Switching of ai / ML model in wireless communications

Abstract

The present disclosure is related to switching of artificial intelligence (AI) / machine learning (ML) model in wireless communications. According to an embodiment of the present disclosure, a method performed by a user equipment (UE) configured to operate in a wireless communication system comprises: activating a first inference function among multiple inference functions; performing an inference task based on the first inference function; evaluating one or more applicability conditions for one or more of the multiple inference functions; based on an applicability condition for the first inference function being not met, deactivating the first inference function; based on an applicability condition for a second inference function among the multiple inference functions being met, activating the second inference function; and resuming performing the inference task based on the second inference function.

Description

SWITCHING OF AI / ML MODEL IN WIRELESS COMMUNICATIONS

[0001] The present disclosure is related to switching of artificial intelligence (AI) / machine learning (ML) model in wireless communications.

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

[0003] Work has started in International Telecommunication Union (ITU) and 3GPP to develop requirements and specifications for New Radio (NR) systems. 3GPP has to identify and develop the technology components needed for successfully standardizing the new RAT timely satisfying both the urgent market needs, and the more long-term requirements set forth by the ITU Radio communication sector (ITU-R) International Mobile Telecommunications (IMT)-2020 process. Further, the NR should be able to use any spectrum band ranging at least up to 100 GHz that may be made available for wireless communications even in a more distant future.

[0004] The 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.

[0005] In wireless communications, AI / ML model (or, inference function) may be used to perform inference task. For the inference task, proper AI / ML model needs to be selected.

[0006] An aspect of the present disclosure is to provide method and apparatus for switching of AI / ML model in a wireless communication system.

[0007] According to an embodiment of the present disclosure, a method performed by a user equipment (UE) configured to operate in a wireless communication system comprises: activating a first inference function among multiple inference functions; performing an inference task based on the first inference function; evaluating one or more applicability conditions for one or more of the multiple inference functions; based on an applicability condition for the first inference function being not met, deactivating the first inference function; based on an applicability condition for a second inference function among the multiple inference functions being met, activating the second inference function; and resuming performing the inference task based on the second inference function.

[0008] According to an embodiment of the present disclosure, a method performed by a network node configured to operate in a wireless communication system comprises: transmitting, to a user equipment (UE), one or more configurations for multiple inference functions, wherein the UE is configured to: activate a first inference function among multiple inference functions; perform an inference task based on the first inference function; evaluate one or more applicability conditions for one or more of the multiple inference functions; based on an applicability condition for the first inference function being not met, deactivate the first inference function; and based on an applicability condition for a second inference function among the multiple inference functions being met, activate the second inference function; and receiving, from the UE, a report comprising information for the second inference function, wherein the UE is further configured to resume performing the inference task based on the second inference function.

[0009] According to various embodiments, apparatuses to implement the above methods are provided.

[0010] The present disclosure may have various advantageous effects.

[0011] For example, it can reduce complexity and / or power consumption for evaluating / reporting the availability of all functionalities / models. It can also prevent unnecessary applicability reporting from UE for functionality / models with lower priority, thereby reducing signaling overhead by notifying functionality / models that can be used immediately.

[0012] 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.

[0013] FIG. 1 shows an example of a communication system to which implementations of the present disclosure is applied.

[0014] FIG. 2 shows an example of wireless devices to which implementations of the present disclosure is applied.

[0015] FIG. 3 shows an example of UE to which implementations of the present disclosure is applied.

[0016] FIGs. 4 and 5 show an example of protocol stacks in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.

[0017] FIG. 6 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.

[0018] FIG. 7 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure is applied.

[0019] FIG. 8 shows an example of an architecture of neuron and neural network.

[0020] FIG. 9 shows an example of a functional framework for AI / ML.

[0021] FIG. 10 shows an example of a method performed by a UE for switching of AI / ML model according to an embodiment of the present disclosure.

[0022] FIG. 11 shows an example of a signal flow between UE and network node for switching of AI / ML model according to an embodiment of the present disclosure.

[0023] FIG. 12 shows an example of model switching in UE side based on applicability condition according to an embodiment of the present disclosure.

[0024] 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, and / or 5G New Radio (NR).

[0025] 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.

[0026] 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.

[0027] 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".

[0028] 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".

[0029] 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".

[0030] 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".

[0031] 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".

[0032] Technical features that are separately described in one drawing in the present disclosure may be implemented separately or simultaneously.

[0033] 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.

[0034] 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.

[0035] FIG. 1 shows an example of a communication system to which implementations of the present disclosure is applied.

[0036] 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.

[0037] 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).

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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).

[0046] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1450MHz - 6000MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0047] 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).

[0048] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1410MHz - 7125MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0049] 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.FIG. 2 shows an example of wireless devices to which implementations of the present disclosure is applied.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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).

[0066] 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.

[0067] 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.

[0068] In the implementations of the present disclosure, a UE may operate as a transmitting device in Uplink (UL) and as a receiving device in Downlink (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.

[0069] In the present disclosure, a BS is also referred to as a node B (NB), an eNode B (eNB), or a gNB.

[0070] FIG. 3 shows an example of UE to which implementations of the present disclosure is applied.

[0071] Referring to FIG. 3, a UE 100 may correspond to the first wireless device 100 of FIG. 2.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] FIGs. 4 and 5 show an example of protocol stacks in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.

[0081] 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 (L1, for example PHY layer) and Layer 2 (L2, for example MAC / RLC / PDCP layer). Referring to FIG. 5, the control plane protocol stack may be divided into Layer 1 (L1, for example PHY layer), Layer 2 (L2, for example MAC / RLC / PDCP layer), Layer 3 (L3, for example 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The RLC sublayer supports three transmission modes: transparent mode (TM), unacknowledged mode (UM), and acknowledged node (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).

[0086] 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.

[0087] 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.

[0088] 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 5GC or 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.

[0089] FIG. 6 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.

[0090] 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., subcarrier spacing (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 CP-OFDM symbols), SC-FDMA symbols (or discrete Fourier transform-spread-OFDM (DFT-s-OFDM) symbols).

[0091] 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 cyclic prefix (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 βf = 2u*15 kHz.

[0092] 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 βf = 2u*15 kHz.

[0093] uNslotsymbNframe,uslotNsubframe,uslot01410111420221440431480841416016

[0094] 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 βf = 2u*15 kHz.

[0095] uNslotsymbNframe,uslotNsubframe,uslot212404

[0096] 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. As shown in FIG. 6, as SCS doubles, the slot length and symbol length are halved. For example, when SCS is 15kHz, the slot length is 1ms, which is the same as the subframe length. When SCS is 30kHz, the slot length is 0.5ms (=500us), and the symbol length is half of that when the SCS is 15kHz. When SCS is 60kHz, the slot length is 0.25ms (=250us), and the symbol length is half of that when the SCS is 30kHz. When SCS is 120kHz, the slot length is 0.125ms (=125us), and the symbol length is half of that when the SCS is 60kHz. When SCS is 240kHz, the slot length is 0.0625ms (=62.5us), and the symbol length is half of that when the SCS is 120kHz.

[0097] 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.

[0098] 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.

[0099] 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 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.

[0100] FIG. 7 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure is applied.

[0101] 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.

[0102] 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 physical uplink control channel (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.

[0103] Hereinafter, contents regarding measurements are described.

