Method and apparatus for supporting ai and ml operation in a wireless communication system

Abstract

A method and apparatus for supporting AI and ML operation in a wireless communication system is provided. A wireless device detects a failure in an operation with a network; and transmits a message including (i) information on the failure, and (ii) information on a predicted information related to the failure, wherein the predicted information is derived by an Artificial Intelligence (AI) and / or Machine Learning (ML) model of the wireless device.

Description

METHOD AND APPARATUS FOR SUPPORTING AI AND ML OPERATION IN A WIRELESS COMMUNICATION SYSTEM

[0001] The present disclosure relates to a method and apparatus for supporting AI and ML operation in a wireless communication system.

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

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

[0004] The NR targets a single technical framework addressing all usage scenarios, requirements and deployment scenarios including enhanced mobile broadband (eMBB), massive machine-type-communications (mMTC), ultra-reliable and low latency communications (URLLC), etc. The NR shall be inherently forward compatible.

[0005] UE supporting AI / ML operations may derive measurements results based on Artificial Intelligence (AI) / Machine Learning (ML) based methodologies or based on non-AI / ML based methodologies. In case the UE experiences connection failure, the UE may report available measurement results as part of failure report to the network. Upon receiving the failure report, network cannot clearly determine which of the following is a likely failure cause:

[0006] - Case a) the failure is expected to be caused by inappropriate AI / ML operations of the UE while the failure is not caused by network coverage problem or other inappropriate UE configuration

[0007] - Case b) the failure is expected to be not caused by inappropriate AI / ML operations of the UE but caused by network coverage problem or other inappropriate UE configuration

[0008] This ambiguity from network side occurs mainly because existing failure report procedure are insufficient in particular when UE is configured or allowed to operate AI / ML based prediction task for measurements and / or other 3GPP procedures. For instance, existing failure report procedure does not indicate whether the UE was performing AI / ML-based for CSI measurement report / RRM measurement report or legacy measurement report, and existing failure report procedure does not indicate whether the failed connection is possibly caused by incorrect predictive mobility based on improper AI / ML operations of the UE or possibly caused by incorrect network decision.

[0009] Even if the network could infer that the failure is possibly related to the AI / ML operation, it is difficult to know how to correct the AI / ML related operations because existing failure report lacks information related to AI / ML operations performed by the UE at the time of the failure. As a result, the network may not recognize whether AI / ML-related settings need to be changed, and erroneous results in AI / ML operation may have been used as input for training or as an interference result.

[0010] Therefore, studies for supporting AI and ML operation in a wireless communication system are required.

[0011] In an aspect, a method performed by a wireless device in a wireless communication system is provided. The method comprises: detecting a failure in an operation with a network; and transmitting a message including (i) information on the failure, and (ii) information on a predicted information related to the failure, wherein the predicted information is derived by an Artificial Intelligence (AI) and / or Machine Learning (ML) model of the wireless device.

[0012] In another aspect, an apparatus for implementing the above method is provided.

[0013] The present disclosure can have various advantageous effects.

[0014] According to some embodiments of the present disclosure, a wireless device could efficiently supporting the AI / ML operation by reporting information related to the AI / ML operation.

[0015] For example, if the network can recognize a problem with the AI / ML model through the failure report, the network manages the AI / ML model well from a model monitoring perspective by updating the parameters of the current AI / ML model or changing a more suitable AI / ML model.

[0016] In other words, for example, the network can efficiently recognize AI / ML problems in the UE. The AI / ML models to be used in the UE can be managed efficiently (for example, the UE could efficiently receive configuration of a new AI / ML model).

[0017] According to some embodiments of the present disclosure, a wireless network system could provide an efficient manage the AI / ML operation of a wireless device by receiving the information related to the AI / ML operation.

[0018] Advantageous effects which can be obtained through specific embodiments of the present disclosure are not limited to the advantageous effects listed above. For example, there may be a variety of technical effects that a person having ordinary skill in the related art can understand and / or derive from the present disclosure. Accordingly, the specific effects of the present disclosure are not limited to those explicitly described herein, but may include various effects that may be understood or derived from the technical features of the present disclosure.

[0019] FIG. 1 shows an example of a communication system to which implementations of the present disclosure is applied.

[0020] FIG. 2 shows an example of wireless devices to which implementations of the present disclosure is applied.

[0021] FIG. 3 shows an example of a wireless device to which implementations of the present disclosure is applied.

[0022] FIG. 4 shows another example of wireless devices to which implementations of the present disclosure is applied.

[0023] FIG. 5 shows an example of UE to which implementations of the present disclosure is applied.

[0024] 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.

[0025] FIG. 8 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.

[0026] FIG. 9 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure is applied.

[0027] FIG. 10 shows an example of a Functional Framework for RAN Intelligence.

[0028] FIG. 11 shows an example of an AI / ML Model Training in OAM and AI / ML Model Inference in NG-RAN node.

[0029] FIG. 12 shows an example of Model Training and Model Inference both located in RAN node.

[0030] FIGS. 13 and 14 show an example of an architecture of neuron and neural network.

[0031] FIG. 15 shows an example of an AI / ML inference.

[0032] FIG. 16 shows an example of an MLP DNN model.

[0033] FIG. 17 shows an example of a CNN model.

[0034] FIG. 18 shows an example of an RNN model.

[0035] FIG. 19 shows an example of Reinforcement learning.

[0036] FIG. 20 shows an example of a method for supporting AI and ML operation in a wireless communication system.

[0037] FIG. 21 shows an example of a method for supporting AI and ML operation in a wireless communication system.

[0038] FIG. 22 shows an example of time elapsed from receiving the AI / ML model configuration.

[0039] The following techniques, apparatuses, and systems may be applied to a variety of wireless multiple access systems. Examples of the multiple access systems include a code division multiple access (CDMA) system, a frequency division multiple access (FDMA) system, a time division multiple access (TDMA) system, an orthogonal frequency division multiple access (OFDMA) system, a single carrier frequency division multiple access (SC-FDMA) system, and a multicarrier frequency division multiple access (MC-FDMA) system. CDMA may be embodied through radio technology such as universal terrestrial radio access (UTRA) or CDMA2000. TDMA may be embodied through radio technology such as global system for mobile communications (GSM), general packet radio service (GPRS), or enhanced data rates for GSM evolution (EDGE). OFDMA may be embodied through radio technology such as institute of electrical and electronics engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, or evolved UTRA (E-UTRA). UTRA is a part of a universal mobile telecommunications system (UMTS). 3rd generation partnership project (3GPP) long term evolution (LTE) is a part of evolved UMTS (E-UMTS) using E-UTRA. 3GPP LTE employs OFDMA in DL and SC-FDMA in UL. LTE-advanced (LTE-A) is an evolved version of 3GPP LTE.

[0040] For convenience of description, implementations of the present disclosure are mainly described in regards to a 3GPP based wireless communication system. However, the technical features of the present disclosure are not limited thereto. For example, although the following detailed description is given based on a mobile communication system corresponding to a 3GPP based wireless communication system, aspects of the present disclosure that are not limited to 3GPP based wireless communication system are applicable to other mobile communication systems.

[0041] For terms and technologies which are not specifically described among the terms of and technologies employed in the present disclosure, the wireless communication standard documents published before the present disclosure may be referenced.

[0042] In the present disclosure, "A or B" may mean "only A", "only B", or "both A and B". In other words, "A or B" in the present disclosure may be interpreted as "A and / or B". For example, "A, B or C" in the present disclosure may mean "only A", "only B", "only C", or "any combination of A, B and C".

[0043] In the present disclosure, slash ( / ) or comma (,) may mean "and / or". For example, "A / B" may mean "A and / or B". Accordingly, "A / B" may mean "only A", "only B", or "both A and B". For example, "A, B, C" may mean "A, B or C".

[0044] In the present disclosure, "at least one of A and B" may mean "only A", "only B" or "both A and B". In addition, the expression "at least one of A or B" or "at least one of A and / or B" in the present disclosure may be interpreted as same as "at least one of A and B".

[0045] In addition, in the present disclosure, "at least one of A, B and C" may mean "only A", "only B", "only C", or "any combination of A, B and C". In addition, "at least one of A, B or C" or "at least one of A, B and / or C" may mean "at least one of A, B and C".

[0046] Also, parentheses used in the present disclosure may mean "for example". In detail, when it is shown as "control information (PDCCH)", "PDCCH" may be proposed as an example of "control information". In other words, "control information" in the present disclosure is not limited to "PDCCH", and "PDCCH" may be proposed as an example of "control information". In addition, even when shown as "control information (i.e., PDCCH)", "PDCCH" may be proposed as an example of "control information".

[0047] Technical features that are separately described in one drawing in the present disclosure may be implemented separately or simultaneously.

[0048] Although not limited thereto, various descriptions, functions, procedures, suggestions, methods and / or operational flowcharts of the present disclosure disclosed herein can be applied to various fields requiring wireless communication and / or connection (e.g., 5G) between devices.

[0049] Hereinafter, the present disclosure will be described in more detail with reference to drawings. The same reference numerals in the following drawings and / or descriptions may refer to the same and / or corresponding hardware blocks, software blocks, and / or functional blocks unless otherwise indicated.

[0050] FIG. 1 shows an example of a communication system to which implementations of the present disclosure is applied.

[0051] 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.

[0052] 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).

[0053] Partial use cases may require a plurality of categories for optimization and other use cases may focus only upon one key performance indicator (KPI). 5G supports such various use cases using a flexible and reliable method.

[0054] 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. In 5G, it is expected that 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. These many application programs require connectivity of an always turned-on state in order to push real-time information and alarm for users. 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.

[0055] In addition, 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.

[0056] 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.

[0057] 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.

[0058] 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. In the future, 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.

[0059] 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.

[0060] Consumption and distribution of energy including heat or gas is distributed at a higher level so that automated control of the distribution sensor network is demanded. 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.

[0061] Mission critical application (e.g., e-health) 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.

[0062] 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. However, in order to achieve this replacement, 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.

[0063] 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.

[0064] Referring to FIG. 1, the communication system 1 includes wireless devices 100a to 100f, base stations (BSs) 200, and a network 300. Although FIG. 1 illustrates a 5G network as an example of the network of the communication system 1, the implementations of the present disclosure are not limited to the 5G system, and can be applied to the future communication system beyond the 5G system.

[0065] 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.

[0066] 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. 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. For example, the vehicles may include a vehicle having a wireless communication function, an autonomous driving vehicle, and a vehicle capable of performing communication between vehicles. The vehicles may include an unmanned aerial vehicle (UAV) (e.g., a drone). The XR device may include an 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.

[0067] In the present disclosure, the wireless devices 100a to 100f may be called user equipments (UEs). A UE may include, for example, a cellular phone, a smartphone, a laptop computer, a digital broadcast terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation system, a slate personal computer (PC), a tablet PC, an ultrabook, a vehicle, a vehicle having an autonomous traveling function, a connected car, an UAV, an AI module, a robot, an AR device, a VR device, an MR device, a hologram device, a public safety device, an MTC device, an IoT device, a medical device, a FinTech device (or a financial device), a security device, a weather / environment device, a device related to a 5G service, or a device related to a fourth industrial revolution field.

[0068] The UAV may be, for example, an aircraft aviated by a wireless control signal without a human being onboard.

[0069] 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.

[0070] 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.

[0071] The MTC device and the IoT device may be, for example, devices that do not require direct human intervention or manipulation. For example, the MTC device and the IoT device may include smartmeters, vending machines, thermometers, smartbulbs, door locks, or various sensors.

[0072] The medical device may be, for example, a device used for the purpose of diagnosing, treating, relieving, curing, or preventing disease. For example, the medical device may be a device used for the purpose of diagnosing, treating, relieving, or correcting injury or impairment. For example, the medical device may be a device used for the purpose of inspecting, replacing, or modifying a structure or a function. For example, the medical device may be a device used for the purpose of adjusting pregnancy. For example, 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.

[0073] The security device may be, for example, a device installed to prevent a danger that may arise and to maintain safety. For example, the security device may be a camera, a closed-circuit TV (CCTV), a recorder, or a black box.

[0074] The FinTech device may be, for example, a device capable of providing a financial service such as mobile payment. For example, the FinTech device may include a payment device or a point of sales (POS) system.

[0075] The weather / environment device may include, for example, a device for monitoring or predicting a weather / environment.

[0076] The wireless devices 100a to 100f may be connected to the network 300 via the BSs 200. An AI technology may be applied to the wireless devices 100a to 100f and the wireless devices 100a to 100f may be connected to the AI server 400 via the network 300. The network 300 may be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, and a beyond-5G network. Although the wireless devices 100a to 100f may communicate with each other through the BSs 200 / network 300, the wireless devices 100a to 100f may perform direct communication (e.g., sidelink communication) with each other without passing through the BSs 200 / network 300. For example, the vehicles 100b-1 and 100b-2 may perform direct communication (e.g., vehicle-to-vehicle (V2V) / vehicle-to-everything (V2X) communication). The IoT device (e.g., a sensor) may perform direct communication with other IoT devices (e.g., sensors) or other wireless devices 100a to 100f.

[0077] Wireless communication / connections 150a, 150b and 150c may be established between the wireless devices 100a to 100f and / or between wireless device 100a to 100f and BS 200 and / or between BSs 200. Herein, the wireless communication / connections may be established through various RATs (e.g., 5G NR) such as uplink / downlink communication 150a, sidelink communication (or device-to-device (D2D) communication) 150b, inter-base station communication 150c (e.g., relay, integrated access and backhaul (IAB)), etc. The wireless devices 100a to 100f and the BSs 200 / the wireless devices 100a to 100f may transmit / receive radio signals to / from each other through the wireless communication / connections 150a, 150b and 150c. For example, the wireless communication / connections 150a, 150b and 150c may transmit / receive signals through various physical channels. To this end, at least a part of various configuration information configuring processes, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, and resource mapping / de-mapping), and resource allocating processes, for transmitting / receiving radio signals, may be performed based on the various proposals of the present disclosure.

