Model applicability reporting based on priority information

Abstract

A method and apparatus for model applicability reporting based on priority information is provided. A wireless device receives a configuration related to one or more inference functions from a network. The configuration includes priority information. A wireless device evaluates an applicability of at least one inference function from among the one or more inference functions, and reports the applicability of an inference function from among the at least one inference function. In this case, evaluating of the applicability and / or reporting of the applicability is based on the priority information.

Description

MODEL APPLICABILITY REPORTING BASED ON PRIORITY INFORMATION

[0001] The present disclosure relates to model applicability reporting based on priority information.

[0002] 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) is a technology for enabling high-speed packet communications. Many schemes have been proposed for the LTE objective including those that aim to reduce user and provider costs, improve service quality, and expand and improve coverage and system capacity. The 3GPP LTE requires reduced cost per bit, increased service availability, flexible use of a frequency band, a simple structure, an open interface, and adequate power consumption of a terminal as an upper-level requirement.

[0003] 3GPP New Radio (NR) targets a single technical framework addressing all usage scenarios, requirements and deployment scenarios including enhanced Mobile BroadBand (eMBB), massive Machine Type Communications (mMTC), Ultra-Reliable and Low Latency Communications (URLLC), etc. The NR shall be inherently forward compatible. Further, the NR should be able to use any spectrum band ranging at least up to 100 GHz that may be made available for wireless communications even in a more distant future.

[0004] 6G is the successor to 5G cellular technology. 6G networks will be able to use higher frequencies than 5G networks and provide substantially higher capacity and much lower latency. The 6G technology market is expected to facilitate large improvements in the areas of imaging, presence technology and location awareness. Working in conjunction with Artificial Intelligence (AI), the 6G computational infrastructure will be able to identify the best place for computing to occur. This includes decisions about data storage, processing and sharing.

[0005] Artificial Intelligence (AI) / Machine Learning (ML) is being used in a range of application domains across industry sectors, realizing significant productivity gains. In particular, in mobile communications systems, mobile devices (e.g., smartphones, smart vehicles, Unmanned Aerial Vehicles (UAVs), mobile robots) are increasingly replacing conventional algorithms (e.g., speech recognition, machine translation, image recognition, video processing, user behavior prediction) with AI / ML models to enable applications like enhanced photography, intelligent personal assistants, Virtual Reality (VR) / Augmented Reality (AR), video gaming, video analytics, personalized shopping recommendation, autonomous driving / navigation, smart home appliances, mobile robotics, mobile medicals, as well as mobile finance.

[0006] In an aspect, a method is provided. The method comprises receiving a configuration related to one or more inference functions from a network. The configuration includes priority information. The method further comprises evaluating an applicability of at least one inference function from among the one or more inference functions, and reporting the applicability of an inference function from among the at least one inference function. Evaluating of the applicability and / or reporting of the applicability is based on the priority information.

[0007] In another aspect, an apparatus for implementing the above method is provided.

[0008] FIG. 1 shows an example of a communication system to which implementations of the present disclosure are applied.

[0009] FIG. 2 shows an example of wireless devices to which implementations of the present disclosure are applied.

[0010] FIG. 3 shows an example of UE to which implementations of the present disclosure are applied.

[0011] FIGS. 4 and 5 show an example of protocol stacks in a 3GPP based wireless communication system to which implementations of the present disclosure are applied.

[0012] FIG. 6 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure are applied.

[0013] FIG. 7 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure are applied.

[0014] FIG. 8 shows an example of a functional framework for RAN intelligence to which implementations of the present disclosure are applied.

[0015] FIG. 9 shows an example of a reactive reporting of applicability-related information to which implementations of the present disclosure are applied.

[0016] FIG. 10 shows an example of a proactive reporting of applicability-related information to which implementations of the present disclosure are applied.

[0017] FIG. 11 shows an example of a method to which implementations of the present disclosure are applied.

[0018] FIG. 12 shows an example of another method to which implementations of the present disclosure are applied.

[0019] FIG. 13 shows an example of model evaluation based on priority information to which implementations of the present disclosure are applied.

[0020] FIG. 14 shows an example of functionality evaluation based on priority information to which implementations of the present disclosure are applied.

[0021] FIGS. 15 and 16 show an example of functionality / model evaluation based on priority information to which implementations of the present disclosure are applied.

[0022] FIG. 17 shows an example of model applicability reporting based on priority information to which implementations of the present disclosure are applied.

[0023] FIG. 18 shows an example of functionality applicability reporting based on priority information to which implementations of the present disclosure are applied.

[0024] FIGS. 19 and 20 show an example of functionality / model applicability reporting based on priority information to which implementations of the present disclosure are applied.

[0025] The following techniques, apparatuses, and systems may be applied to a variety of wireless multiple access systems. Examples of the multiple access systems include a Code Division Multiple Access (CDMA) system, a Frequency Division Multiple Access (FDMA) system, a Time Division Multiple Access (TDMA) system, an Orthogonal Frequency Division Multiple Access (OFDMA) system, a Single Carrier Frequency Division Multiple Access (SC-FDMA) system, and a Multi Carrier Frequency Division Multiple Access (MC-FDMA) system. CDMA may be embodied through radio technology such as Universal Terrestrial Radio Access (UTRA) or CDMA2000. TDMA may be embodied through radio technology such as Global System for Mobile communications (GSM), General Packet Radio Service (GPRS), or Enhanced Data rates for GSM Evolution (EDGE). OFDMA may be embodied through radio technology such as Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, or Evolved UTRA (E-UTRA). UTRA is a part of a Universal Mobile Telecommunications System (UMTS). 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) is a part of Evolved UMTS (E-UMTS) using E-UTRA. 3GPP LTE employs OFDMA in Downlink (DL) and SC-FDMA in Uplink (UL). Evolution of 3GPP LTE includes LTE-Advanced (LTE-A), LTE-A Pro, 5G New Radio (NR) and / or 6G.

[0026] For convenience of description, implementations of the present disclosure are mainly described in regards to a 3GPP based wireless communication system. However, the technical features of the present disclosure are not limited thereto. For example, although the following detailed description is given based on a mobile communication system corresponding to a 3GPP based wireless communication system, aspects of the present disclosure that are not limited to 3GPP based wireless communication system are applicable to other mobile communication systems.

[0027] For terms and technologies which are not specifically described among the terms of and technologies employed in the present disclosure, the wireless communication standard documents published before the present disclosure may be referenced.

[0028] In the present disclosure, "A or B" may mean "only A", "only B", or "both A and B". In other words, "A or B" in the present disclosure may be interpreted as "A and / or B". For example, "A, B or C" in the present disclosure may mean "only A", "only B", "only C", or "any combination of A, B and C".

[0029] In the present disclosure, slash ( / ) or comma (,) may mean "and / or". For example, "A / B" may mean "A and / or B". Accordingly, "A / B" may mean "only A", "only B", or "both A and B". For example, "A, B, C" may mean "A, B or C".

[0030] In the present disclosure, "at least one of A and B" may mean "only A", "only B" or "both A and B". In addition, the expression "at least one of A or B" or "at least one of A and / or B" in the present disclosure may be interpreted as same as "at least one of A and B".

[0031] In addition, in the present disclosure, "at least one of A, B and C" may mean "only A", "only B", "only C", or "any combination of A, B and C". In addition, "at least one of A, B or C" or "at least one of A, B and / or C" may mean "at least one of A, B and C".

[0032] Also, parentheses used in the present disclosure may mean "for example". In detail, when it is shown as "control information (PDCCH)", "PDCCH" may be proposed as an example of "control information". In other words, "control information" in the present disclosure is not limited to "PDCCH", and "PDCCH" may be proposed as an example of "control information". In addition, even when shown as "control information (i.e., PDCCH)", "PDCCH" may be proposed as an example of "control information".

[0033] Technical features that are separately described in one drawing in the present disclosure may be implemented separately or simultaneously.

[0034] Although not limited thereto, various descriptions, functions, procedures, suggestions, methods and / or operational flowcharts of the present disclosure disclosed herein can be applied to various fields requiring wireless communication and / or connection (e.g., 5G) between devices.

[0035] Hereinafter, the present disclosure will be described in more detail with reference to drawings. The same reference numerals in the following drawings and / or descriptions may refer to the same and / or corresponding hardware blocks, software blocks, and / or functional blocks unless otherwise indicated.

[0036] FIG. 1 shows an example of a communication system to which implementations of the present disclosure are applied.

[0037] The 5G usage scenarios shown in FIG. 1 are only exemplary, and the technical features of the present disclosure can be applied to other 5G usage scenarios which are not shown in FIG. 1.

[0038] Three main requirement categories for 5G include (1) a category of enhanced Mobile BroadBand (eMBB), (2) a category of massive Machine Type Communication (mMTC), and (3) a category of Ultra-Reliable and Low Latency Communications (URLLC).

[0039] Referring to FIG. 1, the communication system 1 includes wireless devices 100a to 100f, Base Stations (BSs) 200, and a network 300. Although FIG. 1 illustrates a 5G network as an example of the network of the communication system 1, the implementations of the present disclosure are not limited to the 5G system, and can be applied to the future communication system beyond the 5G system.

[0040] The BSs 200 and the network 300 may be implemented as wireless devices and a specific wireless device may operate as a BS / network node with respect to other wireless devices.

