Method for collecting data from a telecommunication device, telecommunication device and telecommunication system

Abstract

A method for collecting data from a first telecommunication device includes configuring, by a second telecommunication device, a set of data collection parameters. The set of data collection parameters includes a trigger condition for activating a data collection procedure in the first telecommunication device, and further includes an end condition for ending the data collection procedure in the first telecommunication device. The method further includes sending the set of data collection parameters from the second telecommunication device to the first telecommunication device. The method further includes receiving, in the second telecommunication device, data collected by the data collection procedure from the first telecommunication device.

Description

Technical Field

[0001] Various embodiments relate to methods, telecommunications devices, and telecommunications systems for collecting data from telecommunications devices. Background Technology

[0002] Artificial intelligence is increasingly being applied to communication networks. For example, telecommunications networks based on the 3rd Generation Partnership Project (3GPP) standard can employ machine learning models. Generally, the performance of a machine learning model depends heavily on the quality and quantity of the training data used to train it. Therefore, a large amount of data needs to be collected from telecommunications devices to form a training dataset. However, long-duration data transmission between telecommunications devices typically incurs signaling overhead, high energy consumption, and significant computational resource usage to process data transmission.

[0003] In light of the above, it would be helpful to resolve or at least mitigate one or more of the aforementioned problems related to the collection of data from telecommunications devices. Summary of the Invention

[0004] According to a first aspect of the present invention, a method for collecting data from a first telecommunications device is provided. The method includes configuring a set of data collection parameters by a second telecommunications device. The set of data collection parameters includes triggering conditions for activating a data collection process in the first telecommunications device, and further includes termination conditions for ending the data collection process in the first telecommunications device. The method further includes sending the set of data collection parameters from the second telecommunications device to the first telecommunications device. The method further includes receiving data collected by the data collection process from the first telecommunications device in the second telecommunications device.

[0005] According to a second aspect of the present invention, a method for collecting data from a first telecommunications device is provided. The method includes receiving a set of data collection parameters from a second telecommunications device within the first telecommunications device. The set of data collection parameters includes triggering conditions for activating a data collection process in the first telecommunications device, and further includes termination conditions for ending the data collection process in the first telecommunications device. The method further includes collecting data through the data collection process in the first telecommunications device based on the set of data collection parameters.

[0006] According to a third aspect of the invention, a first telecommunications device is provided, comprising a first receiver and a first processor. The first receiver is configured to receive a set of data collection parameters from a second telecommunications device. The set of data collection parameters includes trigger conditions for activating a data collection process in the first telecommunications device, and further includes termination conditions for ending the data collection process in the first telecommunications device. The first processor is configured to run the data collection process based on the set of data collection parameters to collect data therefrom.

[0007] According to a fourth aspect of the present invention, a second telecommunications device is provided, comprising a second processor, a transmitter, and a second receiver. The second processor is configured to configure a set of data collection parameters. The set of data collection parameters includes a trigger condition for activating a data collection process in a first telecommunications device, and further includes a termination condition for terminating the data collection process in the first telecommunications device. The transmitter is configured to transmit the set of data collection parameters to the first telecommunications device. The second receiver is configured to receive data collected through the data collection process from the first telecommunications device.

[0008] According to a fifth aspect of the present invention, a telecommunications system is provided. The telecommunications system includes the first telecommunications device described above and the second telecommunications device described above.

[0009] According to a sixth aspect of the invention, there is a use for training a machine learning model with data collected according to any of the methods described above for collecting data from a first telecommunications device.

[0010] Additional features of advantageous embodiments are provided in the dependent claims. Attached Figure Description

[0011] In the accompanying drawings, similar reference numerals generally refer to the same parts in different views. The drawings are not necessarily drawn to scale, but generally focus on illustrating the principles of the invention. In the following description, various embodiments are described with reference to the following drawings, in which:

[0012] Figure 1 A simplified schematic diagram of a wireless network according to various embodiments is shown.

