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WO2025206697A1 - Procédé et appareil permettant de rapporter une distribution de données relative à une fonction d'inférence - Google Patents

Procédé et appareil permettant de rapporter une distribution de données relative à une fonction d'inférence

Info

Publication number
WO2025206697A1
WO2025206697A1 PCT/KR2025/003773 KR2025003773W WO2025206697A1 WO 2025206697 A1 WO2025206697 A1 WO 2025206697A1 KR 2025003773 W KR2025003773 W KR 2025003773W WO 2025206697 A1 WO2025206697 A1 WO 2025206697A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
wireless device
inference function
model
network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/KR2025/003773
Other languages
English (en)
Inventor
Myoungsoo Kim
Sunghoon Jung
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
LG Electronics Inc
Original Assignee
LG Electronics Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by LG Electronics Inc filed Critical LG Electronics Inc
Publication of WO2025206697A1 publication Critical patent/WO2025206697A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/047—Probabilistic or stochastic networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/20—Control channels or signalling for resource management
    • H04W72/21—Control channels or signalling for resource management in the uplink direction of a wireless link, i.e. towards the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/20—Control channels or signalling for resource management
    • H04W72/23—Control channels or signalling for resource management in the downlink direction of a wireless link, i.e. towards a terminal
    • H04W72/231—Control channels or signalling for resource management in the downlink direction of a wireless link, i.e. towards a terminal the control data signalling from the layers above the physical layer, e.g. RRC or MAC-CE signalling
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports

Definitions

  • the present disclosure relates to a method and apparatus for reporting data distribution related to inference function.
  • 3rd generation partnership project (3GPP) long-term evolution (LTE) is a technology for enabling high-speed packet communications.
  • 3GPP 3rd generation partnership project
  • LTE long-term evolution
  • 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.
  • ITU international telecommunication union
  • NR new radio
  • 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.
  • ITU-R ITU radio communication sector
  • IMT international mobile telecommunications
  • 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.
  • 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.
  • eMBB enhanced mobile broadband
  • mMTC massive machine-type-communications
  • URLLC ultra-reliable and low latency communications
  • the NR shall be inherently forward compatible.
  • data distribution can be utilized. For example, monitoring the validity of the AI/ML input, e.g., out-of-distribution detection, drift detection of input data, delay spread, etc, or output, e.g., drift detection of output data can be evaluated for the UE-sided model or NW-sided model.
  • FIG. 20 shows an example of measurement reporting.
  • FIG. 21 shows an example of location measurement indication.
  • FIG. 25 shows an example of input data and output data of a model.
  • FIG. 26 shows an example of a method for counting abnormal data during a specific time window.
  • FIG. 29 shows an example of a method for counting abnormal data during a specific time window.
  • FIG. 30 shows an example of a method for model monitoring report regarding data distribution based on count threshold.
  • CDMA code division multiple access
  • FDMA frequency division multiple access
  • TDMA time division multiple access
  • OFDMA orthogonal frequency division multiple access
  • SC-FDMA single carrier frequency division multiple access
  • MC-FDMA multicarrier frequency division multiple access
  • 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).
  • GSM global system for mobile communications
  • GPRS general packet radio service
  • EDGE enhanced data rates for GSM evolution
  • 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).
  • IEEE institute of electrical and electronics engineers
  • Wi-Fi Wi-Fi
  • WiMAX IEEE 802.16
  • E-UTRA evolved 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 DL and SC-FDMA in UL.
  • LTE-advanced (LTE-A) is an evolved version of 3GPP LTE.
  • implementations of the present disclosure are mainly described in regards to a 3GPP based wireless communication system.
  • the technical features of the present disclosure are not limited thereto.
  • 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.
  • a or B may mean “only A”, “only B”, or “both A and B”.
  • a or B in the present disclosure may be interpreted as “A and/or B”.
  • A, B or C in the present disclosure may mean “only A”, “only B”, “only C”, or "any combination of A, B and C”.
  • At least one of A and B may mean “only A”, “only B” or “both A and B”.
  • 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”.
  • At least one of A, B and C may mean “only A”, “only B”, “only C”, or “any combination of A, B and C”.
  • 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”.
  • parentheses used in the present disclosure may mean “for example”.
  • control information PDCCH
  • PDCCH control information
  • PDCCH control information
  • PDCCH control information
  • 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).
  • eMBB enhanced mobile broadband
  • mMTC massive machine type communication
  • URLLC ultra-reliable and low latency communications
  • Partial use cases may require a plurality of categories for optimization and other use cases may focus only upon one key performance indicator (KPI).
  • KPI key performance indicator
  • eMBB far surpasses basic mobile Internet access and covers abundant bidirectional work and media and entertainment applications in cloud and augmented reality.
  • Data is one of 5G core motive forces and, in a 5G era, a dedicated voice service may not be provided for the first time.
  • voice will be simply processed as an application program using data connection provided by a communication system.
  • Main causes for increased traffic volume are due to an increase in the size of content and an increase in the number of applications requiring high data transmission rate.
  • a streaming service (of audio and video), conversational video, and mobile Internet access will be more widely used as more devices are connected to the Internet.
  • URLLC includes a new service that will change industry through remote control of main infrastructure and an ultra-reliable/available low-latency link such as a self-driving vehicle.
  • a level of reliability and latency is essential to control a smart grid, automatize industry, achieve robotics, and control and adjust a drone.
  • 5G is a means of providing streaming evaluated as a few hundred megabits per second to gigabits per second and may complement fiber-to-the-home (FTTH) and cable-based broadband (or DOCSIS). Such fast speed is needed to deliver TV in resolution of 4K or more (6K, 8K, and more), as well as virtual reality and augmented reality.
  • Virtual reality (VR) and augmented reality (AR) applications include almost immersive sports games.
  • a specific application program may require a special network configuration. For example, for VR games, gaming companies need to incorporate a core server into an edge network server of a network operator in order to minimize latency.
  • Automotive is expected to be a new important motivated force in 5G together with many use cases for mobile communication for vehicles. For example, entertainment for passengers requires high simultaneous capacity and mobile broadband with high mobility. This is because future users continue to expect connection of high quality regardless of their locations and speeds.