[0041] The wireless devices 100a to 100f represent devices performing communication using Radio Access Technology (RAT) (e.g., 5G NR or LTE) and may be referred to as communication / radio / 5G devices. The wireless devices 100a to 100f may include, without being limited to, a robot 100a, vehicles 100b-1 and 100b-2, an eXtended Reality (XR) device 100c, a hand-held device 100d, a home appliance 100e, an Internet-of-Things (IoT) device 100f, and an Artificial Intelligence (AI) device / server 400. For example, the vehicles may include a vehicle having a wireless communication function, an autonomous driving vehicle, and a vehicle capable of performing communication between vehicles. The vehicles may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone). The XR device may include an Augmented Reality (AR) / Virtual Reality (VR) / Mixed Reality (MR) device and may be implemented in the form of a Head-Mounted Device (HMD), a Head-Up Display (HUD) mounted in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance device, a digital signage, a vehicle, a robot, etc. The hand-held device may include a smartphone, a smartpad, a wearable device (e.g., a smartwatch or a smartglasses), and a computer (e.g., a notebook). The home appliance may include a TV, a refrigerator, and a washing machine. The IoT device may include a sensor and a smartmeter.

[0042] In the present disclosure, the wireless devices 100a to 100f may be called User Equipments (UEs). A UE may include, for example, a cellular phone, a smartphone, a laptop computer, a digital broadcast terminal, a Personal Digital Assistant (PDA), a Portable Multimedia Player (PMP), a navigation system, a slate Personal Computer (PC), a tablet PC, an ultrabook, a vehicle, a vehicle having an autonomous traveling function, a connected car, an UAV, an AI module, a robot, an AR device, a VR device, an MR device, a hologram device, a public safety device, an MTC device, an IoT device, a medical device, a FinTech device (or a financial device), a security device, a weather / environment device, a device related to a 5G service, or a device related to a fourth industrial revolution field.

[0043] The wireless devices 100a to 100f may be connected to the network 300 via the BSs 200. An AI technology may be applied to the wireless devices 100a to 100f and the wireless devices 100a to 100f may be connected to the AI server 400 via the network 300. The network 300 may be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, and a beyond-5G network. Although the wireless devices 100a to 100f may communicate with each other through the BSs 200 / network 300, the wireless devices 100a to 100f may perform direct communication (e.g., sidelink communication) with each other without passing through the BSs 200 / network 300. For example, the vehicles 100b-1 and 100b-2 may perform direct communication (e.g., Vehicle-to-Vehicle (V2V) / Vehicle-to-everything (V2X) communication). The IoT device (e.g., a sensor) may perform direct communication with other IoT devices (e.g., sensors) or other wireless devices 100a to 100f.

[0044] Wireless communication / connections 150a, 150b and 150c may be established between the wireless devices 100a to 100f and / or between wireless device 100a to 100f and BS 200 and / or between BSs 200. Herein, the wireless communication / connections may be established through various RATs (e.g., 5G NR) such as uplink / downlink communication 150a, sidelink communication (or Device-to-Device (D2D) communication) 150b, inter-base station communication 150c (e.g., relay, Integrated Access and Backhaul (IAB)), etc. The wireless devices 100a to 100f and the BSs 200 / the wireless devices 100a to 100f may transmit / receive radio signals to / from each other through the wireless communication / connections 150a, 150b and 150c. For example, the wireless communication / connections 150a, 150b and 150c may transmit / receive signals through various physical channels. To this end, at least a part of various configuration information configuring processes, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, and resource mapping / de-mapping), and resource allocating processes, for transmitting / receiving radio signals, may be performed based on the various proposals of the present disclosure.

[0045] NR supports multiples numerologies (and / or multiple Sub-Carrier Spacings (SCS)) to support various 5G services. For example, if SCS is 15 kHz, wide area can be supported in traditional cellular bands, and if SCS is 30 kHz / 60 kHz, dense-urban, lower latency, and wider carrier bandwidth can be supported. If SCS is 60 kHz or higher, bandwidths greater than 24.25 GHz can be supported to overcome phase noise.

[0046] The NR frequency band may be defined as two types of frequency range, i.e., Frequency Range 1 (FR1) and Frequency Range 2 (FR2). The numerical value of the frequency range may be changed. For example, the frequency ranges of the two types (FR1 and FR2) may be as shown in Table 1 below. For ease of explanation, in the frequency ranges used in the NR system, FR1 may mean "sub 6 GHz range", FR2 may mean "above 6 GHz range," and may be referred to as millimeter Wave (mmW).

[0047] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1450MHz - 6000MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0048] As mentioned above, the numerical value of the frequency range of the NR system may be changed. For example, FR1 may include a frequency band of 410MHz to 7125MHz as shown in Table 2 below. That is, FR1 may include a frequency band of 6GHz (or 5850, 5900, 5925 MHz, etc.) or more. For example, a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or more included in FR1 may include an unlicensed band. Unlicensed bands may be used for a variety of purposes, for example for communication for vehicles (e.g., autonomous driving).

[0049] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1410MHz - 7125MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0050] Here, the radio communication technologies implemented in the wireless devices in the present disclosure may include NarrowBand IoT (NB-IoT) technology for low-power communication as well as LTE, NR and 6G. For example, NB-IoT technology may be an example of Low Power Wide Area Network (LPWAN) technology, may be implemented in specifications such as LTE Cat NB1 and / or LTE Cat NB2, and may not be limited to the above-mentioned names. Additionally and / or alternatively, the radio communication technologies implemented in the wireless devices in the present disclosure may communicate based on LTE-M technology. For example, LTE-M technology may be an example of LPWAN technology and be called by various names such as enhanced MTC (eMTC). For example, LTE-M technology may be implemented in at least one of the various specifications, such as 1) LTE Cat 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-bandwidth limited (non-BL), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and may not be limited to the above-mentioned names. Additionally and / or alternatively, the radio communication technologies implemented in the wireless devices in the present disclosure may include at least one of ZigBee, Bluetooth, and / or LPWAN which take into account low-power communication, and may not be limited to the above-mentioned names. For example, ZigBee technology may generate Personal Area Networks (PANs) associated with small / low-power digital communication based on various specifications such as IEEE 802.15.4 and may be called various names.

[0051] FIG. 2 shows an example of wireless devices to which implementations of the present disclosure are applied.

[0052] In FIG. 2, The first wireless device 100 and / or the second wireless device 200 may be implemented in various forms according to use cases / services. For example, {the first wireless device 100 and the second wireless device 200} may correspond to at least one of {the wireless device 100a to 100f and the BS 200}, {the wireless device 100a to 100f and the wireless device 100a to 100f} and / or {the BS 200 and the BS 200} of FIG. 1. The first wireless device 100 and / or the second wireless device 200 may be configured by various elements, devices / parts, and / or modules.

[0053] The first wireless device 100 may include at least one transceiver, such as a transceiver 106, at least one processing chip, such as a processing chip 101, and / or one or more antennas 108.

[0054] The processing chip 101 may include at least one processor, such a processor 102, and at least one memory, such as a memory 104. Additional and / or alternatively, the memory 104 may be placed outside of the processing chip 101.

[0055] The processor 102 may control the memory 104 and / or the transceiver 106 and may be adapted to implement the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts described in the present disclosure. For example, the processor 102 may process information within the memory 104 to generate first information / signals and then transmit radio signals including the first information / signals through the transceiver 106. The processor 102 may receive radio signals including second information / signals through the transceiver 106 and then store information obtained by processing the second information / signals in the memory 104.

[0056] The memory 104 may be operably connectable to the processor 102. The memory 104 may store various types of information and / or instructions. The memory 104 may store a firmware and / or a software code 105 which implements codes, commands, and / or a set of commands that, when executed by the processor 102, perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. For example, the firmware and / or the software code 105 may implement instructions that, when executed by the processor 102, perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. For example, the firmware and / or the software code 105 may control the processor 102 to perform one or more protocols. For example, the firmware and / or the software code 105 may control the processor 102 to perform one or more layers of the radio interface protocol.

[0057] Herein, the processor 102 and the memory 104 may be a part of a communication modem / circuit / chip designed to implement RAT (e.g., LTE or NR). The transceiver 106 may be connected to the processor 102 and transmit and / or receive radio signals through one or more antennas 108. Each of the transceiver 106 may include a transmitter and / or a receiver. The transceiver 106 may be interchangeably used with Radio Frequency (RF) unit(s). In the present disclosure, the first wireless device 100 may represent a communication modem / circuit / chip.

[0058] The second wireless device 200 may include at least one transceiver, such as a transceiver 206, at least one processing chip, such as a processing chip 201, and / or one or more antennas 208.

[0059] The processing chip 201 may include at least one processor, such a processor 202, and at least one memory, such as a memory 204. Additional and / or alternatively, the memory 204 may be placed outside of the processing chip 201.

[0060] The processor 202 may control the memory 204 and / or the transceiver 206 and may be adapted to implement the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts described in the present disclosure. For example, the processor 202 may process information within the memory 204 to generate third information / signals and then transmit radio signals including the third information / signals through the transceiver 206. The processor 202 may receive radio signals including fourth information / signals through the transceiver 106 and then store information obtained by processing the fourth information / signals in the memory 204.

[0061] The memory 204 may be operably connectable to the processor 202. The memory 204 may store various types of information and / or instructions. The memory 204 may store a firmware and / or a software code 205 which implements codes, commands, and / or a set of commands that, when executed by the processor 202, perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. For example, the firmware and / or the software code 205 may implement instructions that, when executed by the processor 202, perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. For example, the firmware and / or the software code 205 may control the processor 202 to perform one or more protocols. For example, the firmware and / or the software code 205 may control the processor 202 to perform one or more layers of the radio interface protocol.