[0078] Here, 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. For example, NB-IoT technology may be an example of low power wide area network (LPWAN) technology, may be implemented in specifications such as LTE Cat NB1 and / or LTE Cat NB2, and may not be limited to the above-mentioned names. Additionally and / or alternatively, the radio communication technologies implemented in the wireless devices in the present disclosure may communicate based on LTE-M technology. For example, LTE-M technology may be an example of LPWAN technology and be called by various names such as enhanced machine type communication (eMTC). For example, LTE-M technology may be implemented in at least one of the various specifications, such as 1) LTE Cat 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-bandwidth limited (non-BL), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and may not be limited to the above-mentioned names. Additionally and / or alternatively, the radio communication technologies implemented in the wireless devices in the present disclosure may include at least one of ZigBee, Bluetooth, and / or LPWAN which take into account low-power communication, and may not be limited to the above-mentioned names. For example, ZigBee technology may generate personal area networks (PANs) associated with small / low-power digital communication based on various specifications such as IEEE 802.15.4 and may be called various names.

[0079] FIG. 2 shows an example of wireless devices to which implementations of the present disclosure is applied.

[0080] Referring to FIG. 2, 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). In FIG. 2, {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.

[0081] 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. For example, 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. For example, 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. Herein, 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). In the present disclosure, the first wireless device 100 may represent a communication modem / circuit / chip.

[0082] 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. For example, 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. For example, 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. Herein, 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). In the present disclosure, the second wireless device 200 may represent a communication modem / circuit / chip.

[0083] Hereinafter, hardware elements of the wireless devices 100 and 200 will be described more specifically. One or more protocol layers may be implemented by, without being limited to, one or more processors 102 and 202. For example, the one or more processors 102 and 202 may implement one or more layers (e.g., functional layers such as physical (PHY) layer, media access control (MAC) layer, radio link control (RLC) layer, packet data convergence protocol (PDCP) layer, radio resource control (RRC) layer, and service data adaptation protocol (SDAP) layer). The one or more processors 102 and 202 may generate one or more protocol data units (PDUs) 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.

[0084] The one or more processors 102 and 202 may be referred to as controllers, microcontrollers, microprocessors, or microcomputers. The one or more processors 102 and 202 may be implemented by hardware, firmware, software, or a combination thereof. As an example, one or more application specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more digital signal processing devices (DSPDs), one or more programmable logic devices (PLDs), or one or more field programmable gate arrays (FPGAs) may be included in the one or more processors 102 and 202. descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure 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.

[0085] 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.

[0086] The one or more transceivers 106 and 206 may transmit user data, control information, and / or radio signals / channels, mentioned in the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure, to one or more other devices. The one or more transceivers 106 and 206 may receive user data, control information, and / or radio signals / channels, mentioned in the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure, from one or more other devices. For example, the one or more transceivers 106 and 206 may be connected to the one or more processors 102 and 202 and transmit and receive radio signals. For example, the one or more processors 102 and 202 may perform control so that the one or more transceivers 106 and 206 may transmit user data, control information, or radio signals to one or more other devices. The one or more processors 102 and 202 may perform control so that the one or more transceivers 106 and 206 may receive user data, control information, or radio signals from one or more other devices.

[0087] 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. In the present disclosure, the one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports).

[0088] 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. To this end, the one or more transceivers 106 and 206 may include (analog) oscillators and / or filters. For example, 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.

[0089] In the implementations of the present disclosure, a UE may operate as a transmitting device in uplink (UL) and as a receiving device in downlink (DL). In the implementations of the present disclosure, a BS may operate as a receiving device in UL and as a transmitting device in DL. Hereinafter, for convenience of description, it is mainly assumed that the first wireless device 100 acts as the UE, and the second wireless device 200 acts as the BS. For example, the processor(s) 102 connected to, mounted on or launched in the first wireless device 100 may be 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.

[0090] In the present disclosure, a BS is also referred to as a node B (NB), an eNode B (eNB), or a gNB.

[0091] FIG. 3 shows an example of a wireless device to which implementations of the present disclosure is applied.

[0092] The wireless device may be implemented in various forms according to a use-case / service (refer to FIG. 1).

[0093] Referring to FIG. 3, 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. For example, 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. For example, 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. For example, the transceiver(s) 114 may include the one or more transceivers 106 and 206 of FIG. 2 and / or the one or more antennas 108 and 208 of FIG. 2. 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.

[0094] The additional components 140 may be variously configured according to types of the wireless devices 100 and 200. For example, the additional components 140 may include at least one of a power unit / battery, input / output (I / O) unit (e.g., audio I / O port, video I / O port), a driving unit, and a computing unit. 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. 1), a digital broadcast terminal, a hologram device, a public safety device, an MTC device, a medicine device, a FinTech device (or a finance device), a security device, a climate / environment device, the AI server / device (400 of FIG. 1), the BSs (200 of FIG. 1), a network node, etc. The wireless devices 100 and 200 may be used in a mobile or fixed place according to a use-example / service.

[0095] In FIG. 3, 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. For example, in each of the wireless devices 100 and 200, 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. For example, the control unit 120 may be configured by a set of one or more processors. As an example, the 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. As another example, 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.

[0096] FIG. 4 shows another example of wireless devices to which implementations of the present disclosure is applied.

[0097] Referring to FIG. 4, 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.

[0098] 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. For example, the software code 105 may implement instructions that, when executed by the processor 102, perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. For example, the software code 105 may control the processor 102 to perform one or more protocols. For example, the software code 105 may control the processor 102 may perform one or more layers of the radio interface protocol.

[0099] 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. For example, the software code 205 may implement instructions that, when executed by the processor 202, perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. For example, the software code 205 may control the processor 202 to perform one or more protocols. For example, the software code 205 may control the processor 202 may perform one or more layers of the radio interface protocol.

[0100] FIG. 5 shows an example of UE to which implementations of the present disclosure is applied.

[0101] Referring to FIG. 5, a UE 100 may correspond to the first wireless device 100 of FIG. 2 and / or the first wireless device 100 of FIG. 4.

[0102] 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.

[0103] 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). An example of the processor 102 may be found in SNAPDRAGONTMseries of processors made by Qualcomm®, EXYNOSTMseries of processors made by Samsung®, A series of processors made by Apple®, HELIOTMseries of processors made by MediaTek®, ATOMTMseries of processors made by Intel®or a corresponding next generation processor.

[0104] The memory 104 is operatively coupled with the processor 102 and stores a variety of information to operate the processor 102. The memory 104 may include ROM, RAM, flash memory, memory card, storage medium and / or other storage device. When the embodiments are implemented in software, the techniques described herein can be implemented with modules (e.g., procedures, functions, etc.) that perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. The modules can be stored in the memory 104 and executed by the processor 102. The memory 104 can be implemented within the processor 102 or external to the processor 102 in which case those can be communicatively coupled to the processor 102 via various means as is known in the art.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In particular, FIG. 6 illustrates an example of a radio interface user plane protocol stack between a UE and a BS and 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. Referring to FIG. 6, the user plane protocol stack may be divided into Layer 1 (i.e., a PHY layer) and Layer 2. Referring to FIG. 7, the control plane protocol stack may be divided into Layer 1 (i.e., a PHY layer), Layer 2, Layer 3 (e.g., an RRC layer), and a non-access stratum (NAS) layer. Layer 1, Layer 2 and Layer 3 are referred to as an access stratum (AS).

[0112] In the 3GPP LTE system, the Layer 2 is split into the following sublayers: MAC, RLC, and PDCP. In the 3GPP NR system, the Layer 2 is split into the following sublayers: MAC, RLC, PDCP and SDAP. The PHY layer offers to the MAC sublayer transport channels, the MAC sublayer offers to the RLC sublayer logical channels, the RLC sublayer offers to the PDCP sublayer RLC channels, the PDCP sublayer offers to the SDAP sublayer radio bearers. The SDAP sublayer offers to 5G core network quality of service (QoS) flows.

[0113] In the 3GPP NR system, the main services and functions of the MAC sublayer include: mapping between logical channels and transport channels; multiplexing / de-multiplexing of MAC SDUs belonging to one or different logical channels into / from transport blocks (TB) delivered to / from the physical layer on transport channels; scheduling information reporting; error correction through hybrid automatic repeat request (HARQ) (one HARQ entity per cell in case of carrier aggregation (CA)); priority handling between UEs by means of dynamic scheduling; priority handling between logical channels of one UE by means of logical channel prioritization; padding. A single MAC entity may support multiple numerologies, transmission timings and cells. Mapping restrictions in logical channel prioritization control which numerology(ies), cell(s), and transmission timing(s) a logical channel can use.

[0114] Different kinds of data transfer services are offered by MAC. To accommodate different kinds of data transfer services, multiple types of logical channels are defined, i.e., each supporting transfer of a particular type of information. Each logical channel type is defined by what type of information is transferred. Logical channels are classified into two groups: control channels and traffic channels. Control channels are used for the transfer of control plane information only, and traffic channels are used for the transfer of user plane information only. Broadcast control channel (BCCH) is a downlink logical channel for broadcasting system control information, paging control channel (PCCH) is a downlink logical channel that transfers paging information, system information change notifications and indications of ongoing public warning service (PWS) broadcasts, common control channel (CCCH) is a logical channel for transmitting control information between UEs and network and used for UEs having no RRC connection with the network, and dedicated control channel (DCCH) is a point-to-point bi-directional logical channel that transmits dedicated control information between a UE and the network and used by UEs having an RRC connection. Dedicated traffic channel (DTCH) is a point-to-point logical channel, dedicated to one UE, for the transfer of user information. A DTCH can exist in both uplink and downlink. In downlink, the following connections between logical channels and transport channels exist: BCCH can be mapped to broadcast channel (BCH); BCCH can be mapped to downlink shared channel (DL-SCH); PCCH can be mapped to paging channel (PCH); CCCH can be mapped to DL-SCH; DCCH can be mapped to DL-SCH; and DTCH can be mapped to DL-SCH. In uplink, the following connections between logical channels and transport channels exist: CCCH can be mapped to uplink shared channel (UL-SCH); DCCH can be mapped to UL-SCH; and DTCH can be mapped to UL-SCH.

[0115] The RLC sublayer supports three transmission modes: transparent mode (TM), unacknowledged mode (UM), and acknowledged node (AM). The RLC configuration is per logical channel with no dependency on numerologies and / or transmission durations. In the 3GPP NR system, the main services and functions of the RLC sublayer depend on the transmission mode and include: transfer of upper layer PDUs; sequence numbering independent of the one in PDCP (UM and AM); error correction through ARQ (AM only); segmentation (AM and UM) and re-segmentation (AM only) of RLC SDUs; reassembly of SDU (AM and UM); duplicate detection (AM only); RLC SDU discard (AM and UM); RLC re-establishment; protocol error detection (AM only).

[0116] In the 3GPP NR system, the main services and functions of the PDCP sublayer for the user plane include: sequence numbering; header compression and decompression using robust header compression (ROHC); transfer of user data; reordering and duplicate detection; in-order delivery; PDCP PDU routing (in case of split bearers); retransmission of PDCP SDUs; ciphering, deciphering and integrity protection; PDCP SDU discard; PDCP re-establishment and data recovery for RLC AM; PDCP status reporting for RLC AM; duplication of PDCP PDUs and duplicate discard indication to lower layers. The main services and functions of the PDCP sublayer for the control plane include: sequence numbering; ciphering, deciphering and integrity protection; transfer of control plane data; reordering and duplicate detection; in-order delivery; duplication of PDCP PDUs and duplicate discard indication to lower layers.

[0117] In the 3GPP NR system, the main services and functions of SDAP include: mapping between a QoS flow and a data radio bearer; marking QoS flow ID (QFI) in both DL and UL packets. A single protocol entity of SDAP is configured for each individual PDU session.

[0118] In the 3GPP NR system, the main services and functions of the RRC sublayer include: broadcast of system information related to AS and NAS; paging initiated by 5GC or NG-RAN; establishment, maintenance and release of an RRC connection between the UE and NG-RAN; security functions including key management; establishment, configuration, maintenance and release of signaling radio bearers (SRBs) and data radio bearers (DRBs); mobility functions (including: handover and context transfer, UE cell selection and reselection and control of cell selection and reselection, inter-RAT mobility); QoS management functions; UE measurement reporting and control of the reporting; detection of and recovery from radio link failure; NAS message transfer to / from NAS from / to UE.

[0119] FIG. 8 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.

[0120] The frame structure shown in FIG. 8 is purely exemplary and the number of subframes, the number of slots, and / or the number of symbols in a frame may be variously changed. In the 3GPP based wireless communication system, OFDM numerologies (e.g., subcarrier spacing (SCS), transmission time interval (TTI) duration) may be differently configured between a plurality of cells aggregated for one UE. For example, if a UE is configured with different SCSs for cells aggregated for the cell, an (absolute time) duration of a time resource (e.g., a subframe, a slot, or a TTI) including the same number of symbols may be different among the aggregated cells. Herein, symbols may include OFDM symbols (or CP-OFDM symbols), SC-FDMA symbols (or discrete Fourier transform-spread-OFDM (DFT-s-OFDM) symbols).

[0121] Referring to FIG. 8, downlink and uplink transmissions are organized into frames. Each frame has Tf= 10ms duration. Each frame is divided into two half-frames, where each of the half-frames has 5ms duration. Each half-frame consists of 5 subframes, where the duration Tsfper subframe is 1ms. Each subframe is divided into slots and the number of slots in a subframe depends on a subcarrier spacing. Each slot includes 14 or 12 OFDM symbols based on a cyclic prefix (CP). In a normal CP, each slot includes 14 OFDM symbols and, in an extended CP, each slot includes 12 OFDM symbols. The numerology is based on exponentially scalable subcarrier spacing △f = 2u*15 kHz.

[0122] Table 1 shows the number of OFDM symbols per slot Nslotsymb, the number of slots per frameNframe,uslot, and the number of slots per subframe Nsubframe,uslotfor the normal CP, according to the subcarrier spacing △f = 2u*15 kHz.

[0123]

[0124] Table 2 shows the number of OFDM symbols per slot Nslotsymb, the number of slots per frameNframe,uslot, and the number of slots per subframe Nsubframe,uslotfor the extended CP, according to the subcarrier spacing △f = 2u*15 kHz.