  • Another use case of an automotive field is an AR dashboard.
  • the AR dashboard causes a driver to identify an object in the dark in addition to an object seen from a front window and displays a distance from the object and a movement of the object by overlapping information talking to the driver.
  • a wireless module enables communication between vehicles, information exchange between a vehicle and supporting infrastructure, and information exchange between a vehicle and other connected devices (e.g., devices accompanied by a pedestrian).
  • a smart city and a smart home/building mentioned as a smart society will be embedded in a high-density wireless sensor network.
  • a distributed network of an intelligent sensor will identify conditions for costs and energy-efficient maintenance of a city or a home. Similar configurations may be performed for respective households. All of temperature sensors, window and heating controllers, burglar alarms, and home appliances are wirelessly connected. Many of these sensors are typically low in data transmission rate, power, and cost. However, real-time HD video may be demanded by a specific type of device to perform monitoring.
  • the smart grid collects information and connects the sensors to each other using digital information and communication technology so as to act according to the collected information. Since this information may include behaviors of a supply company and a consumer, the smart grid may improve distribution of fuels such as electricity by a method having efficiency, reliability, economic feasibility, production sustainability, and automation.
  • the smart grid may also be regarded as another sensor network having low latency.
  • Wireless and mobile communication gradually becomes important in the field of an industrial application.
  • Wiring is high in installation and maintenance cost. Therefore, a possibility of replacing a cable with reconstructible wireless links is an attractive opportunity in many industrial fields.
  • it is necessary for wireless connection to be established with latency, reliability, and capacity similar to those of the cable and management of wireless connection needs to be simplified. Low latency and a very low error probability are new requirements when connection to 5G is needed.
  • Logistics and freight tracking are important use cases for mobile communication that enables inventory and package tracking anywhere using a location-based information system.
  • the use cases of logistics and freight typically demand low data rate but require location information with a wide range and reliability.
  • the communication system 1 includes wireless devices 100a to 100f, base stations (BSs) 200, and a network 300.
  • 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.
  • the wireless devices 100a to 100f represent devices performing communication using radio access technology (RAT) (e.g., 5G new RAT (NR)) or LTE) and may be referred to as communication/radio/5G devices.
  • RAT radio access technology
  • 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 IoT device 100f, and an artificial intelligence (AI) device/server 400.
  • 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).
  • UAV unmanned aerial vehicle
  • the XR device may include an AR/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.
  • 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.
  • PDA personal digital assistant
  • PMP portable multimedia player
  • PC slate personal computer
  • tablet PC a tablet PC
  • ultrabook a vehicle, a vehicle having an autonomous
  • the VR device may include, for example, a device for implementing an object or a background of the virtual world.
  • the AR device may include, for example, a device implemented by connecting an object or a background of the virtual world to an object or a background of the real world.
  • the MR device may include, for example, a device implemented by merging an object or a background of the virtual world into an object or a background of the real world.
  • the hologram device may include, for example, a device for implementing a stereoscopic image of 360 degrees by recording and reproducing stereoscopic information, using an interference phenomenon of light generated when two laser lights called holography meet.
  • the public safety device may include, for example, an image relay device or an image device that is wearable on the body of a user.
  • 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
  • the IoT device may perform direct communication with other IoT devices (e.g., sensors) or other wireless devices 100a to 100f.
  • 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.
  • 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.
  • the radio communication technologies implemented in the wireless devices in the present disclosure may include narrowband internet-of-things (NB-IoT) technology for low-power communication as well as LTE, NR and 6G.
  • 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.
  • LPWAN low power wide area network
  • the radio communication technologies implemented in the wireless devices in the present disclosure may communicate based on LTE-M technology.
  • LTE-M technology may be an example of LPWAN technology and be called by various names such as enhanced machine type communication (eMTC).
  • eMTC enhanced machine type communication
  • 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.
  • 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.
  • 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.
  • PANs personal area networks
  • FIG. 2 shows an example of wireless devices to which implementations of the present disclosure is applied.
  • a first wireless device 100 and a second wireless device 200 may transmit/receive radio signals to/from an external device through a variety of RATs (e.g., LTE and NR).
  • RATs e.g., LTE and NR
  • ⁇ 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 processor(s) 102 and the memory(s) 104 may be a part of a communication modem/circuit/chip designed to implement RAT (e.g., LTE or NR).
  • the transceiver(s) 106 may be connected to the processor(s) 102 and transmit and/or receive radio signals through one or more antennas 108.
  • Each of the transceiver(s) 106 may include a transmitter and/or a receiver.
  • the transceiver(s) 106 may be interchangeably used with radio frequency (RF) unit(s).
  • the first wireless device 100 may represent a communication modem/circuit/chip.
  • 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.
  • firmware or software may be implemented using firmware or software and the firmware or software may be configured to include the modules, procedures, or functions.
  • Firmware or software configured to perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure may be included in the one or more processors 102 and 202 or stored in the one or more memories 104 and 204 so as to be driven by the one or more processors 102 and 202.
  • the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure may be implemented using firmware or software in the form of code, commands, and/or a set of commands.
  • 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.
  • 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.
  • 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.
  • the one or more transceivers 106 and 206 may be connected to the one or more antennas 108 and 208 and the one or more transceivers 106 and 206 may be configured 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.
  • the one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports).
  • the one or more transceivers 106 and 206 may convert received 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.
  • the one or more transceivers 106 and 206 may include (analog) oscillators and/or filters.
  • the transceivers 106 and 206 can up-convert OFDM baseband signals to a carrier frequency by their (analog) oscillators and/or filters under the control of the processors 102 and 202 and transmit the up-converted OFDM signals at the carrier frequency.
  • the 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 transceivers 102 and 202.
  • a UE may operate as a transmitting device in uplink (UL) and as a receiving device in downlink (DL).
  • a BS may operate as a receiving device in UL and as a transmitting device in DL.
  • the first wireless device 100 acts as the UE
  • the second wireless device 200 acts as the BS.
  • the processor(s) 102 connected to, mounted on or launched in the first wireless device 100 may be configured 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 configured 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.
  • a BS is also referred to as a node B (NB), an eNode B (eNB), or a gNB.
