Method and apparatus for logging aiml model monitoring results in a wireless communication system

Abstract

A method and apparatus for logging AIML model monitoring results in a wireless communication system is provided. Based on that the condition related to the at least one monitoring object is not met, the wireless device log information related to the at least one monitoring object without transmission of the information related to the at least one monitoring object. Based on that the condition related to the at least one monitoring object is met, the wireless device transmits the information related to the at least one monitoring object.

Description

METHOD AND APPARATUS FOR LOGGING AIML MODEL MONITORING RESULTS IN A WIRELESS COMMUNICATION SYSTEM

[0001] The present disclosure relates to a method and apparatus for logging AIML model monitoring results 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] When the entity of training is different from the entity of inference for the one-sided model, or when two sided-model is used, AI / ML model can be transferred / delivered to another entity. AI / ML model information can include either parameters of a model structure known at the receiving end or a new model with parameters. During model transfer / delivery, a full model or a partial model can be delivered.

[0006] In order to use a suitable model in UE and network, there is a model monitoring procedure that monitors the inference performance of the AI / ML model. Based on the model monitoring results, AI / ML model can be updated, (de)activated, selected, switched, etc as one of the processes in LCM(Life Cycle Management).

[0007] In case of UE-sided monitoring, network may configure a threshold criterion to facilitate UE to perform model monitoring. In case of NW-sided monitoring, network my configure UE to report information related to model monitoring. For both sided monitoring, network may require the report from UE. Considering accurate model operation, model monitoring interval can be short.

[0008] However, if UE sends monitoring-related reports whenever conducting model monitoring operations, there would be a lot of power consumption in UE and signalling overhead. Note that several models can be activated at once for various functionality. As many models are activated, many monitoring-related reports can be triggered.

[0009] Therefore, studies for logging AI and ML model monitoring results in a wireless communication system are required.

[0010] In an aspect, a method performed by a wireless device in a wireless communication system is provided. The method comprises: receiving, from a network, a configuration for reporting, wherein the configuration includes information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object; determining whether the condition related to the at least one monitoring object is met; and based on that the condition related to the at least one monitoring object is not met: - logging information related to the at least one monitoring object without transmission of the information related to the at least one monitoring object; based on that the condition related to the at least one monitoring object is met: - transmitting the information related to the at least one monitoring object.

[0011] In another aspect, an apparatus for implementing the above method is provided.

[0012] The present disclosure can have various advantageous effects.

[0013] According to some embodiments of the present disclosure, a wireless device could efficiently log and / or report AIML model monitoring results in a wireless communication system.

[0014] For example, model monitoring results can be transmitted to network efficiently based on the model state. By avoiding the frequent AIML monitoring report, UE power consumption and signalling overhead can be reduced.

[0015] In other words, by reducing the number of AIML monitoring reports, a wireless device could save resource for reporting the AI / ML monitoring results.

[0016] According to some embodiments of the present disclosure, the wireless communication system could provide an efficient solution for logging and / or reporting AIML model monitoring results in a wireless communication system.

[0017] 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.

[0018] FIG. 1 shows an example of a communication system to which implementations of the present disclosure is applied.

[0019] FIG. 2 shows an example of wireless devices to which implementations of the present disclosure is applied.

[0020] FIG. 3 shows an example of a wireless device to which implementations of the present disclosure is applied.

[0021] FIG. 4 shows another example of wireless devices to which implementations of the present disclosure is applied.

[0022] FIG. 5 shows an example of UE to which implementations of the present disclosure is applied.

[0023] 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.

[0024] FIG. 8 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.

[0025] FIG. 9 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure is applied.

[0026] FIG. 10 shows an example of a Functional Framework for RAN Intelligence.

[0027] FIG. 11 shows an example of an AI / ML Model Training in OAM and AI / ML Model Inference in NG-RAN node.

[0028] FIG. 12 shows an example of Model Training and Model Inference both located in RAN node.

[0029] FIGS. 13 and 14 show an example of an architecture of neuron and neural network.

[0030] FIG. 15 shows an example of an AI / ML inference.

[0031] FIG. 16 shows an example of an MLP DNN model.

[0032] FIG. 17 shows an example of a CNN model.

[0033] FIG. 18 shows an example of an RNN model.

[0034] FIG. 19 shows an example of Reinforcement learning.

[0035] FIG. 20 shows an example of a l.ogged measurement configuration.

[0036] FIG. 21 shows an example of UE information procedure.

[0037] FIG. 22 shows an example of monitoring-related reports.

[0038] FIG. 23 shows an example of a method for logging AIML model monitoring results in a wireless communication system.

[0039] FIG. 24 shows an example of a method for determining reporting or logging based on model monitoring results.

[0040] FIG. 25 shows an example of a method for determining reporting or logging based on model monitoring results.

[0041] FIG. 26 shows an example of a generalized method for determining reporting or logging based on model monitoring results.

[0042] FIG. 27 shows an example of a method for determining reporting or logging based on model monitoring results.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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".

[0047] 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".

[0048] 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".

[0049] 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".

[0050] 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".

[0051] Technical features that are separately described in one drawing in the present disclosure may be implemented separately or simultaneously.

[0052] 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.

[0053] 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.

[0054] FIG. 1 shows an example of a communication system to which implementations of the present disclosure is applied.

[0055] 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.

[0056] 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).

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] The UAV may be, for example, an aircraft aviated by a wireless control signal without a human being onboard.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] The weather / environment device may include, for example, a device for monitoring or predicting a weather / environment.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] FIG. 2 shows an example of wireless devices to which implementations of the present disclosure is applied.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] In the present disclosure, a BS is also referred to as a node B (NB), an eNode B (eNB), or a gNB.

[0095] FIG. 3 shows an example of a wireless device to which implementations of the present disclosure is applied.

[0096] The wireless device may be implemented in various forms according to a use-case / service (refer to FIG. 1).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] FIG. 4 shows another example of wireless devices to which implementations of the present disclosure is applied.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] FIG. 5 shows an example of UE to which implementations of the present disclosure is applied.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] FIG. 8 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] uNslotsymbNframe,uslotNsubframe,uslot01410111420221440431480841416016

[0128] 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.

[0129] uNslotsymbNframe,uslotNsubframe,uslot212404

[0130] 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.

[0131] 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.

[0132] 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).

[0133] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1450MHz - 6000MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0134] 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).

[0135] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1410MHz - 7125MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0136] 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.

[0137] 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.

[0138] FIG. 9 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure is applied.

[0139] 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.

[0140] 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.

[0141] Hereinafter, technical features related to AI / ML are described.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Use cases to focus on:

[0152] 1> Initial set of use cases includes:

[0153] a) CSI feedback enhancement, e.g., overhead reduction, improved accuracy, prediction

[0154] b) Beam management, e.g., beam prediction in time, and / or spatial domain for overhead and latency reduction, beam selection accuracy improvement

[0155] c) Positioning accuracy enhancements for different scenarios including, e.g., those with heavy NLOS conditions

[0156] 2> Finalize representative sub use cases for each use case for characterization and baseline performance evaluations

[0157] 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

[0158] - 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.

[0159] AI / ML model, terminology and description to identify common and specific characteristics for framework investigations:

[0160] 3> Characterize the defining stages of AI / ML related algorithms and associated complexity:

[0161] a) Model generation, e.g., model training (including input / output, pre- / post-process, online / offline as applicable), model validation, model testing, as applicable

[0162] b) Inference operation, e.g., input / output, pre- / post-process, as applicable

[0163] 4> Identify various levels of collaboration between UE and gNB pertinent to the selected use cases, e.g.,

[0164] a) No collaboration: implementation-based only AI / ML algorithms without information exchange [for comparison purposes]

[0165] b) Various levels of UE / gNB collaboration targeting at separate or joint ML operation.

[0166] 5> Characterize lifecycle management of AI / ML model: e.g., model training, model deployment , model inference, model monitoring, model updating

[0167] 6> Dataset(s) for training, validation, testing, and inference

[0168] 7> Identify common notation and terminology for AI / ML related functions, procedures and interfaces

[0169] 8> Note: Consider the work done for FS_NR_ENDC_data_collect when appropriate

[0170] For the use cases under consideration:

[0171] - Evaluate performance benefits of AI / ML based algorithms for the agreed use cases in the final representative set:

[0172] a) Methodology based on statistical models, for link and system level simulations.

[0173] i. Extensions of 3GPP evaluation methodology for better suitability to AI / ML based techniques should be considered as needed.

[0174] 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.

[0175] iii. Need for common assumptions in dataset construction for training, validation and test for the selected use cases.

[0176] iv. Consider adequate model training strategy, collaboration levels and associated implications

[0177] v. Consider agreed-upon base AI model(s) for calibration

[0178] vi. AI model description and training methodology used for evaluation should be reported for information and cross-checking purposes

[0179] 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.

[0180] i. Performance, inference latency and computational complexity of AI / ML based algorithms should be compared to that of a state-of-the-art baseline

[0181] 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.

