Determining and reporting applicable combinations of models and functionalities

Abstract

A method and apparatus for determining and reporting applicable combinations of models and functionalities is provided. The wireless device receives a configuration related to one or more inference functions from a base station. The configuration includes report combination information related to which information is to be evaluated or reported. The wireless device performs an operation related to an applicability of at least one inference function based on the report combination information.

Description

DETERMINING AND REPORTING APPLICABLE COMBINATIONS OF MODELS AND FUNCTIONALITIES

[0001] The present disclosure relates to determining and reporting applicable combinations of models and functionalities.

[0002] 3rd Generation Partnership Project (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.

[0003] 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.

[0004] The integration of Artificial Intelligence and Machine Learning (AI / ML) technologies has emerged as a key enabler for enhanced performance and intelligent network operation in 5G and 6G. AI / ML techniques can be applied to various aspects of wireless communications, including channel state prediction, beam management, positioning accuracy improvement, and / or network optimization. By leveraging AI / ML capabilities, communication systems can achieve improved spectral efficiency, reduced latency, and more adaptive resource management compared to conventional rule-based approaches.

[0005] In AI / ML-based wireless communication systems, the applicability of a given model and / or function to specific operating conditions may not be fixed at deployment but may change dynamically over time as the radio environment, traffic load, and / or User Equipment (UE) capability evolve. For example, a model that is initially applicable to a certain set of configurations may later become inapplicable due to changes in channel conditions and / or network topology, and conversely, previously inapplicable configurations may become valid candidates as system conditions shift. Despite this dynamic nature of model / function applicability, existing reporting frameworks may not provide an efficient mechanism by which a UE can convey such changes to the network in a timely and structured manner.

[0006] One approach that may be considered is for the UE to enumerate all possible combinations of parameters and / or configurations along with their respective applicability status. However, this exhaustive enumeration approach may introduce significant drawbacks. As the number of applicable parameters and their possible values increases, the number of combinations grows rapidly, leading to a combinatorial explosion. This may result in the transmission of a large volume of redundant or overlapping information, since many combinations may share common characteristics that could otherwise be represented more compactly. Consequently, signaling overhead may increase substantially, potentially degrading system efficiency and consuming radio resources that could otherwise be utilized for data transmission.

[0007] Furthermore, existing reporting structures may lack support for range-based representations, such as minimum-value-based, maximum-value-based, or bounded-range-based reporting of applicability conditions. In the absence of such structured reporting mechanisms, the network may be unable to effectively interpret and / or control the scope of applicability information reported by the UE. This may make it difficult for the network to determine appropriate operating ranges, configure model activation thresholds, and / or restrict the UE's reporting scope to a relevant subset of conditions, thereby limiting the network's ability to manage AI / ML model lifecycle operations efficiently.

[0008] Accordingly, there may be a need for an improved reporting mechanism that allows a UE to report the applicability of AI / ML models or functions in a compact and range-aware manner, enabling the network to effectively control the scope of UE reporting and reduce unnecessary signaling overhead.

[0009] In an aspect, a method performed by a wireless device is provided. The method includes receiving a configuration related to one or more inference functions from a base station. The configuration includes report combination information related to which information is to be evaluated or reported. The method includes performing an operation related to an applicability of at least one inference function based on the report combination information.

[0010] In another aspect, a wireless device is provided. The wireless device includes 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, cause the wireless device to perform operations. The operations include receiving a configuration related to one or more inference functions from a base station. The configuration includes report combination information related to which information is to be evaluated or reported. The operations include performing an operation related to an applicability of at least one inference function based on the report combination information.

[0011] In another aspect, a processing apparatus is provided. The processing apparatus includes at least one processor that is integrated with a wireless device, and at least one memory comprising processor-executable instructions stored thereon that are configured to cause the at least one processor to perform operations. The operations include obtaining a configuration related to one or more inference functions. The configuration includes report combination information related to which information is to be evaluated or reported. The operations include performing an operation related to an applicability of at least one inference function based on the report combination information.

[0012] In another aspect, a non-transitory Computer Readable Medium (CRM) is provided. The non-transitory CRM stores instructions that, based on being executed by at least one processor, cause a wireless device to perform operations. The operations include receiving a configuration related to one or more inference functions from a base station. The configuration includes report combination information related to which information is to be evaluated or reported. The operations include performing an operation related to an applicability of at least one inference function based on the report combination information.

[0013] In another aspect, a method performed by a base station is provided. The method includes transmitting a configuration related to one or more inference functions to a wireless device. The configuration includes report combination information related to which information is to be evaluated or reported. An operation related to an applicability of at least one inference function is performed based on the report combination information.