[0125]

[0126] A slot includes plural symbols (e.g., 14 or 12 symbols) in the time domain. For each numerology (e.g., subcarrier spacing) and carrier, a resource grid ofNsize,ugrid,x*NRBscsubcarriers andNsubframe,usymbOFDM symbols is defined, starting at common resource block (CRB)Nstart,ugridindicated by higher-layer signaling (e.g., RRC signaling), whereNsize,ugrid,xis the number of resource blocks (RBs) in the resource grid and the subscript x is DL for downlink and UL for uplink.NRBscis the number of subcarriers per RB. In the 3GPP based wireless communication system,NRBscis 12 generally. There is one resource grid for a given antenna portp, subcarrier spacing configurationu, and transmission direction (DL or UL). The carrier bandwidthNsize,ugridfor subcarrier spacing configurationuis given by the higher-layer parameter (e.g., RRC parameter). Each element in the resource grid for the antenna portpand the subcarrier spacing configurationuis referred to as a resource element (RE) and one complex symbol may be mapped to each RE. Each RE in the resource grid is uniquely identified by an indexkin the frequency domain and an indexlrepresenting a symbol location relative to a reference point in the time domain. In the 3GPP based wireless communication system, an RB is defined by 12 consecutive subcarriers in the frequency domain.

[0127] In the 3GPP NR system, RBs are classified into CRBs and physical resource blocks (PRBs). CRBs are numbered from 0 and upwards in the frequency domain for subcarrier spacing configurationu. The center of subcarrier 0 of CRB 0 for subcarrier spacing configurationucoincides with 'point A' which serves as a common reference point for resource block grids. In the 3GPP NR system, PRBs are defined within a bandwidth part (BWP) and numbered from 0 toNsizeBWP,i-1, where i is the number of the bandwidth part. The relation between the physical resource block nPRBin the bandwidth part i and the common resource block nCRBis as follows: nPRB= nCRB+NsizeBWP,i, whereNsizeBWP,iis the common resource block where bandwidth part starts relative to CRB 0. The BWP includes a plurality of consecutive RBs. A carrier may include a maximum of N (e.g., 5) BWPs. A UE may be configured with one or more BWPs on a given component carrier. Only one BWP among BWPs configured to the UE can active at a time. The active BWP defines the UE's operating bandwidth within the cell's operating bandwidth.

[0128] 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. For example, the frequency ranges of the two types (FR1 and FR2) may be as shown in Table 3 below. For ease of explanation, in the frequency ranges used in the NR system, FR1 may mean "sub 6 GHz range", FR2 may mean "above 6 GHz range," and may be referred to as millimeter wave (mmW).

[0129]

[0130] As mentioned above, the numerical value of the frequency range of the NR system may be changed. For example, FR1 may include a frequency band of 410MHz to 7125MHz as shown in Table 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).

[0131]

[0132] In the present disclosure, the term "cell" may refer to a geographic area to which one or more nodes provide a communication system, or refer to radio resources. A "cell" as a geographic area may be understood as coverage within which a node can provide service using a carrier and a "cell" as radio resources (e.g., time-frequency resources) is associated with bandwidth which is a frequency range configured by the carrier. The "cell" associated with the radio resources is defined by a combination of downlink resources and uplink resources, for example, a combination of a DL component carrier (CC) and a UL CC. The cell may be configured by downlink resources only, or may be configured by downlink resources and uplink resources. Since DL coverage, which is a range within which the node is capable of transmitting a valid signal, and UL coverage, which is a range within which the node is capable of receiving the valid signal from the UE, depends upon a carrier carrying the signal, the coverage of the node may be associated with coverage of the "cell" of radio resources used by the node. Accordingly, the term "cell" may be used to represent service coverage of the node sometimes, radio resources at other times, or a range that signals using the radio resources can reach with valid strength at other times.

[0133] In CA, two or more CCs are aggregated. A UE may simultaneously receive or transmit on one or multiple CCs depending on its capabilities. CA is supported for both contiguous and non-contiguous CCs. When CA is configured, the UE only has one RRC connection with the network. At RRC connection establishment / re-establishment / handover, one serving cell provides the NAS mobility information, and at RRC connection re-establishment / handover, one serving cell provides the security input. This cell is referred to as the primary cell (PCell). The PCell is a cell, operating on the primary frequency, in which the UE either performs the initial connection establishment procedure or initiates the connection re-establishment procedure. Depending on UE capabilities, secondary cells (SCells) can be configured to form together with the PCell a set of serving cells. An SCell is a cell providing additional radio resources on top of special cell (SpCell). The configured set of serving cells for a UE therefore always consists of one PCell and one or more SCells. For dual connectivity (DC) operation, the term SpCell refers to the PCell of the master cell group (MCG) or the primary SCell (PSCell) of the secondary cell group (SCG). An SpCell supports PUCCH transmission and contention-based random access, and is always activated. The MCG is a group of serving cells associated with a master node, comprised of the SpCell (PCell) and optionally one or more SCells. The SCG is the subset of serving cells associated with a secondary node, comprised of the PSCell and zero or more SCells, for a UE configured with DC. For a UE in RRC_CONNECTED not configured with CA / DC, there is only one serving cell comprised of the PCell. For a UE in RRC_CONNECTED configured with CA / DC, the term "serving cells" is used to denote the set of cells comprised of the SpCell(s) and all SCells. In DC, two MAC entities are configured in a UE: one for the MCG and one for the SCG.

[0134] FIG. 9 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure is applied.

[0135] Referring to FIG. 9, "RB" denotes a radio bearer, and "H" denotes a header. Radio bearers are categorized into two groups: DRBs for user plane data and SRBs for control plane data. The MAC PDU is transmitted / received using radio resources through the PHY layer to / from an external device. The MAC PDU arrives to the PHY layer in the form of a transport block.

[0136] In the PHY layer, 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. In the PHY layer, uplink control information (UCI) is mapped to PUCCH, and 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, and a MAC PDU related to DL-SCH is transmitted by a BS via a PDSCH based on a DL assignment.

[0137] Hereinafter, technical features related to AI / ML are described.

[0138] The application of AI / ML to wireless communications has been thus far limited to implementation-based approaches, both, at the network and the UE sides. A study on enhancement for data collection for NR and ENDC (FS_NR_ENDC_data_collect) has examined thefunctional framework for RAN intelligence enabled by further enhancement of data collection through use cases, examples etc. and identify the potential standardization impacts on currentNG-RAN nodes and interfaces. In SA WG2 AI / ML related study, a network functionality NWDAF (Network Data Analytics Function) was introduced in Rel-15 and has been enhanced in Rel-16 and Rel-17.

[0139] In this study, we explore the benefits of augmenting the air-interface with features enabling improved support of AI / ML based algorithms for enhanced performance and / or reduced complexity / overhead. Enhanced performance here depends on the use cases under consideration and could be, e.g., improved throughput, robustness, accuracy or reliability, etc.

[0140] Through studying a few carefully selected use cases, assessing their performance in comparison with traditional methods and the associated potential specification impacts that enable their solutions, this SI will　lay the foundation for future air-interface use cases leveraging AI / ML techniques.

[0141] 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.

[0142] 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.

[0143] Evaluations to exercise the attainable gains of AI / ML based techniques for the use cases under consideration will be carried out with the corresponding identification of KPIs with the goal to have a better understanding of the attainable gains and associated complexity requirements.

[0144] Finally, specification impact will be assessed in order to improve the overall understanding of what would be required to enable AI / ML techniques for the air-interface.

[0145] For the study on AI / ML for air-interface, the basic framework and principles agreed forFS_NR_ENDC_data_collectshould be taken into consideration for possible applicability.

[0146] Study the 3GPP framework for AI / ML for air-interface corresponding to each target use case regarding aspects such as performance, complexity, and potential specification impact.

[0147] Use cases to focus on:

[0148] 1> Initial set of use cases includes:

[0149] a) CSI feedback enhancement, e.g., overhead reduction, improved accuracy, prediction

[0150] b) Beam management, e.g., beam prediction in time, and / or spatial domain for overhead and latency reduction, beam selection accuracy improvement

[0151] c) Positioning accuracy enhancements for different scenarios including, e.g., those with heavy NLOS conditions

[0152] 2> Finalize representative sub use cases for each use case for characterization and baseline performance evaluations

[0153] a) The AI / ML approaches for the selected sub use cases need to be diverse enough to support various requirements on the gNB-UE collaboration levels

[0154] - the selection of use cases for this study solely targets the formulation of a framework to apply AI / ML to the air-interface for these and other use cases. The selection itself does not intend to provide any indication of the prospects of any future normative project.

[0155] AI / ML model, terminology and description to identify common and specific characteristics for framework investigations:

[0156] 3> Characterize the defining stages of AI / ML related algorithms and associated complexity:

[0157] a) Model generation, e.g., model training (including input / output, pre- / post-process, online / offline as applicable), model validation, model testing, as applicable

[0158] b) Inference operation, e.g., input / output, pre- / post-process, as applicable

[0159] 4> Identify various levels of collaboration between UE and gNB pertinent to the selected use cases, e.g.,

[0160] a) No collaboration: implementation-based only AI / ML algorithms without information exchange [for comparison purposes]

[0161] b) Various levels of UE / gNB collaboration targeting at separate or joint ML operation.

[0162] 5> Characterize lifecycle management of AI / ML model: e.g., model training, model deployment , model inference, model monitoring, model updating

[0163] 6> Dataset(s) for training, validation, testing, and inference

[0164] 7> Identify common notation and terminology for AI / ML related functions, procedures and interfaces

[0165] 8> Note: Consider the work done for FS_NR_ENDC_data_collect when appropriate

[0166] For the use cases under consideration:

[0167] - Evaluate performance benefits of AI / ML based algorithms for the agreed use cases in the final representative set:

[0168] a) Methodology based on statistical models, for link and system level simulations.

[0169] i. Extensions of 3GPP evaluation methodology for better suitability to AI / ML based techniques should be considered as needed.

[0170] ii. Whether field data are optionally needed to further assess the performance and robustness in real-world environments should be discussed as part of the study.

[0171] iii. Need for common assumptions in dataset construction for training, validation and test for the selected use cases.

[0172] iv. Consider adequate model training strategy, collaboration levels and associated implications

[0173] v. Consider agreed-upon base AI model(s) for calibration

[0174] vi. AI model description and training methodology used for evaluation should be reported for information and cross-checking purposes

[0175] b) 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.

[0176] i. Performance, inference latency and computational complexity of AI / ML based algorithms should be compared to that of a state-of-the-art baseline

[0177] ii. Overhead, power consumption (including computational), memory storage, and hardware requirements (including for given processing delays) associated with enabling respective AI / ML scheme, as well as generalization capability should be considered.

[0178] - Assess potential specification impact, specifically for the agreed use cases in the final representative set and for a common framework:

[0179] c) PHY layer aspects,

[0180] i. Consider aspects related to, e.g., the potential specification of the AI Model lifecycle management, and dataset construction for training, validation and test for the selected use cases

[0181] ii. Use case and collaboration level specific specification impact, such as new signalling, means for training and validation data assistance, assistance information, measurement, and feedback

[0182] d) Protocol aspects, e.g., (RAN2) - RAN2 only starts the work after there is sufficient progress on the use case study in RAN1

[0183] i. Consider aspects related to, e.g., capability indication, configuration and control procedures (training / inference), and management of data and AI / ML model, per RAN1 input

[0184] ii. Collaboration level specific specification impact per use case

[0185] e) Interoperability and testability aspects, e.g., (RAN4) - RAN4 only starts the work after there is sufficient progress on use case study in RAN1 and RAN2

[0186] i. Requirements and testing frameworks to validate AI / ML based performance enhancements and ensuring that UE and gNB with AI / ML meet or exceed the existing minimum requirements if applicable

[0187] ii. Consider the need and implications for AI / ML processing capabilities definition

[0188] - specific AI / ML models are not expected to be specified and are left to implementation. User data privacy needs to be preserved.

[0189] - The study on AI / ML for air interface is based on the current RAN architecture and new interfaces shall not be introduced.

[0190] FIG. 10 shows an example of a Functional Framework for RAN Intelligence.

[0191] > Data Collection is a function that provides input data to Model training and Model inference functions. AI / ML algorithm specific data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) is not carried out in the Data Collection function.

[0192] Examples of input data may include measurements from UEs or different network entities, feedback from Actor, output from an AI / ML model.

[0193] >> Training Data: Data needed as input for the AI / ML Model Training function.

[0194] >> Inference Data: Data needed as input for the AI / ML Model Inference function.

[0195] > 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.

[0196] >> 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.

[0197] > 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.

[0198] >> Output: The inference output of the AI / ML model produced by a Model Inference function.

[0199] >>> Note: Details of inference output are use case specific.

[0200] >> Model Performance Feedback: It may be used for monitoring the performance of the AI / ML model, when available.

[0201] > 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.

[0202] >> 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.

[0203] Hereinafter, technical features related to Mobility Optimization are described.

[0204] Mobility management is the scheme to guarantee the service-continuity during the mobility by minimizing the call drops, RLFs, unnecessary handovers, and ping-pong. For the future high-frequency network, as the coverage of a single node decreases, the frequency for UE to handover between nodes becomes high, especially for high-mobility UE. In addition, for the applications characterized with the stringent QoS requirements such as reliability, latency etc., the QoE is sensitive to the handover performance, so that mobility management should avoid unsuccessful handover and reduce the latency during handover procedure. However, for the conventional method, 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. In addition, the effectiveness of adjustment based on feedback may be weak due to randomness and inconstancy of transmission environment. Besides the baseline case of mobility, areas of optimization for mobility include dual connectivity, CHO, and DAPS, which each has additional aspects to handle in the optimization of mobility.

[0205] Mobility aspects of SON that can be enhanced by the use of AI / ML include

[0206] - Reduction of the probability of unintended events

[0207] - UE Location / Mobility / Performance prediction

[0208] - Traffic Steering

[0209] Reduction of the probability of unintended events associated with mobility.

[0210] Examples of such unintended events are:

[0211] - Intra-system Too Late Handover: A radio link failure (RLF) 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.

[0212] - Intra-system Too Early Handover: 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.

[0213] - Intra-system Handover to Wrong 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.

[0214] - Successful Handover: During a successful handover, there is underlying issue.

[0215] 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.

[0216] UELocation / Mobility / Performance Prediction

[0217] 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.

[0218] Traffic Steering

[0219] Efficient resource handling can be achieved adjusting handover trigger points and selecting optimal combination of Pcell / PSCell / Scells to serve a user.

[0220] Existing traffic steering can also be improved by providing a RAN node with information related to mobility or dual connectivity.

[0221] For example, before initiating a handover, the source gNB could use feedbacks on UE performance collected for successful handovers occurred in the past and received from neighbouring gNBs.

[0222] Similarly, for the case of dual connectivity, before triggering the addition of a secondary gNB or triggering SN change, an eNB could use information (feedbacks) received in the past from the gNB for successfully completed SN Addition or SN Change procedures.

