, optionally, a communication network 106, in accordance with an embodiment of the invention.
  • the apparatus(es) 102 can be coupled to the device(s) 104. Specifically, the apparatus(es) 102 can, for example, be coupled to the device(s) 104 via the communication network 106, in accordance with an embodiment of the invention.
  • the apparatus(es) 102 can be coupled to the communication network 106 and the device(s) 104 can be coupled to the communication network 106. Coupling can be by manner of one or both of wired coupling and wireless coupling.
  • the apparatus(es) 102 can, in general, be configured to communicate with the device(s) 104 via the communication network 106, according to an embodiment of the invention.
  • the apparatus(es) 102 can, for example, be associated with or correspond to or include one or more user equipment (UE) which can carry one or more computers, in accordance with an embodiment of the invention.
  • UE user equipment
  • an apparatus 102 can correspond to a UE (or user device) carrying at least one computer (e.g. an electronic device or module having computing capabilities such as an electronic mobile device which can be carried into a vehicle or an electronic module which can be installed in a vehicle, in accordance with an embodiment of the invention) which can be configured to perform one or more processing tasks in association with the UE (or user device), in accordance with an embodiment of the invention.
  • the device(s) 104 can, for example, be associated with/correspond to at least one base station, where the at least one base station can be a Next Generation Node B (gNB). Moreover, the device(s) 104 can, for example, be configured to carry/be associated with/include one or more computers (e.g., an electronic device/module having computing capabilities) which can, for example, be configured to perform one or more processing tasks in association with the base station. The device(s) 104 can be configured to generate one or more output signals which can be communicated to the apparatus(es) 102, in accordance with an embodiment of the invention. This will be discussed later in further detail in the context of an example scenario, in accordance with an embodiment of the invention.
  • gNB Next Generation Node B
  • the device(s) 104 can, for example, be configured to receive one or more input signals and perform at least one processing task based on the input signal(s) in a manner to generate one or more output signals.
  • the input signal(s) can, for example, be generated by the apparatus(es) 102 and communicated from the apparatus(es) 102 and received by the device(s) 104, in accordance with an embodiment of the invention.
  • the input signal may be generated from a separate device(s) 104.
  • the input signal can be a signal associated with determining a cell (or base station or gNB) quality.
  • the output signal(s) can, for example, be communicated from the device(s) 104, in accordance with an embodiment of the invention.
  • the output signal may correspond to a control signal for predicting measurement by the user device (or UE) to determine the cell quality.
  • the device(s) 104 will be discussed later in further detail with reference to Fig. 2, according to an embodiment of the invention.
  • the communication network 106 can, for example, correspond to an Internet communication network, a cellular-based communication network, a wired-based communication network, a Global Navigation Satellite System (GNSS) based communication network, a wireless-based communication network, or any combination thereof.
  • Communication e.g., between the apparatuses 102 and/or between the apparatus(es) 102 and the device(s) 104) via the communication network 106 can be by manner of one or both of wired communication and wireless communication.
  • the device(s) 104 can, for example, be configured to receive at least one input signal and perform at least one processing task in association with dynamic/adaptive/gradual control on the input signal(s) in a manner so as to generate at least one output signal.
  • the apparatus(es) 102 can, for example, be configured to generate (and communicate) the input signal(s) to the device(s) 104, in accordance with an embodiment of the invention.
  • a separate device(s) 104 may be configured to generate (and communicate) the input signal(s).
  • the apparatus(es) 102 or device(s) 104 can determine or request a cell (or base station or gNB) quality and communicate or transmit at least one input signal associated with the cell quality to the device(s) 104. This will be discussed, in accordance with an embodiment of the invention, in the context of an example scenario with reference to Fig. 1 B, hereinafter.
  • Fig. 1 B shows an example scenario in association with the system of Fig. 1A, according to an embodiment of the invention.
  • Fig. 1 B shows an example embodiment of a measurement model for determining a cell (or base station or gNB) quality.
  • a User Equipment UE or a user device
  • beam level measurements may be obtained to determine the cell quality of a cell (or base station or gNB).
  • the UE can predict beam level measurements in cell level measurements. Together with cell level measurements, the UE (or user device) can predict cell level measurements.