  • NB node B
  • eNB eNode B
  • gNB gNode B
  • FIG. 3 shows an example of a wireless device to which implementations of the present disclosure is applied.
  • the wireless device may be implemented in various forms according to a use-case/service (refer to FIG. 1).
  • wireless devices 100 and 200 may correspond to the wireless devices 100 and 200 of FIG. 2 and may be configured by various elements, components, units/portions, and/or modules.
  • each of the wireless devices 100 and 200 may include a communication unit 110, a control unit 120, a memory unit 130, and additional components 140.
  • the communication unit 110 may include a communication circuit 112 and transceiver(s) 114.
  • the communication circuit 112 may include the one or more processors 102 and 202 of FIG. 2 and/or the one or more memories 104 and 204 of FIG. 2.
  • the transceiver(s) 114 may include the one or more transceivers 106 and 206 of FIG.
  • the control unit 120 is electrically connected to the communication unit 110, the memory 130, and the additional components 140 and controls overall operation of each of the wireless devices 100 and 200. For example, the control unit 120 may control an electric/mechanical operation of each of the wireless devices 100 and 200 based on programs/code/commands/information stored in the memory unit 130.
  • the control unit 120 may transmit the information stored in the memory unit 130 to the exterior (e.g., other communication devices) via the communication unit 110 through a wireless/wired interface or store, in the memory unit 130, information received through the wireless/wired interface from the exterior (e.g., other communication devices) via the communication unit 110.
  • the entirety of the various elements, components, units/portions, and/or modules in the wireless devices 100 and 200 may be connected to each other through a wired interface or at least a part thereof may be wirelessly connected through the communication unit 110.
  • the control unit 120 and the communication unit 110 may be connected by wire and the control unit 120 and first units (e.g., 130 and 140) may be wirelessly connected through the communication unit 110.
  • Each element, component, unit/portion, and/or module within the wireless devices 100 and 200 may further include one or more elements.
  • the control unit 120 may be configured by a set of one or more processors.
  • control unit 120 may be configured by a set of a communication control processor, an application processor (AP), an electronic control unit (ECU), a graphical processing unit, and a memory control processor.
  • the memory 130 may be configured by a RAM, a DRAM, a ROM, a flash memory, a volatile memory, a non-volatile memory, and/or a combination thereof.
  • FIG. 4 shows another example of wireless devices to which implementations of the present disclosure is applied.
  • wireless devices 100 and 200 may correspond to the wireless devices 100 and 200 of FIG. 2 and may be configured by various elements, components, units/portions, and/or modules.
  • the first wireless device 100 may include at least one transceiver, such as a transceiver 106, and at least one processing chip, such as a processing chip 101.
  • the processing chip 101 may include at least one processor, such a processor 102, and at least one memory, such as a memory 104.
  • 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 software code 105 which implements instructions that, when executed by the processor 102, perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
  • the second wireless device 200 may include at least one transceiver, such as a transceiver 206, and at least one processing chip, such as a processing chip 201.
  • the processing chip 201 may include at least one processor, such a processor 202, and at least one memory, such as a memory 204.
  • 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 software code 205 which implements instructions that, when executed by the processor 202, perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
  • 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.
  • the software code 205 may control the processor 202 to perform one or more protocols.
  • the software code 205 may control the processor 202 may perform one or more layers of the radio interface protocol.
  • FIG. 5 shows an example of UE to which implementations of the present disclosure is applied.
  • a UE 100 may correspond to the first wireless device 100 of FIG. 2 and/or the first wireless device 100 of FIG. 4.
  • a UE 100 includes a processor 102, a memory 104, a transceiver 106, one or more antennas 108, a power management module 110, a battery 1112, a display 114, a keypad 116, a subscriber identification module (SIM) card 118, a speaker 120, and a microphone 122.
  • SIM subscriber identification module
  • the processor 102 may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
  • the processor 102 may be configured 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 a digital signal processor (DSP), a central processing unit (CPU), a graphics processing unit (GPU), a modem (modulator and demodulator).
  • DSP digital signal processor
  • CPU central processing unit
  • GPU graphics processing unit
  • modem modulator and demodulator
  • 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.
  • modules e.g., procedures, functions, etc.
  • 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.
  • 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.
  • the power management module 110 manages power for the processor 102 and/or the transceiver 106.
  • the battery 112 supplies power to the power management module 110.
  • the display 114 outputs results processed by the processor 102.
  • the keypad 116 receives inputs to be used by the processor 102.
  • the keypad 16 may be shown on the display 114.
  • the speaker 120 outputs sound-related results processed by the processor 102.
  • the microphone 122 receives sound-related inputs to be used by the processor 102.
  • FIGS. 6 and 7 show an example of protocol stacks in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.
  • FIG. 6 illustrates an example of a radio interface user plane protocol stack between a UE and a BS
  • FIG. 7 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.
  • the user plane protocol stack may be divided into Layer 1 (i.e., a PHY layer) and Layer 2.
  • the control plane protocol stack may be divided into Layer 1 (i.e., a PHY layer), Layer 2, Layer 3 (e.g., an RRC layer), and a non-access stratum (NAS) layer.
  • Layer 1 i.e., a PHY layer
  • Layer 2 e.g., an RRC layer
  • NAS non-access stratum
  • Layer 1 Layer 2 and Layer 3 are referred to as an access stratum (AS).
  • the Layer 2 is split into the following sublayers: MAC, RLC, and PDCP.
  • 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.
  • QoS quality of service
  • MAC Different kinds of data transfer services are offered by MAC.
  • 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 is a downlink logical channel for broadcasting system control information
  • PCCH paging control channel
  • PCCH is a downlink logical channel that transfers paging information
  • common control channel CCCH
  • DCCH dedicated control channel
  • DTCH Dedicated traffic channel
  • 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.
  • ROIHC robust header compression
  • 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.
  • 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.
  • QFI QoS flow ID
  • a single protocol entity of SDAP is configured for each individual PDU session.
  • FIG. 8 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.
  • OFDM numerologies e.g., subcarrier spacing (SCS), transmission time interval (TTI) duration
  • SCCS subcarrier spacing
  • TTI transmission time interval
  • symbols may include OFDM symbols (or CP-OFDM symbols), SC-FDMA symbols (or discrete Fourier transform-spread-OFDM (DFT-s-OFDM) symbols).