[0182] - Assess potential specification impact, specifically for the agreed use cases in the final representative set and for a common framework:

[0183] c) PHY layer aspects,

[0184] 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

[0185] 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

[0186] d) Protocol aspects, e.g., (RAN2) - RAN2 only starts the work after there is sufficient progress on the use case study in RAN1

[0187] 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

[0188] ii. Collaboration level specific specification impact per use case

[0189] 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

[0190] 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

[0191] ii. Consider the need and implications for AI / ML processing capabilities definition

[0192] - specific AI / ML models are not expected to be specified and are left to implementation. User data privacy needs to be preserved.

[0193] - The study on AI / ML for air interface is based on the current RAN architecture and new interfaces shall not be introduced.

[0194] FIG. 10 shows an example of a Functional Framework for RAN Intelligence.

[0195] > 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.

[0196] Examples of input data may include measurements from UEs or different network entities, feedback from Actor, output from an AI / ML model.

[0197] >> Training Data: Data needed as input for the AI / ML Model Training function.

[0198] >> Inference Data: Data needed as input for the AI / ML Model Inference function.

[0199] > 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.

[0200] >> 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.

[0201] > 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.

[0202] >> Output: The inference output of the AI / ML model produced by a Model Inference function.

[0203] >>> Note: Details of inference output are use case specific.

[0204] >> Model Performance Feedback: It may be used for monitoring the performance of the AI / ML model, when available.

[0205] > 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.

[0206] >> 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.

[0207] Hereinafter, technical features related to Mobility Optimization are described.

[0208] 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.

[0209] Mobility aspects of SON that can be enhanced by the use of AI / ML include

[0210] - Reduction of the probability of unintended events

[0211] - UE Location / Mobility / Performance prediction

[0212] - Traffic Steering

[0213] Reduction of the probability of unintended events associated with mobility.

[0214] Examples of such unintended events are:

[0215] - 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.

[0216] - 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.

[0217] - 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.

[0218] - Successful Handover: During a successful handover, there is underlying issue.

[0219] 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.

[0220] UELocation / Mobility / Performance Prediction

[0221] 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.

[0222] Traffic Steering

[0223] Efficient resource handling can be achieved adjusting handover trigger points and selecting optimal combination of Pcell / PSCell / Scells to serve a user.

[0224] Existing traffic steering can also be improved by providing a RAN node with information related to mobility or dual connectivity.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] Locations for AI / ML Model Training and AI / ML Model Inference

[0229] Considering the locations of AI / ML Model Training and AI / ML Model Inference for mobility solution, the following two options are considered:

[0230] - The AI / ML Model Training function is deployed in OAM, while the Model Inference function resides within the RAN node

[0231] - Both the AI / ML Model Training function and the AI / ML Model Inference function reside within the RAN node

[0232] Furthermore, for CU-DU split scenario, following option is possible:

[0233] - AI / ML Model Training is located in CU-CP or OAM, and AI / ML Model Inference function is located in CU-CP

[0234] gNB is also allowed to continue model training based on AI / ML model trained in the OAM.

[0235] FIG. 11 shows an example of an AI / ML Model Training in OAM and AI / ML Model Inference in NG-RAN node.

[0236] 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.

[0237] Step 1. The NG-RAN node configures the measurement information on the UE side and sends configuration message to UE including configuration information.

[0238] Step 2. The UE collects the indicated measurement, e.g., UE measurements related to RSRP, RSRQ, SINR of serving cell and neighbouring cells.

[0239] Step 3. The UE sends measurement report message to NG-RAN node 1 including the required measurement.

[0240] 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.

[0241] 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.

[0242] Step 6. Model Training. Required measurements are leveraged to training AI / ML model for UE mobility optimization.

[0243] 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.

[0244] Step 8. The NG-RAN node 1 obtains the measurement report as inference data for UE mobility optimization.

[0245] 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.

[0246] 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.

[0247] Step 11. The NG-RAN 1 sends the model performance feedback to OAM if applicable.

[0248] Note: This step is out of RAN3 scope.

[0249] 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.

[0250] Step 13. The NG-RAN node 1 sends the feedback information to OAM.

[0251] Step 14. The NG-RAN node 2 sends the feedback information to OAM.

[0252] FIG. 12 shows an example of Model Training and Model Inference both located in RAN node.

[0253] 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.

[0254] Step 1. NG-RAN node1 configures the measurement information on the UE side and sends configuration message to UE including configuration information.

[0255] Step 2. UE collects the indicated measurement, e.g., UE measurements related to RSRP, RSRQ, SINR of serving cell and neighbouring cells.

[0256] Step 3. UE sends measurement report message to NG-RAN node1 including the required measurement.

[0257] 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.

[0258] Step 5. Model training. Required measurements are leveraged to training AI / ML model for mobility optimization.

[0259] Step 6. NG-RAN node1 obtains the measurement report as inference data for real-time UE mobility optimization.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] Step 10. The NG-RAN node 2 sends feedback information after mobility optimization action to the NG-RAN node 1.

[0264] For example, UE mobility information for training purposes is only sent to gNBs that requested such information or when triggered.

[0265] Input of AI / ML-based Mobility Optimization

[0266] The following data is required as input data for mobility optimization.

[0267] From the UE:

[0268] - UE location information (e.g., coordinates, serving cell ID, moving velocity) interpreted by gNB implementation when available.

[0269] - Radio measurements related to serving cell and neighbouring cells associated with UE location information, e.g., RSRP, RSRQ, SINR.

[0270] - UE Mobility History Information.

[0271] From the neighbouring RAN nodes:

[0272] - UE's history information from neighbour

[0273] - Position, QoS parameters and the performance information of historical HO-ed UE (e.g., loss rate, delay, etc.)

[0274] - Current / predicted resource status

[0275] - 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.

[0276] From the local node:

[0277] - UE trajectory prediction

[0278] - Current / predicted resource status

[0279] - Current / predicted UE traffic

[0280] Output of AI / ML-based Mobility Optimization

[0281] AI / ML-based mobility optimization can generate following information as output:

[0282] - UE trajectory prediction (Latitude, longitude, altitude, cell ID of UE over a future period of time)

[0283] 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.

[0284] - Estimated arrival probability in CHO and relevant confidence interval

[0285] - Predicted handover target node, candidate cells in CHO, may together with the confidence of the predication

[0286] - Priority, handover execution timing, predicted resource reservation time window for CHO.

[0287] - UE traffic prediction (will be used by the RAN node internally and the details are left to normative work phase)

[0288] - Model output validity time will be discussed during R18 normative work per inference output.

[0289] Feedback of AI / ML-based Mobility Optimization

[0290] The following data is required as feedback data for mobility optimization.

[0291] - QoS parameters such as throughput, packet delay of the handed-over UE, etc.

[0292] - Resource status information updates from target NG-RAN.

[0293] - Performance information from target NG-RAN. The details of performance information are to be discussed during normative work phase.

[0294] Standard impact

[0295] 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.

[0296] 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.

[0297] MDT procedure enhancements should be discussed during the normative phase.

[0298] PotentialXninterface impact:

[0299] - Predicted resource status info and performance info from candidate target NG-RAN node to source NG-RAN node

[0300] - New signaling procedure or existing procedure to retrieve input information via Xn interface.

[0301] - New signaling procedure or existing procedure to retrieve feedback information via Xn interface.

[0302] Hereinafter, technical features related to AI and ML are described.

[0303] 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.

[0304] Artificial Intelligence (AI) is the science and engineering to build intelligent machines capable of carrying out tasks as humans do.

[0305] Deep neural network

[0306] FIGS. 13 and 14 show an example of an architecture of neuron and neural network.

[0307] 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.

[0308] 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.

[0309] 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.

[0310] 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.

[0311] Training and inference

[0312] 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.

[0313] 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.

[0314] FIG. 15 shows an example of an AI / ML inference.

[0315] 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.

[0316] 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.

[0317] Widely-usedDNNmodels and algorithms

[0318] FIG. 16 shows an example of an MLP DNN model.

[0319] 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.

[0320] FIG. 17 shows an example of a CNN model.

[0321] 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.

[0322] FIG. 18 shows an example of an RNN model.

[0323] 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.

[0324] FIG. 19 shows an example of Reinforcement learning.

[0325] 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.

[0326] Hereinafter, technical features related to logged measurements are described. Section 5.5a of 3GPP TS 38.331 v17.5.0 may be referred.

[0327] FIG. 20 shows an example of a l.ogged measurement configuration.

[0328] The purpose of this procedure is to configure the UE to perform logging of measurement results while in RRC_IDLE and RRC_INACTIVE. The procedure applies to logged measurements capable UEs that are in RRC_CONNECTED.

[0329] For example, NG-RAN may retrieve stored logged measurement information by means of the UE information procedure.

[0330] NG-RAN initiates the logged measurement configuration procedure to UE in RRC_CONNECTED by sending theLoggedMeasurementConfigurationmessage.