[0104] The network may configure an RRC_CONNECTED UE to perform measurements. The network may configure the UE to report them in accordance with the measurement configuration or perform conditional reconfiguration evaluation in accordance with the conditional reconfiguration. The measurement configuration is provided by means of dedicated signalling i.e. using theRRCReconfigurationorRRCResume.

[0105] The network may configure the UE to perform the following types of measurements:

[0106] - NR measurements;

[0107] - Inter-RAT measurements of E-UTRA frequencies;

[0108] - Inter-RAT measurements of UTRA-FDD frequencies;

[0109] - NR sidelink measurements of L2 U2N Relay UEs.

[0110] The network may configure the UE to report the following measurement information based on SS / PBCH block(s):

[0111] - Measurement results per SS / PBCH block;

[0112] - Measurement results per cell based on SS / PBCH block(s);

[0113] - SS / PBCH block(s) indexes.

[0114] The network may configure the UE to report the following measurement information based on CSI-RS resources:

[0115] - Measurement results per CSI-RS resource;

[0116] - Measurement results per cell based on CSI-RS resource(s);

[0117] - CSI-RS resource measurement identifiers.

[0118] The network may configure the UE to perform the following types of measurements for NR sidelink and V2X sidelink:

[0119] - CBR measurements.

[0120] The network may configure the UE to report the following cross link interference (CLI) measurement information based on SRS resources:

[0121] - Measurement results per SRS resource;

[0122] - SRS resource(s) indexes.

[0123] The network may configure the UE to report the following CLI measurement information based on CLI-RSSI resources:

[0124] - Measurement results per CLI-RSSI resource;

[0125] - LI-RSSI resource(s) indexes.

[0126] The network may configure the UE to report the following Rx-Tx time difference measurement information based on CSI-RS for tracking or PRS:

[0127] - UE Rx-Tx time difference measurement result.

[0128] The measurement configuration includes the following parameters:

[0129] 1.Measurement objects:A list of objects on which the UE shall perform the measurements.

[0130] - For intra-frequency and inter-frequency measurements a measurement object indicates the frequency / time location and subcarrier spacing of reference signals to be measured. Associated with this measurement object, the network may configure a list of cell specific offsets, a list of 'exclude-listed' cells and a list of 'allow-listed' cells. Exclude-listed cells are not applicable in event evaluation or measurement reporting. Allow-listed cells are the only ones applicable in event evaluation or measurement reporting.

[0131] - ThemeasObjectIdof the MO which corresponds to each serving cell is indicated byservingCellMOwithin the serving cell configuration.

[0132] - For inter-RAT E-UTRA measurements a measurement object is a single E-UTRA carrier frequency. Associated with this E-UTRA carrier frequency, the network can configure a list of cell specific offsets and a list of 'exclude-listed' cells. Exclude-listed cells are not applicable in event evaluation or measurement reporting.

[0133] - For inter-RAT UTRA-FDD measurements a measurement object is a set of cells on a single UTRA-FDD carrier frequency.

[0134] - For NR sidelink measurements of L2 U2N Relay UEs, a measurement object is a single NR sidelink frequency to be measured.

[0135] - For CBR measurement of NR sidelink communication, a measurement object is a set of transmission resource pool(s) on a single carrier frequency for NR sidelink communication.

[0136] - For CBR measurement of NR sidelink discovery, a measurement object is a set of discovery dedicated resource pool(s) or transmission resource pool(s) also used for NR sidelink discovery on a single carrier frequency for NR sidelink discovery.

[0137] - For CLI measurements a measurement object indicates the frequency / time location of SRS resources and / or CLI-RSSI resources, and subcarrier spacing of SRS resources to be measured.

[0138] 2.Reporting configurations:A list of reporting configurations where there can be one or multiple reporting configurations per measurement object. Each measurement reporting configuration consists of the following:

[0139] - Reporting criterion: The criterion that triggers the UE to send a measurement report. This can either be periodical or a single event description.

[0140] - RS type: The RS that the UE uses for beam and cell measurement results (SS / PBCH block or CSI-RS).

[0141] - Reporting format: The quantities per cell and per beam that the UE includes in the measurement report (e.g. RSRP) and other associated information such as the maximum number of cells and the maximum number beams per cell to report.

[0142] In case of conditional reconfiguration, each configuration consists of the following:

[0143] - Execution criteria: The criteria the UE uses for conditional reconfiguration execution.

[0144] - RS type: The RS that the UE uses for obtaining beam and cell measurement results (SS / PBCH block-based or CSI-RS-based), used for evaluating conditional reconfiguration execution condition.

[0145] 3.Measurement identities:For measurement reporting, a list of measurement identities where each measurement identity links one measurement object with one reporting configuration. By configuring multiple measurement identities, it is possible to link more than one measurement object to the same reporting configuration, as well as to link more than one reporting configuration to the same measurement object. The measurement identity is also included in the measurement report that triggered the reporting, serving as a reference to the network. For conditional reconfiguration triggering, one measurement identity links to exactly one conditional reconfiguration trigger configuration. And up to 2 measurement identities can be linked to one conditional reconfiguration execution condition.

[0146] 4. Quantity configurations:The quantity configuration defines the measurement filtering configuration used for all event evaluation and related reporting, and for periodical reporting of that measurement. For NR measurements, the network may configure up to 2 quantity configurations with a reference in the NR measurement object to the configuration that is to be used. In each configuration, different filter coefficients can be configured for different measurement quantities, for different RS types, and for measurements per cell and per beam.

[0147] 5. Measurement gaps:Periods that the UE may use to perform measurements.

[0148] A UE in RRC_CONNECTED maintains a measurement object list, a reporting configuration list, and a measurement identities list according to signalling and procedures in this specification. The measurement object list possibly includes NR measurement object(s), CLI measurement object(s), inter-RAT objects, and L2 U2N Relay objects. Similarly, the reporting configuration list includes NR, inter-RAT, and L2 U2N Relay reporting configurations. Any measurement object can be linked to any reporting configuration of the same RAT type. Some reporting configurations may not be linked to a measurement object. Likewise, some measurement objects may not be linked to a reporting configuration.

[0149] The measurement procedures distinguish the following types of cells:

[0150] 1. The NR serving cell(s) - these are the SpCell and one or more SCells.

[0151] 2. Listed cells - these are cells listed within the measurement object(s).

[0152] 3. Detected cells - these are cells that are not listed within the measurement object(s) but are detected by the UE on the SSB frequency(ies) and subcarrier spacing(s) indicated by the measurement object(s).

[0153] For NR measurement object(s), the UE measures and reports on the serving cell(s) / serving Relay UE (for L2 U2N Remote UE), listed cells and / or detected cells. For inter-RAT measurements object(s) of E-UTRA, the UE measures and reports on listed cells and detected cells and, for RSSI and channel occupancy measurements, the UE measures and reports on the configured resources on the indicated frequency. For inter-RAT measurements object(s) of UTRA-FDD, the UE measures and reports on listed cells. For CLI measurement object(s), the UE measures and reports on configured measurement resources (i.e. SRS resources and / or CLI-RSSI resources). For L2 U2N Relay object(s), the UE measures and reports on the serving NR cell(s), as well as the discovered L2 U2N Relay UEs.

[0154] The field referred in the measurement related procedure concerns a field included in theVarMeasConfigunless explicitly stated otherwise i.e. only the measurement configuration procedure covers the direct UE action related to the receivedmeasConfig.