[0014] In another aspect, a base station is provided. The base station includes 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, cause the base station to perform operations. The operations include transmitting a configuration related to one or more inference functions to a wireless device. The configuration includes report combination information related to which information is to be evaluated or reported. An operation related to an applicability of at least one inference function is performed based on the report combination information.

[0015] The present disclosure may have various advantageous effects.

[0016] For example, by adopting a range-based and / or structured reporting framework rather than exhaustive enumeration of all parameter combinations, the overall signaling overhead associated with applicability reporting can be significantly reduced, thereby improving spectral efficiency and / or conserving radio resources.

[0017] For example, redundant or overlapping combination entries that would otherwise be transmitted under a conventional reporting scheme can be eliminated, allowing the UE to convey the same applicability information in a more compact and concise form without loss of relevant detail.

[0018] For example, because the reporting structure is designed to accommodate dynamic changes in model or function applicability, the UE can efficiently reflect updated applicability states as operating conditions evolve, ensuring that the network maintains an accurate and current understanding of which models or functions are applicable at any given time.

[0019] For example, the range-aware reporting structure can provide a flexible framework for interaction between the UE and the network, enabling the network to configure and / or control the scope of applicability reporting (e.g., by specifying minimum, maximum, or bounded reporting ranges), thereby allowing both the UE and the network to operate in a coordinated and resource-efficient manner.

[0020] 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.

[0021] FIG. 1 shows an example of a communication system to which implementations of the present disclosure are applied.

[0022] FIG. 2 shows an example of wireless devices to which implementations of the present disclosure are applied.

[0023] FIG. 3 shows an example of UE to which implementations of the present disclosure are applied.

[0024] 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.

[0025] FIG. 6 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure are applied.

[0026] FIG. 7 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure are applied.

[0027] FIG. 8 shows an example of applicability status reporting to which implementations of the present disclosure are applied.

[0028] FIG. 9 shows an example of a method performed by a wireless device to which implementations of the present disclosure are applied.

[0029] FIG. 10 shows an example of a method performed by a base station to which implementations of the present disclosure are applied.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] 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".

[0035] 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".

[0036] 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".

[0037] 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".

[0038] Technical features that are separately described in one drawing in the present disclosure may be implemented separately or simultaneously.

[0039] 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.

[0040] 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.

[0041] FIG. 1 shows an example of a communication system to which implementations of the present disclosure are applied.

[0042] 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.

[0043] 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).

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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).

[0052] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1450MHz - 6000MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0053] 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).

[0054] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1410MHz - 7125MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0055] 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.

[0056] FIG. 2 shows an example of wireless devices to which implementations of the present disclosure are applied.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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).

[0073] 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.

[0074] 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.

[0075] 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.

[0076] In the present disclosure, a BS is also referred to as a node B (NB), an eNode B (eNB), or a gNB.

[0077] FIG. 3 shows an example of UE to which implementations of the present disclosure are applied.

[0078] Referring to FIG. 3, a UE 100 may correspond to the first wireless device 100 of FIG. 2.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] FIG. 6 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure are applied.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] uNslotsymbNframe,uslotNsubframe,uslot01410111420221440431480841416016

[0101] 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.

[0102] uNslotsymbNframe,uslotNsubframe,uslot212404

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] FIG. 7 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure are applied.

[0108] 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.

[0109] 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.

[0110] Features related to Artificial Intelligence (AI) / Machine Learning (ML) are described.

[0111] The objective of AI / ML for NR air interface is to improve network performance and user experience, through AI / ML-enabled enhancements to the following features: beam management, CSI prediction and positioning.

[0112] AI / ML-based beam management may utilize intra-cell downlink beam prediction of the serving cell to reduce measurement / RS overhead and to improve the accuracy of beam selection. Two types of beam prediction may be supported:

[0113] - Spatial-domain downlink transmission beam prediction for one set of beams based on measurement results of another set of beams where the set of beams for prediction can be Synchronization Signal Block (SSB) or CSI-RS beams and another set of beams for measurement can be SSB or CSI-RS beams; and

[0114] - Temporal-domain downlink transmission beam prediction for one set of beams based on historic measurement results of another set of beams (these two sets may be different).

[0115] For AI / ML-based beam management, both network-side model and UE-side model may be supported.

[0116] For UE-side model, the gNB may provide inference configuration or inference related parameters based on UE supported functionalities. The UE may report its applicable functionalities, inapplicable functionalities with its preference to release the configuration and subsequent changes of applicability status of functionalities to gNB.

[0117] FIG. 8 shows an example of applicability status reporting to which implementations of the present disclosure are applied.

[0118] 1. Step 1:

[0119] The network may enquire about the UE capability information.

[0120] 2. Step 2:

[0121] The UE may indicate its supported functionalities to the network viaUECapabilityInformationmessage.

