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WO2025165033A1 - Procédé et appareil de rapport de fonction d'inférence - Google Patents

Procédé et appareil de rapport de fonction d'inférence

Info

Publication number
WO2025165033A1
WO2025165033A1 PCT/KR2025/001180 KR2025001180W WO2025165033A1 WO 2025165033 A1 WO2025165033 A1 WO 2025165033A1 KR 2025001180 W KR2025001180 W KR 2025001180W WO 2025165033 A1 WO2025165033 A1 WO 2025165033A1
Authority
WO
WIPO (PCT)
Prior art keywords
inference function
wireless device
model
reporting
inference
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/KR2025/001180
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 WO2025165033A1 publication Critical patent/WO2025165033A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • 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
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H04W36/0085—Hand-off measurements
    • H04W36/0094—Definition of hand-off measurement parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H04B17/327—Received signal code power [RSCP]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • 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
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0055—Transmission or use of information for re-establishing the radio link
    • H04W36/0058—Transmission of hand-off measurement information, e.g. measurement reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists

Definitions

  • the present disclosure relates to a method and apparatus for inference function reporting.
  • 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.
  • UE For AI/ML operation under network control and management, UE needs to report the applicability for given functionalities (use cases) and models. Applicability can be determined by additional conditions (e.g., scenario, sites, and datasets) as determined/identified between UE-side and NW-side. Additional conditions refer to any aspects that are assumed for the training of the model.
  • additional conditions e.g., scenario, sites, and datasets
  • model ID can represent the additional condition.
  • NW Implicit procedure assisted by monitoring can be used.
  • NW can recognize the model applicability based on monitoring related results.
  • Reactive reporting Two UE reporting types are identified to convey the applicability related information: Reactive reporting and Proactive reporting.
  • the difference between "reactive" and “proactive” operation can be determined by the point when the UE sends applicability related reporting.
  • UE can trigger the report after receiving any action related model operation from NW.
  • the "proactive" reporting can be triggered before receiving action related model operation from NW.
  • NW can decide model activation according to the applicability.
  • UE can determine the applicability based on the additional condition.
  • additional condition related information which is related to below information:
  • Specific location related information e.g., polygon type, latitude/longitude, altitude, angle, indoor/outdoor, etc.
  • Specific time related information e.g., date, time window, start time, stop time, etc.
  • Specific speed related information e.g., 10km/h, 30km/h, 60km/h, 120km/h, etc.
  • Specific radio quality condition e.g., RSRP, RSRQ, SINR, etc
  • Specific cell/frequency related information e.g., bandwidth, size of subband, carrier frequency, numerologies, etc.
  • Specific antenna related information e.g., antenna port layouts, antenna port numbers, rank numbers/layers, antenna spacing, antenna virtualization, etc.
  • additional conditions There can be two types of additional conditions depending on whether they can be measured at the UE. If the additional condition can be specified and measurable in UE, UE can determine the applicability of functionality/model based on NW configuration. If the additional condition is perceivable/measurable in NW, NW can determine the applicability of functionality/model in UE.
  • UE parameters e.g., number of UE Rx beams
  • model applicability cannot be determined. Note that model inference should be performed in the same environment as training. Otherwise, there would be issues related to consistent and accurate AIML operation. Therefore, it is necessary to recognize UE's situation in NW even though the additional condition is not measurable in UE.
  • a method comprises; receiving, by a wireless device from a network, a reporting configuration including at least one reporting condition related to at least one inference function, wherein the at least one reporting condition includes a condition related to a data quality difference and/or a model performance difference related to the at least one inference function; and transmitting, by the wireless device to the network, a measurement report based on the reporting condition related to the at least one inference function being satisfied.
  • an apparatus for implementing the above method is provided.
  • the present disclosure can have various advantageous effects.
  • a wireless device could efficiently perform inference function monitoring.
  • the NW can determine changes in the UE's environment based on the amount of change according to quality criteria and uses it as implicit model applicability information. Using this, NW can perform model Activation/Deactivation/Switching/Fallback, etc.
  • AI/ML models can be operated efficiently.
  • the wireless communication system could provide an efficient solution for Model Applicability Reporting based on the amount of change in quality.
  • FIG. 1 shows an example of a communication system to which implementations of the present disclosure is applied.
  • FIG. 2 shows an example of wireless devices to which implementations of the present disclosure is applied.
  • FIG. 3 shows an example of a wireless device to which implementations of the present disclosure is applied.
  • FIG. 4 shows another example of wireless devices to which implementations of the present disclosure is applied.
  • FIG. 5 shows an example of UE to which implementations of the present disclosure is applied.
  • 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. 8 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.
  • FIG. 9 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure is applied.
  • FIG. 10 shows an example of a Functional Framework for RAN Intelligence.
  • FIG. 11 shows an example of an AI/ML Model Training in OAM and AI/ML Model Inference in NG-RAN node.
  • FIG. 12 shows an example of Model Training and Model Inference both located in RAN node.
  • FIGS. 13 and 14 show an example of an architecture of neuron and neural network.
  • FIG. 15 shows an example of an AI/ML inference.
  • FIG. 16 shows an example of an MLP DNN model.
  • FIG. 17 shows an example of a CNN model.
  • FIG. 18 shows an example of an RNN model.
  • FIG. 19 shows an example of Reinforcement learning.
  • FIG. 20 shows an example of measurement reporting.
  • FIG. 21 shows an example of location measurement indication.
  • FIG. 22 shows an example of a method for inference function reporting.
  • FIG. 23 shows an example of a method for applicability related report based on change in quality.
  • FIG. 24 shows an example of a method for applicability related report based on change in quality.
  • FIG. 25 shows an example of scenarios for model quality.
  • FIG. 26 shows examples of time information for evaluation.
  • FIG. 27 shows an example of a method for model applicability reporting based on the amount of change in quality.
  • FIG. 28 shows an example of a AIML specific method for model applicability reporting based on the amount of change in quality.
  • 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”.
  • slash (/) or comma (,) may mean “and/or”.
  • A/B may mean “A and/or B”.
  • A/B may mean "only A”, “only B”, or “both A and B”.
  • A, B, C may mean "A, B or 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
  • FIG. 1 shows an example of a communication system to which implementations of the present disclosure is applied.
  • 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.
  • 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.
  • Cloud storage and applications are rapidly increasing in a mobile communication platform and may be applied to both work and entertainment.
  • the cloud storage is a special use case which accelerates growth of uplink data transmission rate.
  • 5G is also used for remote work of cloud. When a tactile interface is used, 5G demands much lower end-to-end latency to maintain user good experience.
  • Entertainment for example, cloud gaming and video streaming, is another core element which increases demand for mobile broadband capability. Entertainment is essential for a smartphone and a tablet in any place including high mobility environments such as a train, a vehicle, and an airplane.