[0062] Herein, the processor 202 and the memory 204 may be a part of a communication modem / circuit / chip designed to implement RAT (e.g., LTE or NR). The transceiver 206 may be connected to the processor 202 and transmit and / or receive radio signals through one or more antennas 208. Each of the transceiver 206 may include a transmitter and / or a receiver. The transceiver 206 may be interchangeably used with RF unit. In the present disclosure, the second wireless device 200 may represent a communication modem / circuit / chip.

[0063] Hereinafter, hardware elements of the wireless devices 100 and 200 will be described more specifically. One or more protocol layers may be implemented by, without being limited to, one or more processors 102 and 202. For example, the one or more processors 102 and 202 may implement one or more layers (e.g., functional layers such as Physical (PHY) layer, Media Access Control (MAC) layer, Radio Link Control (RLC) layer, Packet Data Convergence Protocol (PDCP) layer, Radio Resource Control (RRC) layer, and Service Data Adaptation Protocol (SDAP) layer). The one or more processors 102 and 202 may generate one or more Protocol Data Units (PDUs), one or more Service Data Unit (SDUs), messages, control information, data, or information according to the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. The one or more processors 102 and 202 may generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure and provide the generated signals to the one or more transceivers 106 and 206. The one or more processors 102 and 202 may receive the signals (e.g., baseband signals) from the one or more transceivers 106 and 206 and acquire the PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure.

[0064] The one or more processors 102 and 202 may be referred to as controllers, microcontrollers, microprocessors, or microcomputers. The one or more processors 102 and 202 may be implemented by hardware, firmware, software, or a combination thereof. As an example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in the one or more processors 102 and 202. For example, the one or more processors 102 and 202 may be configured by a set of a communication control processor, an Application Processor (AP), an Electronic Control Unit (ECU), a Central Processing Unit (CPU), a Graphic Processing Unit (GPU), and a memory control processor.

[0065] The one or more memories 104 and 204 may be connected to the one or more processors 102 and 202 and store various types of data, signals, messages, information, programs, code, instructions, and / or commands. The one or more memories 104 and 204 may be configured by Random Access Memory (RAM), Dynamic RAM (DRAM), Read-Only Memory (ROM), electrically Erasable Programmable Read-Only Memory (EPROM), flash memory, volatile memory, non-volatile memory, hard drive, register, cash memory, computer-readable storage medium, and / or combinations thereof. The one or more memories 104 and 204 may be located at the interior and / or exterior of the one or more processors 102 and 202. The one or more memories 104 and 204 may be connected to the one or more processors 102 and 202 through various technologies such as wired or wireless connection.

[0066] The one or more transceivers 106 and 206 may transmit user data, control information, and / or radio signals / channels, mentioned in the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure, to one or more other devices. The one or more transceivers 106 and 206 may receive user data, control information, and / or radio signals / channels, mentioned in the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure, from one or more other devices. For example, the one or more transceivers 106 and 206 may be connected to the one or more processors 102 and 202 and transmit and receive radio signals. For example, the one or more processors 102 and 202 may perform control so that the one or more transceivers 106 and 206 may transmit user data, control information, or radio signals to one or more other devices. The one or more processors 102 and 202 may perform control so that the one or more transceivers 106 and 206 may receive user data, control information, or radio signals from one or more other devices.

[0067] The one or more transceivers 106 and 206 may be connected to the one or more antennas 108 and 208. Additionally and / or alternatively, the one or more transceivers 106 and 206 may include one or more antennas 108 and 208. The one or more transceivers 106 and 206 may be adapted to transmit and receive user data, control information, and / or radio signals / channels, mentioned in the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure, through the one or more antennas 108 and 208. In the present disclosure, the one or more antennas 108 and 208 may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports).

[0068] The one or more transceivers 106 and 206 may convert received user data, control information, radio signals / channels, etc., from RF band signals into baseband signals in order to process received user data, control information, radio signals / channels, etc., using the one or more processors 102 and 202. The one or more transceivers 106 and 206 may convert the user data, control information, radio signals / channels, etc., processed using the one or more processors 102 and 202 from the base band signals into the RF band signals. To this end, the one or more transceivers 106 and 206 may include (analog) oscillators and / or filters. For example, the one or more transceivers 106 and 206 can up-convert OFDM baseband signals to OFDM signals by their (analog) oscillators and / or filters under the control of the one or more processors 102 and 202 and transmit the up-converted OFDM signals at the carrier frequency. The one or more transceivers 106 and 206 may receive OFDM signals at a carrier frequency and down-convert the OFDM signals into OFDM baseband signals by their (analog) oscillators and / or filters under the control of the one or more processors 102 and 202.

[0069] Although not shown in FIG. 2, the wireless devices 100 and 200 may further include additional components. The additional components 140 may be variously configured according to types of the wireless devices 100 and 200. For example, the additional components 140 may include at least one of a power unit / battery, an Input / Output (I / O) device (e.g., audio I / O port, video I / O port), a driving device, and a computing device. The additional components 140 may be coupled to the one or more processors 102 and 202 via various technologies, such as a wired or wireless connection.

[0070] In the implementations of the present disclosure, a UE may operate as a transmitting device in UL and as a receiving device in DL. In the implementations of the present disclosure, a BS may operate as a receiving device in UL and as a transmitting device in DL. Hereinafter, for convenience of description, it is mainly assumed that the first wireless device 100 acts as the UE, and the second wireless device 200 acts as the BS. For example, the processor(s) 102 connected to, mounted on or launched in the first wireless device 100 may be adapted to perform the UE behavior according to an implementation of the present disclosure or control the transceiver(s) 106 to perform the UE behavior according to an implementation of the present disclosure. The processor(s) 202 connected to, mounted on or launched in the second wireless device 200 may be adapted to perform the BS behavior according to an implementation of the present disclosure or control the transceiver(s) 206 to perform the BS behavior according to an implementation of the present disclosure.

[0071] In the present disclosure, a BS is also referred to as a node B (NB), an eNode B (eNB), or a gNB.

[0072] FIG. 3 shows an example of UE to which implementations of the present disclosure are applied.

[0073] Referring to FIG. 3, a UE 100 may correspond to the first wireless device 100 of FIG. 2.

[0074] A UE 100 includes a processor 102, a memory 104, a transceiver 106, one or more antennas 108, a power management module 141, a battery 142, a display 143, a keypad 144, a Subscriber Identification Module (SIM) card 145, a speaker 146, and a microphone 147.

[0075] The processor 102 may be adapted to implement the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. The processor 102 may be adapted to control one or more other components of the UE 100 to implement the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. Layers of the radio interface protocol may be implemented in the processor 102. The processor 102 may include ASIC, other chipset, logic circuit and / or data processing device. The processor 102 may be an application processor. The processor 102 may include at least one of DSP, CPU, GPU, a modem (modulator and demodulator). An example of the processor 102 may be found in SNAPDRAGONTMseries of processors made by Qualcomm®, EXYNOSTMseries of processors made by Samsung®, A series of processors made by Apple®, HELIOTMseries of processors made by MediaTek®, ATOMTMseries of processors made by Intel®or a corresponding next generation processor.

[0076] The memory 104 is operatively coupled with the processor 102 and stores a variety of information to operate the processor 102. The memory 104 may include ROM, RAM, flash memory, memory card, storage medium and / or other storage device. When the embodiments are implemented in software, the techniques described herein can be implemented with modules (e.g., procedures, functions, etc.) that perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. The modules can be stored in the memory 104 and executed by the processor 102. The memory 104 can be implemented within the processor 102 or external to the processor 102 in which case those can be communicatively coupled to the processor 102 via various means as is known in the art.

[0077] The transceiver 106 is operatively coupled with the processor 102, and transmits and / or receives a radio signal. The transceiver 106 includes a transmitter and a receiver. The transceiver 106 may include baseband circuitry to process radio frequency signals. The transceiver 106 controls the one or more antennas 108 to transmit and / or receive a radio signal.

[0078] The power management module 141 manages power for the processor 102 and / or the transceiver 106. The battery 142 supplies power to the power management module 141.

[0079] The display 143 outputs results processed by the processor 102. The keypad 144 receives inputs to be used by the processor 102. The keypad 144 may be shown on the display 143.

[0080] The SIM card 145 is an　integrated circuit　that is intended to　securely store the　International Mobile Subscriber Identity　(IMSI) number and its related　key, which are used to identify and authenticate subscribers on　mobile telephony　devices (such as　mobile phones　and　computers). It is also possible to store contact information on many SIM cards.

[0081] The speaker 146 outputs sound-related results processed by the processor 102. The microphone 147 receives sound-related inputs to be used by the processor 102.

[0082] FIGS. 4 and 5 show an example of protocol stacks in a 3GPP based wireless communication system to which implementations of the present disclosure are applied.

[0083] In particular, FIG. 4 illustrates an example of a radio interface user plane protocol stack between a UE and a BS and FIG. 5 illustrates an example of a radio interface control plane protocol stack between a UE and a BS. The control plane refers to a path through which control messages used to manage call by a UE and a network are transported. The user plane refers to a path through which data generated in an application layer, for example, voice data or Internet packet data are transported. Referring to FIG. 4, the user plane protocol stack may be divided into Layer 1 (i.e., a PHY layer) and Layer 2. Referring to FIG. 5, the control plane protocol stack may be divided into Layer 1 (i.e., a PHY layer), Layer 2, Layer 3 (e.g., an RRC layer), and a Non-Access Stratum (NAS) layer. Layer 1, Layer 2 and Layer 3 are referred to as an Access Stratum (AS).