  • cell level measurement for Frequency Range 1 (FR1 ) can be considered for prediction as the number of beams in FR1 are limited.
  • AIML Artificial Intelligence/Machine Learning
  • a cell level measurement prediction model can have the following methods, either alone or in combination.
  • a first method can involve predicting beam level results and subsequently generating cell level results based on the predicted beam results.
  • a second method may involve directly predicting cell level results based on cell level results.
  • a third method can involve directly predicting cell level results based on beam level results. It may not be very efficient for the UE (or user device), from an energy savings perspective, to have all methods work in parallel as the AIML model to predict beam level and cell level measurements can be different.
  • the above-described aspect(s) of the system 100 of the present invention can also apply analogously (all) the aspect(s) of a below described device 104 of the present invention.
  • all below described aspect(s) of the device 104 of the invention can also apply analogously (all) the aspect(s) of above-described system 100 of the invention.
  • FIG. 2 a schematic diagram illustrating a device 104 is shown in further detail in the context of an example implementation 200, according to an embodiment of the invention.
  • the device 104 can correspond to an electronic module 200a.
  • the electronic module 200a can, in one example, correspond to a base station ora cell (or gNB), in accordance with an embodiment of the invention.
  • the electronic module 200a can correspond to an electronic device which can be installed/mounted in the base station (or cell or gNB), in accordance with an embodiment of the invention.
  • the electronic module 200a can be capable of performing one or more processing tasks in association with adaptive/dynamic/gradual control related processing, in accordance with an embodiment of the invention.
  • the electronic module 200a can, for example, include a casing 200b. Moreover, the electronic module 200a can, for example, carry any one of a first module 202, a second module 204, a third module 206, or any combination thereof.
  • the electronic module 200a can carry a first module 202, a second module 204 and/or a third module 206.
  • the electronic module 200a can carry a first module 202, a second module 204 and a third module 206, in accordance with an embodiment of the invention.
  • the casing 200b can be shaped and dimensioned to carry any one of the first module 202, the second module 204 and the third module 206, or any combination thereof.
  • the first module 202 can be coupled to one or both of the second module 204 and the third module 206.
  • the second module 204 can be coupled to one or both of the first module 202 and the third module 206.
  • the third module 206 can be coupled to one or both of the first module 202 and the second module 204.
  • the first module 202 can be coupled to the second module 204 and the second module 204 can be coupled to the third module 206, in accordance with an embodiment of the invention.
  • Coupling between the first module 202, the second module 204 and/or the third module 206 can, for example, be by manner of one or both of wired coupling and wireless coupling.
  • Each of the first module 202, the second module 204 and the third module 206 can correspond to one or both of a hardware-based module and a software-based module, according to an embodiment of the invention.
  • the first module 202 can correspond to a hardware-based receiver which can be configured to receive one or more input signals.
  • the input signal(s) can, for example, be communicated from the apparatus(es) 102 (or user device or User Equipment UE), in accordance with an embodiment of the invention.
  • the second module 204 can, for example, correspond to a hardware-based processor which can be configured to perform one or more processing tasks (e.g., in a manner so as to generate one or more output signals) as will be discussed later in further detail with reference to Fig. 3, in accordance with an embodiment of the invention.
  • the third module 206 can correspond to a hardware-based transmitter which can be configured to communicate one or more output signals from the electronic module 200a.
  • the output signal(s) can, for example, include one or more instructions/commands/control signals in association with the aforementioned dynamic/adaptive/gradual control configuration/determination strategy so as to facilitate efficiency (e.g., power/energy efficiency and/or communication efficiency), in accordance with an embodiment of the invention.
  • the output signal(s) can be a control signal(s) for predicting measurement by the user device (or UE) to determine a cell (or base station or gNB) quality.
  • the present disclosure contemplates the possibility that the first and second modules 202, 204 can be an integrated software-hardware based module, for example, an electronic part which can carry a software program or algorithm in association with receiving and processing functions or an electronic module programmed to perform the functions of receiving and processing.
  • the present disclosure further contemplates the possibility that the first and third modules 202, 206 can be an integrated software-hardware based module, for example an electronic part which can carry a software program or algorithm in association with receiving and transmitting functions or an electronic module programmed to perform the functions of receiving and transmitting.