[0223] In the two reported examples, 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.

[0224] Locations for AI / ML Model Training and AI / ML Model Inference

[0225] Considering the locations of AI / ML Model Training and AI / ML Model Inference for mobility solution, the following two options are considered:

[0226] - The AI / ML Model Training function is deployed in OAM, while the Model Inference function resides within the RAN node

[0227] - Both the AI / ML Model Training function and the AI / ML Model Inference function reside within the RAN node

[0228] Furthermore, for CU-DU split scenario, following option is possible:

[0229] - AI / ML Model Training is located in CU-CP or OAM, and AI / ML Model Inference function is located in CU-CP

[0230] gNB is also allowed to continue model training based on AI / ML model trained in the OAM.

[0231] FIG. 11 shows an example of an AI / ML Model Training in OAM and AI / ML Model Inference in NG-RAN node.

[0232] 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.

[0233] Step 1. The NG-RAN node configures the measurement information on the UE side and sends configuration message to UE including configuration information.

[0234] Step 2. The UE collects the indicated measurement, e.g., UE measurements related to RSRP, RSRQ, SINR of serving cell and neighbouring cells.

[0235] Step 3. The UE sends measurement report message to NG-RAN node 1 including the required measurement.

[0236] 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.

[0237] 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.

[0238] Step 6. Model Training. Required measurements are leveraged to training AI / ML model for UE mobility optimization.

[0239] 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.

[0240] Note: This step is out of RAN3 Rel-17 scope.

[0241] Step 8. The NG-RAN node 1 obtains the measurement report as inference data for UE mobility optimization.

[0242] Step 9. 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.

[0243] 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.

[0244] Step 11. The NG-RAN 1 sends the model performance feedback to OAM if applicable.

[0245] Note: This step is out of RAN3 scope.

[0246] 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.

[0247] Step 13. The NG-RAN node 1 sends the feedback information to OAM.

[0248] Step 14. The NG-RAN node 2 sends the feedback information to OAM.

[0249] FIG. 12 shows an example of Model Training and Model Inference both located in RAN node.

[0250] 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.

[0251] Step 1. NG-RAN node1 configures the measurement information on the UE side and sends configuration message to UE including configuration information.

[0252] Step 2. UE collects the indicated measurement, e.g., UE measurements related to RSRP, RSRQ, SINR of serving cell and neighbouring cells.

[0253] Step 3. UE sends measurement report message to NG-RAN node1 including the required measurement.

[0254] 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.

[0255] Step 5. Model training. Required measurements are leveraged to training AI / ML model for mobility optimization.

[0256] Step 6. NG-RAN node1 obtains the measurement report as inference data for real-time UE mobility optimization.

[0257] 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.

[0258] 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.

[0259] 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.

[0260] Step 10. The NG-RAN node 2 sends feedback information after mobility optimization action to the NG-RAN node 1.

[0261] For example, UE mobility information for training purposes is only sent to gNBs that requested such information or when triggered.

[0262] Input of AI / ML-based Mobility Optimization

[0263] The following data is required as input data for mobility optimization.

[0264] From the UE:

[0265] - UE location information (e.g., coordinates, serving cell ID, moving velocity) interpreted by gNB implementation when available.

[0266] - Radio measurements related to serving cell and neighbouring cells associated with UE location information, e.g., RSRP, RSRQ, SINR.

[0267] - UE Mobility History Information.

[0268] From the neighbouring RAN nodes:

[0269] - UE's history information from neighbour

[0270] - Position, QoS parameters and the performance information of historical HO-ed UE (e.g., loss rate, delay, etc.)

[0271] - Current / predicted resource status

[0272] - UE handovers in the past that were successful and unsuccessful, including too-early, too-late, or handover to wrong (sub-optimal) cell, based on existing SON / RLF report mechanism.

[0273] From the local node:

[0274] - UE trajectory prediction

[0275] - Current / predicted resource status

[0276] - Current / predicted UE traffic

[0277] Output of AI / ML-based Mobility Optimization

[0278] AI / ML-based mobility optimization can generate following information as output:

[0279] - UE trajectory prediction (Latitude, longitude, altitude, cell ID of UE over a future period of time)

[0280] Note: Whether the UE trajectory prediction is an external output to the node hosting the Model Inference function should be discussed during the normative work phase.

[0281] - Estimated arrival probability in CHO and relevant confidence interval

[0282] - Predicted handover target node, candidate cells in CHO, may together with the confidence of the predication

[0283] - Priority, handover execution timing, predicted resource reservation time window for CHO.

[0284] - UE traffic prediction (will be used by the RAN node internally and the details are left to normative work phase)

[0285] - Model output validity time will be discussed during R18 normative work per inference output.

[0286] Feedback of AI / ML-based Mobility Optimization

[0287] The following data is required as feedback data for mobility optimization.

[0288] - QoS parameters such as throughput, packet delay of the handed-over UE, etc.

[0289] - Resource status information updates from target NG-RAN.

[0290] - Performance information from target NG-RAN. The details of performance information are to be discussed during normative work phase.

[0291] Standard impact

[0292] To improve the mobility decisions at a gNB (gNB-CU), a gNB can request mobility feedback from a neighbouring node. Details of the procedure will be determined during the normative phase.

[0293] If existing UE measurements are needed by a gNB for AI / ML-based mobility optimization, RAN3 shall reuse the existing framework (including MDT and RRM measurements). Whether new UE measurements are needed is left to normative phase based on the use case description.

[0294] MDT procedure enhancements should be discussed during the normative phase.

[0295] PotentialXninterface impact:

[0296] - Predicted resource status info and performance info from candidate target NG-RAN node to source NG-RAN node

[0297] - New signaling procedure or existing procedure to retrieve input information via Xn interface.

[0298] - New signaling procedure or existing procedure to retrieve feedback information via Xn interface.

[0299] Hereinafter, technical features related to AI and ML are described.

[0300] Artificial Intelligence (AI) / Machine Learning (ML) is being used in a range of application domains across industry sectors, realizing significant productivity gains. In particular, in mobile communications systems, mobile devices (e.g. smartphones, smart vehicles, UAVs, mobile robots) are increasingly replacing conventional algorithms (e.g. speech recognition, machine translation, image recognition, video processing, user behaviour prediction) with AI / ML models to enable applications like enhanced photography, intelligent personal assistants, VR / AR, video gaming, video analytics, personalized shopping recommendation, autonomous driving / navigation, smart home appliances, mobile robotics, mobile medicals, as well as mobile finance.

[0301] Artificial Intelligence (AI) is the science and engineering to build intelligent machines capable of carrying out tasks as humans do.

[0302] Deep neural network

[0303] FIGS. 13 and 14 show an example of an architecture of neuron and neural network.

[0304] Within the ML field, there is an area that is often referred to as brain-inspired computation, which is a program aiming to emulate some aspects of how we understand the brain to operate. Since it is believed that the main computational elements a human brain are 86 billion neurons, the two subareas of brain-inspired computation are both inspired by the architecture of a neuron, as shown in FIG. 13.

[0305] Compared to spiking computing approaches, the more popular ML approaches are using "neural network" as the model. Neural networks (NN) take their inspiration from the notion that a neuron's computation involves a weighted sum of the input values. But instead of simply outputting the weighted sum, a NN applies a nonlinear function to generate an output only if the inputs cross some threshold, as shown in FIG. 13.

[0306] 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.

[0307] Neural networks having more than three layers, i.e., more than one hidden layer are called deep neural networks (DNN). In contrast to the conventional shallow-structured NN architectures, DNNs, also referred to as deep learning, made amazing breakthroughs since 2010s in many essential application areas because they can achieve human-level accuracy or even exceed human accuracy. Deep learning techniques use supervised and / or unsupervised strategies to automatically learn hierarchical representations in deep architectures for classification. With a large number of hidden layers, 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. In recent years, thanks to the big data obtained from the real world, the rapidly increased computation capacity and continuously-evolved algorithms, DNNs have become the most popular ML models for many AI applications.

[0308] Training and inference

[0309] Training is a process in which a AI / ML model learns to perform its given tasks, more specifically, by optimizing the value of the weights in the DNN. A DNN is trained by inputting a training set, which are often correctly-labelled training samples. Taking image classification for instance, the training set includes correctly-classified images. When training a network, the weights are usually updated using a hill-climbing optimization process called gradient descent. The gradient indicates how the weights should change in order to reduce the loss (the gap between the correct outputs and the outputs computed by the DNN based on its current weights). The training process is repeated iteratively to continuously reduce the overall loss. Until the loss is below a predefined threshold, the DNN with high precision is obtained.

[0310] There are multiple ways to train the network for different targets. The introduced above is supervised learning which uses the labelled training samples to find the correct outputs for a task. Unsupervised learning uses the unlabelled training samples to find the structure or clusters in the data. Reinforcement learning can be used to output what action the agent should take next to maximize expected rewards. Transfer learning is to adjust the previously-trained weights (e.g. weights in a global model) using a new training set, which is used for a faster or more accurate training for a personalized model.

[0311] FIG. 15 shows an example of an AI / ML inference.

[0312] 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. In the model inference process, the inputs from the real world are passed through the DNN. Then the prediction for the task is output, as shown in FIG. 15. For instance, the inputs can be pixels of an image, sampled amplitudes of an audio wave or the numerical representation of the state of some system or game. Correspondingly, the outputs of the network can be a probability that an image contains a particular object, the probability that an audio sequence contains a particular word or a bounding box in an image around an object or the proposed action that should be taken.

[0313] The performance of DNNs is gained at the cost of high computational complexity. Hence more efficient compute engines are often used, e.g. graphics processing units (GPU) and network processing units (NPU). Compared to the inference which only involves the feedforward process, the training often requires more computation and storage resources because it involves also the backpropagation process.

[0314] Widely-usedDNNmodels and algorithms

[0315] FIG. 16 shows an example of an MLP DNN model.

[0316] Many DNN models have been developed over the past two decades. Each of these models has a different "network architecture" in terms of number of layers, layer types, layer shapes (i.e., filter size, number of channels and filters), and connections between layers. FIG. 16 presents three popular structures of DNNs: multilayer perceptrons (MLPs), convolution neural networks (CNNs), and recurrent neural networks (RNNs). 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.

[0317] FIG. 17 shows an example of a CNN model.

[0318] An approach to limiting the number of weights that contribute to an output is to calculate the output only using a function of a fixed-size window of inputs. An extremely popular window-based DNN model uses a convolution operation to structure the computation, hence is named as convolution neural network (CNN). A CNN is composed of multiple convolutional layers, as shown in FIG. 17. Applying various convolutional filters, CNN models can capture the high-level representation of the input data, making it popular for image classification and speech recognition tasks.

[0319] FIG. 18 shows an example of an RNN model.

[0320] 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. As shown in FIG. 18, 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.

[0321] FIG. 19 shows an example of Reinforcement learning.

[0322] Deep reinforcement learning (DRL) 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.

[0323] Hereinafter, technical features related to connection failure are described. Parts of section 5.3.3.7 of 3GPP TS 38.331 v17.2.0 may be referred.

[0324] The UE shall:

[0325] 1> if timer T300 expires:

[0326] 2> reset MAC, release the MAC configuration and re-establish RLC for all RBs that are established;

[0327] 2> if the UE supports RRC Connection Establishment failure with temporary offset and the T300 has expired a consecutiveconnEstFailCounttimes on the same cell for whichconnEstFailureControlis included inSIB1:

[0328] 3> for a period as indicated byconnEstFailOffsetValidity:

[0329] 4> useconnEstFailOffsetfor the parameterQoffsettempfor the concerned cell when performing cell selection and reselection;

[0330] - When performing cell selection, if no suitable or acceptable cell can be found, it is up to UE implementation whether to stop usingconnEstFailOffsetfor the parameterQoffsettempduringconnEstFailOffsetValidityfor the concerned cell.

[0331] 2> if the UE supports multiple CEF report:

[0332] 3> if the UE has connection establishment failure information or connection resume failure information available inVarConnEstFailReportand if the RPLMN is equal toplmn-identitystored inVarConnEstFailReport; and

[0333] 3> if the cell identity of current cell is not equal to the cell identity stored inmeasResultFailedCellinVarConnEstFailReportand if themaxCEFReport-r17has not been reached:

[0334] 4> append theVarConnEstFailReportas a new entry in theVarConnEstFailReportList;

[0335] 2> if the UE has connection establishment failure information or connection resume failure information available inVarConnEstFailReportand if the RPLMN is not equal toplmn-identitystored inVarConnEstFailReport; or

[0336] 2> if the cell identity of current cell is not equal to the cell identity stored inmeasResultFailedCellinVarConnEstFailReport:

[0337] 3> reset thenumberOfConnFailto 0;

[0338] 2> if the UE supports multiple CEF report and if the UE has connection establishment failure information or connection resume failure information available inVarConnEstFailReportListand if the RPLMN is not equal toplmn-identitystored in any entry ofVarConnEstFailReportList:

[0339] 3> clear the content included inVarConnEstFailReportList;

[0340] 2> clear the content included inVarConnEstFailReportexcept for thenumberOfConnFail, if any;

[0341] 2> store the following connection establishment failure information in theVarConnEstFailReportby setting its fields as follows:

[0342] 3> set theplmn-Identityto the PLMN selected by upper layers from the PLMN(s) included in theplmn-IdentityInfoListinSIB1;

[0343] 3> set themeasResultFailedCellto include the global cell identity, tracking area code, the cell level and SS / PBCH block level RSRP, and RSRQ, and SS / PBCH block indexes, of the failed cell based on the available SSB measurements collected up to the moment the UE detected connection establishment failure;

[0344] 3> if available, set themeasResultNeighCells, in order of decreasing ranking-criterion as used for cell re-selection, to include neighbouring cell measurements for at most the following number of neighbouring cells: 6 intra-frequency and 3 inter-frequency neighbours per frequency as well as 3 inter-RAT neighbours, per frequency / set of frequencies per RAT and according to the following:

[0345] 4> for each neighbour cell included, include the optional fields that are available;

[0346] - The UE includes the latest results of the available measurements as used for cell reselection evaluation, which are performed in accordance with the performance requirements.

