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

Abstract

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

Description

METHOD FOR COLLECTING DATA FROM A TELECOMMUNICATION DEVICE, TELECOMMUNICATION DEVICES AND TELECOMMUNICATION SYSTEMTECHNICAL FIELD

[0001] Various embodiments relate to methods for collecting data from a telecommunication device, telecommunication devices and telecommunication systems.BACKGROUND

[0002] Artificial intelligence is increasingly being applied to communication networks. For example, the 3rd Generation Partnership Project (3 GPP) standard-based telecommunications network may adopt the use of machine learning models. Generally, the performance of a machine learning model relies heavily on the quality and quantity of the training data used to train the machine learning model. As a result, there is a need to collect substantial amount of data from telecommunications devices to form training datasets. However, transmission of data between telecommunication devices for extended durations typically incur signaling overheads, high energy consumption, and heavy use of computational resources to process the data transmission.

[0003] In view of the above, it would be helpful to address or at least mitigate one or more of the abovementioned issues, in relation to the collection of data from telecommunication devices.SUMMARY

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

[0005] According to a second aspect of the present invention, there is provided a method for collecting data from a first telecommunications device. The method includes receiving, in the first telecommunications device, a set of data collection parameters from a second telecommunications device. The set of data collection parameters includes a trigger condition for activating a data collection process in the first telecommunications device and further comprises an end condition 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 present invention, there is provided a first telecommunications device that includes 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 a trigger condition for activating a data collection process in the first telecommunications device and further comprises an end condition 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 thereby collect data.

[0007] According to a fourth aspect of the present invention, there is provided a second telecommunications device that includes 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 the first telecommunications device and further comprises an end condition for ending 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, there is provided a telecommunications system. The telecommunications system includes the above-described first telecommunications device and the above-described second telecommunications device.

[0009] According to a sixth aspect of the present invention, there is provided a use of the data collected according to any one of the above-described methods of collecting data from a first telecommunications device, to train a machine learning model.

[0010] Additional features for advantageous embodiments are provided in the dependent claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In the drawings, like reference characters generally refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention. In the following description, various embodiments are described with reference to the following drawings, in which:

[0012] FIG. 1 shows a simplified schematic diagram of a wireless network according to various embodiments.

[0013] FIG. 2 is a flow chart showing a process for obtaining data from a first telecommunications device according to various embodiments.

[0014] FIG. 3 is a flow chart showing a process for obtaining data from a first telecommunications device according to various embodiments.

[0015] FIG. 4 shows a communication scheme between a UE and a node according to various embodiments.

[0016] FIG. 5 shows a flow diagram of a method for collecting data from a first telecommunication device according to various embodiments.

[0017] FIG. 6 shows a flow diagram of a method for collecting data from a first telecommunication device according to various embodiments.

[0018] FIG. 7 shows a block diagram of the first telecommunication device according to various embodiments.

[0019] FIG. 8 shows a block diagram of the second telecommunication device according to various embodiments.

[0020] FIG. 9 shows a block diagram of a telecommunication system according to various embodiments.

[0021] FIG. 10 is a block diagram of a computing system for implementing some embodiments of the present disclosure, in accordance with embodiments of the present disclosure.DESCRIPTION

[0022] Embodiments described below in context of the devices are analogously valid for the respective methods, and vice versa. Furthermore, it will be understood that the embodiments described below may be combined, for example, a part of one embodiment may be combined with a part of another embodiment.

[0023] It will be understood that any property described herein for a specific device may also hold for any device described herein. It will be understood that any property described herein for a specific method may also hold for any method described herein. Furthermore, it will be understood that for any device or method described herein, not necessarily all the components or steps described must be enclosed in the device or method, but only some (but not all) components or steps may be enclosed.

[0024] The term “coupled” (or “connected”) herein may be understood as electrically coupled or as mechanically coupled, for example attached or fixed, or just in contact without any fixation, and it will be understood that both direct coupling or indirect coupling (in other words: coupling without direct contact) may be provided.

[0025] In this context, the device as described in this description may include a memory which is for example used in the processing carried out in the device. A memory used in the embodiments may be a volatile memory, for example a DRAM (Dynamic Random Access Memory) or a nonvolatile memory, for example a PROM (Programmable Read Only Memory), an EPROM (Erasable PROM), EEPROM (Electrically Erasable PROM), or a flash memory, e.g., a floating gate memory, a charge trapping memory, an MRAM (Magnetoresistive Random Access Memory) or a PCRAM (Phase Change Random Access Memory).