[0331] Reception of theLoggedMeasurementConfigurationby theUE

[0332] Upon receiving theLoggedMeasurementConfigurationmessage the UE shall:

[0333] 1> discard the logged measurement configuration as well as the logged measurement information;

[0334] 1> store the receivedloggingDuration,reportTypeandareaConfiguration, if included, inVarLogMeasConfig;

[0335] 1> if theLoggedMeasurementConfigurationmessage includesplmn-IdentityList:

[0336] 2> setplmn-IdentityListinVarLogMeasReportto include the RPLMN as well as the PLMNs included inplmn-IdentityList;

[0337] 1> else:

[0338] 2> setplmn-IdentityListinVarLogMeasReportto include the RPLMN;

[0339] 1> store the receivedabsoluteTimeInfo,traceReference,traceRecordingSessionRef, andtce-IdinVarLogMeasReport;

[0340] 1> store the receivedbt-NameList, if included, inVarLogMeasConfig;

[0341] 1> store the receivedwlan-NameList, if included, inVarLogMeasConfig;

[0342] 1> store the receivedsensor-NameList, if included, inVarLogMeasConfig;

[0343] 1> start timer T330 with the timer value set to theloggingDuration;

[0344] 1> store the receivedsigLoggedMeasType,if included, inVarLogMeasReport;

[0345] 1> store the receivedearlyMeasIndication,if included, inVarLogMeasConfig;

[0346] Release of Logged Measurement Configuration

[0347] The purpose of this procedure is to release the logged measurement configuration as well as the logged measurement information.

[0348] The UE shall initiate the procedure upon receiving a logged measurement configuration in same or another RAT. The UE shall also initiate the procedure upon power off or upon deregistration.

[0349] The UE shall:

[0350] 1> stop timer T330, if running;

[0351] 1> if stored, discard the logged measurement configuration as well as the logged measurement information, i.e. release the UE variablesVarLogMeasConfigandVarLogMeasReport

[0352] Measurements logging

[0353] This procedure specifies the logging of available measurements by a UE in RRC_IDLE and RRC_INACTIVE that has a logged measurement configuration. The actual process of logging within the UE, takes place in RRC IDLE state could continue in RRC INACTIVE state or vice versa.

[0354] While T330 is running and SDT procedure is not ongoing, the UE shall:

[0355] 1> if measurement logging is suspended:

[0356] 2> if during the last logging interval the IDC problems detected by the UE is resolved, resume measurement logging;

[0357] 1> if not suspended, perform the logging in accordance with the following:

[0358] 2> if thereportTypeis set toperiodicalin theVarLogMeasConfig:

[0359] 3> if the UE is in any cell selection state:

[0360] 4> perform the logging at regular time intervals, as defined by theloggingIntervalin theVarLogMeasConfig;

[0361] 3> if the UE is in camped normally state on an NR cell and if the RPLMN is included inplmn-IdentityListstored inVarLogMeasReport:

[0362] 4> if areaConfiguration is not included inVarLogMeasConfig; or

[0363] 4> if the serving cell is part of the area indicated byareaConfiginareaConfigurationinVarLogMeasConfig:

[0364] 5> perform the logging at regular time intervals, as defined by theloggingIntervalin theVarLogMeasConfig;

[0365] 2> else if thereportTypeis set toeventTriggered, andeventTypeis set tooutOfCoverage:

[0366] 3> perform the logging at regular time intervals as defined by theloggingIntervalinVarLogMeasConfigonly when the UE is in any cell selection state;

[0367] 3> upon transition from any cell selection state to camped normally state in NR:

[0368] 4> if the RPLMN is included inplmn-IdentityListstored inVarLogMeasReport; and

[0369] 4> ifareaConfigurationis not included inVarLogMeasConfigor if the current camping cell is part of the area indicated byareaConfigofareaConfigurationinVarLogMeasConfig:

[0370] 5> perform the logging;

[0371] 2> else if thereportTypeis set toeventTriggeredandeventTypeis set toeventL1:

[0372] 3> if the UE is in camped normally state on an NR cell and if the RPLMN is included inplmn-IdentityListstored inVarLogMeasReport:

[0373] 4> ifareaConfigurationis not included inVarLogMeasConfig; or

[0374] 4> if the serving cell is part of the area indicated byareaConfiginareaConfigurationinVarLogMeasConfig;

[0375] 5> perform the logging at regular time intervals as defined by theloggingIntervalinVarLogMeasConfigonly when the conditions indicated by theeventL1are met;

[0376] 2> when performing the logging:

[0377] 3> ifInterFreqTargetInfois configured and if the UE detected IDC problems on at least one of the frequencies included inInterFreqTargetInfoor any inter-RAT frequency during the last logging interval, or

[0378] 3> ifInterFreqTargetInfois not configured and if the UE detected IDC problems during the last logging interval:

[0379] 4> ifmeasResultServingCellin theVarLogMeasReportis not empty:

[0380] 5> includeinDeviceCoexDetected;

[0381] 5> suspend measurement logging from the next logging interval;

[0382] 4> else:

[0383] 5> suspend measurement logging;

[0384] 3> set therelativeTimeStampto indicate the elapsed time since the moment at which the logged measurement configuration was received;

[0385] 3> if location information became available during the last logging interval, set the content of thelocationInfo:

[0386] 3> if the UE is in any cell selection state:

[0387] 4> setanyCellSelectionDetectedto indicate the detection of no suitable or no acceptable cell found;

[0388] 4> if thereportTypeis set toeventTriggeredin theVarLogMeasConfig; and

[0389] 4> if the RPLMN at the time of entering the any cell selection state is included inplmn-IdentityListstored inVarLogMeasReport; and

[0390] 4> ifareaConfigurationis not included inVarLogMeasConfigor if the last suitable cell that the UE was camping on is part of the area indicated byareaConfigofareaConfigurationinVarLogMeasConfig:

[0391] 5> set theservCellIdentityto indicate global cell identity of the last suitable cell that the UE was camping on;

[0392] 5> set themeasResultServingCellto include the quantities of the last suitable cell the UE was camping on;

[0393] 4> else if thereportTypeis set toperiodicalin theVarLogMeasConfig:

[0394] 5> set theservCellIdentityto indicate global cell identity of the last logged cell that the UE was camping on;

[0395] 5> set themeasResultServingCellto include the quantities of the last logged cell the UE was camping on;

[0396] 3> else:

[0397] 4> set theservCellIdentityto indicate global cell identity of the cell the UE is camping on;

[0398] 4> set themeasResultServingCellto include the quantities of the cell the UE is camping on;

[0399] 3> if available, set themeasResultNeighCells, in order of decreasing ranking-criterion as used for cell re-selection, to include measurements of neighbouring cell that became available during the last logging interval and according to the following:

[0400] 4> include measurement results for at most 6 neighbouring cells on the NR serving frequency and for at most 3 cells per NR neighbouring frequency and for the NR neighbouring frequencies in accordance with the following:

[0401] 5> ifinterFreqTargetInfois included inVarLogMeasConfig:

[0402] 6> ifearlyMeasIndicationis included inVarLogMeasConfig;

[0403] 7> include measurement results for NR neighbouring frequencies that are included in bothinterFreqTargetInfoand either inmeasIdleCarrierListNR(within theVarMeasIdleConfig) orSIB4;

[0404] 6> else:

[0405] 7> include measurement results for NR neighbouring frequencies that are included in bothinterFreqTargetInfoandSIB4;

[0406] 5> else:

[0407] 6> ifearlyMeasIndicationis included inVarLogMeasConfig;

[0408] 7> include measurement results for NR neighbouring frequencies that are included in eithermeasIdleCarrierListNR(within theVarMeasIdleConfig) orSIB4;

[0409] 6> else:

[0410] 7> include measurement results for NR neighbouring frequencies that are included inSIB4;

[0411] 4> include measurement results for at most 3 neighbours per inter-RAT frequency in accordance with the following:

[0412] 5> ifearlyMeasIndicationis included inVarLogMeasConfig:

[0413] 6> include measurement results for inter-RAT neighbouring frequencies that are included in eithermeasIdleCarrierListEUTRA(within theVarMeasIdleConfig) orSIB5;

[0414] 5> else:

[0415] 6> include measurement results for inter-RAT frequencies that are included inSIB5;

[0416] 4> for each neighbour cell included, include the optional fields that are available;

[0417] For example, the UE includes the latest results of the available measurements as used for cell reselection evaluation in RRC_IDLE or RRC_INACTIVE, which are performed in accordance with the performance requirements.

[0418] For logging the measurements on frequencies (indicated inmeasIdleCarrierListNR / measIdleCarrierListEUTRA) in the logged measurement, thequalityThresholdinmeasIdleConfigshould not be applied, and how the UE logs the measurements on the frequencies is left to the UE implementation.

[0419] 2> when the memory reserved for the logged measurement information becomes full, stop timer T330 and perform the same actions as performed upon expiry of T330.