[0347] 3> if available, set thelocationInfoas follows:

[0348] 4> if available, set thecommonLocationInfoto include the detailed location information;

[0349] 4> if available, set thebt-LocationInfoto include the Bluetooth measurement results, in order of decreasing RSSI for Bluetooth beacons;

[0350] 4> if available, set thewlan-LocationInfoto include the WLAN measurement results, in order of decreasing RSSI for WLAN APs;

[0351] 4> if available, set thesensor-LocationInfoto include the sensor measurement results as follows;

[0352] 5> if available, include thesensor-MeasurementInformation;

[0353] 5> if available, include thesensor-MotionInformation;

[0354] - Which location information related configuration is used by the UE to make thelocationInfoavailable for inclusion in theVarConnEstFailReportis left to UE implementation.

[0355] 3> setperRAInfoListto indicate the performed random access procedure related information;

[0356] 3> if thenumberOfConnFailis smaller than 8:

[0357] 4> increment thenumberOfConnFailby 1;

[0358] 2> inform upper layers about the failure to establish the RRC connection, upon which the procedure ends;

[0359] The UE may discard the connection establishment failure or connection resume failure information, i.e. release the UE variableVarConnEstFailReport, 48 hours after the last connection establishment failure is detected.

[0360] The L2 U2N Relay UE either indicates to upper layers (to trigger PC5 unicast link release) or sends Notification message to the connected L2 U2N Remote UE(s).

[0361] Hereinafter, technical features related to SCGFailureInformation and MCGFailureInformation are described. Parts of 3GPP TS 38.331 v17.2.0 may be referred.

[0362] TheSCGFailureInformationmessage is used to provide information regarding NR SCG failures detected by the UE.

[0363] - Signalling radio bearer: SRB1

[0364] - RLC-SAP: AM

[0365] - Logical channel: DCCH

[0366] - Direction: UE to Network

[0367] SCGFailureInformation field descriptions:

[0368] - measResultFreqList

[0369] The field contains available results of measurements on NR frequencies the UE is configured to measure by measConfig.

[0370] - measResultSCG-Failure

[0371] The field contains the MeasResultSCG-Failure IE which includes available results of measurements on NR frequencies the UE is configured to measure by the NR SCG RRCReconfiguration message.

[0372] - previousPSCellId

[0373] This field indicates the physical cell id and carrier frequency of the cell that is the source PSCell of the last PSCell change.

[0374] - failedPSCellId

[0375] This field indicates the physical cell id and carrier frequency of the cell in which SCG failure is detected or the target PSCell of the failed PSCell change or failed PSCell addition.

[0376] - timeSCGFailure

[0377] This field is used to indicate the time elapsed since the last execution of RRCReconfiguration with reconfigurationWithSync for the SCG until the SCG failure. Actual value = field value * 100ms. The maximum value 1023 means 102.3s or longer.

[0378] The MCGFailureInformation message is used to provide information regarding NR MCG failures detected by the UE.

[0379] - Signalling radio bearer: SRB1

[0380] - RLC-SAP: AM

[0381] - Logical channel: DCCH

[0382] - Direction: UE to Network

[0383] MCGFailureInformation field descriptions:

[0384] - measResultFreqList

[0385] The field contains available results of measurements on NR frequencies the UE is configured to measure by the measConfig associated with the MCG.

[0386] - measResultFreqListEUTRA

[0387] The field contains available results of measurements on E-UTRA frequencies the UE is configured to measure by measConfig associated with the MCG.

[0388] - measResultFreqListUTRA-FDD

[0389] The field contains available results of measurements on UTRA FDD frequencies the UE is configured to measure by measConfig associated with the MCG.

[0390] - measResultSCG

[0391] The field contains the MeasResultSCG-Failure IE which includes available measurement results on NR frequencies the UE is configured to measure by the measConfig associated with the SCG.

[0392] - measResultSCG-EUTRA

[0393] The field contains the EUTRA MeasResultSCG-FailureMRDC IE which includes available results of measurements on E-UTRA frequencies the UE is configured to measure by the E-UTRA RRCConnectionReconfiguration message.

[0394] Hereinafter, technical features related to support of SON / RLF report may be referred.

[0395] In 3GPP specification, there are many methods to report the failure information.

[0396] MCG / SCG failure information is used to report the connection failure via SCG / MCG, respectively. If the failure is detected in a CG, the failure information can be transmitted through another CG. MCG / SCG failure information can include the followings:

[0397] - Failure type: t310 Expiry, random access problem, rlc max number of retransmissions, synch reconfiguration failure, etc

[0398] - Measurement result of measurements on NR frequencies the UE is configured to measure by measConfig

[0399] - Previous Cell id indicating physical cell id and carrier frequency of the source cell

[0400] - Failed Cell id indicating physical cell id and carrier frequency of the cell in which failure is detected or failed cell change / cell addition

[0401] - Failure time indicating the time elapsed since the last execution of RRCReconfiguration with reconfigurationWithSync until failure

[0402] - Location information

[0403] Self-Organizing Network(SON) and Minimize drive Test(MDT) are standardized mechanism to use user devices in a network to collect mobile network data. For SON / MDT, UE stores some information related to measurement results, connection failure, rlf, mobility history, etc. Network requests that information via UE information request message, and then UE responses to network with stored information via UE information response message. Although the corresponding information is not real-time information, UE can rather notify more detailed failure information. For example, the stored information can include the information as followings:

[0404] - Stored information can be used to notify connection establishment failure information, connection resume failure information, RLF report

[0405] - Stored information can include the following information

[0406] > plmn identity

[0407] > measurement result of last serving Cell to include the cell level RSRP, RSRQ and the available SINR, of the source Pcell (in case HO failure) or Pcell (in case RLF) based on the available SSB / CSI-RS measurements collected up to the moment the UE detected failure

[0408] > RLM configuration information to include the radio link monitoring configuration of the source Pcell(in case HO failure) or Pcell(in case RLF)

[0409] > measurement result of neighbouring Cell to include neighbouring cell measurements

[0410] >> measurement result to include all the available measurement quantities of the best measured cells, other than the source Pcell(in case HO failure) or Pcell(in case RLF)

[0411] >> CHO Config to include conditional reconfiguration information (first triggered event, time between events if two events are configured)

[0412] >> Time elapsed 1) between CHO configuration and CHO execution (If CHO is failed) 2) between CHO configuration and HO execution (If HO is failed)

[0413] > CHO candidate cell list to include the global cell identity / physical cell identity / carrier frequency of each candidate cell for conditional handover at the time of the failed handover

[0414] > Last handover type e.g., cho, daps, etc

[0415] > Connection failure type e.g., hof, rlf

[0416] > Failed Pcell Id to set global cell identity / tracking area code / physical cell identity / carrier frequency where radio link failure is detected

[0417] > Previous Cell to set global cell identity / tracking area code of the Pcell where the last executed CHO message was received

[0418] > RLF cause e.g., random access problem, beam failure recovery Failure, etc

[0419] > Location information

[0420] Hereinafter, technical features related to support of AI / ML model Change during Mobility may be referred.

[0421] One valid question is whether to support mobility for AI / ML operation over air interface. If mobility is not supported for AI / ML over air interface, the AI / ML operation may be disabled before handover is about the happen and then enabled after handover to the target gNB is completed. For collaboration Level z, model transfer may be always needed when the serving gNB for the UE is changed. Considering that the AI / ML model size may be large, it is obvious that model transfer over air interface upon each handover results in large amount of signaling overhead and consumes much system capacity. The mechanism to enable AI / ML model change / reconfiguration during UE mobility is desired.

[0422] There are basically two scenarios to considered:

[0423] 1. UE moves from one cell to another cell without AI / ML model change

[0424] 2. UE moves from one cell to another cell with AI / ML model change

[0425] In the first case, the same AI / ML model is used in both the source gNB and the target gNB. There are two ways to make the AI / ML model continue to be used in the target gNB. One way is that UE uploads or downloads the AI / ML model whenever the anchor gNB or CN is changed. The other way is that the source gNB forwards the AI / ML model or related information to the target gNB when handover happens.

[0426] In the second case, different AI / ML models are used in the source gNB and the target gNB. There are generally two ways to change the AI / ML model. One way is that UE uploads or downloads the new AI / ML model when handover to the target gNB happens, just like full configuration. The other way is that only partial or some parameters of the AI / ML model is changed, using delta configuration.

[0427] The mechanism to support AI / ML change during UE mobility is discussed.

[0428] Support of AI / ML model change during mobility for collaboration level z also needs to consider the model transfer options and the model format. Model transfer through RRC message is compatible with current handover mechanism and easier to support AI / ML change during UE mobility. Model transfer through NAS message is also possible to support AI / ML change during UE mobility. However, if the AI / ML model is transferred through UP traffics, it is impossible to support AI / ML model change during UE mobility with current handover mechanism. If the AI / ML model is transferred in the format of runtime image, it is hard to support delta configuration. It's also a big burden over the Xn interface if the model is transferred from one gNB to another. If the AI / ML model is transferred in the format specified by 3GPP, model change during mobility can be supported in a much more signaling-efficient way.

[0429] Meanwhile, UE supporting AI / ML operations may derive measurements results based on Artificial Intelligence (AI) / Machine Learning (ML) based methodologies or based on non-AI / ML based methodologies. In case the UE experiences connection failure, the UE may report available measurement results as part of failure report to the network. Upon receiving the failure report, network cannot clearly determine which of the following is a likely failure cause:

[0430] - Case a) the failure is expected to be caused by inappropriate AI / ML operations of the UE while the failure is not caused by network coverage problem or other inappropriate UE configuration

[0431] - Case b) the failure is expected to be not caused by inappropriate AI / ML operations of the UE but caused by network coverage problem or other inappropriate UE configuration

[0432] This ambiguity from network side occurs mainly because existing failure report procedure are insufficient in particular when UE is configured or allowed to operate AI / ML based prediction task for measurements and / or other 3GPP procedures. For instance, existing failure report procedure does not indicate whether the UE was performing AI / ML-based for CSI measurement report / RRM measurement report or legacy measurement report, and existing failure report procedure does not indicate whether the failed connection is possibly caused by incorrect predictive mobility based on improper AI / ML operations of the UE or possibly caused by incorrect network decision.

[0433] Even if the network could infer that the failure is possibly related to the AI / ML operation, it is difficult to know how to correct the AI / ML related operations because existing failure report lacks information related to AI / ML operations performed by the UE at the time of the failure. As a result, the network may not recognize whether AI / ML-related settings need to be changed, and erroneous results in AI / ML operation may have been used as input for training or as an interference result.

[0434] Therefore, studies for supporting AI and ML operation in a wireless communication system are required.

[0435] Hereinafter, a method for supporting AI and ML operation in a wireless communication system, according to some embodiments of the present disclosure, will be described with reference to the following drawings.

[0436] The following drawings are created to explain specific embodiments of the present disclosure. The names of the specific devices or the names of the specific signals / messages / fields shown in the drawings are provided by way of example, and thus the technical features of the present disclosure are not limited to the specific names used in the following drawings. Herein, a wireless device may be referred to as a user equipment (UE).

[0437] FIG. 20 shows an example of a method for supporting AI and ML operation in a wireless communication system.

[0438] In particular, FIG. 20 shows an example of a method performed by a wireless device in a wireless communication system.

[0439] In step S2001, a wireless device may detect a failure in an operation with a network.

[0440] For example, the operation may include a predicted operation derived by the AI and / or ML model.

[0441] For example, the operation may include a conditional operation, a conditional handover (CHO), a conditional PScell change (CPC), a cell and / or a cell group (CG) addition, a cell and / or a CG activation, a cell and / or a CG deactivation, and / or a cell and / or a CG release.

[0442] For example, the failure in the operation may include a handover failure, a radio link failure, a beam failure, a random-access failure, a configuration failure, a connection failure, a resume failure, a conditional reconfiguration failure, a cell and / or a CG addition failure, a cell and / or a CG activation failure, and / or a cell and / or a CG deactivation failure.

[0443] In step S2002, a wireless device may transmit a message including (i) information on the failure, and (ii) information on a predicted information related to the failure.

[0444] The predicted information may be derived by an Artificial Intelligence (AI) and / or Machine Learning (ML) model of the wireless device.

[0445] For example, the message further includes information on the operation in which the failure is detected.

[0446] For example, the message further includes information informing that the wireless device derives the predicted information based on the AI and / or ML model.

[0447] For example, the message further includes information on the AI and / or ML model used for deriving the predicted information.

[0448] For example, the message may further include information on a time point at which the operation is performed.

[0449] According to some embodiments of the present disclosure, the wireless device may derive a predicted failure and a predicted time point at which the predicted failure is occurred.

[0450] In this case, the information on the predicted information may include information on the predicted failure and information on the predicted time point.

[0451] For example, the information on the predicted information includes information on time difference between the predicted time point and a time point at which the failure occurred.

[0452] For example, input of the AI and / or ML model may include stored measure results, location information, mobility information, user information, registration information, information on failures, stored messages, stored configurations, and / or UE capabilities.

[0453] That is, the wireless device may use the information on stored measure results, location information, mobility information, user information, registration information, information on failures, stored messages, stored configurations, and / or UE capabilities for deriving the predicted information.

[0454] For example, the predicted information is output of the AI and / or ML model. For example, the output of the AI and / or ML model may include a predicted failure at a future time point, predicted measure results at a future time point, a predicted time point at which the predicted failure is occurs, a predicted operation for the wireless device, a predicted configuration for the wireless device, and / or a predicted location (for example, a predicted cell, a predicted gNB, and / or a predicted tracking area) of the wireless device.

[0455] For example, input of the AI and / or ML model may be used for training the AI / ML model. That is, the information on stored measure results, location information, mobility information, user information, registration information, and / or UE capabilities may be used for training the AI / ML model.

[0456] In addition, the output of the AI and / or ML model may be also used for training the AI / ML model. That is, the information on a predicted failure at a future time point, predicted measure results at a future time point, a predicted time point at which the predicted failure is occurs, a predicted operation for the wireless device, a predicted configuration for the wireless device, and / or a predicted location (for example, a predicted cell, a predicted gNB, and / or a predicted tracking area) of the wireless device may be used for training the AI / ML model.

[0457] According to some embodiments of the present disclosure, the wireless device may receive, from the network, an AI and / or ML model configuration including information on the AI and / or ML model. The wireless device may derive predicted measurement results based on the AI and / or ML model. In this case, the operation may be triggered by the predicted measurement results.

[0458] For example, the message may further include information informing that the operation is triggered by the predicted measurement results.

[0459] For example, the predicted information may include information on the predicted measurement results.

[0460] The wireless device may include an AI and / or ML (in other words, AI / ML) functionality. The AI / ML functionality is an entity for AI / ML operations.

