Method and apparatus for monitoring operations for inference function based on prediction accuracy

Abstract

A method and apparatus for monitoring operations for inference function based on prediction accuracy are provided. The wireless device performs monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object. The wireless device adjusts resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object.

Description

METHOD AND APPARATUS FOR MONITORING OPERATIONS FOR INFERENCE FUNCTION BASED ON PREDICTION ACCURACY

[0001] The present disclosure relates to a method and apparatus for monitoring operations for inference function based on prediction accuracy.

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

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

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

[0005] In 3GPP Release 19, cell-level measurement prediction with inter-frequency is being discussed. This approach has the advantage of reducing the need for inter-frequency measurements, which can also enhance data performance by minimizing the measurements within a specific measurement gap.

[0006] From an AI model monitoring perspective, during cell-measurement prediction with inter-frequency, the UE should perform inter-frequency measurement within a specific measurement gap. By comparing the predicted results with the actual measurement results, the UE can evaluate the model's accuracy. However, in this case, the gain from not performing measurements in the specific measurement gap based on predictions can be diminished. Also, when model monitoring is not necessary every measurement gap, some measurement gap becomes unnecessary. During this period, the UE cannot transmit or receive data, resulting in a loss of data performance.

[0007] Thus, studies for monitoring operations for inference function based on prediction accuracy are required.

[0008] In an aspect, a method is provided. The method comprises: receiving, by a wireless device from a network, a measurement configuration including information related to at least one measurement object; performing, by the wireless device, predictions related to the at least one measurement object based on an inference function; performing, by the wireless device, monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object; and adjusting, by the wireless device, resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object.

[0009] In another aspect, an apparatus for implementing the above method is provided.

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

[0011] According to some embodiments of the present disclosure, a wireless device could efficiently perform monitoring operations for inference function based on prediction accuracy.

[0012] For example, measurement resources can be dynamically adopted by considering the model monitoring periodicity according to model accuracy. It can reduce resource waste and have a positive impact on data performance.

[0013] In other words, the wireless device can save resources for monitoring operations for inference model.

[0014] According to some embodiments of the present disclosure, the wireless communication system could provide an efficient solution for monitoring operations for inference function based on prediction accuracy.

[0015] 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.

[0016] FIG. 1 shows an example of a communication system to which implementations of the present disclosure is applied.

[0017] FIG. 2 shows an example of wireless devices to which implementations of the present disclosure is applied.

[0018] FIG. 3 shows an example of a wireless device to which implementations of the present disclosure is applied.

[0019] FIG. 4 shows another example of wireless devices to which implementations of the present disclosure is applied.

[0020] FIG. 5 shows an example of UE to which implementations of the present disclosure is applied.

[0021] 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.

[0022] FIG. 8 shows a frame structure in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.

[0023] FIG. 9 shows a data flow example in the 3GPP NR system to which implementations of the present disclosure is applied.

[0024] FIG. 10 shows an example of a Functional Framework for RAN Intelligence.

[0025] FIG. 11 shows an example of an AI / ML Model Training in OAM and AI / ML Model Inference in NG-RAN node.

[0026] FIG. 12 shows an example of Model Training and Model Inference both located in RAN node.

[0027] FIGS. 13 and 14 show an example of an architecture of neuron and neural network.

[0028] FIG. 15 shows an example of an AI / ML inference.

[0029] FIG. 16 shows an example of an MLP DNN model.

[0030] FIG. 17 shows an example of a CNN model.

[0031] FIG. 18 shows an example of an RNN model.

[0032] FIG. 19 shows an example of Reinforcement learning.

[0033] FIG. 20 shows an example of Location measurement indication

[0034] FIG. 21 shows an example of a method for monitoring operations for inference function based on prediction accuracy.

[0035] FIG. 22 shows an example of a method for monitoring operations for inference function based on prediction accuracy.

[0036] FIG. 23 shows an example of a method for monitoring operations for inference function based on prediction accuracy based on count threshold.

[0037] FIG. 24 shows an example of a method for monitoring operations related to measurement gap for inference function based on prediction accuracy based on count threshold.

[0038] FIG. 25 shows an example of a method for monitoring operations related to inference model for inference function based on prediction accuracy based on count threshold.

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

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

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

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

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

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

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

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

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

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

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

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

[0051] The 5G usage scenarios shown in FIG. 1 are only exemplary, and the technical features of the present disclosure can be applied to other 5G usage scenarios which are not shown in FIG. 1.

[0052] Three main requirement categories for 5G include (1) a category of enhanced mobile broadband (eMBB), (2) a category of massive machine type communication (mMTC), and (3) a category of ultra-reliable and low latency communications (URLLC).

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

[0054] eMBB far surpasses basic mobile Internet access and covers abundant bidirectional work and media and entertainment applications in cloud and augmented reality. Data is one of 5G core motive forces and, in a 5G era, a dedicated voice service may not be provided for the first time. In 5G, it is expected that voice will be simply processed as an application program using data connection provided by a communication system. Main causes for increased traffic volume are due to an increase in the size of content and an increase in the number of applications requiring high data transmission rate. A streaming service (of audio and video), conversational video, and mobile Internet access will be more widely used as more devices are connected to the Internet. These many application programs require connectivity of an always turned-on state in order to push real-time information and alarm for users. Cloud storage and applications are rapidly increasing in a mobile communication platform and may be applied to both work and entertainment. The cloud storage is a special use case which accelerates growth of uplink data transmission rate. 5G is also used for remote work of cloud. When a tactile interface is used, 5G demands much lower end-to-end latency to maintain user good experience. Entertainment, for example, cloud gaming and video streaming, is another core element which increases demand for mobile broadband capability. Entertainment is essential for a smartphone and a tablet in any place including high mobility environments such as a train, a vehicle, and an airplane. Other use cases are augmented reality for entertainment and information search. In this case, the augmented reality requires very low latency and instantaneous data volume.

[0055] In addition, one of the most expected 5G use cases relates a function capable of smoothly connecting embedded sensors in all fields, i.e., mMTC. It is expected that the number of potential Internet-of-things (IoT) devices will reach 204 hundred million up to the year of 2020. An industrial IoT is one of categories of performing a main role enabling a smart city, asset tracking, smart utility, agriculture, and security infrastructure through 5G.

[0056] URLLC includes a new service that will change industry through remote control of main infrastructure and an ultra-reliable / available low-latency link such as a self-driving vehicle. A level of reliability and latency is essential to control a smart grid, automatize industry, achieve robotics, and control and adjust a drone.

[0057] 5G is a means of providing streaming evaluated as a few hundred megabits per second to gigabits per second and may complement fiber-to-the-home (FTTH) and cable-based broadband (or DOCSIS). Such fast speed is needed to deliver TV in resolution of 4K or more (6K, 8K, and more), as well as virtual reality and augmented reality. Virtual reality (VR) and augmented reality (AR) applications include almost immersive sports games. A specific application program may require a special network configuration. For example, for VR games, gaming companies need to incorporate a core server into an edge network server of a network operator in order to minimize latency.

[0058] Automotive is expected to be a new important motivated force in 5G together with many use cases for mobile communication for vehicles. For example, entertainment for passengers requires high simultaneous capacity and mobile broadband with high mobility. This is because future users continue to expect connection of high quality regardless of their locations and speeds. Another use case of an automotive field is an AR dashboard. The AR dashboard causes a driver to identify an object in the dark in addition to an object seen from a front window and displays a distance from the object and a movement of the object by overlapping information talking to the driver. In the future, a wireless module enables communication between vehicles, information exchange between a vehicle and supporting infrastructure, and information exchange between a vehicle and other connected devices (e.g., devices accompanied by a pedestrian). A safety system guides alternative courses of a behavior so that a driver may drive more safely drive, thereby lowering the danger of an accident. The next stage will be a remotely controlled or self-driven vehicle. This requires very high reliability and very fast communication between different self-driven vehicles and between a vehicle and infrastructure. In the future, a self-driven vehicle will perform all driving activities and a driver will focus only upon abnormal traffic that the vehicle cannot identify. Technical requirements of a self-driven vehicle demand ultra-low latency and ultra-high reliability so that traffic safety is increased to a level that cannot be achieved by human being.

[0059] A smart city and a smart home / building mentioned as a smart society will be embedded in a high-density wireless sensor network. A distributed network of an intelligent sensor will identify conditions for costs and energy-efficient maintenance of a city or a home. Similar configurations may be performed for respective households. All of temperature sensors, window and heating controllers, burglar alarms, and home appliances are wirelessly connected. Many of these sensors are typically low in data transmission rate, power, and cost. However, real-time HD video may be demanded by a specific type of device to perform monitoring.

[0060] Consumption and distribution of energy including heat or gas is distributed at a higher level so that automated control of the distribution sensor network is demanded. The smart grid collects information and connects the sensors to each other using digital information and communication technology so as to act according to the collected information. Since this information may include behaviors of a supply company and a consumer, the smart grid may improve distribution of fuels such as electricity by a method having efficiency, reliability, economic feasibility, production sustainability, and automation. The smart grid may also be regarded as another sensor network having low latency.

[0061] Mission critical application (e.g., e-health) is one of 5G use scenarios. A health part contains many application programs capable of enjoying benefit of mobile communication. A communication system may support remote treatment that provides clinical treatment in a faraway place. Remote treatment may aid in reducing a barrier against distance and improve access to medical services that cannot be continuously available in a faraway rural area. Remote treatment is also used to perform important treatment and save lives in an emergency situation. The wireless sensor network based on mobile communication may provide remote monitoring and sensors for parameters such as heart rate and blood pressure.

[0062] Wireless and mobile communication gradually becomes important in the field of an industrial application. Wiring is high in installation and maintenance cost. Therefore, a possibility of replacing a cable with reconstructible wireless links is an attractive opportunity in many industrial fields. However, in order to achieve this replacement, it is necessary for wireless connection to be established with latency, reliability, and capacity similar to those of the cable and management of wireless connection needs to be simplified. Low latency and a very low error probability are new requirements when connection to 5G is needed.

[0063] Logistics and freight tracking are important use cases for mobile communication that enables inventory and package tracking anywhere using a location-based information system. The use cases of logistics and freight typically demand low data rate but require location information with a wide range and reliability.

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

[0065] The BSs 200 and the network 300 may be implemented as wireless devices and a specific wireless device may operate as a BS / network node with respect to other wireless devices.