[0026] Some portions of the description which follows are explicitly or implicitly presented in terms of algorithms and functional or symbolic representations of operations on data within a computer memory. These algorithmic descriptions and functional or symbolic representations are the means used by those skilled in the data processing arts to convey most effectively the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physicalmanipulations of physical quantities, such as electrical, magnetic or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated.

[0027] The present specification also discloses apparatus for performing the operations of the methods. Such apparatus 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. Alternatively, 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.

[0028] In addition, 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. Moreover, 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.

[0029] Furthermore, one or more of the steps of the computer program may be performed in parallel rather than sequentially. Such a computer program may be stored on any computer readable medium. 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 that implements the steps of the preferred method.

[0030] The present disclosure generally contemplates the facilitation and optimization of a network (for example in association with 3GPP based standard / specifi cation etc.) and / or user equipment (UE) efficiency and mobility (for example energy efficiency or power saving), in accordance with an embodiment of the invention. Specifically, the present disclosure contemplatesthe possibility of initializing Artificial Intelligence and Machine Learning (AIML or AI / ML) of a UE in connection with 3 GPP Release 19 (and beyond) standard(s).

[0031] In order that the invention may be readily understood and put into practical effect, various embodiments will now be described by way of examples and not limitations, and with reference to the figures.

[0032] FIG. 1 shows a simplified schematic diagram of a wireless network 100 according to various embodiments. The wireless network 100 may include a telecommunications node 104, also referred herein simply as “node”. The telecommunications node 104 may be a device capable of at least one of creating, receiving, transmitting information to, and / or from, user equipment 102. Examples of the node 104 include node B such as the Next Generation Node B (gNB), base transceiver station etc. The node 104 may be a component of a mobile communication standard. The node 104 may serve as a radio access network for the UE, and may be responsible for transmitting and receiving data between the UE and the core network. The node 104 may include an artificial intelligence / machine learning (AIML) model 122, also referred herein a ML model 122. The ML model 122 may perform various network functionalities, for example, network analysis, beam management, and other optimizations of the communications.

[0033] The wireless network 100 may further include at least one user equipment (UE) 102. The UE 102 may be a device used by an end user to communicate. Examples of UE 102 may include mobile phones, vehicle radios, computers equipped with mobile broadband adapters etc. In the example of vehicle radios, the vehicle radios may be used for Vehicle-to-Everything (V2X) or Vehicle-to- Vehicle (V2V) communications. A plurality of UEs 102 may communicate with the node 104 via communication channels 118. Each UE 102 may include a respective AIML model 112. Each UE 102 may also include a dataset 114. The datasets 114 may be input to their respective AIML models 112 for inference or training, or may be transmitted to the AIML model 122 of the node 104 for inference or training via model update exchange 116.

[0034] In a scenario where the AIML model 122 of the node 104 requires data from the UEs 102, energy consumption and signaling overhead may be high, if the UEs 102 were to continuously transmit the data to the node 104. An optimized process of data collection is proposed in the following paragraphs. The optimized process of data collection may reduce energy consumption, power usage and the required data bandwidth, as compared to continuous transmission of the data.

[0035] FIG. 2 is a flow chart showing 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”. The process 200 may be carried out by a second telecommunications device 204, that is distinct 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 the process 200, the second device 204 may generate a set of data collection parameters, in 206. The process 200 may then terminate at 208. The second device 204 may generate, in other words, configure, the set of data collection parameters after receiving device information from the first device 202. The device information may indicate the capability of the first device 202, such as its supported radio frequencies, modulation schemes, carrier aggregation capabilities and more. The first device 202 may transmit the device information during an initial registration process when it joins a network. The second device 204 may configure the data collection parameters based on the device information. The set of data collection parameters may include an event that signals the starting point of the data collection process (DCP) and a threshold. The event occurs when a trigger condition is fulfilled. The trigger condition may include at least one of: the remaining battery level of the first device 202, 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 start the data collection process and transmit the collected data when the data collection process is completed.

[0036] The threshold is also referred hereinafter as the end condition. When the threshold is reached, in other words, when the end condition is fulfilled, the first device 202 transmits the collected data to the second device 204. The threshold may be configured as the number of data points in the dataset, or any other factor required for training or inputting to an AIML model 112 / 122 of the second device 204. The second device 204 may transfer the set of data collection parameters to the first device 202 via any one of UE specific message, RRC reconfiguration message, or L1 / L2 signaling message.