[0013] Figure 2 This is a flowchart illustrating a process for obtaining data from a first telecommunications device according to various embodiments.

[0014] Figure 3 This is a flowchart illustrating a process for obtaining data from a first telecommunications device according to various embodiments.

[0015] Figure 4 The communication schemes between the UE and the node are illustrated according to various embodiments.

[0016] Figure 5 A flowchart illustrating a method for collecting data from a first telecommunications device according to various embodiments is shown.

[0017] Figure 6 A flowchart illustrating a method for collecting data from a first telecommunications device according to various embodiments is shown.

[0018] Figure 7A block diagram of a first telecommunications device according to various embodiments is shown.

[0019] Figure 8 A block diagram of a second telecommunications device according to various embodiments is shown.

[0020] Figure 9 Block diagrams of telecommunications systems according to various embodiments are shown.

[0021] Figure 10 This is a block diagram of a computing system for implementing some embodiments of the present disclosure, according to embodiments of the present disclosure. Detailed Implementation

[0022] The embodiments described below in the context of these apparatuses are similarly effective for the corresponding methods, and vice versa. Furthermore, it should be understood that the embodiments described below are combinable; for example, a portion of one embodiment may be combined with a portion of another embodiment.

[0023] It should be understood that any property described herein with respect to a particular apparatus may also apply to any apparatus described herein. Similarly, it should be understood that any property described herein with respect to a particular method may also apply to any method described herein. Furthermore, it should be understood that for any apparatus or method described herein, it is not necessary to include all the described components or steps, but may include only some (but not all) of them.

[0024] The term “coupling” (or “connection”) in this article may be understood as electrical coupling or mechanical coupling, such as attachment or fixation, or simply contact without any fixation, and it should be understood that both direct coupling and indirect coupling (in other words: coupling without direct contact) may be provided.

[0025] In this context, the apparatus described herein may include a memory, for example, for processing performed within the apparatus. The memory used in the embodiments may be volatile memory, such as DRAM (Dynamic Random Access Memory), or non-volatile memory, such as PROM (Programmable Read-Only Memory), EPROM (Erasable PROM), EEPROM (Electrically Erasable PROM), or flash memory (e.g., floating gate memory, charge trap memory), MRAM (Magnetoresistive Random Access Memory), or PCRAM (Phase Change Random Access Memory).

[0026] Some parts of the following description are presented explicitly or implicitly based on the functional or symbolic representation of algorithms and data operations within computer memory. These algorithmic descriptions and functional or symbolic representations are means by which those skilled in the art of data processing most effectively communicate the essence of their work to others skilled in the art. Here and generally, an algorithm is considered as a self-consistent sequence of steps that produces a desired result. Steps are those that require physical manipulation of physical quantities, such as electrical, magnetic, or optical signals that can be stored, transmitted, combined, compared, and otherwise manipulated.

[0027] This specification also discloses apparatus for performing the methods. Such apparatus may be specifically constructed for the desired purpose, or may include a computer or other device selectively activated or reconfigured by a computer program stored in a computer. The algorithms and displays presented herein are inherently independent of any particular computer or other apparatus. Various machines may be used with the program in accordance with the teachings herein. Alternatively, it may be appropriate to construct more specialized apparatus to perform the required method steps. The structure of the computer will become apparent from the description below.

[0028] Furthermore, this specification implicitly discloses a computer program, as it will be apparent to those skilled in the art that individual steps of the methods described herein can be implemented by computer code. The computer program is not intended to be limited to any particular programming language or its implementation. It should be understood that various programming languages ​​and their code processing can be used to implement the teachings of the disclosure contained herein. Moreover, the computer program is not intended to be limited to any particular control flow. Many other variations of the computer program may be used, which may employ different control flows without departing from the spirit or scope of this disclosure.

[0029] Furthermore, one or more steps of the computer program can be executed in parallel, rather than sequentially. Such a computer program can be stored on any computer-readable medium. Computer-readable media may include storage devices such as disks or optical discs, memory chips, or other storage devices suitable for interfacing with a computer. Computer-readable media may also include hardwired media, as exemplified in an Internet system, or wireless media, as exemplified in a mobile phone system. When loaded and executed on such a computer, the computer program effectively produces an apparatus for implementing the steps of the preferred method.