[0066] The wireless devices 100a to 100f represent devices performing communication using radio access technology (RAT) (e.g., 5G new RAT (NR)) or LTE) and may be referred to as communication / radio / 5G devices. The wireless devices 100a to 100f may include, without being limited to, a robot 100a, vehicles 100b-1 and 100b-2, an extended reality (XR) device 100c, a hand-held device 100d, a home appliance 100e, an IoT device 100f, and an artificial intelligence (AI) device / server 400. For example, the vehicles may include a vehicle having a wireless communication function, an autonomous driving vehicle, and a vehicle capable of performing communication between vehicles. The vehicles may include an unmanned aerial vehicle (UAV) (e.g., a drone). The XR device may include an AR / VR / Mixed Reality (MR) device and may be implemented in the form of a head-mounted device (HMD), a head-up display (HUD) mounted in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance device, a digital signage, a vehicle, a robot, etc. The hand-held device may include a smartphone, a smartpad, a wearable device (e.g., a smartwatch or a smartglasses), and a computer (e.g., a notebook). The home appliance may include a TV, a refrigerator, and a washing machine. The IoT device may include a sensor and a smartmeter.

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

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

[0069] The VR device may include, for example, a device for implementing an object or a background of the virtual world. The AR device may include, for example, a device implemented by connecting an object or a background of the virtual world to an object or a background of the real world. The MR device may include, for example, a device implemented by merging an object or a background of the virtual world into an object or a background of the real world. The hologram device may include, for example, a device for implementing a stereoscopic image of 360 degrees by recording and reproducing stereoscopic information, using an interference phenomenon of light generated when two laser lights called holography meet.

[0070] The public safety device may include, for example, an image relay device or an image device that is wearable on the body of a user.

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

[0072] The medical device may be, for example, a device used for the purpose of diagnosing, treating, relieving, curing, or preventing disease. For example, the medical device may be a device used for the purpose of diagnosing, treating, relieving, or correcting injury or impairment. For example, the medical device may be a device used for the purpose of inspecting, replacing, or modifying a structure or a function. For example, the medical device may be a device used for the purpose of adjusting pregnancy. For example, the medical device may include a device for treatment, a device for operation, a device for (in vitro) diagnosis, a hearing aid, or a device for procedure.

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

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

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

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

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

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

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

[0080] Referring to FIG. 2, a first wireless device 100 and a second wireless device 200 may transmit / receive radio signals to / from an external device through a variety of RATs (e.g., LTE and NR). In FIG. 2, {the first wireless device 100 and the second wireless device 200} may correspond to at least one of {the wireless device 100a to 100f and the BS 200}, {the wireless device 100a to 100f and the wireless device 100a to 100f} and / or {the BS 200 and the BS 200} of FIG. 1.

[0081] The first wireless device 100 may include one or more processors 102 and one or more memories 104 and additionally further include one or more transceivers 106 and / or one or more antennas 108. The processor(s) 102 may control the memory(s) 104 and / or the transceiver(s) 106 and may be configured to implement the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts described in the present disclosure. For example, the processor(s) 102 may process information within the memory(s) 104 to generate first information / signals and then transmit radio signals including the first information / signals through the transceiver(s) 106. The processor(s) 102 may receive radio signals including second information / signals through the transceiver(s) 106 and then store information obtained by processing the second information / signals in the memory(s) 104. The memory(s) 104 may be connected to the processor(s) 102 and may store a variety of information related to operations of the processor(s) 102. For example, the memory(s) 104 may store software code including commands for performing a part or the entirety of processes controlled by the processor(s) 102 or for performing the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts described in the present disclosure. Herein, the processor(s) 102 and the memory(s) 104 may be a part of a communication modem / circuit / chip designed to implement RAT (e.g., LTE or NR). The transceiver(s) 106 may be connected to the processor(s) 102 and transmit and / or receive radio signals through one or more antennas 108. Each of the transceiver(s) 106 may include a transmitter and / or a receiver. The transceiver(s) 106 may be interchangeably used with radio frequency (RF) unit(s). In the present disclosure, the first wireless device 100 may represent a communication modem / circuit / chip.

[0082] The second wireless device 200 may include one or more processors 202 and one or more memories 204 and additionally further include one or more transceivers 206 and / or one or more antennas 208. The processor(s) 202 may control the memory(s) 204 and / or the transceiver(s) 206 and may be configured to implement the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts described in the present disclosure. For example, the processor(s) 202 may process information within the memory(s) 204 to generate third information / signals and then transmit radio signals including the third information / signals through the transceiver(s) 206. The processor(s) 202 may receive radio signals including fourth information / signals through the transceiver(s) 106 and then store information obtained by processing the fourth information / signals in the memory(s) 204. The memory(s) 204 may be connected to the processor(s) 202 and may store a variety of information related to operations of the processor(s) 202. For example, the memory(s) 204 may store software code including commands for performing a part or the entirety of processes controlled by the processor(s) 202 or for performing the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts described in the present disclosure. Herein, the processor(s) 202 and the memory(s) 204 may be a part of a communication modem / circuit / chip designed to implement RAT (e.g., LTE or NR). The transceiver(s) 206 may be connected to the processor(s) 202 and transmit and / or receive radio signals through one or more antennas 208. Each of the transceiver(s) 206 may include a transmitter and / or a receiver. The transceiver(s) 206 may be interchangeably used with RF unit(s). In the present disclosure, the second wireless device 200 may represent a communication modem / circuit / chip.

[0083] Hereinafter, hardware elements of the wireless devices 100 and 200 will be described more specifically. One or more protocol layers may be implemented by, without being limited to, one or more processors 102 and 202. For example, the one or more processors 102 and 202 may implement one or more layers (e.g., functional layers such as physical (PHY) layer, media access control (MAC) layer, radio link control (RLC) layer, packet data convergence protocol (PDCP) layer, radio resource control (RRC) layer, and service data adaptation protocol (SDAP) layer). The one or more processors 102 and 202 may generate one or more protocol data units (PDUs) and / or one or more service data unit (SDUs) according to the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. The one or more processors 102 and 202 may generate messages, control information, data, or information according to the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. The one or more processors 102 and 202 may generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure and provide the generated signals to the one or more transceivers 106 and 206. The one or more processors 102 and 202 may receive the signals (e.g., baseband signals) from the one or more transceivers 106 and 206 and acquire the PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure.

[0084] The one or more processors 102 and 202 may be referred to as controllers, microcontrollers, microprocessors, or microcomputers. The one or more processors 102 and 202 may be implemented by hardware, firmware, software, or a combination thereof. As an example, one or more application specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more digital signal processing devices (DSPDs), one or more programmable logic devices (PLDs), or one or more field programmable gate arrays (FPGAs) may be included in the one or more processors 102 and 202. descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure may be implemented using firmware or software and the firmware or software may be configured to include the modules, procedures, or functions. Firmware or software configured to perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure may be included in the one or more processors 102 and 202 or stored in the one or more memories 104 and 204 so as to be driven by the one or more processors 102 and 202. The descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure may be implemented using firmware or software in the form of code, commands, and / or a set of commands.

[0085] The one or more memories 104 and 204 may be connected to the one or more processors 102 and 202 and store various types of data, signals, messages, information, programs, code, instructions, and / or commands. The one or more memories 104 and 204 may be configured by read-only memories (ROMs), random access memories (RAMs), electrically erasable programmable read-only memories (EPROMs), flash memories, hard drives, registers, cash memories, computer-readable storage media, and / or combinations thereof. The one or more memories 104 and 204 may be located at the interior and / or exterior of the one or more processors 102 and 202. The one or more memories 104 and 204 may be connected to the one or more processors 102 and 202 through various technologies such as wired or wireless connection.

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

[0087] The one or more transceivers 106 and 206 may be connected to the one or more antennas 108 and 208 and the one or more transceivers 106 and 206 may be configured to transmit and receive user data, control information, and / or radio signals / channels, mentioned in the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure, through the one or more antennas 108 and 208. In the present disclosure, the one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports).

[0088] The one or more transceivers 106 and 206 may convert received radio signals / channels, etc., from RF band signals into baseband signals in order to process received user data, control information, radio signals / channels, etc., using the one or more processors 102 and 202. The one or more transceivers 106 and 206 may convert the user data, control information, radio signals / channels, etc., processed using the one or more processors 102 and 202 from the base band signals into the RF band signals. To this end, the one or more transceivers 106 and 206 may include (analog) oscillators and / or filters. For example, the transceivers 106 and 206 can up-convert OFDM baseband signals to a carrier frequency by their (analog) oscillators and / or filters under the control of the processors 102 and 202 and transmit the up-converted OFDM signals at the carrier frequency. The transceivers 106 and 206 may receive OFDM signals at a carrier frequency and down-convert the OFDM signals into OFDM baseband signals by their (analog) oscillators and / or filters under the control of the transceivers 102 and 202.

[0089] In the implementations of the present disclosure, a UE may operate as a transmitting device in uplink (UL) and as a receiving device in downlink (DL). In the implementations of the present disclosure, a BS may operate as a receiving device in UL and as a transmitting device in DL. Hereinafter, for convenience of description, it is mainly assumed that the first wireless device 100 acts as the UE, and the second wireless device 200 acts as the BS. For example, the processor(s) 102 connected to, mounted on or launched in the first wireless device 100 may be configured to perform the UE behavior according to an implementation of the present disclosure or control the transceiver(s) 106 to perform the UE behavior according to an implementation of the present disclosure. The processor(s) 202 connected to, mounted on or launched in the second wireless device 200 may be configured to perform the BS behavior according to an implementation of the present disclosure or control the transceiver(s) 206 to perform the BS behavior according to an implementation of the present disclosure.