[0461] The wireless device may configure the AI / ML functionality with an AI / ML model. For example, the wireless device may receive a configuration for the AI / ML model from the network. The wireless device may apply the received configuration for the AI / ML model to the AI / ML functionality.

[0462] For example, the AI / ML functionality may be configured with a first AI / ML model. Then, the AI / ML functionality may derive predicted information (for example, predicted measurements results) based on the first AI / ML model.

[0463] After receiving a configuration for a second AI / ML model, the wireless device may configure the AI / ML functionality with the second AI / ML model. Then, the AI / ML functionality may derive predicted information (for example, predicted measurements results) based on the second AI / ML model.

[0464] For example, the whole AI / ML functionality would be comprised of several different components (for example, Data Collection, Model Training, Model Inference, Actor).

[0465] For example, the AI / ML model used in step S2001 may be described below.

[0466] Hereinafter, examples of AI / ML model for the present disclosure are described.

[0467] An AI / ML model for CSI feedback enhancement is described.

[0468] The following set of objectives have been identified for the two-sided CSI compression use case. Firstly, to ensure that the UE part and network part of the models are configured and applied according to their applicable scenarios and configuration. Secondly, to ensure that models match properly, ensuring that the CSI generation part used at the UE corresponds to the CSI reconstruction part employed at the gNB. Thirdly, to allow for seamless operation, requiring the simultaneous (de)activation and switching of the two-sided model.

[0469] Regarding the last point above, for the two-sided model CSI compression use cases, the selection, (de)activation, switching, and fallback of AI / ML models or AI / ML functionalities can be initiated by either the UE or the gNB. For which it is important to distinguish the various cases and understand their applicability to UE-side versus network-side models.

[0470] For data collection, model transfer / delivery, and function-to-entity mapping analysis, various scenarios unfold for both the two-sided CSI compression use case, as well as for the UE-side CSI prediction use case, when the data generation and termination entities differ. For instance, for:

[0471] 1> Model Training:

[0472] 2> For the two-sided CSI compression use case, training data can be generated by either the UE or the gNB, depending on specific requirements, while the termination point for training data may include the gNB, OAM, Over-The-Top (OTT) server or UE.

[0473] 3> RAN2 identified the case in which Core Network may be used for model training. However, no study was conducted since this is beyond the scope of this Working Group.

[0474] 2> For the UE-side CSI prediction use case, training data can be generated by the UE, while the termination point for training data may include the UE or a UE-side OTT server.

[0475] 3> RAN2 identified the cases in which OAM or Core Network may be used for UE-side model training. However, no study was conducted since this is beyond the scope of this Working Group.

[0476] 3> RAN2 identified the case in which gNB may be used for UE-side model training. However, no conclusion was reached, as this depends on the RAN1 progress.

[0477] 1> Inference:

[0478] 2> For the two-side CSI compression use case:

[0479] 3> For network part of two-sided model inference, the UE can generate the necessary input data while the termination point for this input data lies within the gNB, where the inference process is performed.

[0480] 3> For UE part of two-sided model inference, input data is internally available at UE, where the inference process is performed.

[0481] 2> For the UE-side CSI prediction use case:

[0482] 3> For UE-side model inference, input data is internally available at UE, where the inference process is performed.

[0483] 1> Management:

[0484] 2> For the two-sided CSI compression use case, the model / functionality control (e.g., selection, (de)activation, switching, fallback, etc.) is performed by the gNB.

[0485] 3> RAN2 identified the case in which the control is performed by the UE. However, no conclusion was reached, as this depends on the RAN1 progress.

[0486] 2> For the UE-side CSI prediction use case:

[0487] 3> The model / functionality control (e.g., selection, (de)activation, switching, fallback, etc.) may be performed by the UE when the monitoring resides within the UE.

[0488] 3> The model / functionality control (e.g., selection, (de)activation, switching, fallback, etc.) may be performed by the gNB when the monitoring resides within the gNB or UE.

[0489] 2> Monitoring:

[0490] 3> The UE monitors the performance of its UE-side model.

[0491] 3> For monitoring at the network side of UE-side model, the UE can generate, if needed, calculated performance metrics or data required for performance metric calculation, while the termination point for these is the gNB.

[0492] An AI / ML model for Beam management is described.

[0493] For beam management, the selection, (de)activation, switching, and fallback of models or functionalities can also be initiated by either the UE or the gNB. For which it is important to distinguish the various cases and understand their applicability to UE-side versus network-side models.

[0494] For data collection, model transfer / delivery, and function-to-entity mapping analysis, various scenarios unfold when the data generation and termination entities differ. For instance, for:

[0495] 1> Model Training:

[0496] 2> For UE-side models, training data can be generated by the UE, while the termination point for training data may include the UE or a UE-side OTT server.

[0497] 3> RAN2 identified the cases in which OAM or Core Network may be used for UE-side model training. However, no study was conducted since this is beyond the scope of this Working Group.

[0498] 3> RAN2 identified the case in which gNB may be used for UE-side model training. However, no conclusion was reached, as this depends on the RAN1 progress.

[0499] 2> For gNB-side models, training data can be generated by the gNB or UE, while the termination point for training data may include the gNB, or OAM.

[0500] 3> RAN2 identified the case in which OTT server and Core Network may be used for gNB-side model training. However, no study was conducted since this is beyond the scope of this Working Group.

[0501] 1> Inference:

[0502] 2> For UE-side model inference, input data is internally available at UE, where the inference process is performed.

[0503] 2> For network-side model inference, the UE can generate the necessary input data while the termination point for this input data lies within the gNB, where the inference process is performed.

[0504] 1> Management:

[0505] 2> For UE-side model, the model / functionality control (e.g., selection, (de)activation, switching, fallback, etc.) may be performed by the UE when the monitoring resides within the UE.

[0506] 2> For UE-side model, the model / functionality control (e.g., selection, (de)activation, switching, fallback, etc.) may be performed by the gNB when the monitoring resides within the gNB or UE.

[0507] 2> Monitoring:

[0508] 3> The UE monitors the performance of its UE-side model.

[0509] 3> For monitoring at the network side of UE-side model, the UE can generate, if needed, calculated performance metrics or data required for performance metric calculation, while the termination point for these is the gNB.

[0510] 3> For network-side model, the monitoring resides within the gNB.

[0511] An AI / ML model for Positioning accuracy enhancements is described.

[0512] For the positioning use cases, the selection, (de)activation, switching, and fallback of models or functionalities can be initiated by either the UE, the gNB, or the LMF. For which it is important to distinguish the various cases and understand their applicability to UE-side versus network-side models.

[0513] For data collection, model transfer / delivery, and function-to-entity mapping analysis, various scenarios unfold when the data generation and termination entities differ. For instance, for:

[0514] 1> Model Training:

[0515] 2> For UE-side models, training data can be generated by the UE, while the termination point for training data may include the UE or a UE-side OTT server.

[0516] 3> RAN2 identified the cases in which OAM or Core Network may be used for UE-side model training. However, no study was conducted since this is beyond the scope of this Working Group.

[0517] 3> RAN2 identified the case in which LMF may be used for UE-side model training. However, no conclusion was reached, as this depends on the RAN1 progress.

[0518] 2> For gNB-side model, training data can be generated by the gNB, while the termination point for training data may include the gNB, or OAM.

[0519] 3> RAN2 identified the case in which LMF may be used for gNB-side model training. However, no conclusion was reached, as this depends on the RAN1 progress.

[0520] 2> For LMF-side model, the LMF is the termination point for training data.

[0521] 1> Inference:

[0522] 2> For UE-side model inference, input data is internally available at UE, where the inference process is performed.

[0523] 2> For gNB-side model inference, input data is internally available at gNB. For this case, the UE can also generate the necessary input data while the termination point for this input data lies within the gNB where the inference process is performed.

[0524] 2> For LMF-side model inference, the UE or gNB can generate the necessary input data while the termination point for this input data lies within the LMF where the inference process is performed.

[0525] 1> Management:

[0526] 2> For UE-side model, the model / functionality control (e.g., selection, (de)activation, switching, fallback, etc.) may be performed by the UE when the monitoring resides within the UE.

[0527] 2> For gNB-side model, the model / functionality control (e.g., selection, (de)activation, switching, fallback, etc.) is performed by the gNB.

[0528] 2> The model / functionality control (e.g., selection, (de)activation, switching, fallback, etc.) may be performed by the LMF when the monitoring resides within the LMF or UE.

[0529] 2> Monitoring:

[0530] 3> The UE monitors the performance of its UE-side model.

[0531] 3> For monitoring at the gNB side, and if needed, calculated performance metrics or data required for performance metric calculation, can at least be generated by the gNB.

[0532] 3> For monitoring at the LMF side, the gNB or UE can generate, if needed, calculated performance metrics or data required for performance metric calculation, while the termination points for these metrics is the LMF.

[0533] Hereinafter, examples related to input and output of the AI / ML model for the present disclosure are described.

[0534] For example, the input and the output of the AI / ML model may be used the AI / ML model in FIG. 20.

[0535] For example, the AI / ML model may be used for CSI compression.

[0536] The data contents (that is, input data) for training for the CSI compression may include (i) target CSI, (ii) CSI feedback, and (iii) gradients for CSI feedback.

[0537] The data contents for inference for the CSI compression may include CSI feedback.

[0538] The data contents for monitoring for the CSI compression may include (i) reconstructed CSI from NW to UE, (ii) calculated performance metrics, and (iii) target CSI.

[0539] For example, the AI / ML model may be used for CSI prediction at UE side.

[0540] The data contents for training for CSI prediction at UE side may include target CSI in observation and prediction window.

[0541] The data contents for inference for CSI prediction at UE side may include predicted CSI feedback (AI / ML output).

[0542] The data contents for monitoring for CSI prediction at UE side may include (i) ground truth (i.e., target CSI) corresponding to predicted CSI and (ii) calculated performance metrics / Performance monitoring output.

[0543] For example, the AI / ML model may be used for beam management.

[0544] The data contents for training for beam management at UE-side and at Network-side may include L1-RSRPs and / or beam-IDs.

[0545] The data contents for inference for beam management at UE-side may include beam prediction results.

[0546] The data contents for inference for beam management at Network-side may include L1-RSRPs, and Beam-IDs if needed, for Set B.

[0547] The data contents for monitoring for beam management at UE-side may include (i) Event occurrence and / or calculated performance metrics (from UE to NW) and (ii) L1-RSRP(s) and / or beam-ID(s).

[0548] The data contents for monitoring for beam management at Network-side may include L1-RSRP(s) and / or beam-ID(s).

[0549] For example, the AI / ML model may be used for positioning.

[0550] The data contents for training for positioning may include (i) measurements (corresponding to model input): timing, power, and / or phase info, (ii) Label: Location coordinates as model output, and / or (iii) Label: Intermediate positioning measurement (timing info, LOS / NLOS indicator) as model output.

[0551] The data contents for inference for positioning may include (i) Location coordinates as model output, (ii) intermediate positioning measurement (timing info, LOS / NLOS indicator) as model output, and / or (iii) measurements (corresponding to model input): Timing, power, and / or phase info.

[0552] According to some embodiments of the present disclosure, 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.

[0553] Hereinafter, some embodiments of a method for addition of AI / ML information in self-organizing network are described.

[0554] In the present disclosure, if UE experiences connection failure, UE transmits information related to AI / ML operation that is possibly related to the connection failure.

[0555] For example, 1)Network configures AI / ML configuration 2) UE derives the prediction results such as measurement results, rlf, bf, etc. 3) UE reports the prediction results to network 4-1) Network configures UE to change some parameter or perform handover based on the prediction results and UE applies the configuration 4-2) UE autonomously executes the conditional operation, such as CHO, CPAC 5) UE experience a connection failure 6) The UE constructs conventional failure-related information in an SCG / MCG failure information message or a UE information response message for SON / MDT operation. At this time, AI / ML information related to AI / ML model configuration and AI / ML prediction results is added to the corresponding message 7) UE reports the failure information to network 8) Network updates AI / ML model / configuration

[0556] FIG. 21 shows an example of a method for supporting AI and ML operation in a wireless communication system.

[0557] In step S2101, network may configure UE with an AI / ML model configuration.

[0558] 1> AI / ML model configuration may be transferred via RRC message, NAS message, or data packets (DRB)

[0559] >> In terms of the radio bearer for the AI / ML model configuration, AI / ML specific SRB / DRB may be used

[0560] 1> Each AI / ML model may be related to a specific functionality, or a specific AI / ML model may be commonly used for specific functionalities

[0561] 2> Functionalities means use case, e.g., beam prediction and RRM prediction

[0562] 1> AI / ML model configuration may include full model information, partial model information, or parameters related to each AI / ML model.

[0563] 1> AI / ML model configuration may include report conditions for each AI / ML model

[0564] 1> (Example 1) For example, AI / ML model configuration may comprise the following:

[0565] 2> AI / ML Model A

[0566] 3> Functionality F_a

[0567] 3> Parameters P_a

[0568] 2> AI / ML Model B

[0569] 3> Functionality F_b

[0570] 3> Parameters P_b

[0571] 2> AI / ML Model C

[0572] 3> Parameters P_c

[0573] 2> In this example, Based on the AI / ML model configuration,

[0574] 3> UE can apply AI / ML Model A for Functionality F_a with paratmers P_a

[0575] 3> UE can apply AI / ML Model B for Functionality F_b with parameters P_b

[0576] 3> UE can apply AI / ML Model C for several Functionalities with parameters P_c

[0577] 1> (Example 2) For example, AI / ML model configuration may comprise the following:

[0578] 2> Function F_1

[0579] 2> AI / ML Model A

[0580] 3> Parameters P_a

[0581] 2> AI / ML Model B

[0582] 3> Parameters P_b

[0583] 2> In this example, Based on the AI / ML model configuration,

[0584] 3> UE can apply AI / ML Model A for Functionality F_1 with paratmers P_a

[0585] 3> UE can apply AI / ML Model B for Functionality F_1 with parameters P_b

[0586] In step S2102, for deriving the prediction results, UE may be configured with a more prediction model configuration.

[0587] 1> The prediction model configuration may include prediction model structure information,

[0588] 2> Network may configure a machine learning model to be used by UE.

[0589] 3> Network may include a machine learning type, such as reinforcement learning, supervised learning, or unsupervised learning.

[0590] 3> Network may include a machine learning model, such as DNN, CNN, RNN, and DRL.