[0420] UEinformation Procedure

[0421] FIG. 21 shows an example of UE information procedure.

[0422] The UE information procedure is used by the network to request the UE to report information.

[0423] The network initiates the procedure by sending theUEInformationRequestmessage. The network should initiate this procedure only after successful security activation.

[0424] Upon receiving theUEInformationRequestmessage, the UE shall, only after successful security activation:

[0425] 1> if theidleModeMeasurementReqis included in theUEInformationRequestand the UE has storedVarMeasIdleReportthat contains measurement information concerning cells other than the PCell:

[0426] 2> set themeasResultIdleEUTRAin theUEInformationResponsemessage to the value ofmeasReportIdleEUTRAin theVarMeasIdleReport, if available;

[0427] 2> set themeasResultIdleNRin theUEInformationResponsemessage to the value ofmeasReportIdleNRin theVarMeasIdleReport, if available;

[0428] 2> discard theVarMeasIdleReportupon successful delivery of theUEInformationResponsemessage confirmed by lower layers;

[0429] 1> if thelogMeasReportReqis present and if the RPLMN is included inplmn-IdentityListstored inVarLogMeasReport:

[0430] 2> ifVarLogMeasReportincludes one or more logged measurement entries, set the contents of thelogMeasReportin theUEInformationResponsemessage as follows:

[0431] 3> include theabsoluteTimeStampand set it to the value ofabsoluteTimeInfoin theVarLogMeasReport;

[0432] 3> include thetraceReferenceand set it to the value oftraceReferencein theVarLogMeasReport;

[0433] 3> include thetraceRecordingSessionRefand set it to the value oftraceRecordingSessionRefin theVarLogMeasReport;

[0434] 3> include thetce-Idand set it to the value oftce-Idin theVarLogMeasReport;

[0435] 3> include thelogMeasInfoListand set it to include one or more entries from theVarLogMeasReportstarting from the entries logged first, and for each entry of thelogMeasInfoListthat is included, include all information stored in the correspondinglogMeasInfoListentry inVarLogMeasReport;

[0436] 3> if theVarLogMeasReportincludes one or more additional logged measurement entries that are not included in thelogMeasInfoListwithin theUEInformationResponsemessage:

[0437] 4> include thelogMeasAvailable;

[0438] 4> ifbt-LocationInfois included inlocationInfoof one or more of the additional logged measurement entries inVarLogMeasReportthat are not included in thelogMeasInfoListwithin theUEInformationResponsemessage:

[0439] 5> include thelogMeasAvailableBT;

[0440] 4> ifwlan-LocationInfois included inlocationInfoof one or more of the additional logged measurement entries inVarLogMeasReportthat are not included in thelogMeasInfoListwithin theUEInformationResponsemessage:

[0441] 5> include thelogMeasAvailableWLAN;

[0442] 1> ifra-ReportReqis set totrueand the UE has random access related information available inVarRA-Reportand if the RPLMN is included inplmn-IdentityListstored inVarRA-Report:

[0443] 2> set thera-ReportListin theUEInformationResponsemessage to the value ofra-ReportListinVarRA-Report;

[0444] 2> discard thera-ReportListfromVarRA-Reportupon successful delivery of theUEInformationResponsemessage confirmed by lower layers;

[0445] 1> ifrlf-ReportReqis set totrue:

[0446] 2> if the UE has radio link failure information or handover failure information available inVarRLF-Reportand if the RPLMN is included inplmn-IdentityListstored inVarRLF-Report:

[0447] 3> settimeSinceFailureinVarRLF-Reportto the time that elapsed since the last radio link failure or handover failure in NR;

[0448] 3> set therlf-Reportin theUEInformationResponsemessage to the value ofrlf-ReportinVarRLF-Report;

[0449] 3> discard therlf-ReportfromVarRLF-Reportupon successful delivery of theUEInformationResponsemessage confirmed by lower layers;

[0450] 2> else if the UE is capable of cross-RAT RLF reporting and has radio link failure information or handover failure information available inVarRLF-Reportand if the RPLMN is included inplmn-IdentityListstored inVarRLF-Report:

[0451] 3> settimeSinceFailureinVarRLF-Reportto the time that elapsed since the last radio link failure or handover failure in EUTRA;

[0452] 3> set failedPCellId-EUTRA in therlf-Reportin theUEInformationResponsemessage to indicate the PCell in which RLF was detected or the source PCell of the failed handover in theVarRLF-Report;

[0453] 3> set themeasResult-RLF-Report-EUTRAin therlf-Reportin theUEInformationResponsemessage to the value ofrlf-ReportinVarRLF-Report;

[0454] 3> discard therlf-ReportfromVarRLF-Reportupon successful delivery of theUEInformationResponsemessage confirmed by lower layers;

[0455] 1> ifconnEstFailReportReqis set totrueand the UE has connection establishment failure or connection resume failure information inVarConnEstFailReportorVarConnEstFailReportListand if the RPLMN is equal toplmn-Identitystored inVarConnEstFailReportorin at least one of the entries ofVarConnEstFailReportList:

[0456] 2> settimeSinceFailureinVarConnEstFailReportto the time that elapsed since the last connection establishment failure or connection resume failure in NR;

[0457] 2> set theconnEstFailReportin theUEInformationResponsemessage to the value ofconnEstFailReportinVarConnEstFailReport;

[0458] 2> if the UE supports multiple CEF report:

[0459] 3> for eachconnEstFailReportin theconnEstFailReportListinVarConnEstFailReportList:

[0460] 4> settimeSinceFailureto the time that elapsed since the associated connection establishment failure or connection resume failure in NR;

[0461] 2> for eachconnEstFailReportin theconnEstFailReportListin theUEInformationResponsemessage, set the value to the value ofconnEstFailReportinVarConnEstFailReportinVarConnEstFailReportList;

[0462] 2> discard theconnEstFailReportfromVarConnEstFailReportandVarConnEstFailReportListupon successful delivery of theUEInformationResponsemessage confirmed by lower layers;

[0463] 1> if themobilityHistoryReportReqis set totrue:

[0464] 2> include themobilityHistoryReportand set it to includevisitedCellInfoListfromVarMobilityHistoryReport;

[0465] 2> include in themobilityHistoryReportan entry for the current PCell, possibly after removing the oldest entry if required, and set its fields as follows:

[0466] 3> setvisitedCellIdto the global cell identity or the physical cell identity and carrier frequency of the current PCell:

[0467] 3> set fieldtimeSpentto the time spent in the current PCell;

[0468] 3> if the UE supports PSCell mobility history information and ifvisitedPSCellInfoListis present inVarMobilityHistoryReport:

[0469] 4> for the newest entry of the PCell in themobilityHistoryReport, includevisitedPSCellInfoListfromVarMobilityHistoryReport;

[0470] 4> if the UE is configured with a PSCell:

[0471] 5> for the newest entry of the PCell in themobilityHistoryReport, include the current PSCell information in thevisitedPSCellInfoListReport,possibly after removing the oldest PSCell entry of a PCell in themobilityHistoryReport, if required, and set its fields as follows:

[0472] 6> setvisitedCellIdto the global cell identity or the physical cell identity and carrier frequency of the current PSCell:

[0473] 6> set fieldtimeSpentto the time spent in the current PSCell while being connected to the current PCell;

[0474] 4> else:

[0475] 5> for the newest entry of the PCell in themobilityHistoryReport, include a new entry in thevisitedPSCellInfoListReport,possibly after removing the oldest PSCell entry of a PCell in themobilityHistoryReport, if required, and set its fields as follows:

[0476] 6> set fieldtimeSpentto the time spent without PSCell in the current PCell since last PSCell release since connected to the current PCell in RRC_CONNECTED;

[0477] 3> else if the UE supports PSCell mobility history information:

[0478] 4> if the UE is configured with a PSCell:

[0479] 5> for the newest entry of the PCell in themobilityHistoryReport, include the current PSCell information in thevisitedPSCellInfoListReport,possibly after removing the oldest PSCell entry of a PCell in themobilityHistoryReport, if required, and set its fields as follows:

[0480] 6> setvisitedCellIdto the global cell identity or the physical cell identity and carrier frequency of the current PSCell:

[0481] 6> set fieldtimeSpentto the time spent in the current PSCell while being connected to the current PCell;

[0482] 4> else:

[0483] 5> for the newest entry of the PCell in themobilityHistoryReport, include a new entry in thevisitedPSCellInfoListReport,possibly after removing the oldest PSCell entry of a PCell in themobilityHistoryReport, if required, and set its fields as follows:

[0484] 6> set fieldtimeSpentto the time spent without PSCell in the current PCell since connected to the current PCell in RRC_CONNECTED;

[0485] 1> if thesuccessHO-ReportReqis set totrueand if the UE has successful handover related information available inVarSuccessHO-Reportand if the RPLMN is included in theplmn-IdentityListstored inVarSuccessHO-Report:

[0486] 2> if thesuccessHO-Reportin theVarSuccessHO-Reportconcerns a DAPS handover and if a PDCP PDU has been received from the source cell of the concerned HO and a non-duplicated PDCP PDU has been received from the target cell of the concerned HO:

[0487] 3> setupInterruptionTimeAtHOinVarSuccessHO-Reportto include the time elapsed between the time of arrival of the last PDCP PDU received from the source cell of the concerned handover and the time of arrival of the first non-duplicate PDCP PDU received from the target cell of the concerned handover, as measured at the time of arrival of the first non-duplicate PDCP PDU received from the target cell;

[0488] 2> set thesuccessHO-Reportin theUEInformationResponsemessage to the value ofsuccessHO-Reportin theVarSuccessHO-Report, if available;

[0489] 2> discard theVarSuccessHO-Reportupon successful delivery of theUEInformationResponsemessage confirmed by lower layers;

[0490] 1> if thecoarseLocationRequestis set totrue:

[0491] 2> includecoarseLocationInfo,if available;

[0492] 1> if thelogMeasReportis included in theUEInformationResponse:

[0493] 2> submit theUEInformationResponsemessage to lower layers for transmission via SRB2;

[0494] 2> discard the logged measurement entries included in thelogMeasInfoListfromVarLogMeasReportupon successful delivery of theUEInformationResponsemessage confirmed by lower layers;

[0495] 1> else:

[0496] 2> submit theUEInformationResponsemessage to lower layers for transmission via SRB1.

[0497] Hereinafter technical features related to an AI / ML model are described.

[0498] For example, for the existing (under discussion) AI / ML use cases, proprietary models may be supported and / or open format may be supported.

[0499] For example, from Management or Control point of view mainly some meta info about a model may need to be known.

[0500] For example, a model is identified by a model ID.

[0501] For example, a model ID can be used to identify which AI / ML model is being used in Life Cycle Management (LCM) including model delivery.

[0502] For example, a model ID can be used to identify a model (or models) during model selection / activation / deactivation / switching.

[0503] For example, the wording "model transfer / delivery" may be used.

[0504] For example, aim to at least analyze the feasibility and benefits of model / transfer solutions based on the following:

[0505] - gNB can transfer / deliver AI / ML model(s) to UE via RRC signalling.

[0506] - CN (except LMF) can transfer / deliver AI / ML model(s) to UE via NAS signalling.

[0507] - LMF can transfer / deliver AI / ML model(s) to UE via LPP signalling.

[0508] - gNB can transfer / deliver AI / ML model(s) to UE via UP data.

[0509] - CN (except LMF) can transfer / deliver AI / ML model(s) to UE via UP data.

[0510] - LMF can transfer / deliver AI / ML model(s) to UE via UP data.

[0511] - Server (e.g. OAM, OTT) can transfer / delivery AI / ML model(s) to UE (e.g. transparent to 3GPP).

[0512] For example, Model ID is unique "globally", e.g. in order to manage test certification each retrained version need to be identified.

[0513] For the CSI compression and beam management use cases, model / function selection / (de)activation / switching / fallback can be UE-initiated or gNB-initiated. For the positioning use case, model / function selection / (de)activation / switching / fallback can be UE-initiated or LMF- / gNB-initiated.

[0514] For example, Information such as FFS:vendor info, applicable conditions, model performance indicators, etc. may be required for model management and control, and should, as a starting point, be part of meta information.

[0515] The general AI / ML framework consist of, (i) Data Collection, (ii) Model Training, (iii) Model Management, (iv) Model Inference, and (v) Model Storage.

[0516] Model ID can be used to identify model or models for the following LCM purposes:

[0517] model selection / activation / deactivation / switching (or identification, if that will be supported as a separate step).

[0518] (e.g. for so called "model ID based LCM")

[0519] If model transfer / delivery is supported, model ID can be used for model transfer / delivery LCM purpose.

[0520] Extend the previously endorsed table with 3 columns: Inference, Monitoring and Training, and explain in free text the applicability of the data collection method to the LCM purpose and the use case(s).

[0521] For example, intention is to cover functional arch in general, e.g. covering both be model based and / or functionality based LCM

[0522] "Model Storage" in the figure is only intended as a reference point (if any) for protocol terminations etc for model transfer / delivery etc. It is not intended to limit where models are actually stored. Add a note for this.

[0523] Remove "Model" in Model Managemt and Model Inference and for the actions / the arrow form Management to Inference (to reduce the risk for misunderstanding).

[0524] Management may be model based management, or functionality based management. Add a mote for this.

[0525] For example, that for the data collection in some scenarios (e.g., internal data up to implementation or the existing data are enough), possibly no RAN2 specification effort is needed in some scenarios, e.g. (not exhaustive):

[0526] - For model inference of UE-sided model, input data for model inference is available inside the UE.

[0527] - For UE-side (real time) monitoring of UE-sided model, performance metrics are available inside the UE. UE can independently monitor a model's performance without any data input from NW.

[0528] For CSI enhancement and beam management use cases:

[0529] - For model training, training data can be generated by UE / gNB and terminated at gNB / OAM / OTT server.

[0530] - For NW-sided model inference, input data can be generated by UE and terminated at gNB.

[0531] - For UE-side model inference, input data / assistance information can be generated by gNB and terminated at UE.

[0532] - For model monitoring at NW side, performance metrics can be generated by UE and terminated at gNB.

[0533] For positioning enhancement use case:

[0534] - For model training, training data can be generated by UE / gNB and terminated at LMF / OTT server.

[0535] - For NW-sided model inference, input data can be generated by UE / gNB and terminated at LMF and / or gNB.

[0536] - For UE-side model inference, input data / assistance information can be generated by LMF / gNB and terminated at the UE.

[0537] - For model monitoring at NW side, performance metrics can be generated by UE / gNB and terminated at LMF.

[0538] Meanwhile, when the entity of training is different from the entity of inference for the one-sided model, or when two sided-model is used, AI / ML model can be transferred / delivered to another entity. AI / ML model information can include either parameters of a model structure known at the receiving end or a new model with parameters. During model transfer / delivery, a full model or a partial model can be delivered.

[0539] In order to use a suitable model in UE and network, there is a model monitoring procedure that monitors the inference performance of the AI / ML model. Based on the model monitoring results, AI / ML model can be updated, (de)activated, selected, switched, etc as one of the processes in LCM(Life Cycle Management).

[0540] In case of UE-sided monitoring, network may configure a threshold criterion to facilitate UE to perform model monitoring. In case of NW-sided monitoring, network my configure UE to report information related to model monitoring. For both sided monitoring, network may require the report from UE. Considering accurate model operation, model monitoring interval can be short.

[0541] FIG. 22 shows an example of monitoring-related reports.

[0542] In FIG. 22, the wireless device may send the monitoring-related report at every reporting opportunity with the monitoring interval.

[0543] However, as FIG. 22, if UE sends monitoring-related reports whenever conducting model monitoring operations, there would be a lot of power consumption in UE and signalling overhead. Note that several models can be activated at once for various functionality. As many models are activated, many monitoring-related reports can be triggered.

[0544] Therefore, studies for logging AIML model monitoring results in a wireless communication system are required.

[0545] Hereinafter, a method for logging AIML model monitoring results in a wireless communication system, according to some embodiments of the present disclosure, will be described with reference to the following drawings.

[0546] 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).

[0547] FIG. 23 shows an example of a method for logging AIML model monitoring results in a wireless communication system.

[0548] In particular, FIG. 23 shows an example of a method performed by a wireless device in a wireless communication system.

[0549] In step S2301, a wireless device may receive, from network, a configuration for reporting. The configuration may include information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object.

[0550] For example, the operation of the wireless device may include at least one operation related to an Artificial Intelligence and Machine Learning (AI / ML) model. For example, the at least one monitoring object may include the Artificial Intelligence and Machine Learning (AI / ML) AI / ML model.

[0551] For example, the condition may a condition for prediction results derived from the AI / ML model.

[0552] For example, the information related to the at least one monitoring object may include monitoring results for the at least one monitoring object.

[0553] In step S2302, a wireless device may determine whether the condition related to the at least one monitoring object is met.

[0554] For example, the wireless device may determine whether the condition related to the at least one monitoring object is met at every monitoring opportunity.

[0555] For example, the wireless device may determine whether the at least one operation is in a normal state or in an abnormal state. The condition related to the at least one monitoring object may be met based on that the at least one operation is in the abnormal state.

[0556] For example, the wireless device may determine whether the at least one operation is in the normal state or in the abnormal state, based on a predicted operation of the wireless device which is derived from the AI / ML model.

[0557] That is, when the at least one operation of the wireless device is in a range of the predicted operation, the wireless device may determine that the at least one operation is in the normal state. The wireless device may determine that the condition related to the at least one monitoring object is not met.

[0558] When the at least one operation of the wireless device is not in a range of the predicted operation, the wireless device may determine that the at least one operation is in the abnormal state. The wireless device may determine that the condition related to the at least one monitoring object is met.