  • the present disclosure yet further contemplates the possibility that the first and third modules 202, 206 can be an integrated hardware module, for example a hardware-based transceiver, capable of performing the functions of receiving and transmitting.
  • the device 104 (or base station or UE) can, for example, be further configured to process the input signal(s), as will be discussed later in further detail with reference to Fig. 3, in a manner so as to generate one or more output signals in a manner so as to facilitate efficiency, for example power efficiency or energy efficiency, in accordance with an embodiment of the invention.
  • the output signal(s) can include one or more control signals to facilitate some form of dynamic/adaptive/gradual control configuration/determination strategy so as to facilitate efficiency, for example power efficiency or energy efficiency, in accordance with an embodiment of the invention.
  • the output signal(s) can be a control signal(s) for predicting measurement by the user device (or UE) to determine a cell (or base station or gNB) quality.
  • a method 300 for configuring a user device (or UE) in association with the system 100 is shown, according to an embodiment of the invention.
  • the method 300 can, for example, be suitable for facilitating energy efficiency, network optimization and power saving in accordance with an embodiment of the invention.
  • the method 300 can include any one of an input step 302, a processing step 304 and an output step 306, or any combination thereof, in accordance with an embodiment of the invention.
  • the processing method 300 can include the input step 302. In another embodiment, the processing method 300 can include the input step 302 and the processing step 304. In another embodiment, the processing method 300 can include the input step 302, the processing step 304 and the output step 306. In yet another embodiment, the processing method 300 can include the processing step 304 and one or both of the input step 302 and the output step 306. In yet a further embodiment, the processing method 300 can include the input step 302, the processing step 304 and the output step 306. In yet a further additional embodiment, the processing method 300 can include the processing step 304. In yet another further additional embodiment, the processing method 300 can include any one of or any combination of the input step 302, the processing step 304 and the output step 306 (i.e. , the input step 302, the processing step 304 and/or the output step 306).
  • one or more input signal(s) can be received.
  • the input signal(s) can be communicated from the apparatus 102 and can be received by the device 104, in accordance with an embodiment of the invention.
  • the input signal(s) can be generated and communicated from a different or separate device 104.
  • the input step 302 can include receiving at least one input signal associated with determining a cell (or base station or gNB) quality, which may include receiving data associated with radio resource management (RRM) measurement to derive the cell (or base station or gNB) quality.
  • RRM radio resource management
  • At least a processing task can be performed in association with the received input signal(s) in a manner so as to generate one or more output signals, in accordance with an embodiment of the invention.
  • the processing step 304 may include at least one of: determining a measurement technique for a user device; generating a control signal based on the measurement technique; and communicating the control signal to the user device for predicting measurement.
  • the measurement technique may include a cell level radio resource management (RRM) measurement and a beam level RRM measurement and each of the cell level radio resource management (RRM) measurement and the beam level RRM measurement may include an Artificial Intelligence/Machine Learning (AIML) model.
  • Communicating the control signal to the user device can include communicating via system information block (SIB).
  • SIB system information block
  • the processing step 304 may further include determining the AIML model performance of the cell level RRM measurement and the AIML model performance of the beam level RRM measurement; determining a signal granularity; and communicating the control signal via at least of: Layer 1 signaling, Layer 2 signaling and/or Layer 3 signaling based on the signal granularity, where communicating the control signal to the user device comprises communicating via User Equipment (UE) specific signaling.
  • UE User Equipment
  • the processing step 304 may be performed by the apparatus 102 (user device or UE) and may further include performing the measurement technique to determine the cell (or base station or gNB) quality based on the control signal.
  • the output signal(s) can, for example, be communicated, as an option, in accordance with an embodiment of the invention.
  • the output signal(s) can optionally be communicated from the device 104.
  • the output signal(s) can optionally be communicated from the device 104 to one or both of at least one apparatus 102, in accordance with an embodiment of the invention.
  • the apparatus 102 (or LIE) may also perform the input step 302, the processing step 304 and the output step 306.