[0155] In NR-DC, the UE may receive two independentmeasConfig:

[0156] - ameasConfig, associated with MCG, that is included in theRRCReconfigurationmessage received via SRB1; and

[0157] - ameasConfig, associated with SCG, that is included in theRRCReconfigurationmessage received via SRB3, or, alternatively, included within aRRCReconfigurationmessage embedded in aRRCReconfigurationmessage received via SRB1.

[0158] In this case, the UE maintains two independentVarMeasConfigandVarMeasReportList, one associated with eachmeasConfig, and independently performs all the procedures for eachmeasConfigand the associatedVarMeasConfigandVarMeasReportList, unless explicitly stated otherwise.

[0159] The configurations related to CBR measurements are only included in themeasConfigassociated with MCG.

[0160] The configurations related to Rx-Tx time difference measurement are only included in themeasConfigassociated with MCG.

[0161] An RRC_CONNECTED UE shall derive cell measurement results by measuring one or multiple beams associated per cell as configured by the network. For all cell measurement results, except for RSSI, and CLI measurement results in RRC_CONNECTED, the UE applies the layer 3 filtering, before using the measured results for evaluation of reporting criteria, measurement reporting or the criteria to trigger conditional reconfiguration execution. For cell measurements, the network can configure RSRP, RSRQ, SINR, RSCP or EcN0 as trigger quantity. For CLI measurements, the network can configure SRS-RSRP or CLI-RSSI as trigger quantity. For cell and beam measurements, reporting quantities can be any combination of quantities (i.e. only RSRP; only RSRQ; only SINR; RSRP and RSRQ; RSRP and SINR; RSRQ and SINR; RSRP, RSRQ and SINR; only RSCP; only EcN0; RSCP and EcN0), irrespective of the trigger quantity, and for CLI measurements, reporting quantities can be either SRS-RSRP or CLI-RSSI. For conditional reconfiguration execution, the network can configure up to 2 quantities, both using same RS type. The UE does not apply the layer 3 filtering to derive the CBR measurements. The UE does not apply the layer 3 filtering to derive the Rx-Tx time difference measurements.

[0162] The network may also configure the UE to report measurement information per beam (which can either be measurement results per beam with respective beam identifier(s) or only beam identifier(s)). If beam measurement information is configured to be included in measurement reports, the UE applies the layer 3 beam filtering. On the other hand, the exact L1 filtering of beam measurements used to derive cell measurement results is implementation dependent.

[0163] The UE shall:

[0164] 1> whenever the UE has ameasConfig, perform RSRP and RSRQ measurements for each serving cell for whichservingCellMOis configured as follows:

[0165] 2> if thereportConfigassociated with at least onemeasIdincluded in themeasIdListwithinVarMeasConfigcontains anrsTypeset tossbandssb-ConfigMobilityis configured in themeasObjectindicated by theservingCellMO:

[0166] 3> if thereportConfigassociated with at least onemeasIdincluded in themeasIdListwithinVarMeasConfigcontains areportQuantityRS-IndexesandmaxNrofRS-IndexesToReportand contains anrsTypeset tossb:

[0167] 4> derive layer 3 filtered RSRP and RSRQ per beam for the serving cell based on SS / PBCH block;

[0168] 3> derive serving cell measurement results based on SS / PBCH block;

[0169] 2> if thereportConfigassociated with at least onemeasIdincluded in themeasIdListwithinVarMeasConfigcontains anrsTypeset tocsi-rsandCSI-RS-ResourceConfigMobilityis configured in themeasObjectindicated by theservingCellMO:

[0170] 3> if thereportConfigassociated with at least onemeasIdincluded in themeasIdListwithinVarMeasConfigcontains areportQuantityRS-IndexesandmaxNrofRS-IndexesToReportand contains anrsTypeset tocsi-rs:

[0171] 4> derive layer 3 filtered RSRP and RSRQ per beam for the serving cell based on CSI-RS;

[0172] 3> derive serving cell measurement results based on CSI-RS;

[0173] 1> for each serving cell for whichservingCellMOis configured, if thereportConfigassociated with at least onemeasIdincluded in themeasIdListwithinVarMeasConfigcontains SINR as trigger quantity and / or reporting quantity:

[0174] 2> if thereportConfigcontainsrsTypeset tossbandssb-ConfigMobilityis configured in theservingCellMO:

[0175] 3> if thereportConfigcontains areportQuantityRS-IndexesandmaxNrofRS-IndexesToReport:

[0176] 4> derive layer 3 filtered SINR per beam for the serving cell based on SS / PBCH block;

[0177] 3> derive serving cell SINR based on SS / PBCH block;

[0178] 2> if thereportConfigcontainsrsTypeset tocsi-rsandCSI-RS-ResourceConfigMobilityis configured in theservingCellMO:

[0179] 3> if thereportConfigcontains areportQuantityRS-IndexesandmaxNrofRS-IndexesToReport:

[0180] 4> derive layer 3 filtered SINR per beam for the serving cell based on CSI-RS;

[0181] 3> derive serving cell SINR based on CSI-RS;

[0182] 1> for eachmeasIdincluded in themeasIdListwithinVarMeasConfig:

[0183] 2> if thereportTypefor the associatedreportConfigis set toreportCGIand timer T321 is running:

[0184] 3> ifuseAutonomousGapsis configured for the associatedreportConfig:

[0185] 4> perform the corresponding measurements on the frequency and RAT indicated in the associatedmeasObjectusing autonomous gaps as necessary;

[0186] 3> else:

[0187] 4> perform the corresponding measurements on the frequency and RAT indicated in the associatedmeasObjectusing available idle periods;

[0188] 3> if the cell indicated byreportCGIfield for the associatedmeasObjectis an NR cell and that indicated cell is broadcastingSIB1:

[0189] 4> try to acquireSIB1in the concerned cell;

[0190] 3> if the cell indicated byreportCGIfield is an E-UTRA cell:

[0191] 4> try to acquireSystemInformationBlockType1in the concerned cell;

[0192] 2> if theul-DelayValueConfigis configured for the associatedreportConfig:

[0193] 3> ignore themeasObject;

[0194] 3> for each of the configured DRBs,configure the PDCP layer to perform corresponding average UL PDCP packet delay measurement per DRB;

[0195] 2> if theul-ExcessDelayConfigis configured for the associatedreportConfig:

[0196] 3> ignore themeasObject;

[0197] 3> for each of the configured DRBs,configure the PDCP layer to perform corresponding UL PDCP Excess Packet Delay measurement according to the configured threshold per DRB;

[0198] 2> if thereportTypefor the associatedreportConfigisperiodical,eventTriggered; or

[0199] 2> if thereportTypefor the associatedreportConfigiscondTriggerConfig,themeasIdis within the MCGmeasConfigand is indicated in thecondExecutionCondassociated to acondReconfigIdin the MCGVarConditionalReconfig(for CHO, CPA or MN-initiated inter-SN CPC in NR-DC); or

[0200] 2> if thereportTypefor the associatedreportConfigiscondTriggerConfig, themeasIdis within the SCGVarMeasConfigand is indicated in thecondExecutionCondassociated to acondReconfigIdin the SCGVarConditionalReconfig(for intra-SN CPC); or