[0122] 3. Step 3:

[0123] The network may provide inference configuration with NW-side additional conditions (e.g., associated ID) to UE via CSI report configuration or inference related parameters configuration viaOtherConfig.

[0124] 4. Step 4:

[0125] The UE may determine the applicable AI / ML functionalities based on at least one of NW-side additional conditions (if provided), UE-side additional conditions (internally known by UE) or model availability in the UE. The UE may report its functionality applicability to the network.

[0126] 5. Step 5: RRC Reconfiguration

[0127] Based on the applicability reporting, the network may provide inference configuration inRRCReconfigurationCompletemessage.

[0128] 6. Step 6: Activation / Deactivation / Inference / Monitoring

[0129] When the inference configuration consists of periodic CSI report configuration, upon reporting the applicable functionalities, the UE may autonomously activate the applicable AI / ML functionalities. When the inference configuration consists with semi-persistent CSI and / or aperiodic CSI report configuration, upon reporting the applicable AI / ML functionalities, applicable AI / ML functionality activation can be activated by MAC Control Element (CE) / DCI and aperiodic CSI reporting can be activated by DCI.

[0130] For network-side model, the CSI measurement and reporting may be used to acquire input data for inference.

[0131] For network-side model, the gNB may be responsible for performance monitoring (i.e., calculates performance metrics). There are no additional impacts on the UE for monitoring and management, except for being configured to provide the required measurement / data. Additionally, the UE may not be informed about any gNB-side management decision.

[0132] For UE-side model, the gNB may initiate performance monitoring, and makes management decisions based on the performance monitoring results. The UE may be configured to send either the measurement reports or the calculated performance metrics.

[0133] AI / ML-based CSI prediction is supported with similar principles and procedures for AI / ML-based beam management with the following differences:

[0134] - Only temporal-domain CSI prediction with UE-side model is supported.

[0135] - During the applicability reporting procedure, there is no inference related parameters configuration provided from gNB to UE for CSI prediction.

[0136] Network-side data collection may apply only to AI / ML beam management feature.

[0137] Network-side data collection for network-side model training can be initiated by Operation, Administration, and Maintenance (OAM) or by gNB. In case of NR-DC, the data collection can only be configured for MCG. The following enablers are introduced for Network-side data collection for Network-side model over air interface:

[0138] - The UE can be configured by gNB to log L1 measurements in the AS layer memory and report them via an RRC message(s).

[0139] - Both periodic and L3 measurement event-triggered data collection are supported. The UE may store the logged data at the AS layer memory. When the AS layer memory for storing logged data becomes full, the UE may stop measurement and logging for data collection. When the AS layer memory reserved for storing logged data becomes full or reaches an absolute threshold (if configured), the UE may indicate data availability to the gNB.

[0140] - When low power state is detected, the UE can indicate the low power state to the gNB. Upon reception of the low power state indication, the gNB may release the UE data collection configuration for Network-side model.

[0141] - The gNB can indicate the UE whether the logged data should be kept or not during handover. When indicated to keep the logged data, the UE may retain it during handover and indicates its availability after handover.

[0142] UE-side data collection may apply to AI / ML Beam management and CSI prediction features.

[0143] For UE-side data collection for UE-side model training, the gNB can configure whether the UE is allowed to initiate a request for data collection configuration (e.g., UE's preference to start or to stop data collection, preferred configuration from a list of candidate configurations provided by network). The gNB can also provide UE with data collection configuration or release the data collection configuration at any point in time, with or without UE request.

[0144] The applicability of a specific model and / or functionality may change over time. When the applicability status of a specific model and / or functionality is updated, the UE may trigger an applicable functionality reporting via RRC messages, such asRRCReconfigurationCompletemessage orUEAssistanceInformationmessage.

[0145] To report applicability of a specific model and / or functionality, the following two options may be being considered:

[0146] (1) Option A: The network may provide a full inference configuration, e.g., in step 3 of FIG. 8. For example, the network may configure CSI report configuration for the applicability reporting. The UE may then report the applicableCSI-ReportConfigIdto the network in step 4.

[0147] (2) Option B: The network may provide inference-related parameters, e.g., in step 3 of FIG. 8. The inference-related parameters may include associated IDs, resource set B / A configurations, and / or time instances for Prediction Window (PW) / Observation Window (OW). Based on this information, the UE may report detailed applicable parameters to the network.

[0148] Regardless of whether Option A or B is used, listing all possible applicability-related information may lead to redundant data in the applicability report.

[0149] To address the problem mentioned above, the present disclosure may provide an efficient method for determining and / or report applicable / capable combinations of models / functions.

[0150] According to implementations of the present disclosure, the UE may decide whether to report all possible combinations of applicable / capable functionalities / models or only specific combinations of applicable / capable functionalities / models. The UE may evaluate applicability of combinations of applicable / capable functionalities / models based on network indication / condition and / or UE internal state.