[0084] In the 3GPP LTE system, the Layer 2 is split into the following sublayers: MAC, RLC, and PDCP. In the 3GPP NR system, the Layer 2 is split into the following sublayers: MAC, RLC, PDCP and SDAP. The PHY layer offers to the MAC sublayer transport channels, the MAC sublayer offers to the RLC sublayer logical channels, the RLC sublayer offers to the PDCP sublayer RLC channels, the PDCP sublayer offers to the SDAP sublayer radio bearers. The SDAP sublayer offers to 5G core network Quality of Service (QoS) flows.

[0085] In the 3GPP NR system, the main services and functions of the MAC sublayer include: mapping between logical channels and transport channels; multiplexing / de-multiplexing of MAC SDUs belonging to one or different logical channels into / from Transport Blocks (TB) delivered to / from the physical layer on transport channels; scheduling information reporting; error correction through Hybrid Automatic Repeat reQuest (HARQ) (one HARQ entity per cell in case of Carrier Aggregation (CA)); priority handling between UEs by means of dynamic scheduling; priority handling between logical channels of one UE by means of logical channel prioritization; padding. A single MAC entity may support multiple numerologies, transmission timings and cells. Mapping restrictions in logical channel prioritization control which numerology(ies), cell(s), and transmission timing(s) a logical channel can use.

[0086] Different kinds of data transfer services are offered by MAC. To accommodate different kinds of data transfer services, multiple types of logical channels are defined, i.e., each supporting transfer of a particular type of information. Each logical channel type is defined by what type of information is transferred. Logical channels are classified into two groups: control channels and traffic channels. Control channels are used for the transfer of control plane information only, and traffic channels are used for the transfer of user plane information only. Broadcast Control Channel (BCCH) is a downlink logical channel for broadcasting system control information, Paging Control Channel (PCCH) is a downlink logical channel that transfers paging information, system information change notifications and indications of ongoing Public Warning Service (PWS) broadcasts, Common Control Channel (CCCH) is a logical channel for transmitting control information between UEs and network and used for UEs having no RRC connection with the network, and Dedicated Control Channel (DCCH) is a point-to-point bi-directional logical channel that transmits dedicated control information between a UE and the network and used by UEs having an RRC connection. Dedicated Traffic Channel (DTCH) is a point-to-point logical channel, dedicated to one UE, for the transfer of user information. A DTCH can exist in both uplink and downlink. In downlink, the following connections between logical channels and transport channels exist: BCCH can be mapped to Broadcast Channel (BCH); BCCH can be mapped to Downlink Shared Channel (DL-SCH); PCCH can be mapped to Paging Channel (PCH); CCCH can be mapped to DL-SCH; DCCH can be mapped to DL-SCH; and DTCH can be mapped to DL-SCH. In uplink, the following connections between logical channels and transport channels exist: CCCH can be mapped to Uplink Shared Channel (UL-SCH); DCCH can be mapped to UL-SCH; and DTCH can be mapped to UL-SCH.

[0087] The RLC sublayer supports three transmission modes: Transparent Mode (TM), Unacknowledged Mode (UM), and Acknowledged Mode (AM). The RLC configuration is per logical channel with no dependency on numerologies and / or transmission durations. In the 3GPP NR system, the main services and functions of the RLC sublayer depend on the transmission mode and include: transfer of upper layer PDUs; sequence numbering independent of the one in PDCP (UM and AM); error correction through ARQ (AM only); segmentation (AM and UM) and re-segmentation (AM only) of RLC SDUs; reassembly of SDU (AM and UM); duplicate detection (AM only); RLC SDU discard (AM and UM); RLC re-establishment; protocol error detection (AM only).

[0088] In the 3GPP NR system, the main services and functions of the PDCP sublayer for the user plane include: sequence numbering; header compression and decompression using Robust Header Compression (ROHC); transfer of user data; reordering and duplicate detection; in-order delivery; PDCP PDU routing (in case of split bearers); retransmission of PDCP SDUs; ciphering, deciphering and integrity protection; PDCP SDU discard; PDCP re-establishment and data recovery for RLC AM; PDCP status reporting for RLC AM; duplication of PDCP PDUs and duplicate discard indication to lower layers. The main services and functions of the PDCP sublayer for the control plane include: sequence numbering; ciphering, deciphering and integrity protection; transfer of control plane data; reordering and duplicate detection; in-order delivery; duplication of PDCP PDUs and duplicate discard indication to lower layers.

[0089] In the 3GPP NR system, the main services and functions of SDAP include: mapping between a QoS flow and a data radio bearer; marking QoS Flow ID (QFI) in both DL and UL packets. A single protocol entity of SDAP is configured for each individual PDU session.

[0090] In the 3GPP NR system, the main services and functions of the RRC sublayer include: broadcast of system information related to AS and NAS; paging initiated by 5G Core network (5GC) or Next-Generation Radio Access Network (NG-RAN); establishment, maintenance and release of an RRC connection between the UE and NG-RAN; security functions including key management; establishment, configuration, maintenance and release of Signaling Radio Bearers (SRBs) and Data Radio Bearers (DRBs); mobility functions (including: handover and context transfer, UE cell selection and reselection and control of cell selection and reselection, inter-RAT mobility); QoS management functions; UE measurement reporting and control of the reporting; detection of and recovery from radio link failure; NAS message transfer to / from NAS from / to UE.

[0091] FIG. 6 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure are applied.

[0092] The frame structure shown in FIG. 6 is purely exemplary and the number of subframes, the number of slots, and / or the number of symbols in a frame may be variously changed. In the 3GPP based wireless communication system, OFDM numerologies (e.g., SCS, Transmission Time Interval (TTI) duration) may be differently configured between a plurality of cells aggregated for one UE. For example, if a UE is configured with different SCSs for cells aggregated for the cell, an (absolute time) duration of a time resource (e.g., a subframe, a slot, or a TTI) including the same number of symbols may be different among the aggregated cells. Herein, symbols may include OFDM symbols (or Cyclic Prefix (CP)-OFDM symbols), SC-FDMA symbols (or Discrete Fourier Transform-spread-OFDM (DFT-s-OFDM) symbols).

[0093] Referring to FIG. 6, downlink and uplink transmissions are organized into frames. Each frame has Tf= 10ms duration. Each frame is divided into two half-frames, where each of the half-frames has 5ms duration. Each half-frame consists of 5 subframes, where the duration Tsfper subframe is 1ms. Each subframe is divided into slots and the number of slots in a subframe depends on a subcarrier spacing. Each slot includes 14 or 12 OFDM symbols based on a CP. In a normal CP, each slot includes 14 OFDM symbols and, in an extended CP, each slot includes 12 OFDM symbols. The numerology is based on exponentially scalable subcarrier spacing Δf = 2u*15 kHz.

[0094] Table 3 shows the number of OFDM symbols per slot Nslotsymb, the number of slots per frameNframe,uslot, and the number of slots per subframe Nsubframe,uslotfor the normal CP, according to the subcarrier spacing Δf = 2u*15 kHz.

[0095] uNslotsymbNframe,uslotNsubframe,uslot01410111420221440431480841416016

[0096] Table 4 shows the number of OFDM symbols per slot Nslotsymb, the number of slots per frameNframe,uslot, and the number of slots per subframe Nsubframe,uslotfor the extended CP, according to the subcarrier spacing Δf = 2u*15 kHz.

[0097] uNslotsymbNframe,uslotNsubframe,uslot212404

[0098] A slot includes plural symbols (e.g., 14 or 12 symbols) in the time domain. For each numerology (e.g., subcarrier spacing) and carrier, a resource grid ofNsize,ugrid,x*NRBscsubcarriers andNsubframe,usymbOFDM symbols is defined, starting at Common Resource Block (CRB)Nstart,ugridindicated by higher-layer signaling (e.g., RRC signaling), whereNsize,ugrid,xis the number of Resource Blocks (RBs) in the resource grid and the subscript x is DL for downlink and UL for uplink.NRBscis the number of subcarriers per RB. In the 3GPP based wireless communication system,NRBscis 12 generally. There is one resource grid for a given antenna portp, subcarrier spacing configurationu, and transmission direction (DL or UL). The carrier bandwidthNsize,ugridfor subcarrier spacing configurationuis given by the higher-layer parameter (e.g., RRC parameter). Each element in the resource grid for the antenna portpand the subcarrier spacing configurationuis referred to as a Resource Element (RE) and one complex symbol may be mapped to each RE. Each RE in the resource grid is uniquely identified by an indexkin the frequency domain and an indexlrepresenting a symbol location relative to a reference point in the time domain. In the 3GPP based wireless communication system, an RB is defined by 12 consecutive subcarriers in the frequency domain.