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

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

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

[0093] Referring to FIG. 3, wireless devices 100 and 200 may correspond to the wireless devices 100 and 200 of FIG. 2 and may be configured by various elements, components, units / portions, and / or modules. For example, each of the wireless devices 100 and 200 may include a communication unit 110, a control unit 120, a memory unit 130, and additional components 140. The communication unit 110 may include a communication circuit 112 and transceiver(s) 114. For example, the communication circuit 112 may include the one or more processors 102 and 202 of FIG. 2 and / or the one or more memories 104 and 204 of FIG. 2. For example, the transceiver(s) 114 may include the one or more transceivers 106 and 206 of FIG. 2 and / or the one or more antennas 108 and 208 of FIG. 2. The control unit 120 is electrically connected to the communication unit 110, the memory 130, and the additional components 140 and controls overall operation of each of the wireless devices 100 and 200. For example, the control unit 120 may control an electric / mechanical operation of each of the wireless devices 100 and 200 based on programs / code / commands / information stored in the memory unit 130. The control unit 120 may transmit the information stored in the memory unit 130 to the exterior (e.g., other communication devices) via the communication unit 110 through a wireless / wired interface or store, in the memory unit 130, information received through the wireless / wired interface from the exterior (e.g., other communication devices) via the communication unit 110.

[0094] The additional components 140 may be variously configured according to types of the wireless devices 100 and 200. For example, the additional components 140 may include at least one of a power unit / battery, input / output (I / O) unit (e.g., audio I / O port, video I / O port), a driving unit, and a computing unit. The wireless devices 100 and 200 may be implemented in the form of, without being limited to, the robot (100a of FIG. 1), the vehicles (100b-1 and 100b-2 of FIG. 1), the XR device (100c of FIG. 1), the hand-held device (100d of FIG. 1), the home appliance (100e of FIG. 1), the IoT device (100f of FIG. 1), a digital broadcast terminal, a hologram device, a public safety device, an MTC device, a medicine device, a FinTech device (or a finance device), a security device, a climate / environment device, the AI server / device (400 of FIG. 1), the BSs (200 of FIG. 1), a network node, etc. The wireless devices 100 and 200 may be used in a mobile or fixed place according to a use-example / service.

[0095] In FIG. 3, the entirety of the various elements, components, units / portions, and / or modules in the wireless devices 100 and 200 may be connected to each other through a wired interface or at least a part thereof may be wirelessly connected through the communication unit 110. For example, in each of the wireless devices 100 and 200, the control unit 120 and the communication unit 110 may be connected by wire and the control unit 120 and first units (e.g., 130 and 140) may be wirelessly connected through the communication unit 110. Each element, component, unit / portion, and / or module within the wireless devices 100 and 200 may further include one or more elements. For example, the control unit 120 may be configured by a set of one or more processors. As an example, the control unit 120 may be configured by a set of a communication control processor, an application processor (AP), an electronic control unit (ECU), a graphical processing unit, and a memory control processor. As another example, the memory 130 may be configured by a RAM, a DRAM, a ROM, a flash memory, a volatile memory, a non-volatile memory, and / or a combination thereof.

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

[0097] Referring to FIG. 4, wireless devices 100 and 200 may correspond to the wireless devices 100 and 200 of FIG. 2 and may be configured by various elements, components, units / portions, and / or modules.

[0098] The first wireless device 100 may include at least one transceiver, such as a transceiver 106, and at least one processing chip, such as a processing chip 101. The processing chip 101 may include at least one processor, such a processor 102, and at least one memory, such as a memory 104. The memory 104 may be operably connectable to the processor 102. The memory 104 may store various types of information and / or instructions. The memory 104 may store a software code 105 which implements instructions that, when executed by the processor 102, perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. For example, the software code 105 may implement instructions that, when executed by the processor 102, perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. For example, the software code 105 may control the processor 102 to perform one or more protocols. For example, the software code 105 may control the processor 102 may perform one or more layers of the radio interface protocol.

[0099] The second wireless device 200 may include at least one transceiver, such as a transceiver 206, and at least one processing chip, such as a processing chip 201. The processing chip 201 may include at least one processor, such a processor 202, and at least one memory, such as a memory 204. The memory 204 may be operably connectable to the processor 202. The memory 204 may store various types of information and / or instructions. The memory 204 may store a software code 205 which implements instructions that, when executed by the processor 202, perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. For example, the software code 205 may implement instructions that, when executed by the processor 202, perform the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. For example, the software code 205 may control the processor 202 to perform one or more protocols. For example, the software code 205 may control the processor 202 may perform one or more layers of the radio interface protocol.

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

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

[0102] A UE 100 includes a processor 102, a memory 104, a transceiver 106, one or more antennas 108, a power management module 110, a battery 1112, a display 114, a keypad 116, a subscriber identification module (SIM) card 118, a speaker 120, and a microphone 122.

[0103] The processor 102 may be configured to implement the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. The processor 102 may be configured to control one or more other components of the UE 100 to implement the descriptions, functions, procedures, suggestions, methods and / or operational flowcharts disclosed in the present disclosure. Layers of the radio interface protocol may be implemented in the processor 102. The processor 102 may include ASIC, other chipset, logic circuit and / or data processing device. The processor 102 may be an application processor. The processor 102 may include at least one of a digital signal processor (DSP), a central processing unit (CPU), a graphics processing unit (GPU), a modem (modulator and demodulator). An example of the processor 102 may be found in SNAPDRAGONTMseries of processors made by Qualcomm®, EXYNOSTMseries of processors made by Samsung®, A series of processors made by Apple®, HELIOTMseries of processors made by MediaTek®, ATOMTMseries of processors made by Intel®or a corresponding next generation processor.

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

[0105] The transceiver 106 is operatively coupled with the processor 102, and transmits and / or receives a radio signal. The transceiver 106 includes a transmitter and a receiver. The transceiver 106 may include baseband circuitry to process radio frequency signals. The transceiver 106 controls the one or more antennas 108 to transmit and / or receive a radio signal.

[0106] The power management module 110 manages power for the processor 102 and / or the transceiver 106. The battery 112 supplies power to the power management module 110.

[0107] The display 114 outputs results processed by the processor 102. The keypad 116 receives inputs to be used by the processor 102. The keypad 16 may be shown on the display 114.

[0108] The SIM card 118 is an　integrated circuit　that is intended to　securely store the　international mobile subscriber identity　(IMSI) number and its related　key, which are used to identify and authenticate subscribers on　mobile telephony　devices (such as　mobile phones　and　computers). It is also possible to store contact information on many SIM cards.

[0109] The speaker 120 outputs sound-related results processed by the processor 102. The microphone 122 receives sound-related inputs to be used by the processor 102.

[0110] FIGS. 6 and 7 show an example of protocol stacks in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.

[0111] In particular, FIG. 6 illustrates an example of a radio interface user plane protocol stack between a UE and a BS and FIG. 7 illustrates an example of a radio interface control plane protocol stack between a UE and a BS. The control plane refers to a path through which control messages used to manage call by a UE and a network are transported. The user plane refers to a path through which data generated in an application layer, for example, voice data or Internet packet data are transported. Referring to FIG. 6, the user plane protocol stack may be divided into Layer 1 (i.e., a PHY layer) and Layer 2. Referring to FIG. 7, the control plane protocol stack may be divided into Layer 1 (i.e., a PHY layer), Layer 2, Layer 3 (e.g., an RRC layer), and a non-access stratum (NAS) layer. Layer 1, Layer 2 and Layer 3 are referred to as an access stratum (AS).

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

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

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

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

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

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

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

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

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

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

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

[0123] uNslotsymbNframe,uslotNsubframe,uslot01410111420221440431480841416016

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

[0125] uNslotsymbNframe,uslotNsubframe,uslot212404

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

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

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

[0129] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1450MHz - 6000MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0130] As mentioned above, the numerical value of the frequency range of the NR system may be changed. For example, FR1 may include a frequency band of 410MHz to 7125MHz as shown in Table 4 below. That is, FR1 may include a frequency band of 6GHz (or 5850, 5900, 5925 MHz, etc.) or more. For example, a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or more included in FR1 may include an unlicensed band. Unlicensed bands may be used for a variety of purposes, for example for communication for vehicles (e.g., autonomous driving).

[0131] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1410MHz - 7125MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

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

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

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

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

[0136] In the PHY layer, the uplink transport channels UL-SCH and RACH are mapped to their physical channels PUSCH and PRACH, respectively, and the downlink transport channels DL-SCH, BCH and PCH are mapped to PDSCH, PBCH and PDSCH, respectively. In the PHY layer, uplink control information (UCI) is mapped to PUCCH, and downlink control information (DCI) is mapped to PDCCH. A MAC PDU related to UL-SCH is transmitted by a UE via a PUSCH based on an UL grant, and a MAC PDU related to DL-SCH is transmitted by a BS via a PDSCH based on a DL assignment.

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

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

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

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

[0141] The goal is that sufficient use cases will be considered to enable the identification of a common AI / ML framework, including functional requirements of AI / ML architecture, which could be used in subsequent projects. The study should also identify areas where AI / ML could improve the performance of air-interface functions.

[0142] The study will serve identifying what is required for an adequate AI / ML model characterization and description establishing pertinent notation for discussions and subsequent evaluations. Various levels of collaboration between the gNB and UE are identified and considered.

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

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

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

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

[0147] Use cases to focus on:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0166] For the use cases under consideration:

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

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

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

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

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

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

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

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

[0175] b) KPIs: Determine the common KPIs and corresponding requirements for the AI / ML operations. Determine the use-case specific KPIs and benchmarks of the selected use-cases.

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

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

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

[0179] c) PHY layer aspects,

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

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

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

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

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

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

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

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

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

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

[0190] With existing L3 handover mechanism, handover is triggered and executed based on reported historical measurement result and / or measurement event(s) i.e., it is kind of reactive scheme by its nature. It may work well among macro cells when UE's mobility is low for existing services. But it could be problematic when either UE's mobility is high or among micro cells of high density or both for existing services or future services e.g. XR, where such reactive scheme may result in more unintended event e.g., handover failure, radio link failure, Ping-Pong phenomenon, throughput loss or too early / late handover etc. To improve handover robustness conditional handover is introduced in Rel-16. And to reduce interruption time of frequent handover among small cells LTM HO is introduced in Rel-18. However, these two mechanisms are not sufficient because they are still reactive scheme by design. On the other hand, mechanism based on AI / ML algorithm has the potential to enable proactive scheme.