[0030] According to embodiments of the invention, this disclosure generally envisions promoting and optimizing network (e.g., associated with 3GPP-based standards / specifications, etc.) and / or user equipment (UE) efficiency and mobility (e.g., energy efficiency or power savings). Specifically, this disclosure envisions the possibility of initiating artificial intelligence and machine learning (AIML or AI / ML) for UEs in conjunction with 3GPP Release 19 (and later) standards.

[0031] To facilitate understanding of the invention and its practical application, various embodiments will now be described by way of example rather than limitation and with reference to the figures.

[0032] Figure 1 A simplified schematic diagram of a wireless network 100 according to various embodiments is shown. The wireless network 100 may include a telecommunications node 104, also simply referred to herein as a "node". The telecommunications node 104 may be a device capable of performing at least one of the following: creating information, receiving information from a user equipment 102, and / or sending information to that user equipment. Examples of nodes 104 include nodes B, such as next-generation node B (gNB), base transceivers, etc. Node 104 may be a component of a mobile communication standard. Node 104 may serve as a radio access network for a UE and may be responsible for sending and receiving data between the UE and the core network. Node 104 may include an artificial intelligence / machine learning (AIML) model 122, also referred to herein as ML model 122. ML model 122 may perform various network functionalities, such as network analysis, beam management, and other optimizations of communications.

[0033] The wireless network 100 may further include at least one user equipment (UE) 102. UE 102 may be a device used by an end user for communication. Examples of UE 102 may include a mobile phone, a car radio, a computer equipped with a mobile broadband adapter, etc. In the example of a car radio, the car radio may be used for vehicle-to-everything (V2X) or vehicle-to-vehicle (V2V) communication. Multiple UEs 102 may communicate with node 104 via communication channel 118. Each UE 102 may include a corresponding AIML model 112. Each UE 102 may also include a dataset 114. The dataset 114 may be input to its corresponding AIML model 112 for inference or training, or may be sent to the AIML model 122 of node 104 via model update exchange 116 for inference or training.

[0034] In a scenario where AIML model 122 at node 104 requires data from UE 102, the energy consumption and signaling overhead could be high if UE 102 continuously transmits data to node 104. The following paragraphs propose an optimized data collection process. Compared to continuous data transmission, the optimized data collection process reduces energy consumption, power usage, and required data bandwidth.

[0035] Figure 2This is a flowchart illustrating a process 200 for obtaining data from a first telecommunications device 202, according to various embodiments. The first telecommunications device 202 is also referred to hereinafter as "first device 202". Process 200 may be performed by a second telecommunications device 204, which is different from the first telecommunications device 202. The second telecommunications device 204 is also referred to hereinafter as "second device 204". The first device 202 may be a UE 102, while the second device 204 may be a node 104, or vice versa. In process 200, the second device 204 may generate a set of data collection parameters at 206. Process 200 may then terminate at 208. The second device 204 may generate (in other words, configure) this set of data collection parameters after receiving device information from the first device 202. The device information may indicate the capabilities of the first device 202, such as its supported radio frequencies, modulation schemes, carrier aggregation capabilities, etc. The first device 202 may send the device information during its initial registration process when joining the network. The second device 204 may configure the data collection parameters based on the device information. This set of data collection parameters may include thresholds and an event indicating the start of the data collection process (DCP). This event occurs when a trigger condition is met. The trigger condition may include at least one of the following: the remaining battery power of the first device 202, the length of the data transmission queue of the first device 202, or other factors that may affect the performance of the first device 202. If the data transmission queue is empty or short, the first device 202 may begin the data collection process and transmit the collected data upon completion of the data collection process.