[0201] 2> if thereportTypefor the associatedreportConfigiscondTriggerConfig, themeasIdis within the SCGVarMeasConfigand is indicated in thecondExecutionCondSCGassociated to acondReconfigIdin the MCGVarConditionalReconfig(for SN-initiated inter-SN CPC in NR-DC); or

[0202] 2> if thereportTypefor the associatedreportConfigiscondTriggerConfig, themeasIdis within the SCGVarMeasConfigand is indicated in thetriggerConditionSNassociated to acondReconfigurationIdinVarConditionalReconfigurationas specified in TS 36.331

[0010] (for SN-initiated inter-SN CPC in EN-DC):

[0203] 3> if a measurement gap configuration is setup, or

[0204] 3> if the UE does not require measurement gaps to perform the concerned measurements:

[0205] 4> ifs-MeasureConfigis not configured, or

[0206] 4> ifs-MeasureConfigis set tossb-RSRPand the NR SpCell RSRP based on SS / PBCH block, after layer 3 filtering, is lower thanssb-RSRP,or

[0207] 4> ifs-MeasureConfigis set tocsi-RSRPand the NR SpCell RSRP based on CSI-RS, after layer 3 filtering, is lower thancsi-RSRP:

[0208] 5> if themeasObjectis associated to NR and thersTypeis set tocsi-rs:

[0209] 6> if reportQuantityRS-Indexes and maxNrofRS-IndexesToReport for the associated reportConfig are configured:

[0210] 7> derive layer 3 filtered beam measurements only based on CSI-RS for each measurement quantity indicated inreportQuantityRS-Indexes;

[0211] 6> derive cell measurement results based on CSI-RS for the trigger quantity and each measurement quantity indicated inreportQuantityCellusing parameters from the associatedmeasObject;

[0212] 5> if themeasObjectis associated to NR and thersTypeis set tossb:

[0213] 6> if reportQuantityRS-Indexes and maxNrofRS-IndexesToReport for the associated reportConfig are configured:

[0214] 7> derive layer 3 beam measurements only based on SS / PBCH block for each measurement quantity indicated inreportQuantityRS-Indexes;

[0215] 6> derive cell measurement results based on SS / PBCH block for the trigger quantity and each measurement quantity indicated inreportQuantityCellusing parameters from the associatedmeasObject;

[0216] 5> if themeasObjectis associated to E-UTRA:

[0217] 6> perform the corresponding measurements associated to neighbouring cells on the frequencies indicated in the concernedmeasObject;

[0218] 5> if the measObject is associated to UTRA-FDD:

[0219] 6> perform the corresponding measurements associated to neighbouring cells on the frequencies indicated in the concernedmeasObject;

[0220] 5> if the measObject is associated to L2 U2N Relay UE:

[0221] 6> perform the corresponding measurements associated to candidate Relay UEs on the frequencies indicated in the concernedmeasObject;

[0222] 4> if themeasRSSI-ReportConfigis configured in the associatedreportConfig:

[0223] 5> perform the RSSI and channel occupancy measurements on the frequency configured byrmtc-Frequencyin the associatedmeasObject;

[0224] 2> if thereportTypefor the associatedreportConfigis set toreportSFTDand thenumberOfReportsSentas defined within theVarMeasReportListfor thismeasIdis less than one:

[0225] 3> if thereportSFTD-Measis set totrue:

[0226] 4> if themeasObjectis associated to E-UTRA:

[0227] 5> perform SFTD measurements between the PCell and the E-UTRA PSCell;

[0228] 5> if thereportRSRPis set totrue;

[0229] 6> perform RSRP measurements for the E-UTRA PSCell;

[0230] 4> else if themeasObjectis associated to NR:

[0231] 5> perform SFTD measurements between the PCell and the NR PSCell;

[0232] 5> if thereportRSRPis set totrue;

[0233] 6> perform RSRP measurements for the NR PSCell based on SSB;

[0234] 3> else if thereportSFTD-NeighMeasis included:

[0235] 4> if themeasObjectis associated to NR:

[0236] 5> if thedrx-SFTD-NeighMeasis included:

[0237] 6> perform SFTD measurements between the PCell and the NR neighbouring cell(s) detected based on parameters in the associatedmeasObjectusing available idle periods;

[0238] 5> else:

[0239] 6> perform SFTD measurements between the PCell and the NR neighbouring cell(s) detected based on parameters in the associatedmeasObject;

[0240] 5> if thereportRSRPis set totrue:

[0241] 6> perform RSRP measurements based on SSB for the NR neighbouring cell(s) detected based on parameters in the associatedmeasObject;

[0242] 2> if thereportTypefor the associatedreportConfigiscli-Periodicalorcli-EventTriggered:

[0243] 3> perform the corresponding measurements associated to CLI measurement resources indicated in the concernedmeasObjectCLI;

[0244] 2> perform the evaluation of reporting criteria, except ifreportConfigiscondTriggerConfig.

[0245] The UE capable of Rx-Tx time difference measurement when configured withmeasObjectRxTxDiffshall:

[0246] 1> perform the corresponding Rx-Tx time difference measurements associated with downlink reference signals indicated in the concernedmeasObjectRxTxDiff.

[0247] The UE capable of CBR measurement when configured to transmit NR sidelink communication / discovery shall:

[0248] 1> If the frequency used for NR sidelink communication / discovery is included insl-FreqInfoToAddModListinsl-ConfigDedicatedNRwithinRRCReconfigurationmessage or includedinsl-ConfigCommonNRwithinSIB12:

[0249] 2> if the UE is in RRC_IDLE or in RRC_INACTIVE:

[0250] 3> if configured with NR sidelink communication and the cell chosen for NR sidelink communication providesSIB12which includessl-TxPoolSelectedNormalorsl-TxPoolExceptionalforthe concerned frequency; or

[0251] 3> if configured with NR sidelink discovery and the cell chosen for NR sidelink discovery providesSIB12which includessl-TxPoolSelectedNormalorsl-TxPoolExceptionalbut does not includesl-DiscTxPoolSelectedforthe concerned frequency:

[0252] 4> perform CBR measurement on pool(s) insl-TxPoolSelectedNormalorsl-TxPoolExceptionalfor the concerned frequency inSIB12;

[0253] 3> if configured with NR sidelink discovery and the cell chosen for NR sidelink discovery providesSIB12which includessl-DiscTxPoolSelectedforthe concerned frequency:

[0254] 4> perform CBR measurement on pools insl-DiscTxPoolSelectedandsl-TxPoolExceptionalfor the concerned frequency inSIB12;

[0255] 2> if the UE is in RRC_CONNECTED:

[0256] 3> iftx-PoolMeasToAddModListis included inVarMeasConfig:

[0257] 4> perform CBR measurements on each transmission resource pool indicated in thetx-PoolMeasToAddModList;

[0258] 3> ifsl-DiscTxPoolSelected,sl-TxPoolSelectedNormal,sl-TxPoolSchedulingorsl-TxPoolExceptionalis included insl-ConfigDedicatedNRfor the concerned frequency withinRRCReconfiguration:

[0259] 4> perform CBR measurement on pool(s) insl-DiscTxPoolSelected,sl-TxPoolSelectedNormal,sl-TxPoolSchedulingandsl-TxPoolExceptionalif included insl-ConfigDedicatedNRfor the concerned frequency withinRRCReconfiguration;