[0037] FIG. 3 is a flow chart showing a process 300 for obtaining data from a first telecommunications device 202 according to various embodiments. The process 300 may be carried out by the first telecommunications device 202. In the process 300, the first device 202 may receive the set of data collection parameters from the second device 204, in 302. The firstdevice 202 may determine whether the trigger condition contained in the set of data collection parameters is met, in 304. If the trigger condition is not met, the first device 202 may continue to monitor if the trigger condition is met, in 308. If the first device 202 determines that the trigger condition is met, the first device 202 may activate the DCP, in 306. In the DCP, the first device 202 may collect data from itself, and may store the collected data in a memory in the first device 202. The first device 202 may periodically, or continuously, monitor if the end condition contained in the set of data collection parameters is met, in 310. If the end condition is not met, the first device 202 may continue to run the DCP, in 314. If the end condition is met, the first device 202 may stop the DCP, and may transmit the collected data, in 312. The process 300 thus ends at 316.

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

[0039] FIG. 5 shows a flow diagram of a method 500 for collecting data from a first telecommunication device 202 according to various embodiments. The method 500 may be performed by the second telecommunication device 204. The method 500 may include steps 502, 504, and 506. Step 502 may include configuring, the second telecommunication device 204, a set of data collection parameters 406. The set of data collection parameters 406 may include a trigger condition for activating a data collection process in the first telecommunication device 202 and may further include an end condition for ending the data collection process in the firsttelecommunication device 202. Step 504 may include sending, from the second telecommunication device 204, the set of data collection parameters 406 to the first telecommunication device 202. Step 506 may include receiving, in the second telecommunication device 204, data collected through the data collection process, from the first telecommunication device 202. The method 500 may include the process 200.

[0040] FIG. 6 shows a flow diagram of a method 600 for collecting data from a first telecommunication device 202 according to various embodiments. The method 600 may be performed by the first telecommunication device 202. The method 600 may include steps 610 and 612. Step 610 may include receiving, in the first telecommunication device 202, a set of data collection parameters 406 from the second telecommunication device 204. The set of data collection parameters 406 may include a trigger condition for activating a data collection process in the first telecommunication device 202 and may further include an end condition for ending the data collection process in the first telecommunication device 202. Step 612 may include collecting data through the data collection process in the first telecommunication device 202, based on the set of data collection parameters 406. Having the data collection parameters 406 allow the first telecommunication device 202 to collect and store only the amount of data necessary, for example for the training of an AIML model at the second telecommunication device 204, or to store data only while it has sufficient access to electrical power.

[0041] According to an embodiment which may be combined with any of the above-described embodiment or with any below described further embodiment, the method 600 may further include transmitting, by the first telecommunication device 202, the data collected through the data collection process, to the second telecommunication device 204, upon end of the data collection process. By transmitting the collected data only after the data collection process has ended, the frequency of transmission is reduced, thereby conserving power and data bandwidth for the first telecommunication device 202.

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

[0043] According to an embodiment which may be combined with any of the above-described embodiment or with any below described further embodiment, collecting data through the data collection process in the first telecommunication device 202 based on the set of data collection parameters 406 may further include determining whether the end condition is met, and ending the data collection process based on determining that the end condition is met.

[0044] According to an embodiment which may be combined with any of the above-described embodiment or with any below described further embodiment, the first telecommunication device 202 is a user equipment 102.

[0045] According to an embodiment which may be combined with any of the above-described embodiment or with any below described further embodiment, the second telecommunication device 204 is a telecommunication node 104.

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

[0047] According to an embodiment which may be combined with any of the above-described embodiment or with any below described further embodiment, the trigger condition may include at least one factor associated with performance level of the first telecommunication device 202. The at least one factor may include, for example, the computational capability of the first processor 704. For example, the data collection process may not be initiated if the first telecommunication device 202 is reaching the upper bound of its processing capability, to avoid overloading the first processor 704.

[0048] According to an embodiment which may be combined with any of the above-described embodiment or with any below described further embodiment, the trigger condition may include battery level of the first telecommunication device 202 meeting a battery threshold. Accordingly, the data collection process may take place only when the first telecommunication device 202 has sufficient electrical power to perform the process and to transmit the collected data.

[0049] According to an embodiment which may be combined with any of the above-described embodiment or with any below described further embodiment, the trigger condition may include length of data transmission queue of the first telecommunication device 202. If the data transmission queue is long, it may indicate that non-AIML related data is already in the buffer ofthe first telecommunication device 202 and hence, the first telecommunication device 202 may not be able to transmit more data to the buffer.