  • Other use cases are augmented reality for entertainment and information search. In this case, the augmented reality requires very low latency and instantaneous data volume.
  • one of the most expected 5G use cases relates a function capable of smoothly connecting embedded sensors in all fields, i.e., mMTC. It is expected that the number of potential Internet-of-things (IoT) devices will reach 204 hundred million up to the year of 2020.
  • An industrial IoT is one of categories of performing a main role enabling a smart city, asset tracking, smart utility, agriculture, and security infrastructure through 5G.
  • 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 safety system guides alternative courses of a behavior so that a driver may drive more safely drive, thereby lowering the danger of an accident.
  • the next stage will be a remotely controlled or self-driven vehicle. This requires very high reliability and very fast communication between different self-driven vehicles and between a vehicle and infrastructure. In the future, a self-driven vehicle will perform all driving activities and a driver will focus only upon abnormal traffic that the vehicle cannot identify.
  • Technical requirements of a self-driven vehicle demand ultra-low latency and ultra-high reliability so that traffic safety is increased to a level that cannot be achieved by human being.
  • 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.
  • Mission critical application is one of 5G use scenarios.
  • a health part contains many application programs capable of enjoying benefit of mobile communication.
  • a communication system may support remote treatment that provides clinical treatment in a faraway place. Remote treatment may aid in reducing a barrier against distance and improve access to medical services that cannot be continuously available in a faraway rural area. Remote treatment is also used to perform important treatment and save lives in an emergency situation.
  • the wireless sensor network based on mobile communication may provide remote monitoring and sensors for parameters such as heart rate and blood pressure.
  • 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 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.
  • 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 UAV may be, for example, an aircraft aviated by a wireless control signal without a human being onboard.
  • 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 MTC device and the IoT device may be, for example, devices that do not require direct human intervention or manipulation.
  • the MTC device and the IoT device may include smartmeters, vending machines, thermometers, smartbulbs, door locks, or various sensors.
  • the medical device may be, for example, a device used for the purpose of diagnosing, treating, relieving, curing, or preventing disease.
  • the medical device may be a device used for the purpose of diagnosing, treating, relieving, or correcting injury or impairment.
  • the medical device may be a device used for the purpose of inspecting, replacing, or modifying a structure or a function.
  • the medical device may be a device used for the purpose of adjusting pregnancy.
  • the medical device may include a device for treatment, a device for operation, a device for (in vitro) diagnosis, a hearing aid, or a device for procedure.
  • the security device may be, for example, a device installed to prevent a danger that may arise and to maintain safety.
  • the security device may be a camera, a closed-circuit TV (CCTV), a recorder, or a black box.
  • CCTV closed-circuit TV
  • the FinTech device may be, for example, a device capable of providing a financial service such as mobile payment.
  • the FinTech device may include a payment device or a point of sales (POS) system.
  • POS point of sales
  • the weather/environment device may include, for example, a device for monitoring or predicting a weather/environment.
  • 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.
  • 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.
  • 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 wireless communication/connections 150a, 150b and 150c may transmit/receive signals through various physical channels.
  • various configuration information configuring processes e.g., channel encoding/decoding, modulation/demodulation, and resource mapping/de-mapping
  • resource allocating processes for transmitting/receiving radio signals, may be performed based on the various proposals of the present disclosure.
  • 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 first wireless device 100 may include one or more processors 102 and one or more memories 104 and additionally further include one or more transceivers 106 and/or one or more antennas 108.
  • the processor(s) 102 may control the memory(s) 104 and/or the transceiver(s) 106 and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts described in the present disclosure.
  • the processor(s) 102 may process information within the memory(s) 104 to generate first information/signals and then transmit radio signals including the first information/signals through the transceiver(s) 106.
  • the processor(s) 102 may receive radio signals including second information/signals through the transceiver(s) 106 and then store information obtained by processing the second information/signals in the memory(s) 104.
  • the memory(s) 104 may be connected to the processor(s) 102 and may store a variety of information related to operations of the processor(s) 102.
  • the memory(s) 104 may store software code including commands for performing a part or the entirety of processes controlled by the processor(s) 102 or for performing the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts described in the present disclosure.
  • 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 second wireless device 200 may include one or more processors 202 and one or more memories 204 and additionally further include one or more transceivers 206 and/or one or more antennas 208.
  • the processor(s) 202 may control the memory(s) 204 and/or the transceiver(s) 206 and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts described in the present disclosure.
  • the processor(s) 202 may process information within the memory(s) 204 to generate third information/signals and then transmit radio signals including the third information/signals through the transceiver(s) 206.
  • the processor(s) 202 may receive radio signals including fourth information/signals through the transceiver(s) 106 and then store information obtained by processing the fourth information/signals in the memory(s) 204.
  • the memory(s) 204 may be connected to the processor(s) 202 and may store a variety of information related to operations of the processor(s) 202.
  • the memory(s) 204 may store software code including commands for performing a part or the entirety of processes controlled by the processor(s) 202 or for performing the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts described in the present disclosure.
  • the processor(s) 202 and the memory(s) 204 may be a part of a communication modem/circuit/chip designed to implement RAT (e.g., LTE or NR).
  • the transceiver(s) 206 may be connected to the processor(s) 202 and transmit and/or receive radio signals through one or more antennas 208.
  • Each of the transceiver(s) 206 may include a transmitter and/or a receiver.
  • the transceiver(s) 206 may be interchangeably used with RF unit(s).
  • the second wireless device 200 may represent a communication modem/circuit/chip.
  • One or more protocol layers may be implemented by, without being limited to, one or more processors 102 and 202.
  • 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).
  • 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).
  • PHY physical
  • MAC media access control
  • RLC radio link control
  • PDCP packet data convergence protocol
  • RRC radio resource control
  • SDAP service data adaptation protocol
  • the one or more processors 102 and 202 may generate one or more protocol data units (PDUs) and/or one or more service data unit (SDUs) 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 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.
  • 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.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • 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 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 read-only memories (ROMs), random access memories (RAMs), electrically erasable programmable read-only memories (EPROMs), flash memories, hard drives, registers, cash memories, computer-readable storage media, 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.
  • 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 additional components 140 may be variously configured according to types of the wireless devices 100 and 200.
  • the additional components 140 may include at least one of a power unit/battery, input/output (I/O) unit (e.g., audio I/O port, video I/O port), a driving unit, and a computing unit.
  • I/O input/output
  • the wireless devices 100 and 200 may be implemented in the form of, without being limited to, the robot (100a of FIG. 1), the vehicles (100b-1 and 100b-2 of FIG. 1), the XR device (100c of FIG. 1), the hand-held device (100d of FIG. 1), the home appliance (100e of FIG. 1), the IoT device (100f of FIG.