[0559] For example, the condition related to the at least one monitoring object may be met based on that a performance of the at least one monitoring object is lower than a reference performance.

[0560] Fog example, the condition related to the at least one monitoring object may be met based on that data throughput is less than a threshold value.

[0561] For example, the condition related to the at least one monitoring object may be met based on that amount of allocated resources is less than a threshold value.

[0562] For example, the condition related to the at least one monitoring object may be met based on that a radio quality of the wireless device is less than a threshold value.

[0563] For example, the condition related to the at least one monitoring object may be met based on that error rate of the wireless device is larger than a threshold value.

[0564] In step S2303-1, based on that the condition related to the at least one monitoring object is not met, a wireless device may log information related to the at least one monitoring object without transmission of the information related to the at least one monitoring object.

[0565] In step S2303-2, based on that the condition related to the at least one monitoring object is met, a wireless device may transmit the information related to the at least one monitoring object.

[0566] For example, based on that the condition related to the at least one monitoring object is met, the wireless device may transmit logged information related to the at least one monitoring object. For example, the logged information may be stored at step S2303-1. For example, the logged information related to the at least one monitoring object may be transmitted with the information related to the at least one monitoring object.

[0567] For example, the information related to the at least one monitoring object may include information informing whether the condition related to the at least one monitoring object is met or not at every monitoring opportunity.

[0568] 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.

[0569] Hereinafter, some embodiments of a method of determining reporting or logging based on model monitoring results.

[0570] For example, a method for determining whether to report or log monitoring-related results (information) depending on the UE operation state is provided. The operation state can be determined based on reference performance, e.g., UE data performance, radio quality, and so on.

[0571] UE can do logging (that is, store) the results if the UE operation is stable (that is, normal).

[0572] UE can report the monitoring related results only when the UE operation is unstable (that is, abnormal).

[0573] When reporting the results, logging information can also be delivered together.

[0574] FIG. 24 shows an example of a method for determining reporting or logging based on model monitoring results.

[0575] In FIG. 24, the wireless device may determine whether to send or log the monitoring-related results (or, information related to the AI / ML model) at every reporting opportunity with the monitoring interval. Based on the determination, the wireless device may send or log the monitoring-related results.

[0576] In particular, in the first reporting opportunity, the wireless device may determine to send the report (that is, 'a' report). Then, the wireless device may send the report (that is, 'a' report).

[0577] In the second reporting opportunity, the wireless device may determine not to send the report (that is, 'b' report) and log the report. The wireless device may not send the report at the second reporting opportunity.

[0578] In the third reporting opportunity, the wireless device may determine not to send the report (that is, 'b' report and 'c' report) and log the results. The wireless device may not send the report at the third reporting opportunity.

[0579] In the fourth reporting opportunity, the wireless device may determine to send the report (that is, 'b' report, 'c' report, and 'd' report). The wireless device may send the report including logged results (that is, 'b' report, 'c' report, and 'd' report) at the fourth reporting opportunity.

[0580] In the fifth reporting opportunity, the wireless device may determine not to send the report (that is, 'e' report) and log the results. The wireless device may not send the report at the fifth reporting opportunity.

[0581] With configured / activated AI / ML models, UE can conduct the AI / ML operation and can perform model monitoring. When this method is applied to AIML model operation, network may configure a threshold criterion to facilitate UE to measure model state during model monitoring operation. UE can measure model monitoring related results(information) and compare with the threshold. Based on the comparison, UE may determine the model operation is a normal state or an abnormal state. If it is a normal state, UE may do logging the information. If it is an abnormal state, UE may report the information.

[0582] That is, the UE may perform the following actions:

[0583] 1) Determination of model state based on the model operation Key Performance Indicators (KPIs)

[0584] 2a) Logging the monitoring related results(information) if the model state is stable(normal)

[0585] 2b) Reporting the monitoring related results(information) if the model state is unstable(abnormal)

[0586] FIG. 25 shows an example of a method for determining reporting or logging based on model monitoring results.

[0587] In particular, FIG. 25 shows an example of a method performed by a UE in a wireless communication system.

[0588] In step S2501, the UE may be configured with a more prediction model configuration.

[0589] That is, for deriving the prediction results, UE may be configured with a more prediction model configuration.

[0590] > The prediction model configuration may include prediction model structure information,

[0591] >> Network may configure a machine learning model to be used by UE.

[0592] >>> Network may include a machine learning type, such as reinforcement learning, supervised learning, or unsupervised learning.

[0593] >>> Network may include a machine learning model, such as DNN, CNN, RNN, and DRL.

[0594] >>> The configured ML model may be a pre-trained ML model that has been already trained by network a-priori

[0595] >>>> The configured ML model is described by a model description information including model structure and parameters.

[0596] >>>> 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).

[0597] >>>>> Different layers are connected based on the connections between neurons of different layers

[0598] >>>>>> 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)

[0599] >>>>>> Each neuron may provide input to one or several connected neurons (1 to N connection).

[0600] >>>>>> 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.

[0601] >>>>>> 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.

[0602] >>> The configured ML model may be a ML model to be trained.

[0603] >>>> The configured ML model is described by a model description information including model structure and initial parameters that are to be trained.

[0604] >>>> 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.

[0605] >>> 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.

[0606] >>> Network may include machine learning output, such as UE trajectory prediction, predicted target cell, prediction time for handover, and UE traffic prediction.

[0607] >> UE may perform a machine learning model training, validation, and testing which may generate model performance metrics based on the prediction model configuration.

[0608] >>> UE may perform a model training with the machine learning input parameters.

[0609] >> UE may use the configured ML model to perform ML task such as predictions of measurements.

[0610] >>> UE may derive machine learning output(s).

[0611] >>> UE may infer from the outputs and use the outputs as feedback for the machine learning model.

[0612] >> UE may send feedback to network about the results related to machine learning outputs and the accuracy of the machine learning model.

[0613] >>> Network may update the machine learning model and parameters related to the machine learning model.

[0614] In step S2502, the UE may be configured with logging configuration and / or reporting configuration.

[0615] That is, network may configure UE with logging configuration and / or reporting configuration.

[0616] > Logging configuration may include logging object(s)

[0617] >> A logging object may relate to an AIML model or an AIML model group (model list)

[0618] > Reporting configuration may include condition(s)

[0619] >> The condition may relate to reference performance, for example:

[0620] >>> UE Performance related Key Performance Indicators (KPIs), e.g., Throughput, Reference signal received power (RSRP) / Reference Signal Received Quality (RSRQ) / signal to noise and interference ratio (SINR), Channel Quality Indicator (CQI), Modulation and Coding Scheme (MCS), Resource Block (RB), Block Error Rate (BLER), etc

[0621] >>> Prediction Accuracy related KPIs. e.g., beam prediction accuracy

[0622] >>> Model Performance metric e.g., based on input / output data distribution of AIML, loss, precision score, etc

[0623] >>> Difference between i) predictions at different time / model, ii) actual results and predictions, iii) ground truth value and predictions, etc

[0624] >> The condition / reference performance may be pre-determined in the specification

[0625] >> The condition may be a threshold value (minimum value or maximum value) or a window value (boundary value) related to reference performance

[0626] In step S2503, the UE may measure UE operation related to the logging object.

[0627] > UE operation may relate to AIML model operation

[0628] > UE may derive measurement information for the logging object

[0629] >> The measurement information may be the same type of information with reference performance / threshold / window value

[0630] >> If UE operation is AIML model operation, the measurement information may relate to model related results, e.g., monitoring results

[0631] In step S2504, the UE may determine the UE operation state based on the measurement information and the condition (related to reference performance).

[0632] > If UE operation is AIML model operation, UE may determine AL / ML model state as UE operation state

[0633] > UE may determine the UE operation state as a first(normal) state,

[0634] >> For example,

[0635] >>> If the measurement information fulfils the reference performance

[0636] >>> For UE performance, if data throughput is larger than the threshold, or if radio quality is larger than the threshold, or resource allocation related value (e.g., MCS and RB) is larger than the threshold, or Error rate (e.g., BLER) is smaller than the threshold

[0637] >>> For prediction accuracy, if prediction accuracy is larger than the threshold

[0638] >>> For Model performance metric, if model loss is smaller than the threshold

[0639] >>> If difference between predictions is smaller than the threshold, or if difference between actual results and predictions is smaller than the threshold, or if difference between actual results and ground truth values is smaller than the threshold

[0640] > UE may determine the UE operation state as a second(abnormal) state,

[0641] >> For example,

[0642] >>> If the measurement information does not fulfil the reference performance

[0643] >>> For UE performance, if data throughput is smaller than the threshold, or if radio quality is smaller than the threshold, or resource allocation related value (e.g., MCS and RB) is smaller than the threshold, or Error rate (e.g., BLER) is larger than the threshold

[0644] >>> For prediction accuracy, if prediction accuracy is smaller than the threshold

[0645] >>> For Model performance metric, if model loss is larger than the threshold

[0646] >>> If difference between predictions is larger than the threshold, or if difference between actual results and predictions is larger than the threshold, or if difference between actual results and ground truth values is larger than the threshold

[0647] In step S2505-1, the UE may do logging the measurement information if the UE operation state is the first(normal) state.