[0050] According to an embodiment which may be combined with any of the above-described embodiment or with any below described further embodiment, the end condition may include at least one factor associated with performance of the second telecommunication device 204. This may allow the first telecommunication device 202 to stop the data collection process once sufficient data points are collected for the second telecommunication device 204 to perform its function. Having a suitable end condition defined may optimize the amount of data transferred from the first telecommunication device 202 to the second telecommunication device 204.

[0051] According to an embodiment which may be combined with any of the above-described embodiment or with any below described further embodiment, the end condition may include the data collected having more data points than a predetermined threshold. This may allow the first telecommunication device 202 to stop the data collection process once sufficient data points are collected. The predetermined threshold may be defined based on the amount of data required for training an AIML model 122 of a telecommunication node 104 or the AIML model 112 of a UE 102. Alternatively, the predetermined threshold may be defined based on the amount of data required for inputting to AIML model 122 or 112 during an inference phase.

[0052] FIG. 7 shows a block diagram of the first telecommunication device 202 according to various embodiments. The first telecommunication 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 telecommunication device 204. The set of data collection parameters 406 may include a trigger condition for activating a data collection process in the first telecommunication device 202 and may further include an end condition for ending the data collection process in the first telecommunication device 202. The first processor 704 may be configured to run the 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 one another, for example, electrically, communicatively or mechanically, via the coupling line 710.

[0053] According to an embodiment which may be combined with any of the above-described embodiment or with any below described further embodiment, the first telecommunication device 202 may further include a first transmitter configured to transmit the data collected through thedata collection process, to the second telecommunication device 204, upon end of the data collection process.

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

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

[0056] According to an embodiment which may be combined with any of the above-described embodiment or with any below described further embodiment, the first processor 704 may be further configured to determine whether the end condition is met, and further configured to end the data collection process based on determining that the end condition is met.

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

[0058] FIG. 8 shows a block diagram of the second telecommunication device 204 according to various embodiments. The second telecommunication 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 a trigger condition for activating a data collection process in a first telecommunication device 202 and may further include an end condition for ending the data collection process in the first telecommunication device 202. The transmitter 806 may be configured to transmit the set of data collection parameters 406 to the first telecommunication device 202. The second receiver 802 may be configured to receive data collected through the data collection process, from the first telecommunication device 202. The second processor 804, the second receiver 802 and the transmitter 806 may be coupled to one another, for example, electrically, communicatively or mechanically, via the coupling line 810.

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

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

[0061] FIG. 9 shows a block diagram of a telecommunication system 900 according to various embodiments. The telecommunication system 900 may include the first telecommunication device 202 and the second telecommunication device 204. The first telecommunication device 202 and the second telecommunication device 204 may be coupled to one another communicatively, for example, via wireless communications, via the coupling line 810.

[0062] FIG. 10 is a block diagram of a computing system 1000 for implementing some embodiments of the present disclosure, in accordance with embodiments of the present disclosure. The computing system 1000 may use the data collected according to at least one of the method 500 or the method 600, to train a machine learning model. The machine learning model may be one of the AIML model 122 or 112.

[0063] Computing system 1000 can be a computer connected to a network. Computing system 1000 can be a client or a server. Computing system 1000 can be any suitable type of processorbased system, such as a personal computer, workstation, server, handheld computing device (portable electronic device) such as a phone or tablet, or an embedded system or other dedicated device. The computing system 1000 can include, for example, one or more processors 1010, one or more memory 1014, one or more of input device 1020, one or more of output device 1030, a graphical user interface (GUI) 1034, storage 1040, and communication device 1060. Input device 1020 and / or output device 1030 can generally either be connectable or integrated with the computing system 1000.

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

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

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

[0067] Software 1050 can also be stored and / or transported within 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, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a computer-readable storage medium can be any medium, such as storage 1040, that can contain or store programming for use by or in connection with an instruction execution system, apparatus, or device.

[0068] Software 1050 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device. The transport computer readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared wired or wireless propagation medium.

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

[0070] System 1000 can implement any operating system suitable for operating on the 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 the present disclosure can be deployed in different configurations, such as in a client / server arrangement or through a Web browser as a Web-based application or Web service, for example.

[0071] While embodiments of the invention have been particularly shown and described with reference to specific embodiments, it should be understood by those skilled in the art 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. The scope of the invention is thus indicated by the appended claims and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced. It will be appreciated that common numerals, used in the relevant drawings, refer to components that serve a similar or the same purpose.

[0072] It will be appreciated to a person skilled in the art that the terminology used herein is for the purpose of describing various embodiments only and is not intended to be limiting of the present invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0073] It is understood that the specific order or hierarchy of blocks in the processes / flowcharts disclosed is an illustration of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of blocks in the processes / flowcharts may be rearranged. Further, some blocks may be combined or omitted. The accompanying method claimspresent elements of the various blocks in a sample order, and are not meant to be limited to the specific order or hierarchy presented.