  • the wireless devices 100 and 200 may be used in a mobile or fixed place according to a use-example/service.
  • 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 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.
  • the software code 105 may control the processor 102 to perform one or more protocols.
  • the software code 105 may control the processor 102 may perform one or more layers of the radio interface protocol.
  • 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
  • processor 102 may be found in SNAPDRAGON TM series of processors made by Qualcomm ® , EXYNOS TM series of processors made by Samsung ® , A series of processors made by Apple ® , HELIO TM series of processors made by MediaTek ® , ATOM TM series of processors made by Intel ® or a corresponding next generation processor.
  • 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 SIM card 118 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.
  • IMSI international mobile subscriber identity
  • 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
  • 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.
  • HARQ hybrid automatic repeat request
  • 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.
  • 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
  • a DTCH can exist in both uplink and downlink.
  • 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.
  • PCCH downlink shared channel
  • CCCH can be mapped to DL-SCH
  • DCCH can be mapped to DL-SCH
  • DTCH can be mapped to DL-SCH.
  • 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.
  • 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).
  • 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.
  • 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.
  • SRBs signaling radio bearers
  • DRBs data radio bearers
  • 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
  • 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.
  • RBs are classified into CRBs and physical resource blocks (PRBs).
  • CRBs are numbered from 0 and upwards in the frequency domain for subcarrier spacing configuration u .
  • the center of subcarrier 0 of CRB 0 for subcarrier spacing configuration u coincides with 'point A' which serves as a common reference point for resource block grids.
  • PRBs are defined within a bandwidth part (BWP) and numbered from 0 to N size BWP,i -1, where i is the number of the bandwidth part.
  • BWP bandwidth part
  • 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
  • FR1 may include a frequency band of 410MHz to 7125MHz as shown in Table 4 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).
  • 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.
  • 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.
  • 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.
  • Protocol aspects e.g., (RAN2) - RAN2 only starts the work after there is sufficient progress on the use case study in RAN1
  • Case 1 UE-based positioning with UE-side model, direct AI/ML positioning
  • Case 2b UE-assisted/LMF-based positioning with LMF-side model, direct AI/ML positioning
  • Case 3b NG-RAN node assisted positioning with LMF-side model, direct AI/ML positioning
  • Case 2a UE-assisted/LMF-based positioning with UE-side model, AI/ML assisted positioning
  • Case 3a NG-RAN node assisted positioning with gNB-side model, AI/ML assisted positioning
  • Static/non-static scenarios/conditions and propagation conditions for testing e.g., CDL, field data, etc.
  • Temporal prediction within serving cell is mainly to predict the best or top-K beam(s) or beam pair(s) in time domain in order to improve UE throughput. While predict the best or top-K beam(s) or beam pair(s) among a set of beams by measuring a smaller set of beams could help reduce RS signalling overhead, measurement efforts and UE power consumption etc.
  • LTM HO study majority of the RAN1 work can be reused for e.g. LTM HO study.
  • L3 measurement is based on filtering of L1 measurement
  • the study of AI/ML for air can be leveraged for mobility purpose e.g., temporal prediction can also be used to predict beam(s)/cell(s) becoming worse so that unintended event like radio link failure or short-stay handover can be avoided.
  • Mobility enhancement was also studied in RAN3 in Rel-17 in SID called FS_NR_ENDC_data_collect and is now specified in Rel-18 WID NR_AIML_NGRAN-Core.
  • the study and normative work on mobility enhancement is based on information available in network side e.g. handover and stay of time in history among cells to predict UE's trajectory in single hop and hence potential candidates.
  • RAN3 will further work on UE's trajectory for multiple hops. The predicted UE's trajectory could be helpful for study on AI/ML mobility over air interface to some extent.
  • 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.
  • HO performance KPIs e.g., Ping-pong HO, HOF/RLF, Time of stay, Handover interruption, prediction accuracy, and measurement reduction
  • RAN2 complexity tradeoffs
  • Potential AI mobility specific enhancement should be based on the Rel19 AI/ML-air interface WID general framework (e.g. LCM, performance monitoring etc) [RAN2]
  • WID general framework e.g. LCM, performance monitoring etc
  • - RAN4 scope/work can be defined and confirmed by RAN#105 after some RAN2 discussions (within the RAN4 pre-allocated TUs)
  • FIG. 10 shows an example of a Functional Framework for RAN Intelligence.
  • 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
  • Examples of input data may include measurements from UEs or different network entities, feedback from Actor, output from an AI/ML model.
  • 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.
  • Model Deployment/Update 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.
  • 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.
  • data preparation e.g., data pre-processing and cleaning, formatting, and transformation
  • Model Performance Feedback It may be used for monitoring the performance of the AI/ML model, when available.
  • 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.
  • Feedback 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.
  • Mobility management is the scheme to guarantee the service-continuity during the mobility by minimizing the call drops, RLFs, unnecessary handovers, and ping-pong.
  • the frequency for UE to handover between nodes becomes high, especially for high-mobility UE.
  • the QoE is sensitive to the handover performance, so that mobility management should avoid unsuccessful handover and reduce the latency during handover procedure.
  • it is challengeable for trial-and-error-based scheme to achieve nearly zero-failure handover.
  • the unsuccessful handover cases are the main reason for packet dropping or extra delay during the mobility period, which is unexpected for the packet-drop-intolerant and low-latency applications.
  • the effectiveness of adjustment based on feedback may be weak due to randomness and inconstancy of transmission environment.
  • areas of optimization for mobility include dual connectivity, CHO, and DAPS, which each has additional aspects to handle in the optimization of mobility.
  • Mobility aspects of SON that can be enhanced by the use of AI/ML include
  • 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.
  • An RLF occurs shortly after a successful handover from a source cell to a target cell or a handover failure occurs during the handover procedure; the UE attempts to re-establish the radio link connection in the source cell.
  • An RLF occurs shortly after a successful handover from a source cell to a target cell or a handover failure occurs during the handover procedure; the UE attempts to re-establish the radio link connection in a cell other than the source cell and the target 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.
  • Predicting UE's location is a key part for mobility optimisation, as many RRM actions related to mobility (e.g., selecting handover target cells) can benefit from the predicted UE location/trajectory.
  • UE mobility prediction is also one key factor in the optimization of early data forwarding particularly for CHO.
  • UE Performance prediction when the UE is served by certain cells is a key factor in determining which is the best mobility target for maximisation of efficiency and performance.
  • 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.
  • an eNB could use information (feedbacks) received in the past from the gNB for successfully completed SN Addition or SN Change procedures.