[0648] > UE may do logging information, for example:

[0649] >> the measurement information

[0650] >> the condition related information

[0651] >> the additional information, such as network(cell / beam / TA), time, location, situation awareness information

[0652] >> the AIML model information, such as model / functionality related information

[0653] In step S2505-2, the UE may report the measurement information if the UE operation state is the second(abnormal) state.

[0654] > UE may include the logged information stored in step S2505-1

[0655] >> UE may include the logged information by own its decision

[0656] >> UE may include the logged information based on an indication from network

[0657] > UE may include information, for example:

[0658] >> the measurement information

[0659] >> the condition related information

[0660] >> the additional information, such as network(cell / beam / TA), time, location, situation awareness information (w / or w / o sensing)

[0661] >> the AIML model information, such as model / functionality related information

[0662] FIG. 26 shows an example of a generalized method for determining reporting or logging based on model monitoring results.

[0663] In particular, FIG. 26 shows an example of a method performed by a wireless device in a wireless communication system.

[0664] In step S2601, the wireless device may receive a condition from network.

[0665] In step S2602, the wireless device may generate a RRC message to transfer information.

[0666] In step S2603, the wireless device may determine whether to store information or report information according to condition.

[0667] In step S2604, the wireless device may report the information based on the determination.

[0668] FIG. 27 shows an example of a method for determining reporting or logging based on model monitoring results.

[0669] In particular, FIG. 27 shows an example of a method performed by a wireless device in a wireless communication system.

[0670] In step S2701, the wireless device may receive a configuration for logging and reporting, wherein the configuration includes at least one logging object and a condition.

[0671] In step S2702, the wireless device may measure information related to the logging object.

[0672] In step S2703, the wireless device may determine a state of the logging object based on the measured information and the condition.

[0673] In step S2704-1, the wireless device may log information related to the measured information, based on the determination that the logging object is under a normal state.

[0674] In step S2704-2, the wireless device may report information related to the measured information, based on the determination that the logging object is under abnormal state.

[0675] For example, the logging object may be a ML model.

[0676] For example, the information related to the logging object may be output of the logging object.

[0677] For example, the condition may include a case the performance of the logging object is lower than a reference performance. The reference performance may be configured by network.

[0678] For example, the state of the logging object may be determined as a normal state if the output of the logging object fulfills the reference performance.

[0679] For example, the state of the logging object may be determined as a abnormal state if the output of the logging object does not fulfill the reference performance.

[0680] For example, when reporting the measured information, information indicating the condition may be also included in the reporting

[0681] Some of the detailed steps shown in the examples of FIGS. 23, 24, 25, 26, and 27 may not be essential steps and may be omitted. In addition to the steps shown in FIGS. 23, 24, 25, 26, and 27, other steps may be added, and the order of the steps may vary. Some of the above steps may have their own technical meaning.

[0682] Hereinafter, an apparatus for logging AIML model monitoring results 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.

[0683] 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.

[0684] Referring to FIG. 5, a wireless device 100 may include a processor 102, a memory 104, and a transceiver 106.

[0685] 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.

[0686] The processor 102 may be configured to control the transceiver 106 to receive, from network, a configuration for reporting. The configuration may include information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object. The processor 102 may be configured to determine whether the condition related to the at least one monitoring object is met. Based on that the condition related to the at least one monitoring object is not met, the processor 102 may be configured to log information related to the at least one monitoring object without transmission of the information related to the at least one monitoring object. Based on that the condition related to the at least one monitoring object is met, the processor 102 may be configured to control the transceiver 106 to transmit the information related to the at least one monitoring object.

[0687] For example, the operation of the wireless device may include at least one operation related to an Artificial Intelligence and Machine Learning (AI / ML) model. For example, the at least one monitoring object may include the Artificial Intelligence and Machine Learning (AI / ML) AI / ML model.

[0688] For example, the information related to the at least one monitoring object may include monitoring results for the at least one monitoring object.

[0689] For example, the condition related to the at least one monitoring object may be met based on that a performance of the at least one monitoring object is lower than a reference performance.

[0690] For example, the at least one monitoring object related to the AI / ML model may include at least one operation of the wireless device.

[0691] For example, the processor 102 may be configured to determine whether the at least one operation is in a normal state or in an abnormal state.

[0692] For example, the condition related to the at least one monitoring object may be met based on that the at least one operation is in the abnormal state.

[0693] For example, it may be determined whether the at least one operation is in the normal state or in the abnormal state, based on a predicted operation of the wireless device which is derived from the AI / ML model.

[0694] For example, the condition related to the at least one monitoring object may be met based on that data throughput is less than a threshold value.

[0695] For example, the condition related to the at least one monitoring object may be met based on that amount of allocated resources is less than a threshold value.

[0696] For example, the condition related to the at least one monitoring object may be met based on that a radio quality of the wireless device is less than a threshold value.

[0697] For example, the condition related to the at least one monitoring object may be met based on that error rate of the wireless device is larger than a threshold value.

[0698] For example, based on that the condition related to the at least one monitoring object is met, the processor 102 may be configured to transmit logged information related to the at least one monitoring object.

[0699] For example, the condition may include a condition for prediction results derived from the AI / ML model.

[0700] 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.

[0701] Hereinafter, a processor for a wireless device for logging AIML model monitoring results in a wireless communication system, according to some embodiments of the present disclosure, will be described.

[0702] The processor may be configured to control the wireless device to receive, from network, a configuration for reporting. The configuration may include information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object. The processor may be configured to control the wireless device to determine whether the condition related to the at least one monitoring object is met. Based on that the condition related to the at least one monitoring object is not met, the processor may be configured to control the wireless device to log information related to the at least one monitoring object without transmission of the information related to the at least one monitoring object. Based on that the condition related to the at least one monitoring object is met, the processor may be configured to control the wireless device to transmit the information related to the at least one monitoring object.

[0703] For example, the operation of the wireless device may include at least one operation related to an Artificial Intelligence and Machine Learning (AI / ML) model. For example, the at least one monitoring object may include the Artificial Intelligence and Machine Learning (AI / ML) AI / ML model.

[0704] For example, the information related to the at least one monitoring object may include monitoring results for the at least one monitoring object.

[0705] For example, the condition related to the at least one monitoring object may be met based on that a performance of the at least one monitoring object is lower than a reference performance.

[0706] For example, the at least one monitoring object related to the AI / ML model may include at least one operation of the wireless device.

[0707] For example, the processor may be configured to control the wireless device to determine whether the at least one operation is in a normal state or in an abnormal state.

[0708] For example, the condition related to the at least one monitoring object may be met based on that the at least one operation is in the abnormal state.

[0709] For example, it may be determined whether the at least one operation is in the normal state or in the abnormal state, based on a predicted operation of the wireless device which is derived from the AI / ML model.

[0710] For example, the condition related to the at least one monitoring object may be met based on that data throughput is less than a threshold value.

[0711] For example, the condition related to the at least one monitoring object may be met based on that amount of allocated resources is less than a threshold value.

[0712] For example, the condition related to the at least one monitoring object may be met based on that a radio quality of the wireless device is less than a threshold value.

[0713] For example, the condition related to the at least one monitoring object may be met based on that error rate of the wireless device is larger than a threshold value.

[0714] For example, based on that the condition related to the at least one monitoring object is met, the processor may be configured to control the wireless device to transmit logged information related to the at least one monitoring object.

[0715] For example, the condition may include a condition for prediction results derived from the AI / ML model.

[0716] 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.

[0717] Hereinafter, a non-transitory computer-readable medium has stored thereon a plurality of instructions for logging AIML model monitoring results in a wireless communication system, according to some embodiments of the present disclosure, will be described.

[0718] 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.

[0719] 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.

[0720] The computer-readable medium may include a tangible and non-transitory computer-readable storage medium.

[0721] 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.

[0722] 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.

[0723] 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.

[0724] The stored plurality of instructions may cause the wireless device to receive, from network, a configuration for reporting. The configuration may include information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object. The stored plurality of instructions may cause the wireless device to determine whether the condition related to the at least one monitoring object is met. Based on that the condition related to the at least one monitoring object is not met, the stored plurality of instructions may cause the wireless device to log information related to the at least one monitoring object without transmission of the information related to the at least one monitoring object. Based on that the condition related to the at least one monitoring object is met, the stored plurality of instructions may cause the wireless device to transmit the information related to the at least one monitoring object.

[0725] For example, the operation of the wireless device may include at least one operation related to an Artificial Intelligence and Machine Learning (AI / ML) model. For example, the at least one monitoring object may include the Artificial Intelligence and Machine Learning (AI / ML) AI / ML model.