[0036] The threshold is also referred to hereinafter as the termination condition. When the threshold is reached, in other words, when the termination condition is met, the first device 202 sends the collected data to the second device 204. The threshold can be configured as the number of data points in the dataset, or as any other factor required to train the AIML model 112 / 122 of the second device 204 or as input to that AIML model. The second device 204 can transmit this set of data collection parameters to the first device 202 via any of the following: a UE-specific message, an RRC reconfiguration message, or an L1 / L2 signaling message.

[0037] Figure 3This is a flowchart illustrating a process 300 for obtaining data from a first telecommunications device 202 according to various embodiments. Process 300 can be performed by the first telecommunications device 202. In process 300, the first device 202 may receive a set of data collection parameters from a second device 204 at 302. The first device 202 may determine at 304 whether a trigger condition included in the set of data collection parameters is met. If the trigger condition is not met, the first device 202 may continue monitoring whether the trigger condition is met at 308. If the first device 202 determines that the trigger condition is met, the first device 202 may activate DCP at 306. In DCP, the first device 202 may collect data from itself and may store the collected data in a memory within the first device 202. The first device 202 may periodically or continuously monitor at 310 whether a termination condition included in the set of data collection parameters is met. If the termination condition is not met, the first device 202 may continue running DCP at 314. If the termination condition is met, the first device 202 can stop DCP at 312 and can send the collected data. Therefore, process 300 ends at 316.

[0038] Figure 4 A communication scheme between UE 102 and node 104 according to various embodiments is illustrated. In this example, the first device 202 may be UE 102, and the second device 204 may be node 104. Node 104 may be a gNB node. UE 102 may send device information to node 104 to register itself with node 104. The device information may include UE capability information 402. UE capability information 402 may be a Radio Resource Control (RRC) message. Upon receiving UE capability information 402, the node may configure a set of data collection parameters 406. Node 104 may configure the set of data collection parameters 406 based on UE capability information 402. Then, node 104 may send a Radio Resource Control (RRC) reconfiguration message 404 to UE 102. RRC reconfiguration message 404 may include the set of data collection parameters 406. After receiving data collection parameters 406, UE 102 may determine whether the triggering conditions in data collection parameters 406 are met. If the triggering condition is met, UE 102 can initiate DCP at 410. DCP can continue to run until the threshold in data collection parameter 406 is met at 412. When the threshold is met, UE 102 can send the collected data 408 to node 104.

[0039] Figure 5A flowchart illustrating a method 500 for collecting data from a first telecommunications device 202 according to various embodiments is shown. Method 500 may be performed by a second telecommunications device 204. Method 500 may include steps 502, 504, and 506. Step 502 may include configuring a set of data collection parameters 406 by the second telecommunications device 204. The set of data collection parameters 406 may include triggering conditions for activating the data collection process in the first telecommunications device 202, and may further include termination conditions for ending the data collection process in the first telecommunications device 202. Step 504 may include sending the set of data collection parameters 406 from the second telecommunications device 204 to the first telecommunications device 202. Step 506 may include receiving data collected by the data collection process from the first telecommunications device 202 in the second telecommunications device 204. Method 500 may include process 200.

[0040] Figure 6 A flowchart illustrating a method 600 for collecting data from a first telecommunications device 202 according to various embodiments is shown. Method 600 may be performed by the first telecommunications device 202. Method 600 may include steps 610 and 612. Step 610 may include receiving a set of data collection parameters 406 from a second telecommunications device 204 in the first telecommunications device 202. The set of data collection parameters 406 may include triggering conditions for activating the data collection process in the first telecommunications device 202, and may further include termination conditions for ending the data collection process in the first telecommunications device 202. Step 612 may include collecting data through the data collection process in the first telecommunications device 202 based on the set of data collection parameters 406. Having the data collection parameters 406 allows the first telecommunications device 202 to collect and store only the amount of data required for training, for example, an AIML model at the second telecommunications device 204, or to store data only when the first telecommunications device has sufficient power supply.