[0260] 3> else:

[0261] 4> if configured with NR sidelink communication and the cell chosen for NR sidelink communication providesSIB12which includessl-TxPoolSelectedNormalorsl-TxPoolExceptionalforthe concerned frequency; or

[0262] 4> if configured with NR sidelink discovery and the cell chosen for NR sidelink discovery providesSIB12which includessl-TxPoolSelectedNormalorsl-TxPoolExceptionalbut does not providesl-DiscTxPoolSelectedforthe concerned frequency:

[0263] 5> perform CBR measurement on pool(s) insl-TxPoolSelectedNormalorsl-TxPoolExceptionalfor the concerned frequency inSIB12;

[0264] 4> if configured with NR sidelink discovery and the cell chosen for NR sidelink discovery providesSIB12which includessl-DiscTxPoolSelectedforthe concerned frequency:

[0265] 5> perform CBR measurement on pools insl-DiscTxPoolSelectedandsl-TxPoolExceptionalfor the concerned frequency inSIB12;

[0266] 1> else:

[0267] 2> if configured with NR sidelink communication andsl-TxPoolSelectedNormalis included inSidelinkPreconfigNRfor the concerned frequency; or

[0268] 2> if configured with NR sidelink discovery andsl-TxPoolSelectedNormalis included inSidelinkPreconfigNRbutsl-DiscTxPoolSelectedis not included inSidelinkPreconfigNRfor the concerned frequency:

[0269] 3> perform CBR measurement on pool(s) insl-TxPoolSelectedNormalinSidelinkPreconfigNRfor the concerned frequency.

[0270] 2> if configured with NR sidelink discovery andsl-DiscTxPoolSelectedis included inSidelinkPreconfigNRfor the concerned frequency:

[0271] 3> perform CBR measurement on pools insl-DiscTxPoolSelectedif included inSidelinkPreconfigNR.

[0272] In case the configurations for NR sidelink communication and CBR measurement are acquired via the E-UTRA, configurations for NR sidelink communication inSIB12,sl-ConfigDedicatedNRwithinRRCReconfigurationused in this clause are provided by the configurations inSystemInformationBlockType28,sl-ConfigDedicatedForNRwithinRRCConnectionReconfigurationas specified in TS 36.331

[0010] , respectively.

[0273] If a UE that is configured by upper layers to transmit V2X sidelink communication is configured by NR with transmission resource pool(s) and the measurement objects concerning V2X sidelink communication (i.e. bysl-ConfigDedicatedEUTRA-Info), it shall perform CBR measurement, based on the transmission resource pool(s) and the measurement object(s) concerning V2X sidelink communication configured by NR.

[0274] For V2X sidelink communication, each of the CBR measurement results is associated with a resource pool, as indicated by thepoolReportId, that refers to a pool as included insl-ConfigDedicatedEUTRA-InfoorSIB13.

[0275] Hereinafter, contents regarding artificial intelligence / machine learning (AI / ML) are described.

[0276] Artificial Intelligence (AI) / Machine Learning (ML) is being used in a range of application domains across industry sectors, realizing significant productivity gains. In particular, in mobile communications systems, mobile devices (e.g. smartphones, smart vehicles, UAVs, mobile robots) are increasingly replacing conventional algorithms (e.g. speech recognition, machine translation, image recognition, video processing, user behaviour prediction) with AI / ML models to enable applications like enhanced photography, intelligent personal assistants, VR / AR, video gaming, video analytics, personalized shopping recommendation, autonomous driving / navigation, smart home appliances, mobile robotics, mobile medicals, as well as mobile finance.

[0277] Artificial Intelligence (AI) is the science and engineering to build intelligent machines capable of carrying out tasks as humans do. Within AI is a large subfield called machine learning (ML), which was defined as the field of study that gives computers the ability to learn without being explicitly programmed. Instead of the laborious and hit-or-miss approach of creating a distinct, custom program to solve each individual problem in a domain, a single ML algorithm simply needs to learn, via a process called training, to handle each new problem. Many ML methodologies as exemplified by decision tree, K-means clustering, and Bayesian network have been developed to train the model to make classifications and predictions, based on the data obtained from the real world.

[0278] FIG. 8 shows an example of an architecture of neuron and neural network.

[0279] Within the ML field, there is an area that is often referred to as brain-inspired computation, which is a program aiming to emulate some aspects of how we understand the brain to operate. Since it is believed that the main computational elements a human brain are 86 billion neurons, the two subareas of brain-inspired computation are both inspired by the architecture of a neuron, as shown in FIG. 8.

[0280] Compared to spiking computing approaches, the more popular ML approaches are using "neural network" as the model. Neural networks (NN) take their inspiration from the notion that a neuron's computation involves a weighted sum of the input values. But instead of simply outputting the weighted sum, a NN applies a nonlinear function to generate an output only if the inputs cross some threshold, as shown in FIG. 8. The neurons in the input layer receive some values and propagate them to the neurons in the middle layer of the network, which is also called a "hidden layer". The weighted sums from one or more hidden layers are ultimately propagated to the output layer, which presents the final outputs of the network.

[0281] Many DNN models have been developed over the past two decades. Each of these models has a different "network architecture" in terms of number of layers, layer types, layer shapes (i.e., filter size, number of channels and filters), and connections between layers. The structures of DNNs comprise: multilayer perceptrons (MLPs), convolution neural networks (CNNs), and recurrent neural networks (RNNs).

[0282] Multilayer perceptrons (MLP) model is the most basic DNN, which is composed of a series of fully connected layers. In a fully connected layer, all outputs are connected to all inputs. Hence MLP requires a significant amount of storage and computation.

[0283] An approach to limiting the number of weights that contribute to an output is to calculate the output only using a function of a fixed-size window of inputs. An extremely popular window-based DNN model uses a convolution operation to structure the computation, hence is named as convolution neural network (CNN).

[0284] CNN models can capture the high-level representation of the input data, making it popular for image classification and speech recognition tasks. In recent years, the modern CNN models have dramatically improved the performance of image classification tasks.

[0285] Recurrent neural network (RNN) models are another type of DNNs, which use sequential data feeding. The input of RNN consists of the current input and the previous samples. Each neuron in an RNN owns an internal memory that keeps the information of the computation from the previous samples. The basic unit of RNN is called cell, and further, each cell consists of layers and a series of cells enables the sequential processing of RNN models. RNN models have been widely used in the natural language processing task on mobile devices, e.g., language modelling, machine translation, question answering, word embedding, and document classification.

[0286] Deep reinforcement learning (DRL) is not another DNN model. It is composed of DNNs and reinforcement learning. The goal of DRL is to create an intelligent agent that can perform efficient policies to maximize the rewards of long-term tasks with controllable actions. The typical application of DRL is to solve various scheduling problems, such as decision problems in games, rate selection of video transmission, and so on.

[0287] FIG. 9 shows an example of a functional framework for AI / ML.

[0288] Referring to FIG. 9, data collection is a function that provides input data to Model training and Model inference functions. AI / ML algorithm specific data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) is not carried out in the Data Collection function.

[0289] Examples of input data may include measurements from UEs or different network entities, feedback from Actor, output from an AI / ML model.

[0290] Training Data may be data needed as input for the AI / ML Model Training function. Inference Data may be data needed as input for the AI / ML Model Inference function.