[0191] In Rel-18 SID called FS_NR_AIML_air was studied extensively on physical layer centric use cases including spatial and temporal beam prediction. Temporal prediction within serving cell is mainly to predict the best or top-K beam(s) or beam pair(s) in time domain in order to improve UE throughput. While predict the best or top-K beam(s) or beam pair(s) among a set of beams by measuring a smaller set of beams could help reduce RS signalling overhead, measurement efforts and UE power consumption etc. By extended L1 beam measurement from serving cell to neighbouring cell, majority of the RAN1 work can be reused for e.g. LTM HO study. Since L3 measurement is based on filtering of L1 measurement, the study of AI / ML for air can be leveraged for mobility purpose e.g., temporal prediction can also be used to predict beam(s) / cell(s) becoming worse so that unintended event like radio link failure or short-stay handover can be avoided.

[0192] Mobility enhancement was also studied in RAN3 in Rel-17 in SID called FS_NR_ENDC_data_collect and is now specified in Rel-18 WID NR_AIML_NGRAN-Core. In these RAN3 items the study and normative work on mobility enhancement is based on information available in network side e.g. handover and stay of time in history among cells to predict UE's trajectory in single hop and hence potential candidates. In Rel-19 RAN3 will further work on UE's trajectory for multiple hops. The predicted UE's trajectory could be helpful for study on AI / ML mobility over air interface to some extent.

[0193] Based on progress made in RAN1 and RAN3 so far and assumption on UE's trajectory it is feasible to predict RRM measurement and / or event and hence candidate target cell in UE side. In network side new assistant information, if necessary, and statistics information based on measurement report from UE and / or neighbouring nodes can be also used for smart prediction. If some prediction information could be known by network, handover and / or RRM performance can be improved by proactive measures to either make a better decision or avoid unintended event.

[0194] For AI / ML model identification and model-ID-based LCM of UE-side models and / or UE-part of two-sided models, model-ID-based LCM operates based on identified models, where a model may be associated with specific configurations / conditions associated with UE capability of an AI / ML-enabled Feature / FG and additional conditions (e.g., scenarios, sites, and datasets) as determined / identified between UE-side and NW-side.

[0195] For an AI / ML-enabled feature / FG, additional conditions refer to any aspects that are assumed for the training of the model but are not a part of UE capability for the AI / ML-enabled feature / FG. It does not imply that additional conditions are necessarily specified. Additional conditions can be divided into two categories: NW-side additional conditions and UE-side additional conditions. Whether specification impact is needed is a separate discussion.

[0196] From RAN1 perspective, an AI / ML model identified by a model ID may be logical, and how it maps to physical AI / ML model(s) may be up to implementation. When distinction is necessary for discussion purposes, companies may use the term a logical AI / ML model to refer to a model that is identified and assigned a model ID, and physical AI / ML model(s) to refer to an actual implementation of such a model.

[0197] After model identification, necessity, mechanisms, for UE to report updates on applicable UE part / UE-side model(s), where the applicable models may be a subset of all identified models are studied.

[0198] For inference for UE-side models, to ensure consistency between training and inference regarding NW-side additional conditions (if identified), the following options can be taken as potential approaches (when feasible and necessary):

[0199] - Model identification to achieve alignment on the NW-side additional condition between NW-side and UE-side

[0200] - Model training at NW and transfer to UE, where the model has been trained under the additional condition

[0201] - Information and / or indication on NW-side additional conditions is provided to UE

[0202] - Consistency assisted by monitoring (by UE and / or NW, the performance of UE-side candidate models / functionalities to select a model / functionality)

[0203] - Other approaches are not precluded

[0204] Reporting applicability-related information

[0205] AI / ML models for a given use case may be tailored towards and applicable to specific scenarios, locations, configuration, deployments, among other factors. In this regard, it is acknowledged that AI / ML models may undergo updates, such as model changes, as an inherent part of their development. Therefore, to ensure efficient network control and management, especially associated to what concerns the UE-side, UEs might have the ability to indicate relevant information about their supported AI / ML models and concerning AI / ML functionalities to the network. This can allow the network to perform decisions regarding, e.g., the (de)activation, or switching of AI / ML functionalities and AI / ML models.

[0206] The previously mentioned information could in principle be understood as "applicability-related information" in which the UE could, for example, report to the network conditions under which a model / functionality is applicable / suitable, or whether model(s) / functionality(es) are (non)applicable under the current context. Note, however, that the existing UE capability reporting framework cannot be used for such purposes.

[0207] How and whether there is a need to enable UEs to report applicability-related information can be further discussed and defined in a normative phase. Mechanisms such as UE Assistance Information can eventually be used as example.

[0208] Two UE reporting types are identified to convey this additional information:

[0209] - "reactive" reporting, and - "proactive" reporting.

[0210] A reactive reporting would involve the UE to provide information to the network upon receiving an action from it.

[0211] While a proactive reporting would involve the UE to provide information to the network without necessarily receiving an action from it. For example, the UE might proactively inform the RAN of updates / changes to its supported model(s) or functionality(es).

[0212] Whether necessary signalling from network is needed for proactive UE reporting can be discussed in a normative phase.

[0213] Whether there is a need for the network to report to the UE applicability-related information of AI / ML models and / or AI / ML functionalities can be discussed in a normative phase.

[0214] FIG. 10 shows an example of a Functional Framework for RAN Intelligence.

[0215] > 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.

[0216] Examples of input data may include measurements from UEs or different network entities, feedback from Actor, output from an AI / ML model.

[0217] >> Training Data: Data needed as input for the AI / ML Model Training function.

[0218] >> Inference Data: Data needed as input for the AI / ML Model Inference function.

[0219] > 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.

[0220] >> 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.

[0221] > 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.

[0222] >> Output: The inference output of the AI / ML model produced by a Model Inference function.

[0223] >>> Details of inference output are use case specific.

[0224] >> Model Performance Feedback: It may be used for monitoring the performance of the AI / ML model, when available.

[0225] > 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.

[0226] >> 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.

[0227] Hereinafter, technical features related to Mobility Optimization are described.

[0228] 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.

[0229] Mobility aspects of SON that can be enhanced by the use of AI / ML include

[0230] - Reduction of the probability of unintended events

[0231] - UE Location / Mobility / Performance prediction

[0232] - Traffic Steering

[0233] Reduction of the probability of unintended events associated with mobility.

[0234] Examples of such unintended events are:

[0235] - 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.

[0236] - 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.

[0237] - 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.

[0238] - Successful Handover: During a successful handover, there is underlying issue.

[0239] 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.

[0240] UELocation / Mobility / Performance Prediction

[0241] 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.

[0242] Traffic Steering

[0243] Efficient resource handling can be achieved adjusting handover trigger points and selecting optimal combination of Pcell / PSCell / Scells to serve a user.

[0244] Existing traffic steering can also be improved by providing a RAN node with information related to mobility or dual connectivity.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] Locations for AI / ML Model Training and AI / ML Model Inference

[0249] Considering the locations of AI / ML Model Training and AI / ML Model Inference for mobility solution, the following two options are considered:

[0250] - The AI / ML Model Training function is deployed in OAM, while the Model Inference function resides within the RAN node

[0251] - Both the AI / ML Model Training function and the AI / ML Model Inference function reside within the RAN node

[0252] Furthermore, for CU-DU split scenario, following option is possible:

[0253] - AI / ML Model Training is located in CU-CP or OAM, and AI / ML Model Inference function is located in CU-CP

[0254] gNB is also allowed to continue model training based on AI / ML model trained in the OAM.

[0255] FIG. 11 shows an example of an AI / ML Model Training in OAM and AI / ML Model Inference in NG-RAN node.

[0256] 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.

[0257] Step 1. The NG-RAN node configures the measurement information on the UE side and sends configuration message to UE including configuration information.

[0258] Step 2. The UE collects the indicated measurement, e.g., UE measurements related to RSRP, RSRQ, SINR of serving cell and neighbouring cells.

[0259] Step 3. The UE sends measurement report message to NG-RAN node 1 including the required measurement.

[0260] 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.

[0261] 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.

[0262] Step 6. Model Training. Required measurements are leveraged to training AI / ML model for UE mobility optimization.

[0263] 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.

[0264] Step 8. The NG-RAN node 1 obtains the measurement report as inference data for UE mobility optimization.

[0265] 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.

[0266] 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.

[0267] Step 11. The NG-RAN 1 sends the model performance feedback to OAM if applicable.

[0268] This step is out of RAN3 scope.

[0269] 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.

[0270] Step 13. The NG-RAN node 1 sends the feedback information to OAM.

[0271] Step 14. The NG-RAN node 2 sends the feedback information to OAM.

[0272] FIG. 12 shows an example of Model Training and Model Inference both located in RAN node.

[0273] 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.

[0274] Step 1. NG-RAN node1 configures the measurement information on the UE side and sends configuration message to UE including configuration information.

[0275] Step 2. UE collects the indicated measurement, e.g., UE measurements related to RSRP, RSRQ, SINR of serving cell and neighbouring cells.

[0276] Step 3. UE sends measurement report message to NG-RAN node1 including the required measurement.

[0277] 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.

[0278] Step 5. Model training. Required measurements are leveraged to training AI / ML model for mobility optimization.

[0279] Step 6. NG-RAN node1 obtains the measurement report as inference data for real-time UE mobility optimization.

[0280] 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.

[0281] 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.

[0282] 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.

[0283] Step 10. The NG-RAN node 2 sends feedback information after mobility optimization action to the NG-RAN node 1.

[0284] For example, UE mobility information for training purposes is only sent to gNBs that requested such information or when triggered.

[0285] Input of AI / ML-based Mobility Optimization

[0286] The following data is required as input data for mobility optimization.

[0287] From the UE:

[0288] - UE location information (e.g., coordinates, serving cell ID, moving velocity) interpreted by gNB implementation when available.

[0289] - Radio measurements related to serving cell and neighbouring cells associated with UE location information, e.g., RSRP, RSRQ, SINR.

[0290] - UE Mobility History Information.

[0291] From the neighbouring RAN nodes:

[0292] - UE's history information from neighbour

[0293] - Position, QoS parameters and the performance information of historical HO-ed UE (e.g., loss rate, delay, etc.)

[0294] - Current / predicted resource status

[0295] - 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.

[0296] From the local node:

[0297] - UE trajectory prediction

[0298] - Current / predicted resource status

[0299] - Current / predicted UE traffic

[0300] Output of AI / ML-based Mobility Optimization

[0301] AI / ML-based mobility optimization can generate following information as output:

[0302] - UE trajectory prediction (Latitude, longitude, altitude, cell ID of UE over a future period of time)

[0303] 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.