[0041] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, method 600 may further include, at the end of the data collection process, the first telecommunications device 202 transmitting the data collected during the data collection process to the second telecommunications device 204. By transmitting the collected data only after the data collection process has ended, the transmission frequency is reduced, thereby saving power and data bandwidth for the first telecommunications device 202.

[0042] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, collecting data through a data collection process in the first telecommunications device 202 based on the set of data collection parameters 406 may include determining whether a trigger condition is met, and activating the data collection process based on the determination that the trigger condition is met.

[0043] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, collecting data through a data collection process in the first telecommunications device 202 based on the set of data collection parameters 406 may further include determining whether a termination condition is met, and terminating the data collection process based on the determination that the termination condition is met.

[0044] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, the first telecommunications device 202 is a user equipment 102.

[0045] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, the second telecommunications device 204 is a telecommunications node 104.

[0046] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, the second telecommunications device 204 is a next-generation node B.

[0047] According to embodiments that can be combined with any of the embodiments described above or any of the embodiments further described below, the triggering condition may include at least one factor associated with the performance level of the first telecommunications device 202. At least one factor may include, for example, the computing power of the first processor 704. For example, if the first telecommunications device 202 reaches the upper limit of its processing power, a data collection process may not be initiated to avoid overloading the first processor 704.

[0048] According to embodiments that can be combined with any of the embodiments described above or any of the embodiments further described below, the triggering condition may include the battery level of the first telecommunications device 202 meeting a battery threshold. Therefore, the process can only proceed if the first telecommunications device 202 has sufficient power to perform the data collection process and transmit the collected data.

[0049] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, the triggering condition may include the length of the data transmission queue of the first telecommunications device 202. If the data transmission queue is long, it may indicate that non-AIML related data is already in the buffer of the first telecommunications device 202, and therefore, the first telecommunications device 202 may not be able to send more data to the buffer.

[0050] Depending on the embodiments described above or any of the embodiments further described below, the termination condition may include at least one factor associated with the performance of the second telecommunications device 204. This allows the first telecommunications device 202 to stop the data collection process once it has collected enough data points for the second telecommunications device 204 to perform its functions. Defining a suitable termination condition can optimize the amount of data transmitted from the first telecommunications device 202 to the second telecommunications device 204.

[0051] According to embodiments that can be combined with any of the embodiments described above or any embodiments further described below, a termination condition may include the collected data having more data points than a predetermined threshold. This allows the first telecommunications device 202 to stop the data collection process once sufficient data points have been collected. The predetermined threshold may be defined based on the amount of data required to train the AIML model 122 of the telecommunications node 104 or the AIML model 112 of the UE 102. Alternatively, the predetermined threshold may be defined based on the amount of data required to be input into the AIML model 122 or 112 during the inference phase.

[0052] Figure 7 A block diagram of a first telecommunications device 202 according to various embodiments is shown. The first telecommunications device 202 may include a first processor 704 and a first receiver 702. The first receiver 702 may be configured to receive a set of data collection parameters 406 from a second telecommunications device 204. The set of data collection parameters 406 may include trigger conditions for activating a data collection process in the first telecommunications device 202, and may further include termination conditions for ending the data collection process in the first telecommunications device 202. The first processor 704 may be configured to run a data collection process based on the set of data collection parameters 406, thereby collecting data. The first receiver 702 and the first processor 704 may be coupled to each other, for example, electrically, communicatively, or mechanically, via a coupling line 710.

[0053] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, the first telecommunications device 202 may further include a first transmitter configured to transmit data collected through the data collection process to the second telecommunications device 204 at the end of the data collection process.

[0054] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, the first transmitter may be configured to transmit data collected through the data collection process to the second telecommunications device 204 via at least one of a user equipment-specific message, a radio resource control reconfiguration message, and L1 / L2 control signaling.

[0055] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, the first processor 704 may be configured to determine whether a trigger condition is met, and may be further configured to activate the data collection process based on the determination that the trigger condition is met.

[0056] According to an embodiment that can be combined with any embodiment described above or any embodiment further described below, the first processor 704 may be further configured to determine whether a termination condition is met, and further configured to terminate the data collection process based on the determination that the termination condition is met.