  • the source RAN node of a mobility event or the RAN node acting as Master Node (a eNB for EN-DC, a gNB for NR-DC) can use feedbacks received from the other RAN node, as input to an AI/ML function supporting traffic related decisions (e.g., selection of target cell in case of mobility, selection of a PSCell / Scell(s) in the other case), so that future decisions can be optimized.
  • a eNB for EN-DC a gNB for NR-DC
  • an AI/ML function supporting traffic related decisions e.g., selection of target cell in case of mobility, selection of a PSCell / Scell(s) in the other case
  • the AI/ML Model Training function is deployed in OAM, while the Model Inference function resides within the RAN node
  • AI/ML Model Training is located in CU-CP or OAM
  • AI/ML Model Inference function is located in CU-CP
  • gNB is also allowed to continue model training based on AI/ML model trained in the OAM.
  • FIG. 11 shows an example of an AI/ML Model Training in OAM and AI/ML Model Inference in NG-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 1 The NG-RAN node configures the measurement information on the UE side and sends configuration message to UE including configuration information.
  • Step 2 The 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 3 The UE sends measurement report message to NG-RAN node 1 including the required measurement.
  • Step 4 The NG-RAN node 1 sends the input data for training to OAM, where the input data for training includes the required input information from the NG-RAN node 1 and the measurement from UE.
  • 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 6 Model Training. Required measurements are leveraged to training AI/ML model for UE mobility optimization.
  • 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.
  • 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 10 Model Inference. Required measurements are leveraged into Model Inference to output the prediction, e.g., UE trajectory prediction, target cell prediction, target NG-RAN node prediction, etc.
  • Step 11 The NG-RAN 1 sends the model performance feedback to OAM if applicable.
  • This step is out of RAN3 scope.
  • Step 12 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 13 The NG-RAN node 1 sends the feedback information to OAM.
  • Step 14 The NG-RAN node 2 sends the feedback information to OAM.
  • FIG. 12 shows an example of Model Training and Model Inference both located in 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 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 3 UE sends measurement report message to NG-RAN node1 including the required measurement.
  • Step 4 The NG-RAN node 1 obtains the input data for training from the NG-RAN node2, 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 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 8 Model Inference. Required measurements are leveraged into Model Inference to output the prediction, including e.g., UE trajectory prediction, target cell prediction, target NG-RAN node prediction, etc.
  • 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.
  • UE mobility information for training purposes is only sent to gNBs that requested such information or when triggered.
  • the following data is required as input data for mobility optimization.
  • 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.
  • AI/ML-based mobility optimization can generate following information as output:
  • Predicted handover target node candidate cells in CHO, may together with the confidence of the predication
  • the following data is required as feedback data for mobility optimization.
  • Performance information from target NG-RAN The details of performance information are to be discussed during normative work phase.
  • a gNB can request mobility feedback from a neighbouring node. Details of the procedure will be determined during the normative phase.
  • 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
  • ML Machine Learning
  • mobile devices e.g. smartphones, smart vehicles, UAVs, mobile robots
  • algorithms e.g. speech recognition, machine translation, image recognition, video processing, user behaviour prediction
  • 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.
  • 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. 14 shows a diagrammatic picture of a computational neural network.
  • 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.
  • DNN deep neural networks
  • DNNs Neural networks having more than three layers, i.e., more than one hidden layer
  • DNNs also referred to as deep learning
  • Deep learning techniques use supervised and/or unsupervised strategies to automatically learn hierarchical representations in deep architectures for classification.
  • the superior performance of DNNs comes from its ability to extract high-level features from raw sensory data after using statistical learning over a large amount of data to obtain an effective representation of an input space.
  • DNNs have become the most popular ML models for many AI applications.
  • 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.
  • 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.
  • supervised learning 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.
  • FIG. 15 shows an example of an AI/ML inference.
  • a DNN 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.
  • the inputs from the real world are passed through the DNN.
  • the prediction for the task is output, as shown in FIG. 15.
  • 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.
  • 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.
  • 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
  • FIG. 18 shows an example of an RNN model.
  • 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.
  • model-ID-based LCM operates based on identified models, where a model may be associated with specific configurations/conditions associated with UE capability of an AI/ML-enabled Feature/FG and additional conditions (e.g., scenarios, sites, and datasets) as determined/identified between UE-side and NW-side.
  • additional conditions e.g., scenarios, sites, and datasets
  • 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. whether specification impact is needed is a separate discussion.
  • an AI/ML model identified by a model ID may be logical , and how it maps to physical AI/ML model(s) may be up to implementation.
  • companies may use the term a logical AI/ML model to refer to a model that is identified and assigned a model ID, and physical AI/ML model(s) to refer to an actual implementation of such a model.
  • model identification necessity, mechanisms, for UE to report updates on applicable UE part/UE-side model(s), where the applicable models may be a subset of all identified models are studied.
  • Consistency assisted by monitoring by UE and/or NW, the performance of UE-side candidate models/functionalities to select a model/functionality
  • 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.
  • the previously mentioned information could in principle be understood as "applicability-related information" in which the UE could, for example, report to the network conditions under which a model/functionality is applicable/suitable, or whether model(s)/functionality(es) are (non)applicable under the current context. Note, however, that the existing UE capability reporting framework cannot be used for such purposes.
  • 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).
  • Sections of 3GPP TS 38.331 v17.5.0 may be referred.
  • 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 perform the following types of measurements:
  • the network may configure the UE to report the following measurement information based on SS/PBCH block(s):
  • the network may configure the UE to report the following measurement information based on CSI-RS resources:
  • the network may configure the UE to perform the following types of measurements for NR sidelink and V2X sidelink:
  • 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.
  • 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.
  • 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:
  • Execution criteria The criteria the UE uses for conditional reconfiguration execution.
  • - 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 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.
  • 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.
  • 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.
  • 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.
  • Measurement gaps Periods that the UE may use to perform measurements.
  • 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.
  • 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.
  • the measurement procedures distinguish the following types of cells:
  • 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 D1 Distance between UE and referenceLocation1 is above threshold1 and distance between UE and referenceLocation2 is below threshold2
  • 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)
  • Event Y2 (Candidate L2 U2N Relay UE becomes better than threshold)
  • FIG. 20 shows an example of measurement reporting.
  • 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.
  • 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 NAS signalling.
  • LMF can transfer/deliver AI/ML model(s) to UE via LPP signalling.
  • - gNB can transfer/deliver AI/ML model(s) to UE via UP data.
  • - 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.
  • - Server e.g. OAM, OTT
  • AI/ML model(s) can transfer/delivery AI/ML model(s) to UE (e.g. transparent to 3GPP).
  • Model ID is unique "globally", e.g. in order to manage test certification each retrained version need to be identified.
  • model/function selection/(de)activation/switching/fallback can be UE-initiated or gNB-initiated.