[0304] - Estimated arrival probability in CHO and relevant confidence interval

[0305] - Predicted handover target node, candidate cells in CHO, may together with the confidence of the predication

[0306] - Priority, handover execution timing, predicted resource reservation time window for CHO.

[0307] - UE traffic prediction (will be used by the RAN node internally and the details are left to normative work phase)

[0308] - Model output validity time will be discussed during R18 normative work per inference output.

[0309] Feedback of AI / ML-based Mobility Optimization

[0310] The following data is required as feedback data for mobility optimization.

[0311] - QoS parameters such as throughput, packet delay of the handed-over UE, etc.

[0312] - Resource status information updates from target NG-RAN.

[0313] - Performance information from target NG-RAN. The details of performance information are to be discussed during normative work phase.

[0314] Standard impact

[0315] 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.

[0316] 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.

[0317] MDT procedure enhancements should be discussed during the normative phase.

[0318] PotentialXninterface impact:

[0319] - Predicted resource status info and performance info from candidate target NG-RAN node to source NG-RAN node

[0320] - New signaling procedure or existing procedure to retrieve input information via Xn interface.

[0321] - New signaling procedure or existing procedure to retrieve feedback information via Xn interface.

[0322] Hereinafter, technical features related to AI and ML are described.

[0323] 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.

[0324] Artificial Intelligence (AI) is the science and engineering to build intelligent machines capable of carrying out tasks as humans do.

[0325] Deep neural network

[0326] FIGS. 13 and 14 show an example of an architecture of neuron and neural network.

[0327] 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.

[0328] 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.

[0329] 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.

[0330] 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.

[0331] Training and inference

[0332] 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.

[0333] 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.

[0334] FIG. 15 shows an example of an AI / ML inference.

[0335] 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.

[0336] 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.

[0337] Widely-usedDNNmodels and algorithms

[0338] FIG. 16 shows an example of an MLP DNN model.

[0339] 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.

[0340] FIG. 17 shows an example of a CNN model.

[0341] 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.

[0342] FIG. 18 shows an example of an RNN model.

[0343] 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.

[0344] FIG. 19 shows an example of Reinforcement learning.

[0345] 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.

[0346] Hereinafter, technical features related to measurement gap are described. Sections of 3GPP TS 38.331 v17.5.0 may be referred.

[0347] MUSIM gap configuration

[0348] The UE shall:

[0349] 1> ifmusim-GapConfigis set tosetup:

[0350] 2> for eachmusim-GapIdincluded in the receivedmusim-GapToReleaseList:

[0351] 3> release the periodic MUSIM gap configuration associated with themusim-GapId;

[0352] 2> for eachMUSIM-Gapincluded in the receivedmusim-GapToAddModList:

[0353] 3> setup periodic MUSIM gap configuration indicated by theMUSIM-Gapin accordance with the receivedmusim-GapRepetitionAndOffset(providingmusim-GapRepetitionandOffsetvalue for the following condition) i.e. the first subframe of each periodic MUSIM gap occurs at an SFN and subframe of the NR PCell meeting the following condition:

[0354] SFN modT= FLOOR(Offset / 10);

[0355] subframe =Offsetmod 10;

[0356] withT=musim-GapRepetition / 10;

[0357] 3> set the MUSIM gap priority configuration indicated bymusim-GapPriorityToAddModList,if configured, for each periodic MUSIM gap;

[0358] If network does not configure the relative priorities among MUSIM gaps as indicated by the UE, UE behaviour is not specified.

[0359] 2> ifmusim-AperiodicGapis included:

[0360] 3> setup aperiodic MUSIM gap configuration indicated by themusim-AperiodicGapin accordance with the receivedmusim-Starting-SFN-AndSubframe, i.e. the first subframe of aperiodic MUSIM gap occurs at an SFN and subframe of the NR PCell meeting the following condition:

[0361] SFN =starting-SFN;

[0362] subframe =startingSubframe;

[0363] 2> keep all colliding MUSIM gaps, ifmusim-GapKeepis configured;

[0364] 1> else ifmusim-GapConfigis set torelease:

[0365] 2> release the MUSIM gap configuration.

[0366] Measurement configuration

[0367] 1> if the receivedmeasConfigincludes themeasGapConfig:

[0368] 2> perform the measurement gap configuration procedure;

[0369] 1> if the receivedmeasConfigincludes themeasGapSharingConfig:

[0370] 2> perform the measurement gap sharing configuration procedure;

[0371] Measurement gap configuration

[0372] The UE shall:

[0373] 1> ifgapFR1is set tosetup:

[0374] 2> if an FR1 measurement gap configuration configured bygapFR1is already setup, release the FR1 measurement gap configuration;

[0375] 2> setup the FR1 measurement gap configuration indicated by thegapFR1in accordance with the receivedgapOffset, i.e., the first subframe of each gap occurs at an SFN and subframe meeting the following condition:

[0376] SFN modT= FLOOR(gapOffset / 10);

[0377] subframe =gapOffsetmod 10;

[0378] withT= MGRP / 10;

[0379] 2> apply the specified timing advancemgtato the gap occurrences calculated above (i.e. the UE starts the measurementmgtams before the gap subframe occurrences);

[0380] 1> else ifgapFR1is set torelease:

[0381] 2> release the FR1 measurement gap configuration configured bygapFR1;

[0382] 1> ifgapFR2is set tosetup:

[0383] 2> if an FR2 measurement gap configuration configured bygapFR2is already setup, release the FR2 measurement gap configuration;

[0384] 2> setup the FR2 measurement gap configuration indicated by thegapFR2in accordance with the receivedgapOffset, i.e., the first subframe of each gap occurs at an SFN and subframe meeting the following condition:

[0385] SFN modT= FLOOR(gapOffset / 10);

[0386] subframe =gapOffsetmod 10;

[0387] withT= MGRP / 10;

[0388] 2> apply the specified timing advancemgtato the gap occurrences calculated above (i.e. the UE starts the measurementmgtams before the gap subframe occurrences);

[0389] 1> else ifgapFR2is set torelease:

[0390] 2> release the FR2 measurement gap configuration configured bygapFR2;

[0391] 1> ifgapUEis set tosetup:

[0392] 2> if a per UE measurement gap configuration configured bygapUEis already setup, release the per UE measurement gap configuration;

[0393] 2> setup the per UE measurement gap configuration indicated by thegapUEin accordance with the receivedgapOffset, i.e., the first subframe of each gap occurs at an SFN and subframe meeting the following condition:

[0394] SFN modT= FLOOR(gapOffset / 10);

[0395] subframe =gapOffsetmod 10;

[0396] withT= MGRP / 10;

[0397] 2> apply the specified timing advancemgtato the gap occurrences calculated above (i.e. the UE starts the measurementmgtams before the gap subframe occurrences);

[0398] 1> else ifgapUEis set torelease:

[0399] 2> release the per UE measurement gap configuration configured bygapUE.

[0400] 1> for eachmeasGapIdincluded in the receivedgapToReleaseList:

[0401] 2> release the measurement gap configuration associated with themeasGapId;

[0402] 1> for eachmeasPosPreConfigGapIdincluded in the receivedposMeasGapPreConfigToReleaseList:

[0403] 2> release the measurement gap configuration associated with themeasPosPreConfigGapId;

[0404] 1> for eachGapConfigreceived ingapToAddModList:

[0405] 2> setup measurement gap configuration indicated by theGapConfigin accordance with the receivedgapOffset, i.e., the first subframe of each gap occurs at an SFN and subframe meeting the following condition:

[0406] SFN modT= FLOOR(gapOffset / 10);

[0407] subframe =gapOffsetmod 10;

[0408] withT= MGRP / 10;

[0409] 2> apply the specified timing advancemgtato the gap occurrences calculated above (i.e. the UE starts the measurementmgtams before the gap subframe occurrences);

[0410] 2> apply the measurement gap as per UE measurement gap, FR1 measurement gap, or FR2 measurement gap according to thegapTypeindicated by theGapConfig;

[0411] 2> associate the measurement gap with themeasGapIdindicated by theGapConfig;

[0412] 2> ifgapSharingin theGapConfigis present:

[0413] 3> setup the gap sharing configuration for the measurement gap in accordance with the receivedgapSharing;

[0414] 2> else:

[0415] 3> release the gap sharing configuration (if configured) for the measurement gap;

[0416] 1> for eachPosGapConfigreceived inPosMeasGapPreConfigToAddModList:

[0417] 2> if a measurement gap configuration associated with themeasPosPreConfigGapIdindicated by thePosGapConfigis already setup:

[0418] 3> release the measurement gap configuration;

[0419] 2> setup measurement gap configuration indicated by thePosGapConfigin accordance with the receivedgapOffset, i.e., the first subframe of each gap occurs at an SFN and subframe meeting the following condition:

[0420] SFN modT= FLOOR(gapOffset / 10);

[0421] subframe =gapOffsetmod 10;

[0422] withT= MGRP / 10;

[0423] 2> apply the specified timing advancemgtato the gap occurrences calculated above (i.e. the UE starts the measurementmgtams before the gap subframe occurrences);

[0424] 2> configure the measurement gap as indicated bygapType;

[0425] 1> for each FR1, FR2, and per UE measurement gap that is setup:

[0426] 2> if the measurement gap is configured byGapConfigandpreConfigInd-r17in the correspondingGapConfigis present:

[0427] 3> determine whether the measurement gap is activated or not;

[0428] 2> else if the measurement gap is configured byPosGapConfig:

[0429] 3> consider the measurement gap to be deactivated;

[0430] 2> else:

[0431] 3> consider the measurement gap to be activated.

[0432] For FR2 gap configuration with synchronous CA, for the UE in NE-DC or NR-DC, the SFN and subframe of the serving cell indicated by therefServCellIndicatoris used in the gap calculation. Otherwise, the SFN and subframe of a serving cell on FR2 frequency is used in the gap calculation

[0433] For FR1 gap or per UE gap configuration, for the UE in NE-DC or NR-DC, the SFN and subframe of the serving cell indicated by therefServCellIndicatorin is used in the gap calculation. Otherwise, the SFN and subframe of the PCell is used in the gap calculation.