[0057] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, the first telecommunications device 202 may further include a memory configured to store data collected through a data collection process. The memory may include a non-transitory computer-readable medium.

[0058] Figure 8 A block diagram of a second telecommunications device 204 according to various embodiments is shown. The second telecommunications device 204 may include a second processor 804, a second receiver 802, and a transmitter 806. The second processor 804 may be configured to configure a set of data collection parameters 406. The set of data collection parameters 406 may include trigger conditions for activating a data collection process in the first telecommunications device 202, and may further include termination conditions for ending the data collection process in the first telecommunications device 202. The transmitter 806 may be configured to send the set of data collection parameters 406 to the first telecommunications device 202. The second receiver 802 may be configured to receive data collected by the data collection process from the first telecommunications device 202. The second processor 804, the second receiver 802, and the transmitter 806 may be coupled to each other, for example electrically, communicatively, or mechanically, via a coupling line 810.

[0059] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, the second telecommunications device 204 may further include a machine learning model and a training module configured to train the machine learning model using data collected through a data collection process. For example, the machine learning model may be one of AIML models 122 or 112.

[0060] According to an embodiment that can be combined with any of the embodiments described above or any of the embodiments further described below, the second processor 804 can be configured to configure the set of data collection parameters based on the training requirements of the machine learning model.

[0061] Figure 9A block diagram of a telecommunications system 900 according to various embodiments is shown. The telecommunications system 900 may include a first telecommunications device 202 and a second telecommunications device 204. The first telecommunications device 202 and the second telecommunications device 204 may be coupled to each other via a coupling line 810, for example, via wireless communication.

[0062] Figure 10 This is a block diagram of a computing system 1000 for implementing some embodiments of the present disclosure, according to embodiments of the present disclosure. The computing system 1000 can train a machine learning model using data collected according to at least one of method 500 or method 600. The machine learning model can be one of AIML models 122 or 112.

[0063] The computing system 1000 may be a computer connected to a network. The computing system 1000 may be a client or a server. The computing system 1000 may be any suitable type of processor-based system, such as a personal computer, workstation, server, handheld computing device (portable electronic device) such as a telephone or tablet, or embedded system or other dedicated device. The computing system 1000 may include, for example, one or more processors 1010, one or more memories 1014, one or more input devices 1020, one or more output devices 1030, a graphical user interface (GUI) 1034, a storage device 1040, and a communication device 1060. The input devices 1020 and / or output devices 1030 may typically be connectable to or integrated with the computing system 1000.

[0064] Input device 1020 can be any suitable device that provides input, such as a touchscreen, keyboard or keypad, mouse, gesture recognition component of a virtual / augmented reality system, or voice recognition device. Output device 1030 can be or includes any suitable device that provides output, such as a display, touchscreen, haptic device, virtual / augmented reality display, or speaker.

[0065] Storage device 1040 can be any suitable means of providing storage, such as electrical memory, magnetic memory, or optical memory, including RAM, cache, hard disk drive, removable storage disk, or other non-transitory computer-readable media. Communication device 1060 may include any suitable means of transmitting and receiving signals over a network, such as a network interface chip or device. Components of computing system 1000 can be connected in any suitable manner, such as via a physical bus or wirelessly.

[0066] Processor 1010 can be any suitable processor or combination of processors, including any one or any combination of a central processing unit (CPU), a graphics processing unit (GPU), a field-programmable gate array (FPGA), and an application-specific integrated circuit (ASIC). Software 1050, which can be stored in storage device 1040 and executed by one or more processors 1010, may include programs that embody, for example, functional or functional portions of this disclosure (e.g., as embodied in the apparatus or methods described above). For example, software 1050 may include one or more programs for training AIML model 122 or 112 using collected data, or for running the inference phase of AIML model 122 or 112.