[0291] Model Training is a function that performs the AI / ML model training, validation, and testing which may generate model performance metrics as part of the model testing procedure. The Model Training function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Training Data delivered by a Data Collection function, if required.

[0292] Model Deployment / Update may be used to initially deploy a trained, validated, and tested AI / ML model to the Model Inference function or to deliver an updated model to the Model Inference function.

[0293] For example, training is a process in which a AI / ML model learns to perform its given tasks, more specifically, by optimizing the value of the weights in the DNN. A DNN is trained by inputting a training set, which are often correctly-labelled training samples. Taking image classification for instance, the training set includes correctly-classified images. When training a network, the weights are usually updated using a hill-climbing optimization process called gradient descent. The gradient indicates how the weights should change in order to reduce the loss (the gap between the correct outputs and the outputs computed by the DNN based on its current weights). The training process is repeated iteratively to continuously reduce the overall loss. Until the loss is below a predefined threshold, the DNN with high precision is obtained.

[0294] There are multiple ways to train the network for different targets. The introduced above is supervised learning which uses the labelled training samples to find the correct outputs for a task. Unsupervised learning uses the unlabelled training samples to find the structure or clusters in the data. Reinforcement learning can be used to output what action the agent should take next to maximize expected rewards. Transfer learning is to adjust the previously-trained weights (e.g. weights in a global model) using a new training set, which is used for a faster or more accurate training for a personalized model.

[0295] After a DNN is trained, it can perform its task by computing the output of the network using the weights determined during the training process, which is referred to as inference.

[0296] Model Inference is a function that provides AI / ML model inference output (e.g., predictions or decisions). Model Inference function may provide Model Performance Feedback to Model Training function when applicable. The Model Inference function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Inference Data delivered by a Data Collection function, if required.

[0297] The output of the model inference function may be the inference output of the AI / ML model produced by a Model Inference function.

[0298] Model Performance Feedback may be used for monitoring the performance of the AI / ML model, when available.

[0299] For example, in the model inference process, the inputs from the real world are passed through the DNN. Then the prediction for the task is output. For instance, the inputs can be pixels of an image, sampled amplitudes of an audio wave or the numerical representation of the state of some system or game. Correspondingly, the outputs of the network can be a probability that an image contains a particular object, the probability that an audio sequence contains a particular word or a bounding box in an image around an object or the proposed action that should be taken.

[0300] The performance of DNNs is gained at the cost of high computational complexity. Hence more efficient compute engines are often used, e.g. graphics processing units (GPU) and network processing units (NPU). Compared to the inference which only involves the feedforward process, the training often requires more computation and storage resources because it involves also the backpropagation process.

[0301] Actor is a function that receives the output from the Model Inference function and triggers or performs corresponding actions. The Actor may trigger actions directed to other entities or to itself.

[0302] Feedback may be information that may be needed to derive training data, inference data or to monitor the performance of the AI / ML Model and its impact to the network through updating of KPIs and performance counters.

[0303] In the present disclosure, use cases of AI / ML may comprise:

[0304] - CSI feedback enhancement (e.g., overhead reduction, improved accuracy, prediction);

[0305] - Beam management (e.g., beam prediction in time,　and / or　spatial domain　for overhead and latency reduction, beam selection accuracy improvement); and / or

[0306] - Positioning accuracy enhancements for different scenarios (e.g., those with　heavy non-line of sight (NLOS) conditions.

[0307] Meanwhile, AI / ML may be used to perform a certain inference task in communication to enhance communication performance such as enhancement of channel estimation performance, reduction of channel measurements, reduction of signalling overhead, prediction of future channel states, and / or prediction of future link state. AI / ML models may be valid under restricted circumstances. For example, if the AI / ML model is trained with some constrained circumstances, the AI / ML model can provide good inference performance with a high probability under the similar circumstances. However, it is not ensured that the AI / ML model can provide good inference performance when the model is executed under different circumstances, because model generalization performance depends on the model structure and / or the model training details.

[0308] To ensure UE to use the AI / ML assisted operation for communication only when the AI / ML assisted operation is expected to produce good performance, applicability condition for AI / ML model may be known / configured to the UE. If the condition is configured to a UE, UE may report applicability of the AI / ML model to network. Upon receiving the applicability reporting, network can activate / deactivate the AI / ML model based on the reporting and if needed send model switching command to the UE. Such network-based model control may introduce a duration of using inapplicable model from UE side.

[0309] Therefore, the present disclosure provides various embodiments for switching of AI / ML model.

[0310] FIG. 10 shows an example of a method performed by a UE for switching of AI / ML model according to an embodiment of the present disclosure.

[0311] Referring to FIG. 10, in step S1001, UE may activate a first inference function among multiple inference functions.

[0312] In step S1003, UE may perform an inference task based on the first inference function;

[0313] In step S1005, UE may evaluate one or more applicability conditions for one or more of the multiple inference functions.

[0314] In step S1007, based on an applicability condition for the first inference function being not met, UE may deactivate the first inference function.

[0315] In step S1009, based on an applicability condition for a second inference function among the multiple inference functions being met, UE may activate the second inference function.

[0316] In step S1011, UE may resume performing the inference task based on the second inference function.

[0317] According to various embodiments, UE may receive, from a network, one or more configurations for the multiple inference functions. The one or more configurations may comprise at least one of: a corresponding identifier (ID) of each of the multiple inference functions; a corresponding applicability condition for each of the multiple inference functions; a corresponding priority of each of the multiple inference functions; or a corresponding inference task applicable for each of the multiple inference functions.

[0318] According to various embodiments, UE may receive, from a network, an activation command including an identifier (ID) of the first inference function. UE may activate the first inference function related to the ID included in the activation command.

[0319] According to various embodiments, the first inference function may be a default inference function. UE may activate the default inference function before receiving an activation command for any inference function related to the inference task among the multiple inference functions.

[0320] According to various embodiments, UE may evaluate the applicability condition for the first inference function that is currently activated.

[0321] According to various embodiments, may evaluate the applicability condition for the first inference function that is currently activated, and applicability conditions for one or more other inference functions that are not currently activated.

[0322] According to various embodiments, the one or more of the multiple inference functions may comprise at least one of: inference functions configured by a network; inference functions related to the inference task; or all inference functions other than the first inference function among the multiple inference functions.

[0323] According to various embodiments, UE may suspend the inference task based on deactivating the first inference function.

[0324] According to various embodiments, after deactivating the first inference function, UE may identify one or more candidate inference functions for which applicability condition is met. UE may select the second inference function among the one or more candidate inference functions. The second inference function may be selected as an inference function whose priority is highest among the one or more candidate inference functions, or randomly selected among the one or more candidate inference functions.

[0325] According to various embodiments, UE may transmit, to a network, a report comprising information for the second inference function.

[0326] According to various embodiments, the report may comprise information for whether each of the one or more applicability conditions for the one or more of the multiple inference functions is met or not.

[0327] According to various embodiments, performing the inference task may comprise obtaining output data by applying an inference function to input data. The inference function may comprise an artificial intelligence (AI) / machine learning (ML) model for inferring the output data from the input data.

[0328] FIG. 11 shows an example of a signal flow between UE and network node for switching of AI / ML model according to an embodiment of the present disclosure.

[0329] Referring to FIG. 11, in step S1101, network node may transmit, to UE, one or more configurations for multiple inference functions.