[0151] According to implementations of the present disclosure, to facilitate the decision of the UE, the network may provide information related to the reporting scope and / or reporting range, i.e., whether the UE should report all combinations or only a subset of combinations. Based on the received information, the UE may evaluate applicability of a specific functionality / model and / or report applicability information to the network.

[0152] For evaluation and / or reporting applicability information related to an applicability of a specific functionality / model, the following principles may be considered.

[0153] (1) Principle 1:

[0154] The network may indicate super-set, which means that all of applicable / capable combinations of functionalities / models should be reported. Therefore, the UE may report super-set applicability information related to all of applicable / capable combinations of functionalities / models.

[0155] (2) Principle 2:

[0156] The network may indicate sub-set, which means that a specific applicable / capable combination of functionalities / models should be reported. In other words, the network may indicate a specific range related to applicable / capable combination of functionalities / models.

[0157] - The specific applicable / capable combination of functionalities / models may correspond to a minimum applicable / capable combination of functionalities / models. The minimum applicable / capable combination of functionalities / models may be related to reducing performance burden.

[0158] - The specific applicable / capable combination of functionalities / models may correspond to a maximum applicable / capable combination of functionalities / models. The maximum applicable / capable combination of functionalities / models may be related to reducing signaling burden. Since certain applicable / capable combination of functionalities / models are inherently covered within the maximum applicable / capable combination of functionalities / models, reporting additional applicability information under the maximum applicable / capable combination of functionalities / models may be unnecessary. If the UE supports all of applicable / capable combinations of functionalities / models up to the maximum applicable / capable combination of functionalities / models, any further details could result in redundant signaling, increasing overhead without added benefit.

[0159] If a certain range related to applicable / capable combination of functionalities / models within the maximum applicable / capable combination of functionalities / models is not applicable, the UE may explicitly indicate them by reporting the certain range of related to non-applicable / non-capable combination of functionalities / models.

[0160] - The specific applicable / capable combination of functionalities / models may correspond to a high-priority applicable / capable combination of functionalities / models. In this case, a low-priority applicable / capable combination of functionalities / models may be omitted or reported in a simplified manner. If the UE prefers a certain combination of functionalities / models or if some combinations of functionalities / models are more likely to be used frequently, the UE and / or network may assign weights to prioritize the reporting of a certain combination of functionalities / models.

[0161] According to implementations of the present disclosure, based on evaluation related to applicability of combinations of applicable / capable functionalities / models, the UE may report applicability information related to some combinations of applicable / capable functionalities / models.

[0162] For example, when the super-set is indicated, the UE may report applicability information related to all of combinations of applicable / capable functionalities / models.

[0163] For example, when the minimum sub-set is indicated, the UE may report applicability information related to a minimum combination of applicable / capable functionalities / models.

[0164] For example, when the maximum sub-set is indicated, the UE may report applicability information related to a maximum combination of applicable / capable functionalities / models.

[0165] For example, if necessary, the applicability information may be reported based on a range and / or an offset.

[0166] According to implementations of the present disclosure, the applicability information reporting can be optimized by reducing redundant signaling and ensuring efficient communication between the UE and the network.

[0167] In the present disclosure, the applicability information reporting is described as an example, but the present disclosure is not limited thereto. The present disclosure may be applied to capability information reporting.

[0168] In the present disclosure, the following definitions may apply.

[0169] - ML model: a manageable representation of an ML model algorithm.

[0170] - AI / ML inference: a process of running a set of input data through a trained ML model to produce set of output data, such as predictions.

[0171] - AI / ML inference function: a logical function that employs trained ML model(s) to conduct inference.

[0172] In the present disclosure, "ML model", "AI / ML mode", "model" may be used interchangeably. In the present disclosure, "AI / ML inference", "AI inference", "ML inference", "inference" may be used interchangeably. In the present disclosure, "AI / ML inference function", "AI inference function", "ML inference function", "function", "functionality" may be used interchangeably.

[0173] 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.

[0174] An embodiment of the present disclosure related to a specific drawing described below may be combined with various embodiments of the present disclosure related to other drawings, and some descriptions, functions, procedures, proposals, methods and / or operations of the embodiment may be omitted.

[0175] FIG. 9 shows an example of a method performed by a wireless device to which implementations of the present disclosure are applied.

[0176] In step S900, the method includes receiving a configuration related to one or more inference functions from a base station. The configuration comprises report combination information related to which information is to be evaluated or reported.

[0177] In step S910, the method includes performing an operation related to an applicability of at least one inference function based on the report combination information.

[0178] In some implementations, the operation may include evaluating the applicability of the at least one inference function based on the report combination information.