  • 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 T sf per 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.
  • a slot includes plural symbols (e.g., 14 or 12 symbols) in the time domain.
  • a resource grid of N size,u grid,x * N RB sc subcarriers and N subframe,u symb OFDM symbols is defined, starting at common resource block (CRB) N start,u grid indicated by higher-layer signaling (e.g., RRC signaling), where N size,u grid,x is the number of resource blocks (RBs) in the resource grid and the subscript x is DL for downlink and UL for uplink.
  • N RB sc is the number of subcarriers per RB. In the 3GPP based wireless communication system, N RB sc is 12 generally.
  • Each element in the resource grid for the antenna port p and the subcarrier spacing configuration u is 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 index k in the frequency domain and an index l representing a symbol location relative to a reference point in the time domain.
  • an RB is defined by 12 consecutive subcarriers in the frequency domain.
  • n PRB n CRB + N size BWP,i , where N size BWP,i is 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.
  • the NR frequency band may be defined as two types of frequency range, i.e., FR1 and FR2.
  • the numerical value of the frequency range may be changed.
  • the frequency ranges of the two types may be as shown in Table 3 below.
  • FR1 may mean "sub 6 GHz range”
  • FR2 may mean “above 6 GHz range”
  • mmW millimeter wave
  • 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.
  • 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.
  • 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.
  • the UE When CA is configured, the UE only has one RRC connection with the network.
  • one serving cell 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.
  • secondary cells 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.
  • the term SpCell refers to the PCell of the master cell group (MCG) or the primary SCell (PSCell) of the secondary cell group (SCG).
  • MCG master cell group
  • PSCell primary SCell
  • SCG secondary cell group
  • 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.
  • a UE in RRC_CONNECTED not configured with CA/DC there is only one serving cell comprised of the PCell.
  • serving cells is used to denote the set of cells comprised of the SpCell(s) and all SCells.
  • two MAC entities are configured in a UE: one for the MCG and one for the SCG.
  • FIG. 9 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure is applied.
  • the uplink transport channels UL-SCH and RACH are mapped to their physical channels PUSCH and PRACH, respectively, and the downlink transport channels DL-SCH, BCH and PCH are mapped to PDSCH, PBCH and PDSCH, respectively.
  • uplink control information (UCI) is mapped to PUCCH
  • downlink control information (DCI) is mapped to PDCCH.
  • a MAC PDU related to UL-SCH is transmitted by a UE via a PUSCH based on an UL grant
  • a MAC PDU related to DL-SCH is transmitted by a BS via a PDSCH based on a DL assignment.
  • the goal is that sufficient use cases will be considered to enable the identification of a common AI/ML framework, including functional requirements of AI/ML architecture, which could be used in subsequent projects.
  • the study should also identify areas where AI/ML could improve the performance of air-interface functions.
  • the study will serve identifying what is required for an adequate AI/ML model characterization and description establishing pertinent notation for discussions and subsequent evaluations.
  • Various levels of collaboration between the gNB and UE are identified and considered.
  • Initial set of use cases includes:
  • CSI feedback enhancement e.g., overhead reduction, improved accuracy, prediction
  • Beam management e.g., beam prediction in time, and/or spatial domain for overhead and latency reduction, beam selection accuracy improvement
  • Model generation e.g., model training (including input/output, pre-/post-process, online/offline as applicable), model validation, model testing, as applicable
  • KPIs Determine the common KPIs and corresponding requirements for the AI/ML operations. Determine the use-case specific KPIs and benchmarks of the selected use-cases.
  • Case 1 UE-based positioning with UE-side model, direct AI/ML positioning
  • the study will focus on mobility enhancement in RRC_CONNECTED mode over air interface by following existing mobility framework, i.e., handover decision is always made in network side.
  • Mobility use cases focus on standalone NR PCell change.
  • UE-side and network-side AI/ML model can be both considered, respectively.
  • Training Data Data needed as input for the AI/ML Model Training function.
  • 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.
  • a radio link failure occurs after the UE has stayed for a long period of time in the cell; the UE attempts to re-establish the radio link connection in a different cell.
  • RAN Intelligence could observe multiple HO events with associated parameters, use this information to train its ML model and try to identify sets of parameters that lead to successful Hos and sets of parameters that lead to unintended events.
  • Efficient resource handling can be achieved adjusting handover trigger points and selecting optimal combination of Pcell/PSCell/Scells to serve a user.
  • the source gNB could use feedbacks on UE performance collected for successful handovers occurred in the past and received from neighbouring gNBs.
  • the AI/ML Model Training function is deployed in OAM, while the Model Inference function resides within the RAN node
  • Step 0. NG-RAN node 2 is assumed to optionally have an AI/ML model, which can generate required input such as resource status and utilization prediction/estimation etc.
  • Step 5 The NG-RAN node 2 sends the input data for training to OAM, where the input data for training includes the required input information from the NG-RAN node 2. If the NG-RAN node 2 executes the AI/ML model, the input data for training can include the corresponding inference result from the NG-RAN node 2.
  • Step 7 OAM sends AI/ML Model Deployment Message to deploy the trained/updated AI/ML model into the NG-RAN node(s).
  • the NG-RAN node can also continue model training based on the received AI/ML model from OAM.
  • Step 8 The NG-RAN node 1 obtains the measurement report as inference data for UE mobility optimization.
  • Step 11 The NG-RAN 1 sends the model performance feedback to OAM if applicable.
  • This step is out of RAN3 scope.
  • Step 0. NG-RAN node 2 is assumed to optionally have an AI/ML model, which can generate required input such as resource status and utilization prediction/estimation etc.
  • Step 1 NG-RAN node1 configures the measurement information on the UE side and sends configuration message to UE including configuration information.
  • Step 2 UE collects the indicated measurement, e.g., UE measurements related to RSRP, RSRQ, SINR of serving cell and neighbouring cells.
  • the indicated measurement e.g., UE measurements related to RSRP, RSRQ, SINR of serving cell and neighbouring cells.
  • Step 5 Model training. Required measurements are leveraged to training AI/ML model for mobility optimization.