[0726] For example, the information related to the at least one monitoring object may include monitoring results for the at least one monitoring object.

[0727] For example, the condition related to the at least one monitoring object may be met based on that a performance of the at least one monitoring object is lower than a reference performance.

[0728] For example, the at least one monitoring object related to the AI / ML model may include at least one operation of the wireless device.

[0729] For example, the stored plurality of instructions may cause the wireless device to determine whether the at least one operation is in a normal state or in an abnormal state.

[0730] For example, the condition related to the at least one monitoring object may be met based on that the at least one operation is in the abnormal state.

[0731] For example, it may be determined whether the at least one operation is in the normal state or in the abnormal state, based on a predicted operation of the wireless device which is derived from the AI / ML model.

[0732] For example, the condition related to the at least one monitoring object may be met based on that data throughput is less than a threshold value.

[0733] For example, the condition related to the at least one monitoring object may be met based on that amount of allocated resources is less than a threshold value.

[0734] For example, the condition related to the at least one monitoring object may be met based on that a radio quality of the wireless device is less than a threshold value.

[0735] For example, the condition related to the at least one monitoring object may be met based on that error rate of the wireless device is larger than a threshold value.

[0736] For example, based on that the condition related to the at least one monitoring object is met, the stored plurality of instructions may cause the wireless device to transmit logged information related to the at least one monitoring object.

[0737] For example, the condition may include a condition for prediction results derived from the AI / ML model.

[0738] 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.

[0739] Hereinafter, a method performed by a base station (BS) for logging AIML model monitoring results in a wireless communication system, according to some embodiments of the present disclosure, will be described.

[0740] The BS may transmit, to a wireless device, a configuration for reporting, wherein the configuration includes information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object. The BS may receive, from the wireless device, a message including information related to the at least one monitoring object. The message may be transmitted only based on the condition related to the at least one monitoring object being met. The message further may include information related to the at least one monitoring object which is logged based on the condition related to the at least one monitoring object being not met.

[0741] Hereinafter, a base station (BS) for logging AIML model monitoring results in a wireless communication system, according to some embodiments of the present disclosure, will be described.

[0742] The BS may include a transceiver, a memory, and a processor operatively coupled to the transceiver and the memory.

[0743] The processor may be configured to control the transceiver to transmit, to a wireless device, a configuration for reporting, wherein the configuration includes information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object. The processor may be configured to control the transceiver to receive, from the wireless device, a message including information related to the at least one monitoring object. The message may be transmitted only based on the condition related to the at least one monitoring object being met. The message further may include information related to the at least one monitoring object which is logged based on the condition related to the at least one monitoring object being not met.

[0744] The present disclosure can have various advantageous effects.

[0745] According to some embodiments of the present disclosure, a wireless device could efficiently log and / or report AIML model monitoring results in a wireless communication system.

[0746] For example, model monitoring results can be transmitted to network efficiently based on the model state. By avoiding the frequent AIML monitoring report, UE power consumption and signalling overhead can be reduced.

[0747] In other words, by reducing the number of AIML monitoring reports, a wireless device could save resource for reporting the AI / ML monitoring results.

[0748] According to some embodiments of the present disclosure, the wireless communication system could provide an efficient solution for logging and / or reporting AIML model monitoring results in a wireless communication system.

[0749] 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.

[0750] 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:receiving, from a network, a configuration for reporting, wherein the configuration includes information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object;determining whether the condition related to the at least one monitoring object is met; andbased on that the condition related to the at least one monitoring object is not met:- logging information related to the at least one monitoring object without transmission of the information related to the at least one monitoring object;based on that the condition related to the at least one monitoring object is met:- transmitting the information related to the at least one monitoring object.2.The method of claim 1, wherein the method further comprising:determining whether the at least one operation is in a normal state or in an abnormal state.3.The method of claim 2,wherein the condition related to the at least one monitoring object is met based on that the at least one operation is in the abnormal state.4.The method of claim 2,wherein it is determined whether the at least one operation is in the normal state or in the abnormal state, based on a predicted operation of the wireless device which is derived from the AI / ML model.5.The method of claim 1,wherein the information related to the at least one monitoring object includes monitoring results for the at least one monitoring object.6.The method of claim 1,wherein the condition related to the at least one monitoring object is met based on that a performance of the at least one monitoring object is lower than a reference performance.7.The method of claim 1,wherein the operation of the wireless device includes at least one operation related to an Artificial Intelligence and Machine Learning (AI / ML) model.8.The method of claim 7,wherein the at least one monitoring object includes the AI / ML model.9.The method of claim 7,wherein the condition includes a condition for prediction results derived from the AI / ML model.10.The method of claim 1,wherein the condition related to the at least one monitoring object is met based on that data throughput is less than a threshold value.11.The method of claim 1,wherein the condition related to the at least one monitoring object is met based on that amount of allocated resources is less than a threshold value.12.The method of claim 1,wherein the condition related to the at least one monitoring object is met based on that a radio quality of the wireless device is less than a threshold value.13.The method of claim 1,wherein the condition related to the at least one monitoring object is met based on that error rate of the wireless device is larger than a threshold value.14.The method of claim 1, wherein the method further comprising:based on that the condition related to the at least one monitoring object is met:- transmitting logged information related to the at least one monitoring object.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:receive, from a network, a configuration for reporting, wherein the configuration includes information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object;determine whether the condition related to the at least one monitoring object is met; andbased on that the condition related to the at least one monitoring object is not met:- log information related to the at least one monitoring object without transmission of the information related to the at least one monitoring object;based on that the condition related to the at least one monitoring object is met:- transmit the information related to the at least one monitoring object.17.The wireless device of claim 16, wherein the at least one processor is further adapted to:determine whether the at least one operation is in a normal state or in an abnormal state.AI / ML model18.The wireless device of claim 17,wherein the condition related to the at least one monitoring object is met based on that the at least one operation is in the abnormal state.19.The wireless device of claim 17,wherein it is determined whether the at least one operation is in the normal state or in the abnormal state, based on a predicted operation of the wireless device which is derived from the AI / ML model.20.The wireless device of claim 16,wherein the information related to the at least one monitoring object includes monitoring results for the at least one monitoring object.21.The wireless device of claim 16,wherein the condition related to the at least one monitoring object is met based on that a performance of the at least one monitoring object is lower than a reference performance.22.The wireless device of claim 16,wherein the operation of the wireless device includes at least one operation related to an Artificial Intelligence and Machine Learning (AI / ML) model.23.The wireless device of claim 22,wherein the at least one monitoring object includes the AI / ML model.24.The wireless device of claim 22,wherein the condition includes a condition for prediction results derived from the AI / ML model.25.The wireless device of claim 16,wherein the condition related to the at least one monitoring object is met based on that data throughput is less than a threshold value.26.The wireless device of claim 16,wherein the condition related to the at least one monitoring object is met based on that amount of allocated resources is less than a threshold value.27.The wireless device of claim 16,wherein the condition related to the at least one monitoring object is met based on that a radio quality of the wireless device is less than a threshold value.28.The wireless device of claim 16,wherein the condition related to the at least one monitoring object is met based on that error rate of the wireless device is larger than a threshold value.29.The wireless device of claim 16, wherein the at least one processor is further adapted to:based on that the condition related to the at least one monitoring object is met:- transmit logged information related to the at least one monitoring object.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:receiving, from a network, a configuration for reporting, wherein the configuration includes information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object;determining whether the condition related to the at least one monitoring object is met; andbased on that the condition related to the at least one monitoring object is not met:- logging information related to the at least one monitoring object without transmission of the information related to the at least one monitoring object;based on that the condition related to the at least one monitoring object is met:- transmitting the information related to the at least one monitoring object.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,receiving, from a network, a configuration for reporting, wherein the configuration includes information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object;determining whether the condition related to the at least one monitoring object is met; andbased on that the condition related to the at least one monitoring object is not met:- logging information related to the at least one monitoring object without transmission of the information related to the at least one monitoring object;based on that the condition related to the at least one monitoring object is met:- transmitting the information related to the at least one monitoring object.33.A method performed by a base station in a wireless communication system, the method comprising,transmitting, to a wireless device, a configuration for reporting, wherein the configuration includes information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object; andreceiving, from the wireless device, a message including information related to the at least one monitoring object,wherein the message is transmitted only based on the condition related to the at least one monitoring object being met,wherein the message further includes information related to the at least one monitoring object which is logged based on the condition related to the at least one monitoring object being not met.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:transmit, to a wireless device, a configuration for reporting, wherein the configuration includes information related to at least one monitoring object related to at least one operation of the wireless device and a condition related to the at least one monitoring object; andreceive, from the wireless device, a message including information related to the at least one monitoring object,wherein the message is transmitted only based on the condition related to the at least one monitoring object being met,wherein the message further includes information related to the at least one monitoring object which is logged based on the condition related to the at least one monitoring object being not met.

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