[0591] 3> The configured ML model may be a pre-trained ML model that has been already trained by network a-priori

[0592] 4> The configured ML model is described by a model description information including model structure and parameters.

[0593] 4> For example, neural-network based model may comprise input layer, output layer, and hidden layer(s), where each layer comprises one or more neurons (equivalently nodes).

[0594] 5> Different layers are connected based on the connections between neurons of different layers

[0595] 6> Each connection of two different neurons in two different layers may be directive (e.g. neuron A to neuron B, meaning that the output of neuron A is fed into the neuron B)

[0596] 6> Each neuron may provide input to one or several connected neurons (1 to N connection).

[0597] 6> For a connection between two neurons (neuron A to neuron B), output of one neuron (A) is scaled by a weight, and the other neuron takes the scaled output as its input.

[0598] 6> Each neuron may take input from one or several connected neurons (N to 1 connection), and combines the input from the connected neurons, and produces an output based on activation function.

[0599] 3> The configured ML model may be a ML model to be trained.

[0600] 4> The configured ML model is described by a model description information including model structure and initial parameters that are to be trained.

[0601] 4> When network configures the ML model to be trained, it may also configure training parameters such as optimization objective(s) and optimization-related configuration parameters.

[0602] 3> Network may include machine learning input parameters for the machine learning model, such as UE location information, radio measurements related to serving cell and neighbouring cells, UE mobility history.

[0603] 3> Network may include machine learning output, such as UE trajectory prediction, predicted target cell, prediction time for handover, and UE traffic prediction.

[0604] 2> UE may perform a machine learning model training, validation, and testing which may generate model performance metrics based on the prediction model configuration.

[0605] 3> UE may perform a model training with the machine learning input parameters.

[0606] 2> UE may use the configured ML model to perform ML task such as predictions of measurements.

[0607] 3> UE may derive machine learning output(s).

[0608] 3> UE may infer from the outputs and use the outputs as feedback for the machine learning model.

[0609] 2> UE may send feedback to network about the results related to machine learning outputs and the accuracy of the machine learning model.

[0610] 3> Network may update the machine learning model and parameters related to the machine learning model.

[0611] In step S2103, UE may derive / store the prediction results for a functionality based on the AI / ML model configuration.

[0612] 1> UE may derive predictive measurement results

[0613] 1> UE may derive predictive connection success / connection failure

[0614] 1> UE may derive predictive radio link failure / beam failure

[0615] 1> UE may derive predictive RACH failure

[0616] 1> UE may derive predictive Location information

[0617] 1> UE may derive predictive mobility history

[0618] 1> UE may derive predictive handover success / failure

[0619] 1> In each prediction, UE may also derive the predictive time, location, cell, etc

[0620] In step S2104, UE may report the prediction results based on the report condition of the AI / ML model configuration

[0621] 1> The prediction results may include measurement results

[0622] 1> The prediction results may include connection success / connection failure

[0623] 1> The prediction results may include radio link failure / beam failure

[0624] 1> The prediction results may include RACH failure

[0625] 1> The prediction results may include Location information

[0626] 1> The prediction results may include mobility history

[0627] 1> The prediction results may include handover success / failure

[0628] 1> The prediction results may include the predictive time, location, cell, etc

[0629] In step S2105, UE may apply a new configuration or execute a conditional operation.

[0630] For example, in step S2105-1, network may configure UE with a configuration based on the prediction results and UE may apply the new configuration.

[0631] 1> The configuration may include some parameters change in current cell, e.g., RLM / BFD RS change, Power control change, etc

[0632] 1> The configuration may include handover command

[0633] 1> The configuration may include cell addition, such as CA

[0634] 1> The configuration may include CG addition, such as SCG addition

[0635] 1> The configuration may include conditional reconfiguration for CHO, CPAC, etc

[0636] For another example, in step S2105-2, UE may autonomously execute a conditional operation. That is, UE may automatically apply the new configuration for the conditional operation.

[0637] 1> The conditional operation may be CHO and / or CPC

[0638] 1> The conditional operation may be a cell / CG addition

[0639] 1> The conditional operation may be a cell / CG (de)activation

[0640] 1> The conditional operation may be a cell / CG release

[0641] In step S2106, UE may detect a failure in UE operation.

[0642] 1> The failure may be a handover failure

[0643] 1> The failure may be a radio link failure

[0644] 1> The failure may be a beam failure

[0645] 1> The failure may be a random-access failure

[0646] 1> The failure may be a configuration failure

[0647] 1> The failure may be a connection failure

[0648] 1> The failure may be a resume failure

[0649] 1> The failure may be a conditional reconfiguration (e.g., CHO, CPAC) failure

[0650] 1> The failure may be a cell / CG addition failure

[0651] 1> The failure may be a cell / CG (de)activation failure

[0652] 1> If the UE detects a failure, the UE may increase the number of (connection) failures associated with the AI / ML operation.

[0653] 2> The number can be counted per an AI / ML model

[0654] 2> The number can be counted per an AI / ML function

[0655] 2> The number can be counted for AI / ML configuration

[0656] In step S2107, UE may send a report message including failure-related information.

[0657] 1> The report message may be reported right after the failure is detected (Immediate information)

[0658] 1> The report message may be reported after collecting the failure history (Stored information)

[0659] 1> The report message may be delivered by network request or by UE decision

[0660] 1> The report message may include a first information

[0661] 2> The first information may be related to the detected failure such as connection establishment failure information, connection resume failure information, RLF report

[0662] 2> For example, (R16 / 17 conventional failure information)

[0663] 3> Failure type: t310 Expiry, random access problem, rlc max number of retransmission, synch reconfiguration failure, etc

[0664] 3> Measurement result of measurements on frequencies the UE is configured to measure in idle / inactive state

[0665] 3> Measurement result of measurements on frequencies the UE is configured to measure by measConfig

[0666] 3> Previous Cell id indicating physical cell id and carrier frequency of the source cell

[0667] 3> Failed Cell id indicating physical cell id and carrier frequency of the cell in which failure is detected or failed cell change / cell addition

[0668] 3> Failure time indicating the time elapsed since the last execution of RRCReconfiguration with reconfigurationWithSync until failure

[0669] 3> plmn identity

[0670] 3> measurement result of last serving Cell to include the cell level RSRP, RSRQ and the available SINR, of the source Pcell (in case HO failure) or Pcell (in case RLF) based on the available SSB / CSI-RS measurements collected up to the moment the UE detected failure

[0671] 3> RLM configuration information to include the radio link monitoring configuration of the source Pcell(in case HO failure) or Pcell(in case RLF)

[0672] 3> measurement result of neighbouring Cell to include neighbouring cell measurements

[0673] 3> measurement result to include all the available measurement quantities of the best measured cells, other than the source Pcell(in case HO failure) or Pcell(in case RLF)

[0674] 3> CHO Config to include conditional reconfiguration information (first triggered event, time between events if two events are configured)

[0675] 3> Time elapsed 1) between CHO configuration and CHO execution (If CHO is failed) 2) between CHO configuration and HO execution (If HO is failed)

[0676] 3> CHO candidate cell list to include the global cell identity / physical cell identity / carrier frequency of each candidate cell for conditional handover at the time of the failed handover

[0677] 3> Last handover type e.g., cho, daps, etc

[0678] 3> Connection failure type e.g., hof, rlf

[0679] 3> Failed Pcell Id to set global cell identity / tracking area code / physical cell identity / carrier frequency where radio link failure is detected

[0680] 3> Previous Cell to set global cell identity / tracking area code of the Pcell where the last executed CHO message was received

[0681] 3> RLF cause e.g., random access problem, beam failure recovery Failure, etc

[0682] 3> Location information

[0683] 1> The report message may include a second information

[0684] 2> The second information is related to AI / ML operation and may include the followings:

[0685] 3> Information indicating whether the UE is performing AI / ML based task or not around the time of the failure

[0686] 4> Information whether measurements obtained around the time of the failure is AI / ML-based or not.

[0687] 4> Information whether measurements reported around the time of the failure is AI / ML-based or not.

[0688] 4> Information whether a failed mobility is triggered by AI / ML-based measurements or legacy measurements.

[0689] 3> AI / ML model information

[0690] 4> An indication indicating that AI / ML model was configured / activated / operated

[0691] 4> Configured AI / ML model list, if available

[0692] 4> AI / ML model ID, e.g., Model A

[0693] 4> AI / ML model function, e.g., F_a and F_1

[0694] 4> AI / ML model configuration related to each prediction

[0695] 5> The model configuration may include bitmap-type information to indicate the activated model

[0696] 3> Time information related to prediction

[0697] 4> The time of occurrence below

[0698] 5> Receiving AI / ML model configuration, deriving prediction result, sending prediction report, receiving network configuration / command after the report, UE action based on network command or UE decision, failure occurrence

[0699] 4> Time elapsed between the AI / ML model configuration and the prediction, T1

[0700] 5> If AI / ML model is updated based on the previous AI / ML model configuration,

[0701] 6> Time elapsed between the AI / ML model configuration and the AI / ML model update, T1_1

[0702] 6> Time elapsed between the AI / ML model update and the prediction, T1_2

[0703] 4> Time elapsed between the prediction and the prediction report, T2

[0704] 4> Time elapsed between the prediction report and the network configuration / command, T3

[0705] 4> Time elapsed between the network configuration / command and UE operation based on the network command or UE decision based, T4

[0706] 4> Time elapsed between the UE operation based on the network command or UE decision based and Failure, T5

[0707] 4> Elapsed time information can be included in combination, e.g., T1+T2

[0708] 3> Prediction result information

[0709] 4> Difference between the predictive measurement results and the actual measurement results

[0710] 4> Difference between the predictive failure and the actual failure, e.g., predictive rlf -> actual non-rlf

[0711] 4> Difference between the predictive time and the actual time of the measurement results / failure-related prediction, e.g., predictive time t -> actual time t+T'. T' can be a positive or negative value.

[0712] 4> The predictive measurement results and the actual measurement results, separately

[0713] 4> The predictive failure and the actual failure, separately

[0714] 3> Cell Information

[0715] 4> plmn-identity, global cell identity, tracking area code, physical cell identity, and carrier frequency where AI / ML model configurations are configured

[0716] 4> plmn-identity, global cell identity, tracking area code, physical cell identity, and carrier frequency where UE derived the prediction results

[0717] 4> plmn-identity, global cell identity, tracking area code, physical cell identity, and carrier frequency where UE reported the prediction report

[0718] 4> plmn-identity, global cell identity, tracking area code, physical cell identity, and carrier frequency where UE received the network command based on the prediction result

[0719] 4> plmn-identity, global cell identity, tracking area code, physical cell identity, and carrier frequency where UE operated based on the network command or UE decision based

[0720] 3> Number of failures associated with that AI / ML model

[0721] 2> The second information may be delivered independently of the first information

[0722] 2> The second information may be delivered in each content of the first information

[0723] In step S2108, network may send an AI / ML model configuration.

[0724] 1> The AI / ML model configuration may include an update of the previous AI / ML model configuration with some parameters for a certain AI / ML model

[0725] 1> The AI / ML model configuration may include a change the previous AI / ML model configuration with new AI / ML model

[0726] 1> The AI / ML model configuration may be used to (de)activate a certain AI / ML model

[0727] 1> The AI / ML model configuration may be used to release the AI / ML model operation

[0728] FIG. 22 shows an example of time elapsed from receiving the AI / ML model configuration.

[0729] In particular, FIG. 22 illustrates time information related to prediction described in step S2107.

[0730] T1 is time elapsed between the AI / ML model configuration and the derivation of prediction result.

[0731] T2 is time elapsed between the derivation of prediction result and the prediction report.

[0732] T3 is time elapsed between the prediction report and the the network configuration / command (that is, time at which the UE receives the network configuration / command).

[0733] T4 is time elapsed between the network configuration / command and UE operation based on the network configuration / command. Otherwise, T4 is time elapsed between the network configuration / command and UE operation based on UE decision.

[0734] T5 is time elapsed between UE operation based on the network configuration / command (or UE operation based on UE decision) and Failure.

[0735] The time information (for example, T1, T2, T3, T4, and / or T5) could be included in the report message.

[0736] Some of the detailed steps shown in the examples of FIGS. 20, 21, and 22 may not be essential steps and may be omitted. In addition to the steps shown in FIGS. 20, 21, and 22, other steps may be added, and the order of the steps may vary. Some of the above steps may have their own technical meaning.

[0737] Hereinafter, an apparatus for supporting AI and ML operation in a wireless communication system, according to some embodiments of the present disclosure, will be described. Herein, the apparatus may be a wireless device (100 or 200) in FIGS. 2, 3, and 5.

[0738] For example, a wireless device may perform the methods described above. The detailed description overlapping with the above-described contents could be simplified or omitted.

[0739] Referring to FIG. 5, a wireless device 100 may include a processor 102, a memory 104, and a transceiver 106.

[0740] According to some embodiments of the present disclosure, the processor 102 may be configured to be coupled operably with the memory 104 and the transceiver 106.

[0741] The processor 102 may be configured to detect a failure in an operation with a network. The processor 102 may be configured to control the transceiver 106 to transmit a message including (i) information on the failure, and (ii) information on a predicted information related to the failure. The predicted information may be derived by an Artificial Intelligence (AI) and / or Machine Learning (ML) model of the wireless device.

[0742] For example, the information on the predicted information related to the failure may include information on the operation in which the failure is detected.

[0743] For example, the message further may include information informing that the wireless device derives the predicted information based on the AI and / or ML model.

[0744] For example, the message may further include information on the AI and / or ML model used for deriving the predicted information.

[0745] For example, the processor 102 may be configured to derive a predicted failure and a predicted time point at which the predicted failure is occurred.

[0746] For example, the information on the predicted information may include information on the predicted failure and information on the predicted time point.

[0747] For example, the information on the predicted information may include information on time difference between the predicted time point and a time point at which the failure occurred.

[0748] For example, the message may further include information on a time point at which the operation is performed.

[0749] For example, the operation may include a predicted operation derived from the AI and / or ML model.

[0750] For example, the processor 102 may be configured to control the transceiver 106 to receive, from the network, an AI and / or ML model configuration including information on the AI and / or ML model. The processor 102 may be configured to derive predicted measurement results based on the AI and / or ML model. The operation may be triggered by the predicted measurement results.

[0751] For example, the message may further include information informing that the operation is triggered by the predicted measurement results.