  • Step 6 NG-RAN node1 obtains the measurement report as inference data for real-time UE mobility optimization.
  • Step 7 The NG-RAN node 1 obtains the input data for inference from the NG-RAN node 2 for UE mobility optimization, where the input data for inference includes the required input information from the NG-RAN node 2. If the NG-RAN node 2 executes the AI/ML model, the input data for inference can include the corresponding inference result from the NG-RAN node 2.
  • Step 9 According to the prediction, recommended actions or configuration, the NG-RAN node 1, the target NG-RAN node (represented by NG-RAN node 2 of this step in the flowchart), and UE perform the Mobility Optimization / handover procedure to hand over UE from NG-RAN node 1 to the target NG-RAN node.
  • Step 10 The NG-RAN node 2 sends feedback information after mobility optimization action to the NG-RAN node 1.
  • gNB location information e.g., coordinates, serving cell ID, moving velocity
  • Radio measurements related to serving cell and neighbouring cells associated with UE location information e.g., RSRP, RSRQ, SINR.
  • the following data is required as feedback data for mobility optimization.
  • RAN3 shall reuse the existing framework (including MDT and RRM measurements). Whether new UE measurements are needed is left to normative phase based on the use case description.
  • AI Artificial Intelligence
  • FIGS. 13 and 14 show an example of an architecture of neuron and neural network.
  • brain-inspired computation 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. 13.
  • Neural networks 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. 13.
  • FIG. 15 shows an example of an AI/ML inference.
  • DNNs The performance of DNNs is gained at the cost of high computational complexity.
  • more efficient compute engines are often used, e.g. graphics processing units (GPU) and network processing units (NPU).
  • GPU graphics processing units
  • NPU network processing units
  • the training often requires more computation and storage resources because it involves also the backpropagation process.
  • FIG. 16 shows an example of an MLP DNN model.
  • FIG. 16 presents three popular structures of DNNs: multilayer perceptrons (MLPs), convolution neural networks (CNNs), and recurrent neural networks (RNNs).
  • MLP multilayer perceptrons
  • CNNs convolution neural networks
  • RNNs recurrent neural networks
  • MLP 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, as shown in FIG. 16. Hence MLP requires a significant amount of storage and computation.
  • FIG. 17 shows an example of a CNN model.
  • CNN convolution neural network
  • 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.
  • FIG. 19 shows an example of Reinforcement learning.
  • Deep reinforcement learning is not another DNN model. It is composed of DNNs and reinforcement learning. As illustrated in FIG. 19, 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.
  • additional conditions refer to any aspects that are assumed for the training of the model but are not a part of UE capability for the AI/ML-enabled feature/FG. It does not imply that additional conditions are necessarily specified. Additional conditions can be divided into two categories: NW-side additional conditions and UE-side additional conditions. Note: whether specification impact is needed is a separate discussion.
  • AI/ML models for a given use case may be tailored towards and applicable to specific scenarios, locations, configuration, deployments, among other factors.
  • AI/ML models may undergo updates, such as model changes, as an inherent part of their development. Therefore, to ensure efficient network control and management, especially associated to what concerns the UE-side, UEs might have the ability to indicate relevant information about their supported AI/ML models and concerning AI/ML functionalities to the network. This can allow the network to perform decisions regarding, e.g., the (de)activation, or switching of AI/ML functionalities and AI/ML models.
  • a reactive reporting would involve the UE to provide information to the network upon receiving an action from it.
  • While a proactive reporting would involve the UE to provide information to the network without necessarily receiving an action from it.
  • the UE might proactively inform the RAN of updates/changes to its supported model(s) or functionality(es).
  • 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 the RRCReconfiguration or RRCResume .
  • the network may configure the UE to report the following measurement information based on CSI-RS resources:
  • the network may configure the UE to report the following CLI measurement information based on SRS resources:
  • the network may configure the UE to report the following CLI measurement information based on CLI-RSSI resources:
  • the network may configure the UE to report the following Rx-Tx time difference measurement information based on CSI-RS for tracking or PRS:
  • the measurement configuration includes the following parameters:
  • Measurement objects A list of objects on which the UE shall perform the measurements.
  • a measurement object indicates the frequency/time location and subcarrier spacing of reference signals to be measured.
  • 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.
  • the measObjectId of the MO which corresponds to each serving cell is indicated by servingCellMO within the serving cell configuration.
  • a measurement object is a single 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.
  • a measurement object is a set of cells on a single UTRA-FDD carrier frequency.
  • a measurement object is a single NR sidelink frequency to be measured.
  • a measurement object is a set of transmission resource pool(s) on a single carrier frequency for NR sidelink communication.
  • 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.
  • 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:
  • the criterion that triggers the UE to send a measurement report This can either be periodical or a single event description.
  • - RS type The RS that the UE uses for beam and cell measurement results (SS/PBCH block or CSI-RS).
  • the quantities per cell and per beam that the UE includes in the measurement report e.g. RSRP
  • other associated information such as the maximum number of cells and the maximum number beams per cell to report.
  • each configuration consists of the following:
  • - 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.
  • Measurement gaps Periods that the UE may use to perform measurements.
  • the NR serving cell(s) - these are the SpCell and one or more SCells.
  • 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).
  • the UE measures and reports on the serving cell(s)/serving Relay UE (for L2 U2N Remote UE), listed cells and/or detected cells.
  • 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.
  • the UE measures and reports on listed cells.
  • the UE measures and reports on configured measurement resources (i.e. SRS resources and/or CLI-RSSI resources).
  • 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.
  • the UE may receive two independent measConfig :
  • a measConfig associated with SCG, that is included in the RRCReconfiguration message received via SRB3, or, alternatively, included within a RRCReconfiguration message embedded in a RRCReconfiguration message received via SRB1.
  • the UE maintains two independent VarMeasConfig and VarMeasReportList , one associated with each measConfig , and independently performs all the procedures in clause 5.5 for each measConfig and the associated VarMeasConfig and VarMeasReportList , unless explicitly stated otherwise.
  • the configurations related to CBR measurements are only included in the measConfig associated with MCG.
  • the configurations related to Rx-Tx time difference measurement are only included in the measConfig associated with MCG.