  • model/function selection/(de)activation/switching/fallback can be UE-initiated or LMF-/ gNB-initiated.
  • 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")
  • model ID can be used for model transfer/delivery LCM purpose.
  • 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.
  • performance metrics are available inside the UE.
  • UE can independently monitor a model's performance without any data input from NW.
  • training data can be generated by UE/gNB and terminated at gNB/OAM/OTT server.
  • 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.
  • Whether/how OAM is to be involved may need to consult RAN3, SA5.
  • CN may need to consult RAN3, SA2.
  • 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.
  • Table 6 shows the 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.
  • CN may need to consult RAN3, SA2.
  • 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.
  • Mapped entities a) Model training (offline training) gNB, OAM b) Model transfer/delivery OAM->gNB c) Inference gNB d) Model/functionality monitoring gNB e) Model/functionality control (selection, (de)activation, switching, fallback) gNB
  • Whether/how OAM is to be involved may need to consult RAN3, SA5.
  • CN may need to consult RAN3, SA2.
  • the Table 8 can be used as starting point for discussion on mapping of AI/ML functions to physical entities for positioning with UE -side model (case 1 and 2a).
  • Table 8 shows the mapping of functions to physical entities for positioning with UE-side model (case 1 and 2a).
  • Whether/how OAM is to be involved may need to consult RAN3, SA5.
  • CN/LMF Mobility Management Function
  • 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:
  • an AI/ML model is downloaded when it is needed due to changes in scenarios, configurations, or sites.
  • 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.
  • 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.
  • the UE memory, processing power, energy consumption, signalling overhead should be taken into account.
  • UE collects and directly transfers training data to the OTT server
  • UE collects training data and transfers it to CN.
  • CN transfers the training data to the OTT server.
  • UE collects training data and transfers it to OAM.
  • OAM transfers the needed data to the OTT server.
  • RAN2 did not study or analyze the proposals and did not agree to requirements or recommendations.
  • Applicability can be determined by additional conditions (e.g., scenario, sites, and datasets) as determined/identified between UE-side and NW-side. Additional conditions refer to any aspects that are assumed for the training of the model.
  • model ID can represent the additional condition.
  • NW Implicit procedure assisted by monitoring can be used.
  • NW can recognize the model applicability based on monitoring related results.
  • Reactive reporting Two UE reporting types are identified to convey the applicability related information: Reactive reporting and Proactive reporting.
  • the difference between "reactive" and “proactive” operation can be determined by the point when the UE sends applicability related reporting.
  • UE can trigger the report after receiving any action related model operation from NW.
  • the "proactive" reporting can be triggered before receiving action related model operation from NW.
  • NW can decide model activation according to the applicability.
  • UE can determine the applicability based on the additional condition.
  • additional condition related information which is related to below information:
  • Specific location related information e.g., polygon type, latitude/longitude, altitude, angle, indoor/outdoor, etc.
  • Specific time related information e.g., date, time window, start time, stop time, etc.
  • Specific speed related information e.g., 10km/h, 30km/h, 60km/h, 120km/h, etc.
  • Specific radio quality condition e.g., RSRP, RSRQ, SINR, etc
  • Specific cell/frequency related information e.g., bandwidth, size of subband, carrier frequency, numerologies, etc.
  • Specific antenna related information e.g., antenna port layouts, antenna port numbers, rank numbers/layers, antenna spacing, antenna virtualization, etc.
  • additional conditions There can be two types of additional conditions depending on whether they can be measured at the UE. If the additional condition can be specified and measurable in UE, UE can determine the applicability of functionality/model based on NW configuration. If the additional condition is perceivable/measurable in NW, NW can determine the applicability of functionality/model in UE.
  • UE parameters e.g., number of UE Rx beams
  • model applicability cannot be determined. Note that model inference should be performed in the same environment as training. Otherwise, there would be issues related to consistent and accurate AIML operation. Therefore, it is necessary to recognize UE's situation in NW even though the additional condition is not measurable in UE.
  • a wireless device may be referred to as a user equipment (UE).
  • UE user equipment
  • FIG. 22 shows an example of a method for inference function reporting.
  • FIG. 22 shows an example of a method performed by a wireless device in a wireless communication system.
  • a wireless device may receive, from a network, a reporting configuration including at least one reporting condition related to at least one inference function.
  • the at least one reporting condition may include a condition related to a data quality difference and/or a model performance difference related to the at least one inference function.
  • the reporting configuration may include information related to a threshold value.
  • the reporting configuration may include time information related to evaluation for the data quality difference and/or model performance difference.
  • the time information may include at least one of (i) multiple time periods, (ii) multiple time points, and/or (iii) multiple time windows, when the wireless device should perform measurements for the at least one inference function.
  • a wireless device may transmit, the wireless device to the network, a measurement report based on the reporting condition related to the at least one inference function being satisfied.
  • the measurement report may include applicability related information for the at least one inference function.
  • the applicability related information may inform (i) whether the at least one inference function is applicable or not.
  • the applicability related information may inform (i) whether configuration for the at least one inference function is applicable or not.
  • the measurement report may include information related to the data quality difference and/or the model performance difference.
  • the wireless device may evaluate whether the at least one reporting condition related to the at least one inference function is met.
  • the reporting condition related to the at least one inference function may be satisfied based on that the data quality difference and/or the model performance difference related to the at least one inference function is greater than or equal to a threshold value.
  • the wireless device may perform first evaluation related to the at least one inference function in a first time.
  • the wireless device may perform second evaluation related to the at least one inference function in a second time.
  • the wireless device may derive the data quality difference and/or the model performance difference related to the at least one inference function based on the first evaluation and the second evaluation.
  • the wireless device may perform measurements related to the at least one inference function for (i) reference signal received power (RSRP), (ii) reference signal received quality (RSRQ), (iii) signal-to-interference-plus-noise ratio (SINR), (iv) data throughput, and/or (v) model accuracy.
  • RSRP reference signal received power
  • RSRQ reference signal received quality
  • SINR signal-to-interference-plus-noise ratio
  • the wireless device may perform measurements related to the at least one inference function for (i) block error rate (BLER), (ii) acknowledgement (ACK)/negative acknowledgment (NACK) rate, (iii) input/output data distribution, and/or (iv) number of abnormal value from input/output data.
  • BLER block error rate
  • ACK acknowledgement
  • NACK negative acknowledgment
  • the data quality difference and/or the model performance may be derived based on (i) BLER, (ii) ACK/NACK rate, (iii) input/output data distribution, and/or (iv) number of abnormal value from input/output data.
  • the wireless device may receive, from the network, a management message related to the at least one inference function in response to the measurement report.
  • the management message may include information related to (i) activating the at least one inference function, (ii) deactivating the at least one inference function, (iii) switching the at least one inference function, (iv) updating the at least one inference function, and/or (v) fallback operation for the at least one inference function.