[0434] For FR2 gap configuration with asynchronous CA, for the UE in NE-DC or NR-DC, the SFN and subframe of the serving cell indicated by therefServCellIndicatorandrefFR2ServCellAsyncCAis used in the gap calculation. Otherwise, the SFN and subframe of a serving cell on FR2 frequency indicated by therefFR2ServCellAsyncCAis used in the gap calculation

[0435] Performing measurements

[0436] 2> if thereportTypefor the associatedreportConfigisperiodical,eventTriggered; or

[0437] 2> if thereportTypefor the associatedreportConfigiscondTriggerConfig,themeasIdis within the MCGVarMeasConfigand is indicated in thecondExecutionCondor in thecondExecutionCondPSCellassociated to acondReconfigIdin the MCGVarConditionalReconfig(for CHO, CPA, MN-initiated inter-SN CPC, or subsequent CPAC in NR-DC); or

[0438] 2> if thereportTypefor the associatedreportConfigiscondTriggerConfig, themeasIdis within the SCGVarMeasConfigand is indicated in thecondExecutionCondassociated to acondReconfigIdin the SCGVarConditionalReconfig(for intra-SN CPC or subsequent CPAC); or

[0439] 2> if thereportTypefor the associatedreportConfigiscondTriggerConfig, themeasIdis within the SCGVarMeasConfigand is indicated in thecondExecutionCondSCGassociated to acondReconfigIdin the MCGVarConditionalReconfig(for SN-initiated inter-SN CPC or subsequent CPAC in NR-DC); or

[0440] 2> if thereportTypefor the associatedreportConfigiscondTriggerConfig, themeasIdis within the SCGVarMeasConfigand is indicated in thetriggerConditionSNassociated to acondReconfigurationIdinVarConditionalReconfiguration(for SN-initiated inter-SN CPC in EN-DC):

[0441] 3> if a measurement gap configuration is setup, or

[0442] 3> if the UE does not require measurement gaps to perform the concerned measurements:

[0443] 4> ifs-MeasureConfigis not configured, or

[0444] 4> ifs-MeasureConfigis set tossb-RSRPand the NR SpCell RSRP based on SS / PBCH block, after layer 3 filtering, is lower thanssb-RSRP,or

[0445] 4> ifs-MeasureConfigis set tocsi-RSRPand the NR SpCell RSRP based on CSI-RS, after layer 3 filtering, is lower thancsi-RSRP:

[0446] 5> if themeasObjectis associated to NR and thersTypeis set tocsi-rs:

[0447] 6> if reportQuantityRS-Indexes and maxNrofRS-IndexesToReport for the associated reportConfig are configured:

[0448] 7> derive layer 3 filtered beam measurements only based on CSI-RS for each measurement quantity indicated inreportQuantityRS-Indexes;

[0449] 6> derive cell measurement results based on CSI-RS for the trigger quantity and each measurement quantity indicated inreportQuantityCellusing parameters from the associatedmeasObject;

[0450] 5> if themeasObjectis associated to NR and thersTypeis set tossb:

[0451] 6> if reportQuantityRS-Indexes and maxNrofRS-IndexesToReport for the associated reportConfig are configured:

[0452] 7> derive layer 3 beam measurements only based on SS / PBCH block for each measurement quantity indicated inreportQuantityRS-Indexes;

[0453] 6> derive cell measurement results based on SS / PBCH block for the trigger quantity and each measurement quantity indicated inreportQuantityCellusing parameters from the associatedmeasObject;

[0454] 5> if themeasObjectis associated to E-UTRA:

[0455] 6> perform the corresponding measurements associated to neighbouring cells on the frequencies indicated in the concernedmeasObject;

[0456] 5> if the measObject is associated to UTRA-FDD:

[0457] 6> perform the corresponding measurements associated to neighbouring cells on the frequencies indicated in the concernedmeasObject;

[0458] 5> if the measObject is associated to L2 U2N Relay UE:

[0459] 6> perform the corresponding measurements associated to candidate Relay UEs on the frequencies indicated in the concernedmeasObject;

[0460] 4> if themeasRSSI-ReportConfigis configured in the associatedreportConfig:

[0461] 5> perform the RSSI and channel occupancy measurements on the frequency configured byrmtc-Frequencyin the associatedmeasObject;

[0462] The network avoids configuring UEs supporting only CHO and / or Rel-16 CPC with measurements not referred to by any execution condition.

[0463] Location measurement indication

[0464] FIG. 20 shows an example of Location measurement indication

[0465] The purpose of this procedure is to indicate to the network that the UE is going to start / stop location related measurements towards E-UTRA or NR which require measurement gaps or start / stop detection of subframe and slot timing towards E-UTRA (eutra-FineTimingDetection)which requires measurement gaps. UE shall initiate this procedure only after successful AS security activation.

[0466] It is a network decision to configure the measurement gap.

[0467] Initiation

[0468] The UE shall:

[0469] 1> if and only if upper layers indicate to start performing location measurements towards E-UTRA or NR or start subframe and slot timing detection towards E-UTRA, and the UE requires measurement gaps for these operations while measurement gaps are either not configured or not sufficient:

[0470] 2> if preconfigured measurement gaps for positioning andposMG-Requestare configured and the UE considers that at least one of the preconfigured measurement gaps for positioning is sufficient for the location measurement when activated:

[0471] 3> trigger the lower layers to initiate the measurement gap activation request using UL MAC CE];

[0472] 2> else:

[0473] 3> initiate the procedure to indicate start;

[0474] The UE verifies the measurement gap situation only upon receiving the indication from upper layers. If at this point in time sufficient gaps are available, the UE does not initiate the procedure. Unless it receives a new indication from upper layers, the UE is only allowed to further repeat the procedure in the same PCell once per frequency of the target RAT if the provided measurement gaps are insufficient.

[0475] When indication is received from upper layers for performing location measurement and there is pre-configured measurement gap configured (not preconfigured measurement gap for positioning), the UE considers this preconfigured measurement gap to be not sufficient if the measurement gap is not considered to be always activated.

[0476] 1> if and only if upper layers indicate to stop performing location measurements towards E-UTRA or NR or stop subframe and slot timing detection towards E-UTRA:

[0477] 2> if there is no activated preconfigured measurement gap for positioning:

[0478] 3> if there is previously triggered UL MAC CE transmission for the measurement gap activation for positioning:

[0479] 4> indicate to the lower layers to cancel the triggered UL MAC CE transmission for the measurement gap activation;

[0480] 2> else if there is activated preconfigured measurement gap for positioning:

[0481] 3> trigger the lower layers to deactivate all the activated measurement gap(s) for positioning.

[0482] 2> if there is configured measurement gap used for positioning and the measurement gap is not the activated preconfigured measurement gap for positioning:

[0483] 3> initiate the procedure to indicate stop.

[0484] The UE may initiate the procedure to indicate stop even if it did not previously initiate the procedure to indicate start.

[0485] Reception of an RRCReconfiguration by the UE

[0486] 1> if theRRCReconfigurationmessage includes theneedForGapsConfigNR:

[0487] 2> ifneedForGapsConfigNRis set tosetup:

[0488] 3> consider itself to be configured to provide the measurement gap requirement information of NR target bands;

[0489] 2> else:

[0490] 3> consider itself not to be configured to provide the measurement gap requirement information of NR target bands;

[0491] 1> if theRRCReconfigurationmessage includes theneedForGapNCSG-ConfigNR:

[0492] 2> ifneedForGapNCSG-ConfigNRis set tosetup:

[0493] 3> consider itself to be configured to provide the measurement gap and NCSG requirement information of NR target bands;

[0494] 2> else:

[0495] 3> consider itself not to be configured to provide the measurement gap and NCSG requirement information of NR target bands;

[0496] 1> if theRRCReconfigurationmessage includes theneedForGapNCSG-ConfigEUTRA:

[0497] 2> ifneedForGapNCSG-ConfigEUTRAis set tosetup:

[0498] 3> consider itself to be configured to provide the measurement gap and NCSG requirement information of E-UTRA target bands;

[0499] 2> else:

[0500] 3> consider itself not to be configured to provide the measurement gap and NCSG requirement information of E-UTRA target bands;

[0501] Reception of theRRCResumeby the UE

[0502] 1> if theRRCResumemessage includes theneedForGapsConfigNR:

[0503] 2> ifneedForGapsConfigNRis set tosetup:

[0504] 3> consider itself to be configured to provide the measurement gap requirement information of NR target bands;

[0505] 2> else:

[0506] 3> consider itself not to be configured to provide the measurement gap requirement information of NR target bands;

[0507] 1> if theRRCResumemessage includes theneedForGapNCSG-ConfigNR:

[0508] 2> ifneedForGapNCSG-ConfigNRis set tosetup:

[0509] 3> consider itself to be configured to provide the measurement gap and NCSG requirement information of NR target bands;

[0510] 2> else:

[0511] 3> consider itself not to be configured to provide the measurement gap and NCSG requirement information of NR target bands;

[0512] 1> if theRRCResumemessage includes theneedForGapNCSG-ConfigEUTRA:

[0513] 2> ifneedForGapNCSG-ConfigEUTRAis set tosetup:

[0514] 3> consider itself to be configured to provide the measurement gap and NCSG requirement information of E-UTRA target bands;

[0515] 2> else:

[0516] 3> consider itself not to be configured to provide the measurement gap and NCSG requirement information of E-UTRA target bands;

[0517] UE Assistance Information

[0518] 1> if configured to provide MUSIM assistance information for temporary capability restriction:

[0519] 2> if the UE has temporary capability restriction on the current configuration and timer T348 is not running:

[0520] 3> initiate transmission of theUEAssistanceInformationmessage to provide the currentmusim-Cell-SCG-ToReleaseand / ormusim-CellToAffectList;

[0521] 3> start the timer T348 with the timer value set to themusim-WaitTimer.

[0522] 2> if the UE has temporary capability restriction on the combination(s) of bands comprising of band(s) included inmusim-CandidateBandListand the UE did not transmit aUEAssistanceInformationmessage withmusim-AffectedBandsListand / ormusim-AvoidedBandsListsince it was configured to provide MUSIM assistance information for temporary capability restriction; or

[0523] 2> if the currentmusim-AffectedBandsListand / ormusim-AvoidedBandsListis different from the one indicated in the last transmission of theUEAssistanceInformationmessage includingmusim-CapRestrictionand timer T346n is not running:

[0524] 3> initiate transmission of theUEAssistanceInformationmessage to provide the currentmusim-AffectedBandsListand / ormusim-AvoidedBandsList;

[0525] 3> start the timer T346n with the timer value set to themusim-ProhibitTimer.