  • Event A1 (Serving becomes better than threshold)
  • Event A2 (Serving becomes worse than threshold)
  • Event A5 (SpCell becomes worse than threshold1 and neighbour becomes better than threshold2)
  • Event B1 (Inter RAT neighbour becomes better than threshold)
  • Event B2 (PCell becomes worse than threshold1 and inter RAT neighbour becomes better than threshold2)
  • Event C1 (The NR sidelink channel busy ratio is above a threshold)
  • Event C2 (The NR sidelink channel busy ratio is below a threshold)
  • Event X1 (Serving L2 U2N Relay UE becomes worse than threshold1 and NR Cell becomes better than threshold2)
  • Event X2 (Serving L2 U2N Relay UE becomes worse than threshold)
  • This procedure is to transfer measurement results from the UE to the network.
  • the UE shall initiate this procedure only after successful AS security activation.
  • FIG. 21 shows an example of location measurement indication.
  • This procedure is to indicate to the network that the UE is going to start/stop location related measurements towards E-UTRA or NR (eutra-RSTD, nr-RSTD, nr-UE-RxTxTimeDiff, nr-PRS-RSRP) which require measurement gaps or start/stop detection of subframe and slot timing towards E-UTRA (eutra-FineTimingDetection) which requires measurement gaps.
  • UE shall initiate this procedure only after successful AS security activation.
  • the UE shall:
  • each cell measurement quantity for each cell measurement quantity, each beam measurement quantity, each sidelink measurement quantity, for each CLI measurement quantity that the UE performs measurements, and for each candidate L2 U2N Relay UE measurement quantity:
  • M n is the latest received measurement result from the physical layer
  • F n is the updated filtered measurement result, that is used for evaluation of reporting criteria or for measurement reporting;
  • the filtering is performed in the same domain as used for evaluation of reporting criteria or for measurement reporting, i.e., logarithmic filtering for logarithmic measurements.
  • proprietary models may be supported and/or open format may be supported.
  • a model is identified by a model ID.
  • a model ID can be used to identify which AI/ML model is being used in Life Cycle Management (LCM) including model delivery.
  • LCM Life Cycle Management
  • a model ID can be used to identify a model (or models) during model selection/activation/deactivation/switching.
  • model transfer/delivery may be used.
  • - gNB can transfer/deliver AI/ML model(s) to UE via RRC signalling.
  • - CN (except LMF) can transfer/deliver AI/ML model(s) to UE via UP data.
  • - LMF can transfer/deliver AI/ML model(s) to UE via UP data.
  • Model ID is unique "globally", e.g. in order to manage test certification each retrained version need to be identified.
  • Information such as FFS:vendor info, applicable conditions, model performance indicators, etc. may be required for model management and control, and should, as a starting point, be part of meta information.
  • the general AI/ML framework consist of, (i) Data Collection, (ii) Model Training, (iii) Model Management, (iv) Model Inference, and (v) Model Storage.
  • Model ID can be used to identify model or models for the following LCM purposes:
  • model selection/activation/deactivation/switching (or identification, if that will be supported as a separate step).
  • model ID based LCM (for example, for so called “model ID based LCM")
  • intention is to cover functional arch in general, e.g. covering both be model based and/or functionality based LCM
  • Model Storage in the figure is only intended as a reference point (if any) for protocol terminations etc for model transfer/delivery etc. It is not intended to limit where models are actually stored. Add a note for this.
  • Management may be model based management, or functionality based management. Add a mote for this.
  • input data can be generated by UE and terminated at gNB.
  • input data/assistance information can be generated by gNB and terminated at UE.
  • performance metrics can be generated by UE and terminated at gNB.
  • training data can be generated by UE/gNB and terminated at LMF/OTT server.
  • input data can be generated by UE/gNB and terminated at LMF and/or gNB.
  • input data/assistance information can be generated by LMF/gNB and terminated at the UE.
  • performance metrics can be generated by UE/gNB and terminated at LMF.
  • the Table 5 can be used as starting point for discussion on mapping of AI/ML functions to physical entities for CSI compression with two-sided model.
  • Table 5 shows the mapping of functions to physical entities for CSI compression with two-sided model.
  • the Table 6 can be used as starting point for discussion on mapping of AI/ML functions to physical entities for beam management with UE -side model.
  • Whether/how OAM is to be involved may need to consult RAN3, SA5.
  • the Table 7 can be used as starting point for discussion on mapping of AI/ML functions to physical entities for beam management with NW-side model.
  • Table 7 shows the mapping of functions to physical entities for beam management with NW-side model.
  • Whether/how OAM is to be involved may need to consult RAN3, SA5.
  • CN may need to consult RAN3, SA2.
  • Whether/how OAM is to be involved may need to consult RAN3, SA5.
  • R2 confirms that for UE-side AIML functionality, current UE capability reporting by RRC and LPP and handling is expected to be used, at least for static capabilities.
  • AIML algorithm for a certain use case may be tailored towards and applicable to certain scenarios/location/configuration/deployment etc.
  • AIML algorithm may be updated, e.g. by model change (these are observations):
  • the UE informs the RAN about applicability conditions of AIML algorithm(s) available to the UE, to support RAN control (e.g. activation/deactivation/switching).
  • - RAN2 further observes that current UE capability reporting and handling is designed for Capabilities that do not dynamically change.
  • the procedure for UE reporting of AIML applicability conditions is FFS.
  • Model transfer/delivery can be initiated in following two ways:
  • Proactive model transfer/delivery AI/ML models are pre-download to UE, and a model switch is performed when changes in scenarios, configurations, or sites occur.
  • the legacy UE capability framework serves as the baseline to report UE's supported AI/ML-enabled Feature/FG:
  • UECapabilityEnquiry/UECapabilityInformation For CSI and beam management use cases, it is indicated in UE AS capability in RRC (i.e., UECapabilityEnquiry/UECapabilityInformation).
  • RAN2 confirm that stage 3 details of AI/ML-enabled Feature/FG (e.g. granularity of Feature/FG) in legacy UE capability are postponed to discuss in the normative phase.
  • UAI can be used as an example. This can be defined and discussed in normative phase. FSS signaling of additional conditions from network to UE
  • the gNB-centric data collection implies that the gNB configures the UE to initiate/terminate the data collection procedure.