[0099] In the 3GPP NR system, RBs are classified into CRBs and Physical Resource Blocks (PRBs). CRBs are numbered from 0 and upwards in the frequency domain for subcarrier spacing configurationu. The center of subcarrier 0 of CRB 0 for subcarrier spacing configurationucoincides with 'point A' which serves as a common reference point for resource block grids. In the 3GPP NR system, PRBs are defined within a BandWidth Part (BWP) and numbered from 0 toNsizeBWP,i-1, where i is the number of the bandwidth part. The relation between the physical resource block nPRBin the bandwidth part i and the common resource block nCRBis as follows: nPRB= nCRB+NsizeBWP,i, whereNsizeBWP,iis the common resource block where bandwidth part starts relative to CRB 0. The BWP includes a plurality of consecutive RBs. A carrier may include a maximum of N (e.g., 5) BWPs. A UE may be configured with one or more BWPs on a given component carrier. Only one BWP among BWPs configured to the UE can active at a time. The active BWP defines the UE's operating bandwidth within the cell's operating bandwidth.

[0100] In the present disclosure, the term "cell" may refer to a geographic area to which one or more nodes provide a communication system, or refer to radio resources. A "cell" as a geographic area may be understood as coverage within which a node can provide service using a carrier and a "cell" as radio resources (e.g., time-frequency resources) is associated with bandwidth which is a frequency range configured by the carrier. The "cell" associated with the radio resources is defined by a combination of downlink resources and uplink resources, for example, a combination of a DL Component Carrier (CC) and a UL CC. The cell may be configured by downlink resources only, or may be configured by downlink resources and uplink resources. Since DL coverage, which is a range within which the node is capable of transmitting a valid signal, and UL coverage, which is a range within which the node is capable of receiving the valid signal from the UE, depends upon a carrier carrying the signal, the coverage of the node may be associated with coverage of the "cell" of radio resources used by the node. Accordingly, the term "cell" may be used to represent service coverage of the node sometimes, radio resources at other times, or a range that signals using the radio resources can reach with valid strength at other times.

[0101] In CA, two or more CCs are aggregated. A UE may simultaneously receive or transmit on one or multiple CCs depending on its capabilities. CA is supported for both contiguous and non-contiguous CCs. When CA is configured, the UE only has one RRC connection with the network. At RRC connection establishment / re-establishment / handover, one serving cell provides the NAS mobility information, and at RRC connection re-establishment / handover, one serving cell provides the security input. This cell is referred to as the Primary Cell (PCell). The PCell is a cell, operating on the primary frequency, in which the UE either performs the initial connection establishment procedure or initiates the connection re-establishment procedure. Depending on UE capabilities, Secondary Cells (SCells) can be configured to form together with the PCell a set of serving cells. An SCell is a cell providing additional radio resources on top of Special Cell (SpCell). The configured set of serving cells for a UE therefore always consists of one PCell and one or more SCells. For Dual Connectivity (DC) operation, the term SpCell refers to the PCell of the Master Cell Group (MCG) or the Primary SCell (PSCell) of the Secondary Cell Group (SCG). An SpCell supports Physical Uplink Control Channel (PUCCH) transmission and contention-based random access, and is always activated. The MCG is a group of serving cells associated with a master node, comprised of the SpCell (PCell) and optionally one or more SCells. The SCG is the subset of serving cells associated with a secondary node, comprised of the PSCell and zero or more SCells, for a UE configured with DC. For a UE in RRC_CONNECTED not configured with CA / DC, there is only one serving cell comprised of the PCell. For a UE in RRC_CONNECTED configured with CA / DC, the term "serving cells" is used to denote the set of cells comprised of the SpCell(s) and all SCells. In DC, two MAC entities are configured in a UE: one for the MCG and one for the SCG.

[0102] FIG. 7 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure are applied.

[0103] Referring to FIG. 7, "RB" denotes a radio bearer, and "H" denotes a header. Radio bearers are categorized into two groups: DRBs for user plane data and SRBs for control plane data. The MAC PDU is transmitted / received using radio resources through the PHY layer to / from an external device. The MAC PDU arrives to the PHY layer in the form of a transport block.

[0104] In the PHY layer, the uplink transport channels UL-SCH and Random Access Channel (RACH) are mapped to their physical channels Physical Uplink Shared Channel (PUSCH) and Physical Random Access Channel (PRACH), respectively, and the downlink transport channels DL-SCH, BCH and PCH are mapped to Physical Downlink Shared Channel (PDSCH), Physical Broadcast Channel (PBCH) and PDSCH, respectively. In the PHY layer, Uplink Control Information (UCI) is mapped to PUCCH, and Downlink Control Information (DCI) is mapped to Physical Downlink Control Channel (PDCCH). A MAC PDU related to UL-SCH is transmitted by a UE via a PUSCH based on an UL grant, and a MAC PDU related to DL-SCH is transmitted by a BS via a PDSCH based on a DL assignment.

[0105] FIG. 8 shows an example of a functional framework for RAN intelligence to which implementations of the present disclosure are applied.

[0106] In FIG. 8, each function is described as follows.

[0107] (1) Data Collection is a function that provides input data to model training and model inference functions. Artificial Intelligence (AI) / Machine Learning (ML) algorithm specific data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) is not carried out in the data collection function.

[0108] Examples of input data may include measurements from UEs and / or different network entities, feedback from actor, output from an AI / ML model.

[0109] - Training data is data needed as input for the AI / ML Model training function.

[0110] - Inference data is data needed as input for the AI / ML Model Inference function.

[0111] (2) Model training is a function that performs the AI / ML model training, validation, and / or testing which may generate model performance metrics as part of the model testing procedure. The model training function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on training data delivered by a data collection function, if required.

[0112] - Model deployment / update is used to initially deploy a trained, validated, and tested AI / ML model to the model inference function or to deliver an updated model to the model inference function.

[0113] (3) Model inference is a function that provides AI / ML model inference output (e.g., predictions or decisions). Model inference function may provide model performance feedback to model training function when applicable. The model inference function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on inference data delivered by a data collection function, if required.

[0114] - Output is the inference output of the AI / ML model produced by a model inference function.

[0115] - Model performance feedback may be used for monitoring the performance of the AI / ML model, when available.

[0116] (4) Actor is a function that receives the output from the model inference function and triggers or performs corresponding actions. The actor may trigger actions directed to other entities or to itself.

[0117] - Feedback is information that may be needed to derive training data, inference data or to monitor the performance of the AI / ML model and its impact to the network through updating of Key Performance Indicators (KPIs) and performance counters.

[0118] AI / ML models for a given use case may be tailored towards and applicable to specific scenarios, locations, configuration, deployments, among other factors. In this regard, it is acknowledged that AI / ML models may undergo updates, such as model changes, as an inherent part of their development. Therefore, to ensure efficient network control and management, especially associated to what concerns the UE-side, UEs might have the ability to indicate relevant information about their supported AI / ML models and concerning AI / ML functionalities to the network. This can allow the network to perform decisions regarding, e.g., the (de)activation, or switching of AI / ML functionalities and AI / ML models.

[0119] The relevant information mentioned above could in principle be understood as "applicability-related information" in which the UE could, for example, report to the network conditions under which a model / functionality is applicable / suitable, or whether model(s) / functionality(es) are (non)applicable under the current context. The existing UE capability reporting framework may not be used for such purposes.

[0120] More specifically, for AI / ML operation under network control and management, the UE may need to report the applicability for given functionalities (use cases) and / or models. Applicability may be determined by additional conditions (e.g., scenario, sites, and datasets) as determined / identified between UE-side and network-side. Additional conditions may refer to any aspects that are assumed for the training of the model.

[0121] To configure additional conditions, the following options may be taken as potential approaches:

[0122] - Model identification to achieve alignment on the network-side additional condition between the network and the UE. In this case, model Identifier (ID) may represent the additional condition.

[0123] - Model transfer from the network to the UE, where the model has been trained under the additional condition

[0124] - Using separate procedure, e.g., other configuration for UE Assistance Information (UAI). Information and / or indication may be provided to the UE

[0125] - Implicit procedure assisted by monitoring may be used. In this case, without additional condition configuration, the network may recognize the model applicability based on monitoring related results.

[0126] Two UE reporting types may be identified to convey the applicability-related information:

[0127] - reactive reporting, and

[0128] - proactive reporting.

[0129] The difference between reactive reporting and proactive reporting may be determined by the point when the UE sends applicability-related information.

[0130] The reactive reporting would involve the UE to provide information to the network upon receiving an action from it. For example, the UE may trigger the reactive reporting after receiving any action-related model operation from the network.

[0131] The proactive reporting would involve the UE to provide information to the network without necessarily receiving an action from it. For example, the UE may trigger the proactive reporting before receiving action-related model operation from the network. For example, the UE might proactively inform the RAN of updates / changes to its supported model(s) or functionality(es). For the proactive reporting, the network may decide model activation according to the applicability.

[0132] FIG. 9 shows an example of a reactive reporting of applicability-related information to which implementations of the present disclosure are applied.

[0133] A procedure for the reactive reporting of applicability-related information may include at least one of the following steps.

[0134] (1) The network may configure functionality / model-related configuration. The configuration may include additional condition-related information for functionalities / models and / or a list of models for a specific functionality. The additional condition-related information may be linked to a specific functionality / model.

[0135] (2) The network may activate a specific functionality / model from among the configured functionalities / models.

[0136] (3) The UE may determine whether the specific functionality / model is applicable. For example, it is assumed that the network configures models A_1, A_2 and A_3 for functionality A, and models B_1, B_2 and B_3 for functionality B and the network activates the model A_1. In this case, the UE may evaluate whether the model A_1 is applicable.

[0137] (4) The UE may report applicability-related information based on the evaluation. For example, the applicability-related information may include information related to the applicability of the model A_1.

[0138] (5) The network may determine the operation for the activated functionality / model of which applicability-related information is reported. For example, the network may deactivate the activated functionality / model and / or may switch a configuration of the activated functionality / model.