[0179] In some implementations, the operation may include reporting applicability information or capability information of the at least one inference function based on the report combination information.

[0180] In some implementations, the operation may include reporting capability information of the at least one inference function based on the report combination information.

[0181] In some implementations, the report combination information may inform that a super-set combination is to be evaluated or reported. The super-set combination may correspond to all applicable or capable combinations of the one or more inference functions. In this case, the operation may be performed for the all applicable or capable combinations of the one or more inference functions including the at least one inference function.

[0182] In some implementations, the report combination information may inform that a sub-set combination is to be evaluated or reported. The sub-set combination may correspond to a minimum applicable or capable combination of the one or more inference functions. The sub-set combination may correspond to a maximum applicable or capable combination of the one or more inference functions. The sub-set combination may correspond to an applicable or capable combination of the one or more inference functions with a specific priority and / or a specific preference.

[0183] In some implementations, the operation may be performed only for the at least one inference function which is the sub-set combination. Or, the operation may be performed for the sub-set combination including the at least one inference function.

[0184] In some implementations, the operation may be performed for the sub-set combination based on an offset and / or a range.

[0185] Furthermore, 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. The wireless device may be in communication with at least one of a mobile device, a network, and / or autonomous vehicles other than the wireless device.

[0186] The wireless device may comprise 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, cause the wireless device to perform the method described in FIG. 9.

[0187] More specifically, the wireless device receives a configuration related to one or more inference functions from a base station. The configuration comprises report combination information related to which information is to be evaluated or reported.

[0188] The wireless device performs an operation related to an applicability of at least one inference function based on the report combination information.

[0189] In some implementations, the operation may include evaluating the applicability of the at least one inference function based on the report combination information.

[0190] In some implementations, the operation may include reporting applicability information or capability information of the at least one inference function based on the report combination information.

[0191] In some implementations, the operation may include reporting capability information of the at least one inference function based on the report combination information.

[0192] In some implementations, the report combination information may inform that a super-set combination is to be evaluated or reported. The super-set combination may correspond to all applicable or capable combinations of the one or more inference functions. In this case, the operation may be performed for the all applicable or capable combinations of the one or more inference functions including the at least one inference function.

[0193] In some implementations, the report combination information may inform that a sub-set combination is to be evaluated or reported. The sub-set combination may correspond to a minimum applicable or capable combination of the one or more inference functions. The sub-set combination may correspond to a maximum applicable or capable combination of the one or more inference functions. The sub-set combination may correspond to an applicable or capable combination of the one or more inference functions with a specific priority and / or a specific preference.

[0194] In some implementations, the operation may be performed only for the at least one inference function which is the sub-set combination. Or, the operation may be performed for the sub-set combination including the at least one inference function.

[0195] In some implementations, the operation may be performed for the sub-set combination based on an offset and / or a range.

[0196] Furthermore, the method described above in FIG. 9 may be performed by control of a processing apparatus. 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.

[0197] The processing apparatus comprises at least one processor that is integrated with a wireless device, and at least one memory comprising processor-executable instructions stored thereon that are configured to cause the at least one processor to perform the method described in FIG. 9.

[0198] Furthermore, the method described above in FIG. 9 may be performed by a software code 105 stored in the memory 104 included in the first wireless device 100 shown in FIG. 2.

[0199] 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.

[0200] 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.

[0201] The computer-readable medium may include a tangible and non-transitory computer-readable storage medium.

[0202] 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.

[0203] 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.

[0204] 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. 9.

[0205] FIG. 10 shows an example of a method performed by a base station to which implementations of the present disclosure are applied.

[0206] In step S1000, the method includes transmitting a configuration related to one or more inference functions to a wireless device. The configuration includes report combination information related to which information is to be evaluated or reported. An operation related to an applicability of at least one inference function is performed based on the report combination information.

[0207] Furthermore, the base station may be implemented by the second wireless device 200 shown in FIG. 2.

[0208] The base station may comprise 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, cause the base station to perform the method described in FIG. 12.

[0209] More specifically, the base station transmits a configuration related to one or more inference functions to a wireless device. The configuration includes report combination information related to which information is to be evaluated or reported. An operation related to an applicability of at least one inference function is performed based on the report combination information.

[0210] Detailed description of an example of UE operation according to implementations of the present disclosure is described.

[0211] 1. Step 1

[0212] The network may configure the UE with model / functionality-related configurations / settings.

[0213] The model / functionality-related configuration may include at least one of the following information.

[0214] For example, the model / functionality-related configuration may include model / functionality identification information, e.g., model / functionality group ID / group list.

[0215] For example, the model / functionality-related configuration may include information related to model / functionality related parameters.

[0216] For example, in the case of L1 measurement related tasks, e.g., beam management, CSI feedback, the model / functionality related parameters may include at least one of following information.