  • an OAM-centric data collection implies that the OAM provides the configuration (via the gNB) needed for the UE to initiate/terminate the data collection procedure.
  • MDT framework can be considered.
  • RAN2 studies the potential impact on L3 signalling for the reporting of collected data, taking into account RAN1 further inputs/progress.
  • RAN2 assumes LPP protocol should be applied to the data collected by UE and terminated at LMF, while the NRPPa protocol should be applied to the data collected by gNB and terminated at LMF.
  • UE collects training data and transfers it to CN.
  • CN transfers the training data to the OTT server.
  • RAN2 did not study or analyze the proposals and did not agree to requirements or recommendations.
  • data distribution can be utilized. For example, monitoring the validity of the AI/ML input, e.g., out-of-distribution detection, drift detection of input data, delay spread, etc, or output, e.g., drift detection of output data can be evaluated for the UE-sided model or NW-sided model.
  • NW can configure range of input/output data for a specific functionality or model. If abnormal input/output data is detected, UE may report the abnormal input/output data as a monitoring result.
  • reporting frequency may vary depending on UE, model, functionality, etc. In this case, it may be difficult for NW to perform model management by only looking at data distribution information.
  • a wireless device may receive, from a network, a monitoring configuration related to at least one inference function.
  • the monitoring configuration may include information related to (1) at least one data range and (2) a threshold value.
  • the wireless device may evaluate that the current data (the input data and/or the output data) are abnormal when the difference between the historic measured data and the current data is greater than or equal to a threshold value.
  • the monitoring report may include information related to applicability of the the at least one inference function.
  • the monitoring report may include the applicable functionality report for the at least one inference function.
  • the monitoring report may include information related to number of abnormal input data and/or abnormal output data related to the at least one inference function.
  • the wireless device may be in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the wireless device.
  • a method of monitoring report regarding data distribution based on count threshold is provided.
  • the network may configure data distribution related configuration and a count value for a specific functionality or model.
  • UE may evaluate the input data and/or output data for the specific functionality or model.
  • the UE detects abnormal data based on the data distribution related configuration, it may increment the number of occurrences of abnormal data.
  • the UE may report the monitoring results.
  • the NW can manage AI/ML related operation, e.g., (de)activation, switching, fallback, etc.
  • FIG. 23 shows an example of a method for model monitoring report regarding data distribution based on count threshold.
  • the UE may activate the functionality and/or a model (for example, inference function).
  • the network may activate the functionality and/or the model.
  • the UE may perform evaluation of input/out data for the functionality and/or the model and count abnormal input and/or output data.
  • the Data Distribution related configuration may include:
  • the detection period value e.g., 10ms
  • the UE may evaluate the data difference between historic measured result and current measured result
  • the UE When the UE detects abnormal data, it may increment the number of occurrences of abnormal data as followings
  • UE may not expire the time window when detecting normal data
  • FIG. 26 shows an example of a method for counting abnormal data during a specific time window.
  • UE may count number of abnormal data (out of range) within time window. For example, UE may increment a counter upon detecting the abnormal data within time window.
  • UE may count number of abnormal data (out of range) within time window, and reset the number of counts when detecting normal data. For example, the UE may increment the counter upon detecting the abnormal data. When the UE detects a data within the specific range, the UE may reset the counter.
  • UE may expire the time window when detecting normal data
  • UE may reset the number of count when detecting normal data
  • FIG. 28 shows an example of a method for counting abnormal data during a specific time window.
  • FIG. 29 shows an example of a method for counting abnormal data during a specific time window.
  • UE may count consecutive abnormal data during a specific time window. After the time window, the UE may reset the number of consecutive abnormal data. For example, the UE may increment the counter upon detecting the abnormal data and may clear the counter upon expiry of the time of the time window. In this example, for example, the wireless device may terminate the time window upon detecting the data within the specific data range and clear the counter.
  • UE may not reset the number of count when detecting normal data
  • the UE can recalculate the count value based on the frequency of occurrence of abnormal data (e.g., percentage type).
  • step S2404 when the number of occurrences of abnormal data is larger than the count value, the UE may report the monitoring results.
  • the report may include:
  • Functionality/model information e.g., Functionality/model ID
  • the data distribution can be utilized in applicability related report based on additional conditions, which can include condition used for the model training, and additional condition such as scenario, configuration, site, etc.
  • additional conditions can include condition used for the model training, and additional condition such as scenario, configuration, site, etc.
  • the UE can evaluate the applicability of the functionality/model based on the data distribution and can report the applicability information.
  • step S2405 based on the report, the NW may determine AI/ML related management.
  • the NW may update the functionality/model related configuration
  • the NW may re-train the model
  • FIG. 30 shows an example of a method performed by a wireless device in a wireless communication system.
  • the wireless device may receive information on a monitoring configuration associated with the Artificial Intelligence (AI) and/or Machine Learning (ML) model.
  • the configuration includes data threshold and count value for a model/functionality.
  • the wireless device may evaluate value of input and/or output data for the model/functionality.
  • the wireless device may count abnormal data for the input and/or output based on the data threshold.
  • Some of the detailed steps shown in the examples of FIGS. 22-30 may not be essential steps and may be omitted. In addition to the steps shown in FIGS. 22-30, other steps may be added, and the order of the steps may vary. Some of the above steps may have their own technical meaning.
  • the apparatus may be a wireless device (100 or 200) in FIGS. 2, 3, and 5.
  • a wireless device may perform the methods described above.
  • the detailed description overlapping with the above-described contents could be simplified or omitted.
  • the processor 102 may be configured to be coupled operably with the memory 104 and the transceiver 106.
  • the processor 102 may be adapted to perform operations.
  • the operations comprise: receiving, from a network, a monitoring configuration related to at least one inference function, wherein the monitoring configuration includes information related to (1) at least one data range and (2) a threshold value; incrementing a counter based on determining that input data and/or output data related to the at least one inference function are abnormal based on the at least one data range; and transmitting, to the network, a monitoring report based on the counter reaches the threshold value.
  • the monitoring report may include information related to applicability of the the at least one inference function.
  • the monitoring configuration related to the at least one inference function includes information related to at least one time window.