  • the wireless device may activate a first inference function, deactivate a second inference function, update a third inference function, and/or perform fallback operation for a fourth inference function, respectively, upon receiving the management message.
  • 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 for model applicability report based on the amount of change in quality, thereby recognizing UE's situation implicitly in NW, is provided.
  • NW may configure additional condition related configuration, wherein the configuration may include the condition to recognize the change of UE's situation implicitly.
  • the condition may be related to change in radio quality, data quality, etc.
  • UE may evaluate the change of quality based on the configuration. Based on the evaluation, UE may report the change of quality when it satisfies threshold(s). NW may recognize the change of UE's situation based on the change of quality, and it may utilize it as implicit applicability information. NW may determine functionality or model management, such as activation, deactivation, switch, fallback, etc.
  • FIG. 23 shows an example of a method for applicability related report based on change in quality.
  • a network may activate model A.
  • the model A has trained in outdoor condition.
  • step S2302 UE may report applicability information when the change of quality is larger than a threshold.
  • the network may switch model with model B.
  • the model B has trained in indoor condition.
  • FIG. 24 shows an example of a method for applicability related report based on change in quality.
  • NW may configure functionality/model related configuration with additional conditions, wherein the additional condition may include a change of specific (performance) metric related condition.
  • UE may evaluate whether at least one functionality/model is applicable based on additional condition.
  • condition is related to the change of specific metric
  • UE may compare the difference between previous measurement results and current measured results.
  • UE may report applicability related report based on the evaluation. If the condition is related to the change of specific metric, UE may report applicability related report when the difference satisfies the condition.
  • NW may determine functionality/model management related results, such as model (de)activation, switching, update, fallback, etc.
  • NW may configure functionality/model management related results or reconfigure functionality/model.
  • NW may configure functionality/model related configuration with additional conditions
  • Functionality/model related configuration may include following information:
  • Additional conditions may include conditions under which the UE can perform functionality/model related operations
  • Additional condition may include following information:
  • Specific location related information e.g., polygon type, latitude/longitude, altitude, angle, indoor/outdoor, etc.
  • Specific time related information e.g., date, time window, start time, stop time, etc.
  • Specific speed related information e.g., 10km/h, 30km/h, 60km/h, 120km/h, etc.
  • Specific radio quality condition e.g., RSRP, RSRQ, SINR, etc
  • Specific cell/frequency related information e.g., bandwidth, size of subband, carrier frequency, numerologies, etc.
  • Specific antenna related information e.g., antenna port layouts, antenna port numbers, rank numbers/layers, antenna spacing, antenna virtualization, etc.
  • Additional conditions may include a change specific (performance) metric related condition:
  • Radio quality difference information e.g., radio quality difference for RSRP, RSRQ, or SINR. It can be denoted by R rsrp , R rsrq , and R sinr , respectively.
  • Data quality difference information e.g., data quality difference for throughput, BLER, and ACK/NACK rate. It can be denoted by D tput , D bler , and D ack rate or nack rate , respectively.
  • Model performance difference information e.g., model quality difference for model accuracy rate, input/output data distribution, out of input/output data(abnormal value). It can be denoted by M accuracy , M distribution , and M outofdata , respectively.
  • Location difference information e.g., a threshold for change of location from a specific reference point/previous location/etc.
  • Speed difference information e.g., a threshold for change of speed
  • Antenna related difference information e.g., a threshold for change of number of antenna port/rank/layers/etc
  • Time window information e.g., to determine how long UE compares the difference. It can be denoted by T window .
  • Start/Stop time information indicating start/stop of evaluation, e.g., to determine when UE start/stop evaluation the difference. It may be related to a specific time value, and can be denoted by T start and T stop
  • Start/Stop condition information indicating start/stop of evaluation, e.g., to determine when UE start/stop evaluation the difference. It may be related to a specific measurable value, e.g., a specific radio(e.g., a rsrp value)/data(e.g., a data throughput value)/monitoring(e.g., a accuracy value) quality. It can be denoted by C_start and C_stop
  • Period information e.g., periodicity for evaluation for the difference. It can be denoted by T period .
  • Additional condition may consist of one condition or a combination of several conditions
  • Additional condition may have a specific condition ID
  • Additional condition may be linked to a specific functionality/functionality group or a specific model/model group
  • FIG. 25 shows an example of scenarios for model quality.
  • UE may evaluate whether at least one functionality/model is applicable based on additional conditions.
  • UE may compare the difference between previous measurement results and current measured results.
  • quality-based measurement results may vary depending on where the NW is located, i.e., indoor specific NW/model or outdoor specific NW/model
  • RSRP/RSRQ/SINR/Data Throughput/Model accuracy may become high as shown in (a) of FIG. 25.
  • BLER/ACK or NACK rate, input/output data distribution, and input/output out of data may become low as shown in (b) of FIG. 25.
  • BLER/ACK or NACK rate, input/output data distribution, and input/output out of data may become high as shown in (b) of FIG. 25.
  • the quality-based measurement result may be derived in the opposite direction in the case of indoor specific NW/model case, as shown in (c) and (d) of FIG. 25.
  • 3> UE may evaluate whether the difference satisfies the condition as below:
  • UE may derive the current measurement value and compare with previous measurement value
  • 3> UE may evaluate whether the difference satisfies the condition as below:
  • UE may utilize the time information to determine when start or stop evaluation, and/or how long the measure the difference
  • FIG. 26 shows examples of time information for evaluation.
  • UE may count the point where the condition is satisfied
  • UE may report applicability related information based on evaluation.
  • Applicability related information may include at least one of information below:
  • UE may report when the satisfaction is changed, i.e., from applicable to not-applicable, after reporting applicability related report
  • UE may report based on the difference between previous measurement results and current measured results.
  • 3> UE may report when the difference is within the threshold, i.e., when the difference does not satisfy the condition (after reporting)
  • NW may determine functionality/model management related results, such as model (de)activation, switching, update, fallback, etc
  • NW may determine whether the functionality/model configuration needs to be updated
  • NW may configure functionality/model management related results
  • NW may reconfigure functionality/model.
  • FIG. 27 shows an example of a method for model applicability reporting based on the amount of change in quality.
  • FIG. 27 shows an example of a method performed by a wireless device in a wireless communication system.
  • a wireless device may receive information on a report configuration, wherein the configuration includes measurement objects and report condition.
  • a wireless device may evaluate the measurement results for the measurement objects in a first time.
  • a wireless device may evaluate the measurement results for the measurement objects in a second time.
  • a wireless device may determine whether the difference between the first and second measurement results satisfies the report condition.
  • a wireless device may report information related to the difference.
  • the difference between the first and second measurement results can be determined within a specific time window.