[0217] - Resource related configuration, e.g., resource / resource set of CSI-RS and SSB: It may include resource set B / A information, representing prediction ratio, e.g., 4:16, 8:64, etc.

[0218] - Report related configuration, e.g.,CSI-ReportConfig

[0219] - Report contents related configuration, e.g., L1-Reference Signal Received Power (RSRP), beam index, CSI feedback related information (e.g., Precoding Matrix Indicator (PMI), Rank Indicator (RI), Channel Quality Indicator (CQI), etc.)

[0220] - Time instance related information, e.g., time instance for temporal prediction: It may represent OW / PW information, e.g., 200ms / 400ms, etc.

[0221] For example, in the case of L3 measurement related tasks, e.g., L3 filtered beam / cell level measurement, the model / functionality related parameters may include at least one of following information.

[0222] - Measurement object related configuration (e.g., target cell, frequency, sub-carrier spacing, SSB / CSI-RS information for measurement, etc.): It may represent prediction ratio, e.g., 4:16, 8:64, etc. (e.g., for spatial domain prediction, cell cluster ratio (number of neighbor cells: number of target cell))

[0223] - Report related configuration, such as report condition, report interval, report amount, etc.

[0224] - Report contents related configuration, e.g., L3 quality (e.g., RSRP, Reference Signal Received Quality (RSRQ), Signal-to-Noise and Interference Ratio (SINR), Received Signal Strength Indicator (RSSI), etc.) for a specific beam / cell

[0225] - Frequency related information, e.g., collocated / correlated frequency / frequency range

[0226] - Time instance related information, e.g., time instance for temporal prediction: It may represent OW / PW information, e.g., 200ms / 400ms, etc.

[0227] For example, in the case of model related information, the model / functionality related parameters may include a model type and / or configuration. The network may configure the type of AI / ML models available for use, such as deep learning models, machine learning models, regression models, clustering models, etc.

[0228] The network may also set configuration parameters for each model (e.g., number of layers, hyperparameters, learning rate, etc.). The configuration parameters may include at least one of following information.

[0229] - Resource allocation: The network may allocate resources (e.g., computational power, memory, bandwidth) for each model. The network may configure which resources are required for each task and model execution.

[0230] - Optimization goals: The network may set optimization goals for the models, such as optimizing for model accuracy, response time, or resource consumption.

[0231] - Prediction accuracy and model complexity configuration: The network may set the trade-off between prediction accuracy and model complexity. For example, selecting a more complex model for higher accuracy, which might consume more resources.

[0232] For example, the model / functionality-related configuration may include information related to additional condition. The additional conditions may include conditions under which the UE can perform model / functionality related operations.

[0233] Additional condition may include at least one of following information.

[0234] - Specific location related information (e.g., polygon type, latitude / longitude, altitude, angle, indoor / outdoor, etc.)

[0235] - Specific time related information (e.g., date, time window, start time, stop time, etc.)

[0236] - Specific speed related information (e.g., 10km / h, 30km / h, 60km / h, 120km / h, etc.)

[0237] - Specific radio quality condition (e.g., RSRP, RSRQ, SINR, etc.)

[0238] - Specific deployment scenario (e.g., Urban Macro (UMa), Urban Micro (UMi), Indoor Hotspot (InH), etc.)

[0239] - Specific cell / frequency related information (e.g., bandwidth, size of subband, carrier frequency, numerologies, etc).)

[0240] - Specific antenna related information (e.g., antenna port layouts, antenna port numbers, rank numbers / layers, antenna spacing, antenna virtualization, etc.)

[0241] The additional condition may consist of one condition or a combination of several conditions. The additional condition may have a specific condition ID, e.g., associated ID. The additional condition may be linked to a specific functionality / functionality group or a specific model / model group.

[0242] For example, the model / functionality-related configuration may include information related to whether the UE should perform operations related to all possible / capable combinations of applicable model / functionality (e.g., super-set), or the UE should perform operations related to only specific possible / capable combination of applicable model / functionality (e.g., sub-set (e.g., report combination range (max / min / range))).

[0243] 2. step 2

[0244] The UE may the applicability of model / functionality-related tasks (e.g., training, inference, monitoring, etc.). The UE may evaluate whether at least one model / functionality is applicable based on at least one of followings.

[0245] - Configured additional conditions from the network

[0246] - UE internal conditions such as internal problems (e.g., memory, battery, thermal, etc.)

[0247] - UE status (e.g., speed, area, etc.)

[0248] - Model availability (e.g., whether the UE has available / applicable model)

[0249] - The need of (re)training / inference / monitoring / management for a specific model / functionality.