[0067] The software 1050 may also be stored and / or transported in any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device (such as those described above), from which instructions associated with the software may be retrieved and executed. In the context of this disclosure, a computer-readable storage medium may be any medium capable of containing or storing programs for use by or in connection with an instruction execution system, apparatus, or device, such as storage device 1040.

[0068] The software 1050 can also be propagated within any delivery medium for use by or in conjunction with an instruction execution system, apparatus, or device (such as those described above), which can retrieve and execute instructions associated with the software. In the context of this disclosure, the delivery medium can be any medium capable of communicating, propagating, or delivering a program for use by or in conjunction with an instruction execution system, apparatus, or device. Delivery computer-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, or infrared wired or wireless propagation media.

[0069] The computing system 1000 can be connected to a network, which can be any suitable type of interconnected communication system. The network can implement any suitable communication protocol and can be protected by any suitable security protocol. The network may include network links of any suitable arrangement that enable the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, wired networks, DSL, or telephone lines.

[0070] System 1000 can be implemented with any operating system suitable for operation on a network. Software 1050 can be written in any suitable programming language, such as C, C++, Java, or Python. In various embodiments, application software embodying the functionality of this disclosure can be deployed in different configurations, such as, for example, as a client / server setup or as a web-based application or web service via a web browser.

[0071] While embodiments of the invention have been specifically shown and described with reference to specific examples, those skilled in the art will understand that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the scope of the invention is indicated by the appended claims, and thus, it is intended to cover all changes falling within the meaning and scope equivalent to the claims. It should be understood that general numerals used in the related drawings refer to components of similar or identical use.

[0072] Those skilled in the art will understand that the terminology used herein is intended to describe various embodiments only and is not intended to limit the invention. As used herein, the singular forms “a,” “an,” and “described” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of the stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof.

[0073] It should be understood that the specific order or hierarchy of boxes in the disclosed process / flowchart is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of boxes in the process / flowchart may be rearranged. Furthermore, some boxes may be combined or omitted. The appended method claims present elements of various boxes in a sample order and are not intended to limit one to the specific order or hierarchy presented.

[0074] The above description is provided to enable those skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. Therefore, the claims are not intended to be limited to the aspects shown herein, but rather to conform to the full scope consistent with the language of the claims, wherein reference to a singular element is not intended to mean “one and only one” unless so expressly stated, but rather means “one or more”. The word “exemplary” is used herein to mean “as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred over or advantageous over other aspects. Unless expressly stated otherwise, the term “some” refers to one or more. Combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof” include any combination of A, B, and / or C, and may include multiple A, multiple B, or multiple C. Specifically, combinations such as "at least one of A, B, or C", "one or more of A, B, or C", "at least one of A, B, and C", "one or more of A, B, and C", and "A, B, C, or any combination thereof" may be only A, only B, only C, A and B, A and C, B and C, or A and B and C, wherein any such combination may include one or more members of A, B, or C. All structural and functional equivalents of the elements throughout the various aspects described in this disclosure that are known or will be known in the future by those skilled in the art are expressly incorporated herein by reference and are intended to be covered by the claims.

Claims

1. A method (500) for collecting data from a first telecommunications device (202), the method (500) comprising: A set of data collection parameters (406) are configured by the second telecommunications device (204). The set of data collection parameters (406) includes triggering conditions for activating the data collection process in the first telecommunications device (202), and further includes termination conditions for ending the data collection process in the first telecommunications device (202). The set of data collection parameters is sent from the second telecommunications device to the first telecommunications device (202); as well as The second telecommunications device (204) receives data collected through the data collection process from the first telecommunications device (202).

2. A method (600) for collecting data from a first telecommunications device (202), the method comprising: A set of data collection parameters is received from the second telecommunications device (204) in the first telecommunications device (202). The set of data collection parameters (406) includes triggering conditions for activating the data collection process in the first telecommunications device (202), and further includes termination conditions for ending the data collection process in the first telecommunications device (202). Data is collected through the data collection process in the first telecommunications device (202) based on the set of data collection parameters (406).