  • the present disclosure further contemplates a computer program (not shown) which can include instructions which, when the program is executed by a computer (not shown), cause the computer to carry out the input step 302, the processing step 304 and/or the output step 306 as discussed with reference to the method 300.
  • the computer program can include instructions which, when the program is executed by a computer, cause the computer to carry out the input step 302 and/or the processing step 304, in accordance with an embodiment of the invention.
  • the present disclosure yet further contemplates a computer readable storage medium (not shown) having data stored therein representing software executable by a computer (not shown), the software including instructions, when executed by the computer, to carry out the input step 302, the processing step 304 and/or the output step 306 as discussed with reference to the method 300.
  • the computer readable storage medium can have data stored therein representing software executable by a computer, the software including instructions, when executed by the computer, cause the computer to carry out the input step 302 and/or the processing step 304, in accordance with an embodiment of the invention.
  • a device 104 for predicting measurement in a network which can include a first module 202, a second module 204 and/or a third module 206.
  • the first module 202 can be configured to receive one or more input signals.
  • the input signal(s) can, for example, be associated with determining a cell (or base station or gNB) quality.
  • the second module 204 can be configured to process and/or facilitate processing of the input signal(s) according to the method 300 as discussed earlier to generate one or more output signals.
  • the third module 206 can be configured to communicate one or more output signals.
  • the output signal(s) can, for example, correspond to one or more control signals for predicting measurement by the user device (or UE) to determine a cell (or base station or gNB) quality.
  • the apparatus 102 can correspond to a User Equipment (UE) which can communicate with a device 104 corresponding to a base station.
  • the base station can, for example, correspond to a Next generation Node B (gNB) which can be configured to communicate one or more signals (e.g., output signal(s)) to the UE.
  • gNB Next generation Node B
  • the present disclosure generally contemplates a system 100 which can include one or more apparatuses 102 and one or more devices 104.
  • the apparatus(es) 102 and the device(s) 104 can, for example, be capable of being coupled via wired coupling and/or wireless coupling.
  • the output signal(s) can, for example, correspond to internal command(s)/instruction(s) (e.g., communicated only within an apparatus 102) for adaptively controlling operational configuration of an apparatus 102, in accordance with an embodiment of the invention.
  • Fig. 4A to Fig. 4B show schematic diagrams illustrating the flow of information in association with the method of Fig. 3, according to an embodiment of the invention.
  • a gNB (or cell or base station) can, for example, be configured to provide a configuration for the User Equipment UE (or user device) whether to use cell level radio resource management (RRM) measurement or beam level RRM measurement, in accordance with an embodiment of the invention.
  • the gNB (or cell or base station) can signal the cell level or beam level RRM measurement using system information block (SIB).
  • SIB system information block
  • 1 -bit can be configured to indicate the type of RRM Measurement to be used for RRM prediction.
  • the network (or cell or base station or gNB) can change this dynamically based on the granularity using Layer 1 signaling, Layer 2 signaling and/or Layer 3 signaling.
  • the network can also signal this to the UE (or user device) in a UE specific manner using a UE specific signaling.
  • the gNB (or cell or base station) can switch cell level measurement to beam level management based on its model performance. For example, 1 -bit indication can be provided in the SIB to indicate the switch from cell level measurement to beam level measurement while the default setting can be cell level RRM measurement.
  • the User Equipment UE (or user device) can, for example, be configured to perform cell level or beam level measurement prediction based on the received configuration.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Sont divulgués un système (100), un dispositif (104) et un procédé (300) de prédiction de mesure dans un réseau. Le procédé (300) consiste en la détermination d'une technique de mesure pour un dispositif utilisateur; la génération d'un signal de commande sur la base de la technique de mesure; et la communication du signal de commande au dispositif utilisateur pour prédire une mesure.
PCT/EP2025/060605 2024-04-30 2025-04-17 Système et appareil de prédiction d'une mesure dans un réseau et procédé associé Pending WO2025228705A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102024204047.9 2024-04-30
DE102024204047.9A DE102024204047A1 (de) 2024-04-30 2024-04-30 System und einrichtung zum vorhersagen einer messung in einem netz und zugehöriges verfahren

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WO2025228705A1 true WO2025228705A1 (fr) 2025-11-06

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