[0250] Furthermore, the UE may decide which configuration is required / applicable for the task (e.g., model, functionality). The UE may determine whether the certain configuration is necessary / applicable based on the current configuration from the network, e.g., configuration selection among the configured configuration. The UE may determine whether the certain configuration is necessary regardless of the current configuration from the network. The certain configuration may be a setting for a model within the UE and / or a setting for a model not within the UE.

[0251] For example, in the case of the L1 measurement, the certain configuration may be related to report configuration, resource configuration, physical layer configuration, etc., e.g., resource set, resource / report type (aperiodic, periodic, and semi-persistent), report quantity (RSRP, RSRQ, SINR, etc.), number of reported RS, subband size, BWP ID, repetition, offset value, number of ports, frequency / time domain allocation related information, RB information, power control offset, CQI / PMI format (e.g., wide band), CSI reporting band (e.g., subband), codebook related configuration (e.g., panel type, number of antenna ports, etc), cqi-table, etc.

[0252] For example, in the case of the L3 measurement, the certain configuration may be related to measurement object, report configuration, etc., e.g., SSB frequency, subcarrier spacing, SSB-based Measurement Timing Configuration (SMTC), absolute threshold for SSB / CSI-RS consolidation, number of SSB / CSI-RS to average, allowed / excluded cell list, frequency band, measurement cycle, measurement gap, reference signal configuration (e.g., SSB / CSI-RS), report type (e.g., periodic, event triggered, report Cell Global ID (CGI), report Subband Full Duplex (SBFD), etc.), conditional trigger configuration (e.g., conditional event A3, A4, A5, etc), event trigger configuration (e.g., event A1, A2, A3, A4, A5, A6, etc.), RS type, report interval, report amount, maximum number of report cells, include beam measurement indication, etc.

[0253] For, in the case of the failure detection, the certain configuration may be related to Radio Link Monitoring (RLM) configuration, e.g., beam failure instance max count, beam failure detection timer, radio link monitoring RS, purpose (e.g., beam failure, radio link failure, both, etc.), etc.

[0254] Furthermore, the UE may identify whether the applicability information corresponds to a super-set, or a sub-set (e.g., maximum / minimum / set within a specific range) for the task (e.g., inference).

[0255] For example, if a specific combination (super-set) encompasses all possible / capable combinations of applicable model / functionality, the UE may consider the specific combination corresponds to the super-set. For example, if a specific combination represents the maximum / minimum / specific model / functionality within a specific range of models / functionalities, the UE may consider the specific combination corresponds to the sub-set.

[0256] If it is initial report, the UE may trigger applicability reporting.

[0257] The UE may evaluate whether the status of the model / functionality has changed from applicable to non-applicable or vice versa. If the status has changed, the UE may trigger applicability reporting. If the status has changed, the UE may trigger applicability reporting. If the previous report needs to be reset, the UE may trigger applicability reporting.

[0258] 3. Step 3

[0259] Based on evaluation, the UE may report the applicability information to the network. The UE may determine which information is to be included in the applicability information based on the network configuration (e.g., report combination range) received in step 1.

[0260] For example, the UE may report applicability information related to the super-set for all applicable / capable combinations of model / functionality.

[0261] For example, the UE may report applicability information related to the sub-set indicating / including a specific applicable / capable combination of model / functionality (e.g., range). For example, the specific applicable / capable combination of model / functionality may be a minimum applicable / capable combination of model / functionality. For example, the specific applicable / capable combination of model / functionality may be a maximum applicable / capable combination of model / functionality. For example, the specific applicable / capable combination of model / functionality may be a specific applicable / capable combination of model / functionality with a specific preference or priority based on UE's preference / priority (UE decision based or NW configuration based).

[0262] In case of the sub-set, the applicability information may include only the sub-set. Or, the applicability information may also contain offset information between different combinations. For example, when the maximum OW is 600ms, and if offset information indicates that interval is 200ms, the applicability information may include OW information including 400ms and 200ms, as well. This applicability information may also include the number of subsets as well.

[0263] For example, when determining applicability / capability, it may be assumed that the UE's model has an applicable resource set B / A ratio of 4:16 / 8:16, and the prediction OW / PW ratio is 200ms:400ms / 200ms:800ms. For super-set report, all applicable / capable functionality / model related information may be evaluated and / or reported (e.g., including combination of the resource set B / A ratio of 4:16 / 8:16 and the prediction OW / PW ratio 200ms:400ms / 200ms:800ms). For sub-set report, a maximum applicable / capable functionality / model related information may be evaluated and / or reported (e.g., including maximum combination of the resource set B / A ratio of 4:16 and the prediction OW / PW ratio 200ms:800ms). Or, for the sub-set report, a minimum applicable / capable functionality / model related information may be evaluated and / or reported (e.g., including minimum combination of the resource set B / A ratio of 8:16 and the prediction OW / PW ratio 200ms:400ms).