[0139] FIG. 10 shows an example of a proactive reporting of applicability-related information to which implementations of the present disclosure are applied.

[0140] A procedure for the proactive reporting of applicability-related information may include at least one of the following steps.

[0141] (1) The network may configure functionality / model-related configuration. The configuration may include additional condition-related information for functionalities / models and / or a list of models for a specific functionality. The additional condition-related information may be linked to a specific functionality / model.

[0142] (2) The UE may determine whether each model is applicable per a functionality. For example, if functionality A is linked to model A_1, model A_2, and model A_3, and if functionality B is linked to model B_1, model B_2, and model B_3, the UE may evaluate the applicability of mode A_1, model A_2, and model_A_3 for functionality A, and model B_1, model B_2, and model B_3 for functionality B.

[0143] (3) The UE may report applicability-related information based on the evaluation. For example, the applicability-related information may include all information related applicability of models for functionality A / B.

[0144] Additionally, step 3 may be repeated. For example, the UE may report individual applicability-related information for each model of each functionality depending on the change of environment.

[0145] (4) The network may determine which model can be activated in current environment based on the reporting of applicability-related information, and activate applicable functionality / model by transmitting a configuration of the applicable functionality / model to the UE.

[0146] Since the network cannot recognize and / or understand UE's situation at any time, reporting of the applicability-related information from the UE may be helpful for the network to decide for model management. However, if the UE is set up with multiple models for multiple functionalities, the UE may evaluate multiple models for multiple functionalities. If the UE determines the applicability of an individual functionality / model and reports applicability-related information every time the applicability changes, the UE's power consumption may increase, and signal overhead may occur due to frequent transmission.

[0147] Additionally, from a network perspective, if the UE is currently operating in an appropriate model, reporting of the applicability-related information may be unnecessary, but the UE may continuously report applicability-related information depending on the network configuration.

[0148] Therefore, a method is needed for the UE to efficiently transmit applicability-related information to the network.

[0149] According to implementations of the present disclosure, a method of reporting applicability of functionality / model based on priority information is provided. To this end, the network may configure functionality / model-related configuration with additional conditions, and the functionality / model-related configuration may include priority information related to functionality / model.

[0150] According to implementations of the present disclosure, the UE may utilize the priority information when evaluating and / or reporting applicability of functionalities / models. For example, the UE may evaluate applicability of functionalities / models in order of higher priority based on the priority information. For example, the UE may report applicability-related information of the highest priority functionality / model based on the priority information, and / or may report applicability-related information of N number of functionalities / models with higher priority based on the priority information.

[0151] According to implementations of the present disclosure, based on the reporting of the applicability-related information from the UE, the network may determine suitable model management, e.g., model (de)activation, switch, update, fallback, etc., and / or suitable model condition / configuration.

[0152] Hereinafter, for example, "function", "functionality", "inference function", "model inference function", "AI / ML model inference function", "model", "AI / ML model" may be used interchangeably in the description below.

[0153] Hereinafter, for example, "model", "AI / ML model" may be sub-group of "function", "functionality", "inference function", "model inference function", "AI / ML model inference function". For example, multiple models may belong to and / or may be linked to a specific function.

[0154] The following drawings are created to explain specific embodiments of the present disclosure. The names of the specific devices or the names of the specific signals / messages / fields shown in the drawings are provided by way of example, and thus the technical features of the present disclosure are not limited to the specific names used in the following drawings.

[0155] FIG. 11 shows an example of a method to which implementations of the present disclosure are applied.

[0156] In step S1100, the method comprises receiving a configuration related to one or more inference functions from a network. The configuration includes priority information.

[0157] For example, the network may configure functionality / model-related configuration with additional conditions. The functionality / model-related configuration may include priority information related to functionality / model.

[0158] In some implementations, the priority information may include a priority value. The priority value may be configured per inference function and / or per inference function group.

[0159] In some implementations, the priority information may be liked to a specific inference function and / or specific inference function group.

[0160] In some implementations, the configuration may further include a maximum number for evaluating of the applicability and / or reporting of the applicability based on the priority information. The maximum number may be configured per inference function and / or per inference function group.

[0161] In step S1110, the method comprises evaluating an applicability of at least one inference function from among the one or more inference functions.

[0162] In some implementations, the evaluation of the applicability of the at least one inference function may be based on the priority information. For example, the wireless device may evaluate whether at least one functionality / model is applicable in order of higher priority based on the priority information.

[0163] In step S1120, the method comprises reporting the applicability of an inference function from among the at least one inference function.

[0164] In some implementations, the reporting of the applicability of the inference function may be based on the priority information. For example, the wireless device may report applicability-related information of highest priority functionality / model from among the evaluated functionalities / models based on the priority information. For example, the wireless device may report applicability-related information of N number of functionalities / models with higher priority from among the evaluated functionalities / models based on the priority information. The applicability of the N number of functionalities / models with higher priority may be reported in order of higher priority based on the priority information. The N number of functionalities / models with higher priority may be determined based on a maximum number of interference functions.

[0165] In some implementations, the reporting may be a reactive reporting or a proactive reporting.

[0166] In some implementations, the method may further comprise receiving a command related to life cycle management from the network. The life cycle management includes at least one of activation, deactivation, switch, update, or fallback related to the inference function.

[0167] In some implementations, the method may be performed by a wireless device. The wireless device may be in communication with at least one of a mobile device, a network, and / or autonomous vehicles other than the first wireless device.

[0168] Furthermore, the method described above in FIG. 11 may be performed by a wireless device. The wireless device may be implemented by the first wireless device 100 shown in FIG. 2 and / or the UE 100 shown in FIG. 3.

[0169] The wireless device comprises at least one transceiver, at least one processor, and at least one memory operably connectable to the at least one processor and storing instructions that, based on being executed by the at least one processor, perform the method described in FIG. 11.

[0170] More specifically, the wireless device receives a configuration related to one or more inference functions from a network. The configuration includes priority information.

[0171] For example, the network may configure functionality / model-related configuration with additional conditions. The functionality / model-related configuration may include priority information related to functionality / model.

[0172] In some implementations, the priority information may include a priority value. The priority value may be configured per inference function and / or per inference function group.

[0173] In some implementations, the priority information may be liked to a specific inference function and / or specific inference function group.

[0174] In some implementations, the configuration may further include a maximum number for evaluating of the applicability and / or reporting of the applicability based on the priority information. The maximum number may be configured per inference function and / or per inference function group.

[0175] The wireless device evaluates an applicability of at least one inference function from among the one or more inference functions.

[0176] In some implementations, the evaluation of the applicability of the at least one inference function may be based on the priority information. For example, the wireless device may evaluate whether at least one functionality / model is applicable in order of higher priority based on the priority information.

[0177] The wireless device reports the applicability of an inference function from among the at least one inference function.

[0178] In some implementations, the reporting of the applicability of the inference function may be based on the priority information. For example, the wireless device may report applicability-related information of highest priority functionality / model from among the evaluated functionalities / models based on the priority information. For example, the wireless device may report applicability-related information of N number of functionalities / models with higher priority from among the evaluated functionalities / models based on the priority information. The applicability of the N number of functionalities / models with higher priority may be reported in order of higher priority based on the priority information. The N number of functionalities / models with higher priority may be determined based on a maximum number of interference functions.

[0179] In some implementations, the reporting may be a reactive reporting or a proactive reporting.

[0180] The wireless device may further receive a command related to life cycle management from the network. The life cycle management includes at least one of activation, deactivation, switch, update, or fallback related to the inference function.

[0181] Furthermore, the method described above in FIG. 11 may be performed by control of a processing apparatus adapted to control a wireless device. The processing apparatus may be implemented by the processor 102 included in the first wireless device 100 shown in FIG. 2 and / or the processor 102 included in the UE 100 shown in FIG. 3.

[0182] The processing apparatus adapted to control the wireless device comprises at least one processor, and at least one memory operably connectable to the at least one processor. The at least one processor is adapted to perform the method described in FIG. 11.

[0183] Furthermore, the method described above in FIG. 11 may be performed by a software code 105 stored in the memory 104 included in the first wireless device 100 shown in FIG. 2.

[0184] The technical features of the present disclosure may be embodied directly in hardware, in a software executed by a processor, or in a combination of the two. For example, a method performed by a wireless device in a wireless communication may be implemented in hardware, software, firmware, or any combination thereof. For example, a software may reside in RAM, flash memory, ROM, EPROM, EEPROM, registers, hard disk, a removable disk, a CD-ROM, or any other storage medium.

[0185] Some example of storage medium may be coupled to the processor such that the processor can read information from the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. For other example, the processor and the storage medium may reside as discrete components.

[0186] The computer-readable medium may include a tangible and non-transitory computer-readable storage medium.

[0187] For example, non-transitory computer-readable media may include RAM such as Synchronous DRAM (SDRAM), ROM, Non-Volatile RAM (NVRAM), EEPROM, flash memory, magnetic or optical data storage media, or any other medium that can be used to store instructions or data structures. Non-transitory computer-readable media may also include combinations of the above.

[0188] In addition, the method described herein may be realized at least in part by a computer-readable communication medium that carries or communicates code in the form of instructions or data structures and that can be accessed, read, and / or executed by a computer.

[0189] According to some implementations of the present disclosure, a non-transitory Computer-Readable Medium (CRM) stores instructions that, based on being executed by at least one processor, perform the method described in FIG. 11.