[0074] The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects. Unless specifically stated otherwise, the term “some” refers to one or more. Combinations such as “at least one of A, B, or C,” “one or more ofA, 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 multiples of A, multiples of B, or multiples of 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 A only, B only, C only, A and B, A and C, B and C, or A and B and C, where any such combinations may contain one or more member or members of A, B, or C. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims.

Claims

CLAIMS1. A method (500) for collecting data from a first telecommunication device (202), the method (500) comprising: configuring, by a second telecommunication device (204), a set of data collection parameters (406), wherein the set of data collection parameters (406) comprises a trigger condition for activating a data collection process in the first telecommunication device (202) and further comprises an end condition for ending the data collection process in the first telecommunication device (202); sending, from the second telecommunication device, the set of data collection parameters to the first telecommunication device (202); and receiving, in the second telecommunication device (204), data collected through the data collection process, from the first telecommunication device (202).

2. A method (600) for collecting data from a first telecommunication device (202), the method comprising: receiving, in the first telecommunication device (202), a set of data collection parameters from a second telecommunication device (204), wherein the set of data collection parameters (406) comprises a trigger condition for activating a data collection process in the first telecommunication device (202) and further comprises an end condition for ending the data collection process in the first telecommunication device (202); collecting data through the data collection process in the first telecommunication device (202), based on the set of data collection parameters (406).

3. The method (600) of claim 2, further comprising: transmitting, by the first telecommunication device (202), the data collected through the data collection process, to the second telecommunication device (204), upon end of the data collection process.

4. The method (600) of any one of claims 2 to 3, wherein collecting data through the data collection process in the first telecommunication device (202), based on the set of data collection parameters (406), comprises determining whether the trigger condition is met, and activating the data collection process based on determining that the trigger condition is met.

5. The method (600) of claim 4, wherein collecting data through the data collection process in the first telecommunication device (202), based on the set of data collection parameters, further comprises determining whether the end condition is met, and ending the data collection process based on determining that the end condition is met.

6. The method (500, 600) of any preceding claim, wherein the first telecommunication device (202) is a user equipment (102).

7. The method (500, 600) of any preceding claim, wherein the second telecommunication device (204) is a telecommunication node (104).

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

9. The method (500, 600) of any preceding claim, wherein the trigger condition comprises at least one factor associated with performance level of the first telecommunication device (202).

10. The method (500, 600) of any preceding claim, wherein the trigger condition comprises at least one of(i) battery level of the first telecommunication device (202) meeting a battery threshold, and(ii) length of data transmission queue of the first telecommunication device (202).

11. The method (500, 600) of any preceding claim, wherein the end condition comprises at least one factor associated with performance of the second telecommunication device (204).

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

13. A first telecommunication device (202) comprising: a first receiver (702) configured to receive a set of data collection parameters (406) from a second telecommunication device (204), wherein the set of data collection parameters (406) comprises a trigger condition for activating a data collection process in the first telecommunication device (202) and further comprises an end condition for ending the data collection process in the first telecommunication device (202); and a first processor (704) configured to run the data collection process based on the set of data collection parameters (406), thereby collecting data.

14. The first telecommunication device (202) of claim 13, further comprising: a first transmitter configured to transmit the data collected through the data collection process, to the second telecommunication device (204), upon end of the data collection process.

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

16. The first telecommunication device (202) of any one of claims 13 to 15, wherein the first processor (704) is configured to determine whether the trigger condition is met, and further configured to activate the data collection process based on determining that the trigger condition is met.

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

18. The first telecommunication device (202) of 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 telecommunication device (204) comprising: a second processor (804) configured to configure a set of data collection parameters (406), wherein the set of data collection parameters (406) comprises a trigger condition for activating a data collection process in a first telecommunication device (202) and further comprises an end condition for ending the data collection process in the first telecommunication device (202); a transmitter (806) configured to transmit the set of data collection parameters (406) to the first telecommunication device (202); and a second receiver (802) configured to receive data collected through the data collection process, from the first telecommunication device (202).

20. The second telecommunication device (204) of claim 19, further comprising: a machine learning model; and a training module configured to train the machine learning model using the data collected through the data collection process.

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

22. A telecommunication system (900) comprising: the first telecommunication device (202); andthe second telecommunication device (204).

23. Use of the data collected according to the method of any one of claims 1 to 12, to train a machine learning model.

Images