3. The method (600) of claim 2, further comprising: At the end of the data collection process, the first telecommunications device (202) sends the data collected through the data collection process to the second telecommunications device (204).

4. The method (600) of any one of claims 2 to 3, wherein collecting data through the data collection process in the first telecommunications device (202) based on the set of data collection parameters (406) comprises: Determine whether the triggering condition is met, and The data collection process is activated based on the determination that the triggering conditions are met.

5. The method (600) of claim 4, wherein collecting data through the data collection process in the first telecommunications device (202) based on the set of data collection parameters further comprises: Determine whether the termination condition is met, and The data collection process is terminated based on the determination that the termination condition is met.

6. The method (500, 600) as claimed in any of the preceding claims, wherein the first telecommunications device (202) is a user equipment (102).

7. The method (500, 600) as described in any of the preceding claims, wherein the second telecommunications device (204) is a telecommunications node (104).

8. The method (500, 600) of claim 7, wherein the second telecommunications device (204) is a next-generation node B.

9. The method (500, 600) of any of the preceding claims, wherein the triggering condition includes at least one factor associated with the performance level of the first telecommunications device (202).

10. The method (500, 600) as claimed in any of the preceding claims, wherein the triggering condition includes at least one of the following: (i) The battery power of the first telecommunications device (202) meets the battery threshold, and (ii) The length of the data transmission queue of the first telecommunications device (202).

11. The method (500, 600) of any of the preceding claims, wherein the termination condition includes at least one factor associated with the performance of the second telecommunications device (204).

12. The method (500, 600) of any of the preceding claims, wherein the termination condition includes the collected data having more data points than a predetermined threshold.

13. A first telecommunications device (202), comprising: A first receiver (702) is configured to receive a set of data collection parameters (406) from a second telecommunications device (204). The set of data collection parameters (406) includes triggering conditions for activating the data collection process in the first telecommunications device (202), and further includes termination conditions for ending the data collection process in the first telecommunications device (202). as well as A first processor (704) is configured to run the data collection process based on the set of data collection parameters (406), thereby collecting data.

14. The first telecommunications device (202) as claimed in claim 13, further comprising: A first transmitter is configured to send the data collected through the data collection process to the second telecommunications device (204) at the end of the data collection process.

15. The first telecommunications device (202) of claim 14, wherein the first transmitter is configured to transmit the data collected through the data collection process to the second telecommunications device (204) via at least one of a user equipment-specific message, a radio resource control reconfiguration message, and L1 / L2 control signaling.

16. The first telecommunications device (202) as claimed in any one of claims 13 to 15, wherein the first processor (704) is configured to determine whether the triggering condition is met, and is further configured to activate the data collection process based on the determination that the triggering condition is met.

17. The first telecommunications device (202) of claim 16, wherein the first processor (704) is further configured to determine whether the termination condition is met, and is further configured to terminate the data collection process based on the determination that the termination condition is met.

18. The first telecommunications device (202) as claimed in any one of claims 13 to 17, further comprising: A memory configured to store the data collected through the data collection process.

19. A second telecommunications device (204), comprising: A second processor (804) is configured to configure a set of data collection parameters (406). The set of data collection parameters (406) includes triggering conditions for activating the data collection process in the first telecommunications device (202), and further includes termination conditions for ending the data collection process in the first telecommunications device (202); A transmitter (806) configured to send the set of data collection parameters (406) to the first telecommunications device (202); as well as A second receiver (802) is configured to receive data collected through the data collection process from the first telecommunications device (202).

20. The second telecommunications device (204) as claimed in claim 19, further comprising: Machine learning models; as well as A training module configured to train the machine learning model using the data collected through the data collection process.

21. The second telecommunications device (204) of claim 20, wherein the second processor (804) is configured to configure the set of data collection parameters (406) based on the training requirements of the machine learning model.

22. A telecommunications system (900), comprising: The first telecommunications device (202); as well as The second telecommunications device (204).

23. The use of data collected by the method according to any one of claims 1 to 12 for training a machine learning model.

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