[0190] FIG. 12 shows an example of another method to which implementations of the present disclosure are applied.

[0191] In step S1200, the method comprises transmitting a configuration related to one or more inference functions to a wireless device. The configuration includes priority information.

[0192] In step S1210, the method comprises receiving an applicability of an inference function from among the one or more inference functions from the wireless device. Evaluating of the applicability and / or reporting of the applicability is based on the priority information.

[0193] In step S1220, the method comprises determining management results related to the inference function based on the applicability of the inference function.

[0194] In step S1230, the method comprises transmitting a command related to life cycle management to the wireless device. The life cycle management includes at least one of activation, deactivation, switch, update, or fallback related to the inference function.

[0195] Furthermore, the method described above in FIG. 12 may be performed by a base station. The base station may be implemented by the second wireless device 200 shown in FIG. 2.

[0196] The base station comprises at least one transceiver, at least one processor, and at least one memory operably connectable to the at least one processor and storing instructions that, based on being executed by the at least one processor, perform the method described in FIG. 12.

[0197] More specifically, the network transmits a configuration related to one or more inference functions to a wireless device. The configuration includes priority information.

[0198] The network receives an applicability of an inference function from among the one or more inference functions from the wireless device. Evaluating of the applicability and / or reporting of the applicability is based on the priority information.

[0199] The network determines management results related to the inference function based on the applicability of the inference function.

[0200] The network transmits a command related to life cycle management to the wireless device. The life cycle management includes at least one of activation, deactivation, switch, update, or fallback related to the inference function.

[0201] Hereinafter, each step described in FIGS. 11 and / or 12 is described in detail.

[0202] 1. Configuration step (i.e., step S1100 in FIG. 11 and / or step S1200 in FIG. 12)

[0203] The network may configure functionality / model-related configuration with additional conditions.

[0204] Functionality / model-related configuration may include at least one of the following information.

[0205] - Functionality / Functionality group ID

[0206] - Model / Model group ID

[0207] - Functionality / Functionality group list

[0208] - Model / Model group list

[0209] - Functionality / Model related parameters

[0210] Additional conditions may refer to conditions under which the UE can perform functionality / model-related operations. The additional conditions may include at least one of the following information.

[0211] - Specific location related information (e.g., polygon type, latitude / longitude, altitude, angle, indoor / outdoor, etc.)

[0212] - Specific time related information (e.g., date, time window, start time, stop time, etc.)

[0213] - Specific speed related information (e.g., 10km / h, 30km / h, 60km / h, 120km / h, etc.)

[0214] - Specific radio quality condition (e.g., Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal-to-Interference plus Noise Ratio (SINR), etc.)

[0215] - Specific deployment scenario (e.g., Urban Macro-cells (Uma), Urban Micro-cells (UMi), InH, etc.)

[0216] - Specific cell / frequency related information, (e.g., bandwidth, size of subband, carrier frequency, numerologies, etc.)

[0217] - Specific antenna related information (e.g., antenna port layouts, antenna port numbers, rank numbers / layers, antenna spacing, antenna virtualization, etc.)

[0218] The additional conditions may consist of one condition or a combination of several conditions. The additional condition may have a specific condition ID. The additional condition may be linked to a specific functionality / functionality group and / or a specific model / model group.

[0219] The functionality / model-related configuration may include priority information related to functionality / model.

[0220] The priority information may correspond to a priority value. The priority value may be an integer value. For example, low priority value may indicate high priority (e.g., priority 1 is higher than priority 2). For example, high priority value may indicate high priority (e.g., priority 2 is higher than priority 1).

[0221] The priority information may be configured per functionality / functionality group and / or per model / model group.

[0222] The priority information may be linked to a specific functionality / functionality group and / or a specific model / model group.

[0223] Maximum number that can be evaluated and / or reported based on the priority information may be configured. The maximum number may be configured per functionality / functionality group and / or per model / model group.

[0224] 2. Evaluation step (i.e., step S1110 in FIG. 11)

[0225] The UE may evaluate whether at least one functionality / model is applicable based on the additional conditions.

[0226] At evaluation step, the UE may utilize the priority information.

[0227] FIG. 13 shows an example of model evaluation based on priority information to which implementations of the present disclosure are applied.

[0228] In FIG. 13-(a), the UE evaluates applicability of the currently activated model and applicability of models that is higher priority than the currently activated model. When model C is activated, the UE evaluates applicability of models A, B and C. When model C is deactivated and model A is activated, the UE only evaluates applicability of model A.

[0229] In FIG. 13-(b), the UE evaluates applicability of the currently activated model and applicability of the model that is highest priority. When model C is activated, the UE evaluates applicability of models A and C. When model C is deactivated and model A is activated, the UE only evaluates applicability of model A.

[0230] The UE may evaluate applicability of the models in order of higher priority as many as maximum number configured by the network.

[0231] FIG. 14 shows an example of functionality evaluation based on priority information to which implementations of the present disclosure are applied.

[0232] In FIG. 14-(a), the UE evaluates applicability of the currently activated functionality and applicability of the functionalities that is higher priority than the currently activated functionality. When functionality C is activated, the UE evaluates applicability of functionalities A, B and C. When functionality C is deactivated and functionality A is activated, the UE only evaluates applicability of functionality A.

[0233] In FIG. 14-(b), the UE evaluates applicability of the currently activated functionality and applicability of the functionality that is highest priority. When functionality C is activated, the UE evaluates applicability of functionalities A and C. When functionality C is deactivated and functionality A is activated, the UE only evaluates applicability of functionality A.

[0234] The UE may evaluate applicability of the functionalities in order of higher priority as many as maximum number configured by the network.

[0235] FIGS. 15 and 16 show an example of functionality / model evaluation based on priority information to which implementations of the present disclosure are applied.

[0236] Referring to FIGS. 15 and 16, the UE may evaluate applicability of the activated functionalities / models. The UE may evaluate applicability of functionalities / models that is higher priority than currently activated functionality / model. The UE may evaluate applicability of functionality / model that is the highest priority. The UE may evaluate applicability of the functionalities / models in order of higher priority as many as maximum number configured by the network.

[0237] In FIG. 15-(a), the UE evaluates applicability of the currently activated functionality and applicability of the functionalities that is higher priority than the currently activated functionality. When functionality A is activated, the UE only evaluates applicability of functionality A. When functionality A is deactivated and functionality B is activated, the UE evaluates applicability of functionalities A and B.

[0238] Furthermore, the UE evaluates applicability of models related to the evaluated functionalities. When the UE evaluates applicability of functionality A, the UE also evaluates applicability of models A, B, C, and D related to functionality A. When the UE evaluates applicability of functionalities A and B, the UE also evaluates applicability of models A, B, C, D, E and F related to functionalities A and B.

[0239] In FIG. 15-(b), the UE evaluates applicability of the currently activated functionality and applicability of the functionality that is higher priority than the currently activated functionality. When functionality A is activated, the UE only evaluates applicability of functionality A. When functionality A is deactivated and functionality B is activated, the UE evaluates applicability of functionalities A and B.

[0240] Furthermore, the UE evaluates applicability of the highest priority model related to the evaluated functionalities. When the UE evaluates applicability of functionality A, the UE also evaluates applicability of model A related to functionality A. When the UE evaluates applicability of functionalities A and B, the UE also evaluates applicability of models A and E related to functionalities A and B respectively.

[0241] In FIG. 16-(a), the UE only evaluates applicability of the currently activated functionality. When functionality A is activated, the UE only evaluates applicability of functionality A. When functionality A is deactivated and functionality B is activated, the UE only evaluates applicability of functionality B.

[0242] Furthermore, the UE evaluates applicability of the highest priority model related to the evaluated functionality. When the UE evaluates applicability of functionality A, the UE also evaluates applicability of model A related to functionality A. When the UE evaluates applicability of functionality B, the UE also evaluates applicability of model E related to functionality B.

[0243] In FIG. 16-(b), the UE evaluates applicability of the currently activated functionality / model and applicability of functionalities / models that is higher priority than the currently activated functionality / model. When functionality A with model B is activated, the UE evaluates applicability of functionality A, and also applicability of models A and B. When functionality A is deactivated and functionality B with model E is activated, the UE evaluates applicability of functionalities A and B, and also applicability of models A, B, C, D, and E.

[0244] 3. Reporting step (i.e., step S1100 in FIG. 11 and / or step S1210 in FIG. 12)

[0245] The UE may report applicability-related information based on the evaluation. The applicability-related information may include at least one of the following information.

[0246] - Applicability of functionality / model;

[0247] - No applicability of functionality / model;

[0248] - Applicable condition / configuration;

[0249] - Not applicable condition / configuration;

[0250] - Update / change applicability

[0251] The reporting of the applicability-related information may be conducted by UE internal conditions, such as internal problem (memory, battery, thermal, etc.).

[0252] For the reactive reporting, the UE may report applicability-related information for a specific functionality / model that is instructed to take a specific action from the network. The specific action may be related to functionality / model operation, e.g., model (de)activation, switching, update, etc. The specific action may be related to functionality / model configuration for a specific functionality / model.

[0253] For the proactive reporting, the UE may report applicability-related information for (pre)-configured / installed / determined / known model between the network and the UE.

[0254] At reporting step, the UE may utilize the priority information The UE may report applicability-related information of Top-N / 1 priority functionalities / models based on the priority information. The order of the applicability-related information may be determined by the priority information, e.g.., in order of higher priority. The number of functionalities / models of which the applicability-related information is reported may be determined by the maximum number of functionalities / models configured by the network.