  • the operations further comprises: incrementing the counter within the time window.
  • the operations further comprises: resetting the counter upon expiry of the time window.
  • the operations further comprises: activating the at least one inference function.
  • the at least one inference function is activated based on (i) an activating command from the network, (ii) a decision of the wireless device, and/or (iii) at least one condition for activating the at least one inference function.
  • the operations further comprises: resetting the counter upon detecting normal input data and/or normal output data related to the at least one inference function, wherein the normal input data and/or the normal output data related to the at least one inference function are a data within the at least one data range.
  • the monitoring report is included in a radio resource control (RRC) message, a Medium Access Control (MAC) Control Element (CE), and/or an Uplink Control Information (UCI).
  • RRC radio resource control
  • MAC Medium Access Control
  • CE Control Element
  • UCI Uplink Control Information
  • the monitoring report includes information related to number of abnormal input data and/or abnormal output data related to the at least one inference function.
  • the processor 102 may be configured to control the transceiver 106 to be in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the wireless device.
  • the processor may be configured to control the wireless device to perform operations.
  • the operations comprise: receiving, from a network, a monitoring configuration related to at least one inference function, wherein the monitoring configuration includes information related to (1) at least one data range and (2) a threshold value; incrementing a counter based on determining that input data and/or output data related to the at least one inference function are abnormal based on the at least one data range; and transmitting, to the network, a monitoring report based on the counter reaches the threshold value.
  • the monitoring report may include information related to applicability of the the at least one inference function.
  • the monitoring configuration related to the at least one inference function includes information related to at least one time window.
  • the operations further comprises: incrementing the counter within the time window.
  • the operations further comprises: resetting the counter upon expiry of the time window.
  • the at least one inference function is activated based on (i) an activating command from the network, (ii) a decision of the wireless device, and/or (iii) at least one condition for activating the at least one inference function.
  • the operations further comprises: resetting the counter upon detecting normal input data and/or normal output data related to the at least one inference function, wherein the normal input data and/or the normal output data related to the at least one inference function are a data within the at least one data range.
  • the monitoring report is included in a radio resource control (RRC) message, a Medium Access Control (MAC) Control Element (CE), and/or an Uplink Control Information (UCI).
  • RRC radio resource control
  • MAC Medium Access Control
  • CE Control Element
  • UCI Uplink Control Information
  • the monitoring report includes information related to the input data and/or the output data related to the at least one inference function.
  • the monitoring report includes information related to number of abnormal input data and/or abnormal output data related to the at least one inference function.
  • non-transitory computer-readable medium has stored thereon a plurality of instructions for reporting data distribution related to inference function, according to some embodiments of the present disclosure, will be described.
  • the computer-readable medium may include a tangible and non-transitory computer-readable storage medium.
  • non-transitory computer-readable media may include random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, or any other medium that can be used to store instructions or data structures.
  • RAM random access memory
  • SDRAM synchronous dynamic random access memory
  • ROM read-only memory
  • NVRAM non-volatile random access memory
  • EEPROM electrically erasable programmable read-only memory
  • FLASH memory magnetic or optical data storage media, or any other medium that can be used to store instructions or data structures.
  • Non-transitory computer-readable media may also include combinations of the above.
  • the method described herein may be realized at least in part by a computer-readable communication medium that carries or communicates code in the form of instructions or data structures and that can be accessed, read, and/or executed by a computer.
  • a non-transitory computer-readable medium has stored thereon a plurality of instructions.
  • the stored plurality of instructions may be executed by a processor of a wireless device.
  • the stored plurality of instructions may cause the wireless device to perform operations.
  • the operations comprise: receiving, from a network, a monitoring configuration related to at least one inference function, wherein the monitoring configuration includes information related to (1) at least one data range and (2) a threshold value; incrementing a counter based on determining that input data and/or output data related to the at least one inference function are abnormal based on the at least one data range; and transmitting, to the network, a monitoring report based on the counter reaches the threshold value.
  • the monitoring report may include information related to applicability of the the at least one inference function.
  • the monitoring configuration related to the at least one inference function includes information related to at least one time window.
  • the operations further comprises: incrementing the counter within the time window.
  • the operations further comprises: resetting the counter upon expiry of the time window.
  • the operations further comprises: resetting the counter upon detecting normal input data and/or normal output data related to the at least one inference function, wherein the normal input data and/or the normal output data related to the at least one inference function are a data within the at least one data range.
  • the monitoring report includes information related to the input data and/or the output data related to the at least one inference function.
  • the monitoring report includes information related to number of abnormal input data and/or abnormal output data related to the at least one inference function.
  • the operations further comprises: receiving, from the network, a configuration for management of the at least one inference function.
  • the stored plurality of instructions may cause the wireless device to be in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the wireless device.
  • BS base station
  • the method comprises: transmitting, by a base station to a wireless device, a monitoring configuration related to at least one inference function, wherein the monitoring configuration includes information related to (1) at least one data range and (2) a threshold value, and wherein the wireless device increments a counter based on determining that input data and/or output data related to the at least one inference function are abnormal based on the at least one data range; and receiving, by the base station from the wireless device, a monitoring report based on the counter reaches the threshold value.
  • the BS may include a transceiver, a memory, and a processor operatively coupled to the transceiver and the memory.

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Abstract

L'invention concerne un procédé et un appareil permettant de rapporter une distribution de données relative à une fonction d'inférence. Un dispositif sans fil reçoit, en provenance d'un réseau, une configuration de surveillance associée à au moins une fonction d'inférence. La configuration de surveillance comprend des informations relatives à (1) au moins une plage de données et (2) une valeur seuil. Le dispositif sans fil incrémente un compteur sur la base de la détermination que des données d'entrée et/ou des données de sortie relatives à la ou aux fonctions d'inférence sont anormales sur la base de la ou des plages de données. Le dispositif sans fil transmet, au réseau, un rapport de surveillance sur la base du compteur atteignant la valeur seuil.
PCT/KR2025/003773 2024-03-28 2025-03-25 Procédé et appareil permettant de rapporter une distribution de données relative à une fonction d'inférence Pending WO2025206697A1 (fr)

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US202463570834P 2024-03-28 2024-03-28
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