[0330] In step S1103, UE may activate a first inference function among multiple inference functions.

[0331] In step S1105, UE may perform an inference task based on the first inference function;

[0332] In step S1107, UE may evaluate one or more applicability conditions for one or more of the multiple inference functions.

[0333] In step S1109, based on an applicability condition for the first inference function being not met, UE may deactivate the first inference function.

[0334] In step S1111, based on an applicability condition for a second inference function among the multiple inference functions being met, UE may activate the second inference function.

[0335] In step S1113, network node may receive, from UE, a report comprising information for the second inference function.

[0336] In step S1115, UE may resume performing the inference task based on the second inference function.

[0337] Hereinafter, detailed implementations regarding switching of AI / ML model are described.

[0338] In the present disclosure, the terms "model", "AI / ML model", and "(model) inference function" can be used inter-changeably.

[0339] To minimize UE from using inapplicable model and / or to maximize AI-assisted performance enhancement, the present disclosure provides method / apparatus for model switching in UE side based on applicability condition and / or priority information of related to the model.

[0340] FIG. 12 shows an example of model switching in UE side based on applicability condition according to an embodiment of the present disclosure.

[0341] Referring to FIG. 12, in step S1201, network (NW) may transmit configurations for models to UE, comprising model identifiers (IDs), applicability conditions, and / or priority information. The NW may configure the UE with multiple models. The NW may configure an applicability condition associated with each model. The NW may also configure priority of each model.

[0342] In step S1203, UE may activate one of the multiple models. For the activation, NW may explicitly activate one model by sending an activation command including an ID of the model for activation via RRC message, MAC CE and / or L1 control information (e.g., DCI). UE may be configured with a default model to be activated among the configured models for the case where no model for the inference task is yet activated.

[0343] In step S1205, UE may evaluate the applicability condition associated with the activated model. In addition, UE may evaluate the applicability condition associated with each model of all or a subset of the configured models. Network may configure UE with a subset of the configured models to evaluate their applicability conditions. If network does not configure UE with such subset of the configured models, UE may evaluate at least one applicability condition associated with all configured models.

[0344] In step S1207, UE may detect that the activated model is inapplicable, if the applicability condition of the activated model is not satisfied.

[0345] In step S1209, if the applicability condition of the activated model is not satisfied (and / or if UE detects that the activated model is inapplicable), UE may deactivate the activated model. Upon deactivation of the model, UE may fall back to the legacy operation that is not assisted by the model for the concerned functionality. Deactivation of the activated model may be skipped if the following model (re)selection and / or activation of the (re)selected model can be done in a short time.

[0346] In step S1211, UE may select a model to activate based on the evaluation results of the applicability conditions and / or priority information of the models. UE may consider a model as model selection candidate if the applicability condition of the model is satisfied. If the applicability condition of the model is not satisfied, the model may be precluded from model selection candidates. If there are one or more models with which associated applicability condition is satisfied, UE may select a model based on priority information of the one or more models - for example, UE may select a model whose priority is highest among the one or more models with which associated applicability condition is met. If the priority information is not available, UE may randomly select a model among those with which associated applicability condition is met. This model (re)selection step can be initiated before the step S1209, and for this early model (re)selection, network may configure a precondition to the UE such that upon satisfaction of the precondition, UE may initiate selecting a model to activate.

[0347] Upon deactivating the concerned activated model and selecting the new model, in step S1213, UE may activate the selected model. That is, the UE may use the model to perform inference task associated with the model.

[0348] In step S1215, UE may report the selected model to network. When reporting the selected model, UE may also include applicability of the other non-activated models.

[0349] According to various embodiments, NW may configure functionality / model related configuration with the applicability conditions. The functionality / model related configuration may include at least one of:

[0350] - Functionality / Functionality group ID;

[0351] - Model / Model group ID;

[0352] - Functionality / Functionality group list;

[0353] - Model / Model group list; or

[0354] - Functionality / Model related parameters.

[0355] According to various embodiments, the applicability conditions may include conditions under which the UE can perform functionality / model related operations. The applicability condition may include at least one of:

[0356] - Specific location related information (e.g., polygon type, latitude / longitude, altitude, angle, indoor / outdoor);

[0357] - Specific time related information (e.g., date, time window, start time, stop time);

[0358] - Specific speed related information (e.g., 10km / h, 30km / h, 60km / h, 120km / h);

[0359] - Specific radio quality condition (e.g., RSRP, RSRQ, SINR);

[0360] - Specific deployment scenario (e.g., urban macro (UMa), urban micro (UMi), indoor hotspot (InH));

[0361] - Specific cell / frequency related information, (e.g., bandwidth, size of subband, carrier frequency, numerologies); or

[0362] - Specific antenna related information (e.g., antenna port layouts, antenna port numbers, rank numbers / layers, antenna spacing, antenna virtualization).

[0363] The applicability condition may comprise / consist of one condition or a combination of several conditions. The applicability condition may have a specific condition ID. The applicability condition may be linked to a specific functionality / functionality group and / or a specific model / model group.

[0364] According to various embodiments, UE may configure applicability conditions for multiple models. Each applicability condition may be associated with at least one of the multiple models and the model may be used to perform inference task, if activated. UE may activate a fist model among the multiple models. UE may evaluate the applicability of the models based on the applicable conditions. Based on the evaluation result that the activated model is not applicable, UE may deactivate the activated model. UE may activate a second model among the applicable models. The second model may be the highest priority of the applicable models.

[0365] Through applicability related information reporting based on priority information, UE can reduce complexity and / or power consumption for evaluating / reporting the availability of all functionalities / models. UE can also prevent unnecessary applicability reporting for functionality / models with lower priority, thereby reducing signaling overhead by notifying functionality / models that can be used immediately.

[0366] Furthermore, the method in perspective of the UE described in the present disclosure (e.g., in FIG. 10) may be performed by the first wireless device 100 shown in FIG. 2 and / or the UE 100 shown in FIG. 3.

[0367] More specifically, the UE comprises at least one transceiver, at least processor, and at least one computer memory operably connectable to the at least one processor and storing instructions that, based on being executed by the at least one processor, perform operations.

[0368] The operations comprise: activating a first inference function among multiple inference functions; performing an inference task based on the first inference function; evaluating one or more applicability conditions for one or more of the multiple inference functions; based on an applicability condition for the first inference function being not met, deactivating the first inference function; based on an applicability condition for a second inference function among the multiple inference functions being met, activating the second inference function; and resuming performing the inference task based on the second inference function.

[0369] Furthermore, the method in perspective of the UE described in the present disclosure (e.g., in FIG. 10) may be performed by a software code 105 stored in the memory 104 included in the first wireless device 100 shown in FIG. 2.

[0370] More specifically, at least one computer readable medium (CRM) stores instructions that, based on being executed by at least one processor, perform operations comprising: activating a first inference function among multiple inference functions; performing an inference task based on the first inference function; evaluating one or more applicability conditions for one or more of the multiple inference functions; based on an applicability condition for the first inference function being not met, deactivating the first inference function; based on an applicability condition for a second inference function among the multiple inference functions being met, activating the second inference function; and resuming performing the inference task based on the second inference function.

[0371] Furthermore, the method in perspective of the UE described in the present disclosure (e.g., in FIG. 10) may be performed by control of the processor 102 included in the first wireless device 100 shown in FIG. 2 and / or by control of the processor 102 included in the UE 100 shown in FIG. 3.