[0526] 2> if the currentmusim-MaxCCis different from the one indicated in the last transmission of theUEAssistanceInformationmessage includingmusim-CapRestrictionand timer T346n is not running:

[0527] 3> initiate transmission of theUEAssistanceInformationmessage to provide the currentmusim-MaxCC;

[0528] 3> start the timer T346n with the timer value set to themusim-ProhibitTimer.

[0529] 2> if the UE has a preference on the measurement gap requirement information and the UE did not transmit aUEAssistanceInformationmessage with measurement gap requirement information orRRCReconfigurationCompletemessage orRRCResumeCompletemessage with measurement gap requirement information since it was configured to provide its preference on the measurement gap requirement information for MUSIM; or

[0530] 2> if the currentmusim-NeedForGapsInfoNRis different from the one indicated in the last transmission of theUEAssistanceInformationmessage includingmusim-NeedForGapsInfoNRorRRCReconfigurationCompletemessage orRRCResumeCompletemessage includingneedForGapsInfoNR:

[0531] 3> initiate transmission of theUEAssistanceInformationmessage to provide the currentmusim-NeedForGapsInfoNR.

[0532] Meanwhile, in 3GPP Release 19, cell-level measurement prediction with inter-frequency is being discussed. This approach has the advantage of reducing the need for inter-frequency measurements, which can also enhance data performance by minimizing the measurements within a specific measurement gap.

[0533] From an AI model monitoring perspective, during cell-measurement prediction with inter-frequency, the UE should perform inter-frequency measurement within a specific measurement gap. By comparing the predicted results with the actual measurement results, the UE can evaluate the model's accuracy. However, in this case, the gain from not performing measurements in the specific measurement gap based on predictions can be diminished. Also, when model monitoring is not necessary every measurement gap, some measurement gap becomes unnecessary. During this period, the UE cannot transmit or receive data, resulting in a loss of data performance.

[0534] Thus, studies for monitoring operations for inference function based on prediction accuracy are required.

[0535] Hereinafter, a method for monitoring operations for inference function based on prediction accuracy, according to some embodiments of the present disclosure, will be described with reference to the following drawings.

[0536] 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).

[0537] FIG. 21 shows an example of a method for monitoring operations for inference function based on prediction accuracy.

[0538] In particular, FIG. 21 shows an example of a method performed by a wireless device in a wireless communication system.

[0539] In step S2101, the wireless device may receive, from a network, a measurement configuration including information related to at least one measurement object.

[0540] For example, the measurement configuration may include information related to measurements number and / or measurements frequency for monitoring operations for deriving the prediction accuracy of the inference function.

[0541] In step S2102, the wireless device may perform predictions related to the at least one measurement object based on an inference function.

[0542] For example, the wireless device may perform measurements for the at least one measurement object and log the measurement results. The wireless device may perform predictions related to the at least one measurement object using the inference function based on the logged measurement results.

[0543] In step S2103, the wireless device may perform monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object.

[0544] For example, the wireless device may derive prediction accuracy of the inference function by i) performing measurements for the at least one measurement object and ii) comparing the measurements results with the derived prediction results related to the at least one measurement object.

[0545] In step S2104, the wireless device may adjust resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object.

[0546] For example, the wireless device may decrease measurements frequency of the monitoring operations, based on that the measurement results for the at least one measurement object are equal to or greater than a first threshold value.

[0547] For example, the wireless device may increase measurements frequency of the monitoring operations, based on that the measurement results for the at least one measurement object are less than a second threshold value.

[0548] For example, the wireless device may increase measurements frequency of the monitoring operations, based on that the prediction accuracy of the inference function do not satisfy a first performance key performance indicator (KPI).

[0549] For example, the wireless device may decrease measurements frequency of the monitoring operations based on that the prediction accuracy of the inference function satisfy a second KPI.

[0550] For example, the first performance KPI may be different from the second performance KPI. Alternatively, the first performance KPI may be same as the second performance KPI.

[0551] For example, the wireless device may consider that the inference function is unstable based on that the prediction accuracy of the inference function do not satisfy a first performance KPI.

[0552] For example, the wireless device may consider that the inference function is stable based on that the prediction accuracy of the inference function satisfy a second performance KPI.

[0553] For example, the wireless device and / or the network may determine the common KPIs and corresponding requirements for the AI / ML operations.

[0554] For example, the wireless device may increase number of measurements for the at least one measurement object within a certain time period, based on considering that the inference function is unstable.

[0555] For example, the wireless device may decrease number of measurements for the at least one measurement object within a certain time period, based on considering that the inference function is stable.

[0556] According to some embodiments of the present disclosure, the wireless device may transmit, to the network, a message including information related to the adjusted resources for the monitoring operations. For example, the information related to the adjusted resources for the monitoring operations may include information related to adjusted measurements frequency of the monitoring operations. For example, the message may include information informing whether the inference function is considered as stable or unstable.

[0557] 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.

[0558] Hereinafter, some embodiments of a method for monitoring operations for inference function based on prediction accuracy based on count threshold are provided.

[0559] For example, a method of applying the measurement gap based on the AI model / functionality accuracy may include the following technical features:

[0560] - If the AI model / functionality operation is unstable, the UE may frequently perform measurements on specific objects.

[0561] - If the AI model / functionality operation is stable, the UE may perform relaxed measurements for that specific object.

[0562] - In both methods, the UE may notify or informs the network to receive frequent or relaxed measurement resources from the network.

[0563] FIG. 22 shows an example of a method for monitoring operations for inference function based on prediction accuracy.

[0564] In step S2201, the UE may receive a measurement configuration for resources associated with one or more measurement object.

[0565] For example, the network may configure measurement related resource associated with one or more measurement object.

[0566] In step S2202, the UE may perform AI-based prediction (for specific AI model / functionality) related to measurement object.

[0567] In step S2203, the UE may derive the model / functionality accuracy for the prediction.

[0568] In step S2204, the UE may adjust measurement related resource depending on the results of model / functionality accuracy.

[0569] In step S2205, the UE may notify or inform the network updated measurement resource related information. Based on updated measurement resource related information, the UE may perform measurement.

[0570] Regarding step S2201,

[0571] 1> The network may configure measurement related resource associated with one or more measurement object

[0572] 2> The measurement related resource may include following:

[0573] 3> Time domain information

[0574] 4> Start / end validity time / time window

[0575] 5> e.g., with a specific SFN / Subframe / Slot / Symbol

[0576] 5> e.g., with UTC time (YYYY:MM:DD: HH:MM:SS)

[0577] 5> e.g., with time value (e.g., unit of ms)

[0578] 4> Measurement periodicity, e.g., measurement repetition period (e.g., the number of SFN / Subframe / Slot / Symbol or time window (e.g., ms)

[0579] 4> Measurement length, e.g., measurement length of measurement (e.g., the number of SFN / Subframe / Slot / Symbol or time window (e.g., ms)

[0580] 4> Measurement offset, e.g., measurement timing advanced (e.g., the number of SFN / Subframe / Slot / Symbol or time window (e.g., ms)

[0581] 3> Spatial domain information (e.g., specific RF port, reference signal, the number of layers, etc)

[0582] 3> Frequency domain information (e.g., specific frequency range)

[0583] 2> The measurement object may be the following:

[0584] 3> L1 / L2 / L3 measurement related object

[0585] 4> E.g., Frequency / Band

[0586] 4> E.g., Cell / Zone (geographical or multiple cell-based location)

[0587] 4> E.g., Synchronization signal block (SSB)

[0588] 4> E.g., Reference signal (CSI-RS, SRS, PRS, etc)

[0589] 4> E.g., RACH resources

[0590] 3> Data related object

[0591] 4> E.g., data bearer

[0592] 4> E.g., LCH / LCG

[0593] 4> E.g., data type

[0594] 4> E.g., QoS Flow

[0595] 4> E.g., Slice

[0596] 3> Location related object

[0597] 4> E.g., altitude / latitude / longitude

[0598] 4> E.g., speed (e.g., vertical / horizontal)

[0599] 4> E.g., vector / direction / angle

[0600] 4> E.g., PLMN / TA / Cell

[0601] 4> E.g., Polygon type location

[0602] 2> The NW may configure a validity time window

[0603] Regarding step S2202,

[0604] 1> The UE may perform AI-based prediction with a specific AI model / functionality related to measurement object

[0605] 2> When performing prediction, UE may be configured with a more prediction model configuration. The AIML model configuration may include prediction model structure information,

[0606] 3> Network may configure a machine learning model to be used by UE.

[0607] 4> Network may include a machine learning type, such as reinforcement learning, supervised learning, or unsupervised learning.

[0608] 4> Network may include a machine learning model, such as DNN, CNN, RNN, and DRL.

[0609] 4> The configured ML model may be a pre-trained ML model that has been already trained by network a-priori

[0610] 5> The configured ML model is described by a model description information including model structure and parameters.

[0611] 6> 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).

[0612] 7> Different layers are connected based on the connections between neurons of different layers

[0613] 8> 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)

[0614] 8> Each neuron may provide input to one or several connected neurons (1 to N connection).

[0615] 8> 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.

[0616] 8> 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.

[0617] 4> The configured ML model may be a ML model to be trained.

[0618] 5> The configured ML model is described by a model description information including model structure and initial parameters that are to be trained.

[0619] 5> 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.

[0620] 4> 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.

[0621] 4> Network may include machine learning output, such as UE trajectory prediction, predicted target cell, prediction time for handover, and UE traffic prediction.

[0622] 3> UE may perform a machine learning model training, validation, and testing which may generate model performance metrics based on the prediction model configuration.

[0623] 4> UE may perform a model training with the machine learning input parameters.

[0624] 3> UE may use the configured ML model to perform ML task such as predictions of measurements.

[0625] 4> UE may derive machine learning output(s).

[0626] 4> UE may infer from the outputs and use the outputs as feedback for the machine learning model.

[0627] 3> UE may send feedback to network about the results related to machine learning outputs and the accuracy of the machine learning model.

[0628] 4> Network may update the machine learning model and parameters related to the machine learning model.