[0255] FIG. 17 shows an example of model applicability reporting based on priority information to which implementations of the present disclosure are applied.

[0256] In FIG. 17-(a), the UE reports applicability of the currently activated model and applicability of models that is higher priority than the currently activated model. When model C is activated, the UE reports applicability of models A, B and C. When model B is activated, the UE reports applicability of models A and B. The higher priority may mean the higher priority value regardless of applicability of models. Or, the higher priority may mean the higher priority value among the applicable models.

[0257] In FIG. 17-(b), the UE reports applicability of the currently activated model and applicability of the model that is highest priority. When model C is activated, the UE reports applicability of models A and C. When model B is activated, the UE reports applicability of models A and B. The highest priority may mean the highest priority value regardless of applicability of model. Or, the highest priority may mean the highest priority value among the applicable models.

[0258] In FIG. 17-(c), the UE reports applicability of models in order of higher priority as many as maximum number configured by the network.

[0259] FIG. 18 shows an example of functionality applicability reporting based on priority information to which implementations of the present disclosure are applied.

[0260] In FIG. 18-(a), the UE reports applicability of the currently activated functionality and applicability of functionalities that is higher priority than the currently activated functionality. When functionality C is activated, the UE reports applicability of functionalities A, B and C. When functionality B is activated, the UE reports applicability of functionalities A and B. The higher priority may mean the higher priority value regardless of applicability of functionalities. Or, the higher priority may mean the higher priority value among the applicable functionalities.

[0261] In FIG. 18-(b), the UE reports applicability of the currently activated functionality and applicability of the functionality that is highest priority. When functionality C is activated, the UE reports applicability of functionalities A and C. When functionality B is activated, the UE reports applicability of functionalities A and B. The highest priority may mean the highest priority value regardless of applicability of functionality. Or, the highest priority may mean the highest priority value among the applicable functionalities.

[0262] In FIG. 18-(c), the UE reports applicability of functionalities in order of higher priority as many as maximum number configured by the network.

[0263] FIGS. 19 and 20 show an example of functionality / model applicability reporting based on priority information to which implementations of the present disclosure are applied.

[0264] Referring to FIGS. 19 and 20, the UE may report applicability-related information of the activated functionalities / models. The UE may report applicability-related information of the functionalities / models that is higher priority than currently activated functionality / model. The higher priority may mean the higher priority value regardless of applicability of functionalities / models. The higher priority may mean the higher priority value among the applicable functionalities / models. The UE may report applicability-related information of the functionality / model that is the highest priority. The highest priority may mean the highest priority value regardless of applicability of functionalities / models. The highest priority may mean the highest priority value among the applicable functionalities / models. The UE may report applicability-related information of the functionalities / models in order of higher priority as many as maximum number configured by the network.

[0265] In FIG. 19-(a), the UE reports applicability of the currently activated functionality and applicability of the functionalities that is higher priority than the currently activated functionality. When functionality A is activated, the UE only reports applicability of functionality A. When functionality A is deactivated and functionality B is activated, the UE reports applicability of functionalities A and B.

[0266] Furthermore, the UE reports applicability of models related to the reported functionalities. When the UE reports applicability of functionality A, the UE also reports applicability of models A, B, C, and D related to functionality A. When the UE reports applicability of functionalities A and B, the UE also reports applicability of models A, B, C, D, E and F related to functionalities A and B.

[0267] In FIG. 19-(b), the UE reports applicability of the currently activated functionality and applicability of the functionalities that is higher priority than the currently activated functionality. When functionality A is activated, the UE only reports applicability of functionality A. When functionality A is deactivated and functionality B is activated, the UE reports applicability of functionalities A and B.

[0268] Furthermore, the UE reports applicability of the highest priority model related to the evaluated functionalities. When the UE reports applicability of functionality A, the UE also reports applicability of model A related to functionality A. When the UE reports applicability of functionalities A and B, the UE also reports applicability of models A and E related to functionalities A and B respectively.

[0269] In FIG. 20-(a), the UE only reports applicability of the currently activated functionality. When functionality A is activated, the UE only reports applicability of functionality A. When functionality A is deactivated and functionality B is activated, the UE only reports applicability of functionality B.

[0270] Furthermore, the UE reports applicability of the highest priority model related to the reported functionality. When the UE reports applicability of functionality A, the UE also reports applicability of model A related to functionality A. When the UE reports applicability of functionality B, the UE also reports applicability of model E related to functionality B.

[0271] In FIG. 20-(b), the UE reports applicability of the currently activated functionality / model and applicability of functionalities / models that is higher priority than the currently activated functionality / model. When functionality A with model B is activated, the UE reports applicability of functionality A, and also applicability of models A and B. When functionality A is deactivated and functionality B with model E is activated, the UE reports applicability of functionalities A and B, and also applicability of models A, B, C, D, and E.

[0272] 4. Determination step (i.e., step S1220 / S1230 in FIG. 12)

[0273] The network may determine functionality / model management related results, such as model (de)activation, switching, update, fallback, etc. The network may determine whether the functionality / model configuration needs to be updated.

[0274] Based on determination, the network may configure functionality / model management related results. Or, based on determination, the network may reconfigure functionality / model.

[0275] The present disclosure may have various advantageous effects.

[0276] For example, through evaluating and / or reporting of inference function applicability based on priority information, complexity and / or power consumption for evaluating and / or reporting of the applicability of all inference functions can be reduced.

[0277] For example, the UE can prevent unnecessary applicability reporting for inference functions with lower priority, thereby reducing signaling overhead by notifying inference functions that can be used immediately.

[0278] Advantageous effects which can be obtained through specific embodiments of the present disclosure are not limited to the advantageous effects listed above. For example, there may be a variety of technical effects that a person having ordinary skill in the related art can understand and / or derive from the present disclosure. Accordingly, the specific effects of the present disclosure are not limited to those explicitly described herein, but may include various effects that may be understood or derived from the technical features of the present disclosure.

[0279] Claims in the present disclosure can be combined in a various way. For instance, technical features in method claims of the present disclosure can be combined to be implemented or performed in an apparatus, and technical features in apparatus claims can be combined to be implemented or performed in a method. Further, technical features in method claim(s) and apparatus claim(s) can be combined to be implemented or performed in an apparatus. Further, technical features in method claim(s) and apparatus claim(s) can be combined to be implemented or performed in a method. Other implementations are within the scope of the following claims.

Claims

1.A method comprising:receiving a configuration related to one or more inference functions from a network, wherein the configuration includes priority information;evaluating an applicability of at least one inference function from among the one or more inference functions; andreporting the applicability of an inference function from among the at least one inference function,wherein evaluating of the applicability and / or reporting of the applicability is based on the priority information.2.The method of claim 1, wherein the applicability of the at least one inference function is evaluated in order of higher priority based on the priority information.3.The method of claim 1 or 2, wherein the inference function of which the applicability is reported is an inference function with highest priority based on the priority information.4.The method of claim 1 or 2, wherein the inference function of which the applicability is reported is included in N number of inference functions with higher priority based on the priority information.5.The method of claim 4, wherein the applicability of the N number of inference functions with higher priority is reported in order of higher priority based on the priority information.6.The method of claim 4, wherein the N number of inference functions with higher priority is determined based on a maximum number of interference functions.7.The method of any claims 1 to 6, wherein the reporting is a reactive reporting or a proactive reporting.8.The method of any claims 1 to 7, wherein the priority information includes a priority value.9.The method of claim 8, wherein the priority value is configured per inference function and / or per inference function group.10.The method of any claims 1 to 9, wherein the priority information is liked to a specific inference function and / or specific inference function group.11.The method of any claims 1 to 10, wherein the configuration further includes a maximum number for evaluating of the applicability and / or reporting of the applicability based on the priority information.12.The method of claim 11, wherein the maximum number is configured per inference function and / or per inference function group.13.The method of any claims 1 to 12, wherein the method further comprises receiving a command related to life cycle management from the network, andwherein the life cycle management includes at least one of activation, deactivation, switch, update, or fallback related to the inference function.14.The method of any claims 1 to 13, wherein the method is performed by a wireless device.15.The method of any claims 1 to 14, wherein the wireless device is in communication with at least one of a mobile device, a network, and / or autonomous vehicles other than the wireless device.16.A wireless device comprising:at least one transceiver;at least one processor; andat least one memory operably connectable to the at least one processor and storing instructions that, based on being executed by the at least one processor, perform the method of any claims 1 to 15.17.A processing apparatus adapted to control a wireless device comprising:at least one processor; andat least one memory operably connectable to the at least one processor,wherein the at least one processor is adapted to perform the method of any claims 1 to 15.18.A non-transitory Computer Readable Medium (CRM) storing instructions that, based on being executed by at least one processor, perform the method of any claims 1 to 15.19.A method comprising:transmitting a configuration related to one or more inference functions to a wireless device, wherein the configuration includes priority information;receiving an applicability of an inference function from among the one or more inference functions from the wireless device, wherein evaluating of the applicability and / or reporting of the applicability is based on the priority information;determining management results related to the inference function based on the applicability of the inference function; andtransmitting a command related to life cycle management to the wireless device,wherein the life cycle management includes at least one of activation, deactivation, switch, update, or fallback related to the inference function.20.A base station comprising:at least one transceiver;at least one processor; andat least one memory operably connectable to the at least one processor and storing instructions that, based on being executed by the at least one processor, perform the method of claim 19.

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