[0264] The UE may indicate which combination is the super-set or sub-set (with whether it is maximum / minimum applicable / capable combination of model / functionality).

[0265] The UE may report the applicability information via RRC message (e.g.,RRCReconfigurationComplete,UEAssistanceInformation, etc), MAC CE, UCI, etc.

[0266] 4. Step 4

[0267] Based on the received applicability information, the network may determine whether to activate or deactivate a specific model / functionality for the related tasks (e.g., training, inference, monitoring, model transfer / delivery, data set / parameter transfer, model update, etc.). Or, based on the received applicability information, the network may determine whether to update the configuration accordingly.

[0268] The present disclosure may have various advantageous effects.

[0269] For example, by adopting a range-based and / or structured reporting framework rather than exhaustive enumeration of all parameter combinations, the overall signaling overhead associated with applicability reporting can be significantly reduced, thereby improving spectral efficiency and / or conserving radio resources.

[0270] For example, redundant or overlapping combination entries that would otherwise be transmitted under a conventional reporting scheme can be eliminated, allowing the UE to convey the same applicability information in a more compact and concise form without loss of relevant detail.

[0271] For example, because the reporting structure is designed to accommodate dynamic changes in model or function applicability, the UE can efficiently reflect updated applicability states as operating conditions evolve, ensuring that the network maintains an accurate and current understanding of which models or functions are applicable at any given time.

[0272] For example, the range-aware reporting structure can provide a flexible framework for interaction between the UE and the network, enabling the network to configure and / or control the scope of applicability reporting (e.g., by specifying minimum, maximum, or bounded reporting ranges), thereby allowing both the UE and the network to operate in a coordinated and resource-efficient manner.

[0273] 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.

[0274] 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, by a wireless device, a configuration related to one or more inference functions from a base station,wherein the configuration comprises report combination information related to which information is to be evaluated or reported; andperforming, by the wireless device, an operation related to an applicability of at least one inference function based on the report combination information.2.The method of claim 1, wherein the operation comprises evaluating the applicability of the at least one inference function based on the report combination information.3.The method of claim 1, wherein the operation comprises reporting applicability information or capability information of the at least one inference function based on the report combination information.4.The method of claim 1, wherein the operation comprises reporting capability information of the at least one inference function based on the report combination information.5.The method of claim 1, wherein the report combination information informs that a super-set combination is to be evaluated or reported,wherein the super-set combination corresponds to all applicable or capable combinations of the one or more inference functions, andwherein the operation is performed for the all applicable or capable combinations of the one or more inference functions including the at least one inference function.6.The method of claim 1, wherein the report combination information informs that a sub-set combination is to be evaluated or reported.7.The method of claim 6, wherein the sub-set combination corresponds to a minimum applicable or capable combination of the one or more inference functions.8.The method of claim 6, wherein the sub-set combination corresponds to a maximum applicable or capable combination of the one or more inference functions.9.The method of claim 6, wherein the sub-set combination corresponds to an applicable or capable combination of the one or more inference functions with a specific priority and / or a specific preference.10.The method of claim 6, wherein the operation is performed only for the at least one inference function which is the sub-set combination.11.The method of claim 6, wherein the operation is performed for the sub-set combination including the at least one inference function.12.The method of claim 6, wherein the operation is performed for the sub-set combination based on an offset and / or a range.13.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, cause the wireless device to perform operations comprising:receiving a configuration related to one or more inference functions from a base station,wherein the configuration comprises report combination information related to which information is to be evaluated or reported; andperforming an operation related to an applicability of at least one inference function based on the report combination information.14.A processing apparatus comprising:at least one processor that is integrated with a wireless device; andat least one memory comprising processor-executable instructions stored thereon that are configured to cause the at least one processor to perform operation comprising:obtaining a configuration related to one or more inference functions,wherein the configuration comprises report combination information related to which information is to be evaluated or reported; andperforming an operation related to an applicability of at least one inference function based on the report combination information.15.A non-transitory Computer Readable Medium (CRM) storing instructions that, based on being executed by at least one processor, cause a wireless device to perform operations comprising:receiving a configuration related to one or more inference functions from a base station,wherein the configuration comprises report combination information related to which information is to be evaluated or reported; andperforming an operation related to an applicability of at least one inference function based on the report combination information.16.A method comprising:transmitting, by a base station, a configuration related to one or more inference functions to a wireless device,wherein the configuration comprises report combination information related to which information is to be evaluated or reported, andwherein an operation related to an applicability of at least one inference function is performed based on the report combination information.17.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, cause the base station to perform operations comprising:transmitting a configuration related to one or more inference functions to a wireless device,wherein the configuration comprises report combination information related to which information is to be evaluated or reported, andwherein an operation related to an applicability of at least one inference function is performed based on the report combination information.

Images