[0372] More specifically, an apparatus configured to / adapted to operate in a wireless communication system (e.g., communication device / UE) comprises at least processor, and at least one computer memory operably connectable to the at least one processor. The at least one processor is configured to / adapted to perform operations comprising: activating a first inference function among multiple inference functions; performing an inference task based on the first inference function; evaluating one or more applicability conditions for one or more of the multiple inference functions; based on an applicability condition for the first inference function being not met, deactivating the first inference function; based on an applicability condition for a second inference function among the multiple inference functions being met, activating the second inference function; and resuming performing the inference task based on the second inference function.

[0373] Furthermore, the method in perspective of a network node described in the present disclosure (e.g., in FIG. 11) may be performed by the second wireless device 200 shown in FIG. 2. The network node may be related to a serving cell.

[0374] More specifically, the network node comprises at least one transceiver, at least processor, and at least one computer memory operably connectable to the at least one processor and storing instructions that, based on being executed by the at least one processor, perform operations.

[0375] The operations comprise: transmitting, to a user equipment (UE), one or more configurations for multiple inference functions, wherein the UE is configured to: activate a first inference function among multiple inference functions; perform an inference task based on the first inference function; evaluate one or more applicability conditions for one or more of the multiple inference functions; based on an applicability condition for the first inference function being not met, deactivate the first inference function; and based on an applicability condition for a second inference function among the multiple inference functions being met, activate the second inference function; and receiving, from the UE, a report comprising information for the second inference function, wherein the UE is further configured to resume performing the inference task based on the second inference function.

[0376] The present disclosure may have various advantageous effects.

[0377] For example, it can reduce complexity and / or power consumption for evaluating / reporting the availability of all functionalities / models. It can also prevent unnecessary applicability reporting from UE for functionality / models with lower priority, thereby reducing signaling overhead by notifying functionality / models that can be used immediately.

[0378] 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.

[0379] 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:activating a first inference function among multiple inference functions;performing an inference task based on the first inference function;evaluating one or more applicability conditions for one or more of the multiple inference functions;based on an applicability condition for the first inference function being not met, deactivating the first inference function;based on an applicability condition for a second inference function among the multiple inference functions being met, activating the second inference function; andresuming performing the inference task based on the second inference function.2.The method of claim 1, further comprising receiving, from a network, one or more configurations for the multiple inference functions,wherein the one or more configurations comprise at least one of:a corresponding identifier (ID) of each of the multiple inference functions;a corresponding applicability condition for each of the multiple inference functions;a corresponding priority of each of the multiple inference functions; ora corresponding inference task applicable for each of the multiple inference functions.3.The method of claim 1, further comprising receiving, from a network, an activation command including an identifier (ID) of the first inference function,wherein the activating of the first inference function comprises activating the first inference function related to the ID included in the activation command.4.The method of claim 1, wherein the first inference function is a default inference function, andwherein the activating of the first inference function comprises activating the default inference function before receiving an activation command for any inference function related to the inference task among the multiple inference functions.5.The method of claim 1, wherein the evaluating of the one or more applicability conditions comprises evaluating the applicability condition for the first inference function that is currently activated.6.The method of claim 1, wherein the evaluating of the one or more applicability conditions comprises evaluating the applicability condition for the first inference function that is currently activated, and applicability conditions for one or more other inference functions that are not currently activated.7.The method of claim 1, wherein the one or more of the multiple inference functions comprise at least one of:inference functions configured by a network;inference functions related to the inference task; orall inference functions other than the first inference function among the multiple inference functions.8.The method of claim 1, further comprising suspending the inference task based on deactivating the first inference function.9.The method of claim 1, after deactivating the first inference function, further comprising:identifying one or more candidate inference functions for which applicability condition is met; andselecting the second inference function among the one or more candidate inference functions,wherein the second inference function is selected as an inference function whose priority is highest among the one or more candidate inference functions, or is randomly selected among the one or more candidate inference functions.10.The method of claim 1, further comprising transmitting, to a network, a report comprising information for the second inference function.11.The method of claim 10, wherein the report comprises information for whether each of the one or more applicability conditions for the one or more of the multiple inference functions is met or not.12.The method of claim 1, wherein the performing of the inference task comprises obtaining output data by applying an inference function to input data, andwherein the inference function comprises an artificial intelligence (AI) / machine learning (ML) model for inferring the output data from the input data.13.The method of claims 1, wherein the method is performed by a user equipment (UE) in communication with at least one of a mobile device, a network, or autonomous vehicles.14.A user equipment (UE) comprising:at least one transceiver;at least one processor; andat least one memory operatively coupled to the at least one processor and storing instructions that, based on being executed by the at least one processor, perform operations comprising:activating a first inference function among multiple inference functions;performing an inference task based on the first inference function;evaluating one or more applicability conditions for one or more of the multiple inference functions;based on an applicability condition for the first inference function being not met, deactivating the first inference function;based on an applicability condition for a second inference function among the multiple inference functions being met, activating the second inference function; andresuming performing the inference task based on the second inference function.15.An apparatus comprising:at least processor; andat least one memory operatively coupled to the at least one processor and storing instructions that, based on being executed by the at least one processor, perform operations comprising:activating a first inference function among multiple inference functions;performing an inference task based on the first inference function;evaluating one or more applicability conditions for one or more of the multiple inference functions;based on an applicability condition for the first inference function being not met, deactivating the first inference function;based on an applicability condition for a second inference function among the multiple inference functions being met, activating the second inference function; andresuming performing the inference task based on the second inference function.16.A non-transitory computer readable medium (CRM) having stored thereon a program code implementing instructions that, based on being executed by at least one processor, perform operations comprising:activating a first inference function among multiple inference functions;performing an inference task based on the first inference function;evaluating one or more applicability conditions for one or more of the multiple inference functions;based on an applicability condition for the first inference function being not met, deactivating the first inference function;based on an applicability condition for a second inference function among the multiple inference functions being met, activating the second inference function; andresuming performing the inference task based on the second inference function.17.A method comprising:transmitting, to a user equipment (UE), one or more configurations for multiple inference functions,wherein the UE is configured to:activate a first inference function among multiple inference functions;perform an inference task based on the first inference function;evaluate one or more applicability conditions for one or more of the multiple inference functions;based on an applicability condition for the first inference function being not met, deactivate the first inference function; andbased on an applicability condition for a second inference function among the multiple inference functions being met, activate the second inference function; andreceiving, from the UE, a report comprising information for the second inference function,wherein the UE is further configured to resume performing the inference task based on the second inference function.18.A network node comprising:at least one transceiver;at least one processor; andat least one memory operatively coupled to the at least one processor and storing instructions that, based on being executed by the at least one processor, perform operations comprising:transmitting, to a user equipment (UE), one or more configurations for multiple inference functions,wherein the UE is configured to:activate a first inference function among multiple inference functions;perform an inference task based on the first inference function;evaluate one or more applicability conditions for one or more of the multiple inference functions;based on an applicability condition for the first inference function being not met, deactivate the first inference function; andbased on an applicability condition for a second inference function among the multiple inference functions being met, activate the second inference function; andreceiving, from the UE, a report comprising information for the second inference function,wherein the UE is further configured to resume performing the inference task based on the second inference function.

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