  • the first time can be determined by the point where UE sends measurement results related to the beam/cell.
  • FIG. 28 shows an example of a AIML specific method for model applicability reporting based on the amount of change in quality.
  • FIG. 28 shows an example of a method performed by a wireless device in a wireless communication system.
  • a wireless device may receive information on a report configuration associated with a model, wherein the configuration includes measurement objects and report condition,.
  • a wireless device may evaluate the measurement results for the measurement objects in a first time.
  • a wireless device may evaluate the measurement results for the measurement objects in a second time.
  • a wireless device may determine whether the difference between the first and second measurement results satisfies the report condition.
  • a wireless device may report information related to the difference.
  • a wireless device may receive life cycle management related command.
  • the life cycle management may include AIML model and/or functionality activation, deactivation, switch, update, and fallback.
  • Some of the detailed steps shown in the examples of FIGS. 22-28 may not be essential steps and may be omitted. In addition to the steps shown in FIGS. 22-28, 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.
  • a wireless device 100 may include a processor 102, a memory 104, and a transceiver 106.
  • 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 reporting configuration including at least one reporting condition related to at least one inference function, wherein the at least one reporting condition includes a condition related to a data quality difference and/or a model performance difference related to the at least one inference function; and transmitting, to the network, a measurement report based on the reporting condition related to the at least one inference function being satisfied.
  • the measurement report may include applicability related information for the at least one inference function.
  • the operations further comprise: evaluating whether the at least one reporting condition related to the at least one inference function is met.
  • the operations further comprise: performing first evaluation related to the at least one inference function in a first time; and performing second evaluation related to the at least one inference function in a second time.
  • the operations further comprise: deriving the data quality difference and/or the model performance difference related to the at least one inference function based on the first evaluation and the second evaluation.
  • the reporting condition related to the at least one inference function is satisfied based on that the data quality difference and/or the model performance difference related to the at least one inference function is greater than or equal to a threshold value.
  • the reporting configuration includes information related to a threshold value.
  • the measurement report includes information related to the data quality difference and/or the model performance difference.
  • the operations further comprise: performing, by the wireless device, measurements related to the at least one inference function for (i) reference signal received power (RSRP), (ii) reference signal received quality (RSRQ), (iii) signal-to-interference-plus-noise ratio (SINR), (iv) data throughput, and/or (v) model accuracy.
  • RSRP reference signal received power
  • RSS reference signal received quality
  • SINR signal-to-interference-plus-noise ratio
  • data throughput iv
  • model accuracy e.g., the operations related to the at least one inference function for (i) reference signal received power (RSRP), (ii) reference signal received quality (RSRQ), (iii) signal-to-interference-plus-noise ratio (SINR), (iv) data throughput, and/or (v) model accuracy.
  • RSRP reference signal received power
  • RSRQ reference signal received quality
  • SINR signal-to-interference-plus-noise ratio
  • the operations further comprise: performing measurements related to the at least one inference function for (i) block error rate (BLER), (ii) acknowledgement (ACK)/negative acknowledgment (NACK) rate, (iii) input/output data distribution, and/or (iv) number of abnormal value from input/output data.
  • BLER block error rate
  • ACK acknowledgement
  • NACK negative acknowledgment
  • the operations further comprise: performing measurements related to the at least one inference function for (i) block error rate (BLER), (ii) acknowledgement (ACK)/negative acknowledgment (NACK) rate, (iii) input/output data distribution, and/or (iv) number of abnormal value from input/output data.
  • the operations further comprise: receiving, from the network, a management message related to the at least one inference function in response to the measurement report.
  • the management message includes information related to (i) activating the at least one inference function, (ii) deactivating the at least one inference function, (iii) switching the at least one inference function, (iv) updating the at least one inference function, and/or (v) fallback operation for the at least one inference function.
  • the reporting configuration includes time information related to evaluation for the data quality difference and/or model performance difference.
  • 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 reporting configuration including at least one reporting condition related to at least one inference function, wherein the at least one reporting condition includes a condition related to a data quality difference and/or a model performance difference related to the at least one inference function; and transmitting, to the network, a measurement report based on the reporting condition related to the at least one inference function being satisfied.
  • the measurement report may include applicability related information for the at least one inference function.
  • the operations further comprise: evaluating whether the at least one reporting condition related to the at least one inference function is met.
  • the operations further comprise: performing first evaluation related to the at least one inference function in a first time; and performing second evaluation related to the at least one inference function in a second time.
  • the operations further comprise: deriving the data quality difference and/or the model performance difference related to the at least one inference function based on the first evaluation and the second evaluation.
  • the reporting condition related to the at least one inference function is satisfied based on that the data quality difference and/or the model performance difference related to the at least one inference function is greater than or equal to a threshold value.
  • the reporting configuration includes information related to a threshold value.
  • the measurement report includes information related to the data quality difference and/or the model performance difference.
  • the operations further comprise: performing, by the wireless device, measurements related to the at least one inference function for (i) reference signal received power (RSRP), (ii) reference signal received quality (RSRQ), (iii) signal-to-interference-plus-noise ratio (SINR), (iv) data throughput, and/or (v) model accuracy.
  • RSRP reference signal received power
  • RSS reference signal received quality
  • SINR signal-to-interference-plus-noise ratio
  • data throughput iv
  • model accuracy e.g., the operations related to the at least one inference function for (i) reference signal received power (RSRP), (ii) reference signal received quality (RSRQ), (iii) signal-to-interference-plus-noise ratio (SINR), (iv) data throughput, and/or (v) model accuracy.
  • RSRP reference signal received power
  • RSRQ reference signal received quality
  • SINR signal-to-interference-plus-noise ratio
  • the operations further comprise: performing measurements related to the at least one inference function for (i) block error rate (BLER), (ii) acknowledgement (ACK)/negative acknowledgment (NACK) rate, (iii) input/output data distribution, and/or (iv) number of abnormal value from input/output data.
  • BLER block error rate
  • ACK acknowledgement
  • NACK negative acknowledgment
  • the operations further comprise: performing measurements related to the at least one inference function for (i) block error rate (BLER), (ii) acknowledgement (ACK)/negative acknowledgment (NACK) rate, (iii) input/output data distribution, and/or (iv) number of abnormal value from input/output data.
  • the operations further comprise: receiving, from the network, a management message related to the at least one inference function in response to the measurement report.
  • the management message includes information related to (i) activating the at least one inference function, (ii) deactivating the at least one inference function, (iii) switching the at least one inference function, (iv) updating the at least one inference function, and/or (v) fallback operation for the at least one inference function.
  • the reporting configuration includes time information related to evaluation for the data quality difference and/or model performance difference.
  • the processor may be configured to control 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.