[0752] For example, the predicted information may include information on the predicted measurement results.

[0753] For example, the operation may include a conditional operation, a conditional handover (CHO), a conditional PScell change (CPC), a cell and / or a cell group (CG) addition, a cell and / or a CG activation, a cell and / or a CG deactivation, and / or a cell and / or a CG release.

[0754] For example, the failure in the operation may include a handover failure, a radio link failure, a beam failure, a random-access failure, a configuration failure, a connection failure, a resume failure, a conditional reconfiguration failure, a cell and / or a CG addition failure, a cell and / or a CG activation failure, and / or a cell and / or a CG deactivation failure.

[0755] For example, 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.

[0756] Hereinafter, a processor for a wireless device for supporting AI and ML operation in a wireless communication system, according to some embodiments of the present disclosure, will be described.

[0757] The processor may be configured to control the wireless device to detect a failure in an operation with a network. The processor may be configured to control the wireless device to transmit a message including (i) information on the failure, and (ii) information on a predicted information related to the failure. The predicted information may be derived by an Artificial Intelligence (AI) and / or Machine Learning (ML) model of the wireless device.

[0758] For example, the information on the predicted information related to the failure may include information on the operation in which the failure is detected.

[0759] For example, the message further may include information informing that the wireless device derives the predicted information based on the AI and / or ML model.

[0760] For example, the message may further include information on the AI and / or ML model used for deriving the predicted information.

[0761] For example, the processor may be configured to control the wireless device to derive a predicted failure and a predicted time point at which the predicted failure is occurred.

[0762] For example, the information on the predicted information may include information on the predicted failure and information on the predicted time point.

[0763] For example, the information on the predicted information may include information on time difference between the predicted time point and a time point at which the failure occurred.

[0764] For example, the message may further include information on a time point at which the operation is performed.

[0765] For example, the operation may include a predicted operation derived from the AI and / or ML model.

[0766] For example, the processor may be configured to control the wireless device to receive, from the network, an AI and / or ML model configuration including information on the AI and / or ML model. The processor may be configured to control the wireless device to derive predicted measurement results based on the AI and / or ML model. The operation may be triggered by the predicted measurement results.

[0767] For example, the message may further include information informing that the operation is triggered by the predicted measurement results.

[0768] For example, the predicted information may include information on the predicted measurement results.

[0769] For example, the operation may include a conditional operation, a conditional handover (CHO), a conditional PScell change (CPC), a cell and / or a cell group (CG) addition, a cell and / or a CG activation, a cell and / or a CG deactivation, and / or a cell and / or a CG release.

[0770] For example, the failure in the operation may include a handover failure, a radio link failure, a beam failure, a random-access failure, a configuration failure, a connection failure, a resume failure, a conditional reconfiguration failure, a cell and / or a CG addition failure, a cell and / or a CG activation failure, and / or a cell and / or a CG deactivation failure.

[0771] For example, 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.

[0772] Hereinafter, a non-transitory computer-readable medium has stored thereon a plurality of instructions for supporting AI and ML operation in a wireless communication system, according to some embodiments of the present disclosure, will be described.

[0773] According to some embodiment of the present disclosure, 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. For example, a method performed by a wireless device in a wireless communication may be implemented in hardware, software, firmware, or any combination thereof. For example, a software may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other storage medium.

[0774] Some example of storage medium is coupled to the processor such that the processor can read information from the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. For other example, the processor and the storage medium may reside as discrete components.

[0775] The computer-readable medium may include a tangible and non-transitory computer-readable storage medium.

[0776] For example, 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. Non-transitory computer-readable media may also include combinations of the above.

[0777] In addition, the method described herein may be realized at least in part by a computer-readable communication medium that carries or communicates code in the form of instructions or data structures and that can be accessed, read, and / or executed by a computer.

[0778] According to some embodiment of the present disclosure, 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.

[0779] The stored plurality of instructions may cause the wireless device to detect a failure in an operation with a network. The stored plurality of instructions may cause the wireless device to transmit a message including (i) information on the failure, and (ii) information on a predicted information related to the failure. The predicted information may be derived by an Artificial Intelligence (AI) and / or Machine Learning (ML) model of the wireless device.

[0780] For example, the information on the predicted information related to the failure may include information on the operation in which the failure is detected.

[0781] For example, the message further may include information informing that the wireless device derives the predicted information based on the AI and / or ML model.

[0782] For example, the message may further include information on the AI and / or ML model used for deriving the predicted information.

[0783] For example, the stored plurality of instructions may cause the wireless device to derive a predicted failure and a predicted time point at which the predicted failure is occurred.

[0784] For example, the information on the predicted information may include information on the predicted failure and information on the predicted time point.

[0785] For example, the information on the predicted information may include information on time difference between the predicted time point and a time point at which the failure occurred.

[0786] For example, the message may further include information on a time point at which the operation is performed.

[0787] For example, the operation may include a predicted operation derived from the AI and / or ML model.

[0788] For example, the stored plurality of instructions may cause the wireless device to receive, from the network, an AI and / or ML model configuration including information on the AI and / or ML model. The stored plurality of instructions may cause the wireless device to derive predicted measurement results based on the AI and / or ML model. The operation may be triggered by the predicted measurement results.

[0789] For example, the message may further include information informing that the operation is triggered by the predicted measurement results.

[0790] For example, the predicted information may include information on the predicted measurement results.

[0791] For example, the operation may include a conditional operation, a conditional handover (CHO), a conditional PScell change (CPC), a cell and / or a cell group (CG) addition, a cell and / or a CG activation, a cell and / or a CG deactivation, and / or a cell and / or a CG release.

[0792] For example, the failure in the operation may include a handover failure, a radio link failure, a beam failure, a random-access failure, a configuration failure, a connection failure, a resume failure, a conditional reconfiguration failure, a cell and / or a CG addition failure, a cell and / or a CG activation failure, and / or a cell and / or a CG deactivation failure.

[0793] According to some embodiments of the present disclosure, 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.

[0794] Hereinafter, a method performed by a base station (BS) for supporting AI and ML operation in a wireless communication system, according to some embodiments of the present disclosure, will be described.

[0795] The BS may provide, to a wireless device, a configuration for an Artificial Intelligence (AI) and / or Machine Learning (ML) model. The BS may receive, from the wireless device, a message including (i) information on a failure detected in an operation of the wireless device, and (ii) information on a predicted information related to the failure.

[0796] Hereinafter, a base station (BS) for supporting AI and ML operation in a wireless communication system, according to some embodiments of the present disclosure, will be described.

[0797] The BS may include a transceiver, a memory, and a processor operatively coupled to the transceiver and the memory.

[0798] The processor may be configured to control the transceiver to provide, to a wireless device, a configuration for an Artificial Intelligence (AI) and / or Machine Learning (ML) model. The processor may be configured to control the transceiver to receive, from the wireless device, a message including (i) information on a failure detected in an operation of the wireless device, and (ii) information on a predicted information related to the failure.

[0799] The present disclosure can have various advantageous effects.

[0800] According to some embodiments of the present disclosure, a wireless device could efficiently supporting the AI / ML operation by reporting information related to the AI / ML operation.

[0801] For example, if the network can recognize a problem with the AI / ML model through the failure report, the network manages the AI / ML model well from a model monitoring perspective by updating the parameters of the current AI / ML model or changing a more suitable AI / ML model.

[0802] In other words, for example, the network can efficiently recognize AI / ML problems in the UE. The AI / ML models to be used in the UE can be managed efficiently (for example, the UE could efficiently receive configuration of a new AI / ML model).

[0803] According to some embodiments of the present disclosure, a wireless network system could provide an efficient manage the AI / ML operation of a wireless device by receiving the information related to the AI / ML operation.

[0804] Advantageous effects which can be obtained through specific embodiments of the present disclosure are not limited to the advantageous effects listed above. For example, there may be a variety of technical effects that a person having ordinary skill in the related art can understand and / or derive from the present disclosure. Accordingly, the specific effects of the present disclosure are not limited to those explicitly described herein, but may include various effects that may be understood or derived from the technical features of the present disclosure.

[0805] Claims in the present disclosure can be combined in a various way. For instance, technical features in method claims of the present disclosure can be combined to be implemented or performed in an apparatus, and technical features in apparatus claims can be combined to be implemented or performed in a method. Further, technical features in method claim(s) and apparatus claim(s) can be combined to be implemented or performed in an apparatus. Further, technical features in method claim(s) and apparatus claim(s) can be combined to be implemented or performed in a method. Other implementations are within the scope of the following claims.

Claims

1.A method performed by a wireless device in a wireless communication system, the method comprising:detecting a failure in an operation with a network; andtransmitting a message including (i) information on the failure, and (ii) information on a predicted information related to the failure,wherein the predicted information is derived by an Artificial Intelligence (AI) and / or Machine Learning (ML) model of the wireless device.2.The method of claim 1,wherein the information on the predicted information related to the failure includes information on the operation in which the failure is detected.3.The method of claim 1,wherein the message further includes information informing that the wireless device derives the predicted information based on the AI and / or ML model.4.The method of claim 1,wherein the message further includes information on the AI and / or ML model used for deriving the predicted information.5.The method of claim 1, wherein the method further comprises,deriving a predicted failure and a predicted time point at which the predicted failure is occurred.6.The method of claim 5,wherein the information on the predicted information includes information on the predicted failure and information on the predicted time point.7.The method of claim 5,wherein the information on the predicted information includes information on time difference between the predicted time point and a time point at which the failure occurred.8.The method of claim 1,wherein the message further includes information on a time point at which the operation is performed.9.The method of claim 1,wherein the operation includes a predicted operation derived from the AI and / or ML model.10.The method of claim 1, wherein the method further comprises,receiving, from the network, an AI and / or ML model configuration including information on the AI and / or ML model; andderiving predicted measurement results based on the AI and / or ML model,wherein the operation is triggered by the predicted measurement results.11.The method of claim 10,wherein the message further includes information informing that the operation is triggered by the predicted measurement results.12.The method of claim 10,wherein the predicted information includes information on the predicted measurement results.13.The method of claim 1,wherein the operation includes a conditional operation, a conditional handover (CHO), a conditional PScell change (CPC), a cell and / or a cell group (CG) addition, a cell and / or a CG activation, a cell and / or a CG deactivation, and / or a cell and / or a CG release.14.The method of claim 1,wherein the failure in the operation includes a handover failure, a radio link failure, a beam failure, a random-access failure, a configuration failure, a connection failure, a resume failure, a conditional reconfiguration failure, a cell and / or a CG addition failure, a cell and / or a CG activation failure, and / or a cell and / or a CG deactivation failure.15.The method of claim 1,wherein the wireless device is in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the wireless device.16.A wireless device in a wireless communication system comprising:a transceiver;a memory; andat least one processor operatively coupled to the transceiver and the memory, and adapted to:detect a failure in an operation with a network; andtransmit a message including (i) information on the failure, and (ii) information on a predicted information related to the failure,wherein the predicted information is derived by an Artificial Intelligence (AI) and / or Machine Learning (ML) model of the wireless device.17.The wireless device of claim 16,wherein the information on the predicted information related to the failure includes information on the operation in which the failure is detected.18.The wireless device of claim 16,wherein the message further includes information informing that the wireless device derives the predicted information based on the AI and / or ML model.19.The wireless device of claim 16,wherein the message further includes information on the AI and / or ML model used for deriving the predicted information.20.The wireless device of claim 16, wherein the at least one processor is further adapted to:derive a predicted failure and a predicted time point at which the predicted failure is occurred.21.The wireless device of claim 20,wherein the information on the predicted information includes information on the predicted failure and information on the predicted time point.22.The wireless device of claim 20,wherein the information on the predicted information includes information on time difference between the predicted time point and a time point at which the failure occurred.23.The wireless device of claim 16,wherein the message further includes information on a time point at which the operation is performed.24.The wireless device of claim 16,wherein the operation includes a predicted operation derived from the AI and / or ML model.25.The wireless device of claim 16, wherein the at least one processor is further adapted to:receive, from the network, an AI and / or ML model configuration including information on the AI and / or ML model; andderive predicted measurement results based on the AI and / or ML model,wherein the operation is triggered by the predicted measurement results.26.The wireless device of claim 25,wherein the message further includes information informing that the operation is triggered by the predicted measurement results.27.The wireless device of claim 25,wherein the predicted information includes information on the predicted measurement results.28.The wireless device of claim 16,wherein the operation includes a conditional operation, a conditional handover (CHO), a conditional PScell change (CPC), a cell and / or a cell group (CG) addition, a cell and / or a CG activation, a cell and / or a CG deactivation, and / or a cell and / or a CG release.29.The wireless device of claim 16,wherein the failure in the operation includes a handover failure, a radio link failure, a beam failure, a random-access failure, a configuration failure, a connection failure, a resume failure, a conditional reconfiguration failure, a cell and / or a CG addition failure, a cell and / or a CG activation failure, and / or a cell and / or a CG deactivation failure.30.The wireless device of claim 16,wherein the wireless device is in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the wireless device.31.A processor for a wireless device in a wireless communication system, wherein the processor is configured to control the wireless device to perform operations comprising:detecting a failure in an operation with a network; andtransmitting a message including (i) information on the failure, and (ii) information on a predicted information related to the failure,wherein the predicted information is derived by an Artificial Intelligence (AI) and / or Machine Learning (ML) model of the wireless device.32.A non-transitory computer-readable medium having stored thereon a plurality of instructions, which, when executed by a processor of a wireless device, cause the wireless device to perform operations, the operations comprises,detecting a failure in an operation with a network; andtransmitting a message including (i) information on the failure, and (ii) information on a predicted information related to the failure,wherein the predicted information is derived by an Artificial Intelligence (AI) and / or Machine Learning (ML) model of the wireless device.33.A method performed by a base station in a wireless communication system, the method comprising,providing, to a wireless device, a configuration for an Artificial Intelligence (AI) and / or Machine Learning (ML) model; andreceiving, from the wireless device, a message including (i) information on a failure detected in an operation of the wireless device, and (ii) information on a predicted information related to the failure.34.A base station in a wireless communication system comprising:a transceiver;a memory; anda processor operatively coupled to the transceiver and the memory, and adapted to:provide, to a wireless device, a configuration for an Artificial Intelligence (AI) and / or Machine Learning (ML) model; andreceive, from the wireless device, a message including (i) information on a failure detected in an operation of the wireless device, and (ii) information on a predicted information related to the failure.

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