  • non-transitory computer-readable medium has stored thereon a plurality of instructions for inference function reporting, according to some embodiments of the present disclosure, will be described.
  • the technical features of the present disclosure could be embodied directly in hardware, in a software executed by a processor, or in a combination of the two.
  • a method performed by a wireless device in a wireless communication may be implemented in hardware, software, firmware, or any combination thereof.
  • a software may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other storage medium.
  • storage medium is coupled to the processor such that the processor can read information from the storage medium.
  • the storage medium may be integral to the processor.
  • the processor and the storage medium may reside in an ASIC.
  • the processor and the storage medium may reside as discrete components.
  • 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 reporting configuration including at least one reporting condition related to at least one inference function, wherein the at least one reporting condition includes a condition related to a data quality difference and/or a model performance difference related to the at least one inference function; and transmitting, to the network, a measurement report based on the reporting condition related to the at least one inference function being satisfied.
  • the measurement report may include applicability related information for the at least one inference function.
  • the operations further comprise: evaluating whether the at least one reporting condition related to the at least one inference function is met.
  • the operations further comprise: performing first evaluation related to the at least one inference function in a first time; and performing second evaluation related to the at least one inference function in a second time.
  • the operations further comprise: deriving the data quality difference and/or the model performance difference related to the at least one inference function based on the first evaluation and the second evaluation.
  • the reporting condition related to the at least one inference function is satisfied based on that the data quality difference and/or the model performance difference related to the at least one inference function is greater than or equal to a threshold value.
  • the reporting configuration includes information related to a threshold value.
  • the measurement report includes information related to the data quality difference and/or the model performance difference.
  • the operations further comprise: performing, by the wireless device, measurements related to the at least one inference function for (i) reference signal received power (RSRP), (ii) reference signal received quality (RSRQ), (iii) signal-to-interference-plus-noise ratio (SINR), (iv) data throughput, and/or (v) model accuracy.
  • RSRP reference signal received power
  • RSS reference signal received quality
  • SINR signal-to-interference-plus-noise ratio
  • data throughput iv
  • model accuracy e.g., the operations related to the at least one inference function for (i) reference signal received power (RSRP), (ii) reference signal received quality (RSRQ), (iii) signal-to-interference-plus-noise ratio (SINR), (iv) data throughput, and/or (v) model accuracy.
  • RSRP reference signal received power
  • RSRQ reference signal received quality
  • SINR signal-to-interference-plus-noise ratio
  • the operations further comprise: performing measurements related to the at least one inference function for (i) block error rate (BLER), (ii) acknowledgement (ACK)/negative acknowledgment (NACK) rate, (iii) input/output data distribution, and/or (iv) number of abnormal value from input/output data.
  • BLER block error rate
  • ACK acknowledgement
  • NACK negative acknowledgment
  • the operations further comprise: performing measurements related to the at least one inference function for (i) block error rate (BLER), (ii) acknowledgement (ACK)/negative acknowledgment (NACK) rate, (iii) input/output data distribution, and/or (iv) number of abnormal value from input/output data.
  • the operations further comprise: receiving, from the network, a management message related to the at least one inference function in response to the measurement report.
  • the management message includes information related to (i) activating the at least one inference function, (ii) deactivating the at least one inference function, (iii) switching the at least one inference function, (iv) updating the at least one inference function, and/or (v) fallback operation for the at least one inference function.
  • the reporting configuration includes time information related to evaluation for the data quality difference and/or model performance difference.
  • 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 BS may transmit, to the wireless device, a reporting configuration including at least one reporting condition related to at least one inference function, wherein the at least one reporting condition includes a condition related to a data quality difference and/or a model performance difference related to the at least one inference function.
  • the BS may receive, from the wireless device, a measurement report based on the reporting condition related to the at least one inference function being satisfied.
  • BS base station
  • the BS may include a transceiver, a memory, and a processor operatively coupled to the transceiver and the memory.
  • the processor may be configured to control the transceiver to transmit, to the wireless device, a reporting configuration including at least one reporting condition related to at least one inference function, wherein the at least one reporting condition includes a condition related to a data quality difference and/or a model performance difference related to the at least one inference function.
  • the processor may be configured to control the transceiver to receive, from the wireless device, a measurement report based on the reporting condition related to the at least one inference function being satisfied.
  • the present disclosure can have various advantageous effects.
  • a wireless device could efficiently perform inference function monitoring.
  • the NW can determine changes in the UE's environment based on the amount of change according to quality criteria and uses it as implicit model applicability information. Using this, NW can perform model Activation/Deactivation/Switching/Fallback, etc.
  • AI/ML models can be operated efficiently.
  • the wireless communication system could provide an efficient solution for Model Applicability Reporting based on the amount of change in quality.

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Abstract

L'invention concerne un procédé et un appareil de rapport de fonction d'inférence. Un dispositif sans fil reçoit une configuration de rapport comprenant au moins une condition de rapport relative à au moins une fonction d'inférence. La ou les conditions de rapport comprennent une condition liée à une différence de qualité de données et/ou une différence de performance de modèle liée à la ou aux fonctions d'inférence. Le dispositif sans fil transmet un rapport de mesure sur la base de la condition de rapport relative à la satisfaction de la ou des fonctions d'inférence.
PCT/KR2025/001180 2024-01-29 2025-01-22 Procédé et appareil de rapport de fonction d'inférence Pending WO2025165033A1 (fr)

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Citations (2)

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US20230209390A1 (en) * 2020-08-24 2023-06-29 Huawei Technologies Co., Ltd. Intelligent Radio Access Network
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US20230209390A1 (en) * 2020-08-24 2023-06-29 Huawei Technologies Co., Ltd. Intelligent Radio Access Network
WO2023148665A1 (fr) * 2022-02-04 2023-08-10 Lenovo (Singapore) Pte. Ltd. Mesure et rapport pour positionnement basé sur l'intelligence artificielle

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XUEMING PAN, VIVO: "Other aspects on AI/ML for positioning accuracy enhancement", 3GPP DRAFT; R1-2306745; TYPE DISCUSSION; FS_NR_AIML_AIR, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. RAN WG1, no. Toulouse, FR; 20230821 - 20230825, 11 August 2023 (2023-08-11), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France, XP052435977 *
YIZHI YAO, INTEL, NEC, NOKIA, NOKIA SHANGHAI BELL, HUAWEI, CATT, ERICSSON, DEUTSCHE TELEKOM, TELUS, CHINA MOBILE, NTT DOCOMO: "Enhancements for AI-ML management", 3GPP DRAFT; S5-240191; TYPE CR; CR 0076; AIML_MGT, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. SA WG5, no. Sevilla, ES; 20240129 - 20240202, 19 January 2024 (2024-01-19), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France, XP052555968 *

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