[0629] 2> The UE may derive AI-based prediction results as below:

[0630] 3> For L1 / L3 measurement related object

[0631] 4> E.g., L1 measurement result (e.g., RSRP / RSRQ / SINR)

[0632] 4> E.g., L3 measurement result (e.g., RSRP / RSRQ / SINR)

[0633] 4> E.g., CQI / PMI / RI

[0634] 4> E.g., Radio link failure, Beam failure, and / or Handover failure

[0635] 3> For Data related object

[0636] 4> E.g., pack arrival / transmit time

[0637] 4> E.g., retransmission time

[0638] 4> E.g., the number of repetition number

[0639] 4> E.g., data packet size / data volute (e.g., bytes)

[0640] 4> E.g., latency budget

[0641] 4> E.g., data throughput

[0642] 4> E.g., BLER

[0643] 4> E.g., Rx / Tx failure (ratio)

[0644] 3> For Location related object

[0645] 4> E.g., arriving location / time

[0646] 4> E.g., distance between specific locations

[0647] 4> E.g., time required between specific locations

[0648] Regarding step S2203,

[0649] 1> The UE may derive prediction accuracy for the model / functionality (monitoring operation)

[0650] 2> The UE may perform legacy measurement without prediction

[0651] 3> The UE may perform measurement on measurement related resource

[0652] 4> If the prediction was related to time-domain(temporal) prediction, the UE may perform the measurement at the time of prediction

[0653] 4> If the prediction was related to spatial / frequency prediction, the UE may perform measure

[0654] 2> The UE may evaluate the difference between predictive measurement results and actual prediction results.

[0655] 2> The UE may evaluate accuracy ratio based on prediction results and actual results

[0656] 2> The UE may evaluate the throughput, BLER, NACK / ACK, L1 / L3-RSRP, L1 / L3-SINR

[0657] 2> The UE may evaluate Input / output data-based monitoring (such as data drift between training dataset and observed dataset and out-of-distribution detection)

[0658] 2> If the evaluation results do not satisfy a first performance KPI, model / functionality can be considered as unstable. If the evaluation results do not satisfy a second performance KPI, model / functionality can be considered as stable.

[0659] 3> The first performance KPI and the second performance KPI may be a threshold configured by the network, determined in accordance with a specification, or determined by the UE's implementation

[0660] 3> The first performance KPI may be the same with the second performance

[0661] 3> For example, if the prediction accuracy is lower than a first threshold, the model / functionality operation may be considered as unstable. If the prediction accuracy is higher than a second threshold, the model / functionality operation may be considered as stable

[0662] Regarding step S2204,

[0663] 1> The UE may adjust measurement related resource depending on the results of model / functionality accuracy

[0664] 2> If the model / functionality operation is considered as unstable, the UE may determine to frequently perform measurements on specific objects. (model monitoring)

[0665] 3> For the frequent model monitoring operation, the UE may adjust measurement related resource accordingly

[0666] 4> E.g., shorter measurement periodicity

[0667] 4> E.g., shorter measurement length

[0668] 4> E.g., wider beam id

[0669] 4> E.g., wider bandwidth

[0670] 2> If the model / functionality operation is considered as stable, the UE may determine to perform relaxed measurements on specific objects. (model monitoring)

[0671] 3> For the relaxed model monitoring operation, the UE may adjust measurement related resource to relax

[0672] 4> E.g., longer measurement periodicity

[0673] 4> E.g., longer measurement length

[0674] 4> E.g., narrower beam id

[0675] 4> E.g., narrower bandwidth

[0676] 4> E.g., no measurement (measurement skip)

[0677] Regarding step S2205,

[0678] 1> The UE may notify or inform the network updated measurement related resource information

[0679] 2> The UE may include the adjusted(updated) measurement related resource information

[0680] 2> The UE may apply the adjusted(updated) measurement related resource during the validity time configured. Alternatively, the UE may determine the validity time on its own.

[0681] 3> UE may inform the network of start / end(validity) time / time window

[0682] 3> The UE may perform measurement with adjusted measurement related resource within the valid time

[0683] 3> The UE may perform measurement with initial measurement related resource outside the valid time

[0684] 2> The UE may notify the information to the network and perform measurement operation on the updated measurement related resource

[0685] 2> The NW may inform the network of the information.

[0686] 3> The network may re-configure the measurement related resource based on the UE's information

[0687] 3> The UE may apply the new measurement related resource based on the network re-configuration

[0688] Additionally, when measurement related resource is associated with a model / functionality, the measurement related resource can be activated only when the model / functionality is activated. If the model / functionality is deactivated, the measurement related resource can be deactivated as well.

[0689] FIG. 23 shows an example of a method for monitoring operations for inference function based on prediction accuracy based on count threshold.

[0690] In particular, FIG. 23 shows a method performed by a wireless device.

[0691] In step S2301, a wireless device may perform a first type measurement.

[0692] In step S2302, a wireless device may perform a second type measurement.

[0693] In step S2303, a wireless device may evaluate accuracy of the first type measurement based on the first type measurement results and the second type measurement results.

[0694] In step S2304, based on the evaluation, a wireless device may adjust frequency of the second type measurement.

[0695] For example, (i) when the accuracy is worse, measurement frequency increases or (ii) when the accuracy is better, measurement frequency decreases.

[0696] For example, measurement frequency may not have periodicity such as a constant cycle / periodicity.

[0697] For example, the first type measurements may include predictive measurements.

[0698] For example, the second type measurement may include legacy measurements.

[0699] For example, for adjusting frequency, when the accuracy is worse: measurement frequency increases (for example, measurement frequency increases to maximum frequency). For example, for adjusting frequency, when the accuracy is better, measurement frequency decreases (for example, measurement frequency decreases to minimum frequency).

[0700] FIG. 24 shows an example of a method for monitoring operations related to measurement gap for inference function based on prediction accuracy based on count threshold.

[0701] In particular, FIG. 24 shows a method performed by a wireless device.

[0702] In step S2401, a wireless device may receive, from network, measurement gap configuration associated with one or more frequency, wherein the configuration includes a periodicity of gap.

[0703] In step S2402, a wireless device may derive the predictive measurement results for the frequency(ies).

[0704] In step S2403, a wireless device may perform the measurement of the frequency(ies) in the measurement gap periodically with the periodicity.

[0705] In step S2404, a wireless device may derive the model accuracy.

[0706] In step S2405, based on the model accuracy, a wireless device may adjust the timing of the gap; and informing the periodicity to the network.

[0707] FIG. 25 shows an example of a method for monitoring operations related to inference model for inference function based on prediction accuracy based on count threshold.

[0708] In particular, FIG. 25 shows a method performed by a wireless device.

[0709] In step S2501, a wireless device may activate a model for an intended functionality.

[0710] In step S2502, a wireless device may evaluate the model accuracy with a periodicity.

[0711] In step S2503, if the model accuracy is stable, a wireless device may increase the periodicity of evaluation of the model accuracy.

[0712] In step S2504, if the model accuracy is unstable, a wireless device may decrease the periodicity of evaluation of the model accuracy.

[0713] In step S2505, a wireless device may inform the periodicity related information to the network.

[0714] Some of the detailed steps shown in the examples of FIGS. 21-25 may not be essential steps and may be omitted. In addition to the steps shown in FIGS. 21-25, other steps may be added, and the order of the steps may vary. Some of the above steps may have their own technical meaning.

[0715] Hereinafter, an apparatus for monitoring operations for inference function based on prediction accuracy, 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.

[0716] 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.

[0717] Referring to FIG. 5, a wireless device 100 may include a processor 102, a memory 104, and a transceiver 106.

[0718] 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.

[0719] The processor 102 may be adapted to perform operations. The operations comprise: receiving, from a network, a measurement configuration including information related to at least one measurement object; performing predictions related to the at least one measurement object based on an inference function; performing monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object; and adjusting resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object.

[0720] For example, the operations further comprise: decreasing measurements frequency of the monitoring operations based on that the measurement results for the at least one measurement object are equal to or greater than a first threshold value.

[0721] For example, the operations further comprise: increasing measurements frequency of the monitoring operations based on that the measurement results for the at least one measurement object are less than a second threshold value.

[0722] For example, the operations further comprise: increasing measurements frequency of the monitoring operations based on that the prediction accuracy of the inference function do not satisfy a first performance key performance indicator (KPI).

[0723] For example, the operations further comprise: decreasing measurements frequency of the monitoring operations based on that the prediction accuracy of the inference function satisfy a second KPI.

[0724] For example, the operations further comprise: considering that the inference function is unstable based on that the prediction accuracy of the inference function do not satisfy a first performance KPI.

[0725] For example, the operations further comprise: considering that the inference function is stable based on that the prediction accuracy of the inference function satisfy a second performance KPI.

[0726] For example, the operations further comprise: increasing number of measurements for the at least one measurement object within a certain time period, based on considering that the inference function is unstable.

[0727] For example, the operations further comprise: decreasing number of measurements for the at least one measurement object within a certain time period, based on considering that the inference function is stable.

[0728] For example, the operations further comprise: transmitting, to the network, a message including information related to the adjusted resources for the monitoring operations.

[0729] For example, the information related to the adjusted resources for the monitoring operations includes information related to adjusted measurements frequency of the monitoring operations.

[0730] For example, the message includes information informing whether the inference function is considered as stable or unstable.

[0731] For example, the measurement configuration includes information related to measurements number and / or measurements frequency for monitoring operations for deriving the prediction accuracy of the inference function.

[0732] 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.

[0733] Hereinafter, a processor for a wireless device for monitoring operations for inference function based on prediction accuracy, according to some embodiments of the present disclosure, will be described.

[0734] The processor may be configured to control the wireless device to perform operations. The operations comprise: receiving, from a network, a measurement configuration including information related to at least one measurement object; performing predictions related to the at least one measurement object based on an inference function; performing monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object; and adjusting resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object.

[0735] For example, the operations further comprise: decreasing measurements frequency of the monitoring operations based on that the measurement results for the at least one measurement object are equal to or greater than a first threshold value.

[0736] For example, the operations further comprise: increasing measurements frequency of the monitoring operations based on that the measurement results for the at least one measurement object are less than a second threshold value.

[0737] For example, the operations further comprise: increasing measurements frequency of the monitoring operations based on that the prediction accuracy of the inference function do not satisfy a first performance key performance indicator (KPI).

[0738] For example, the operations further comprise: decreasing measurements frequency of the monitoring operations based on that the prediction accuracy of the inference function satisfy a second KPI.

[0739] For example, the operations further comprise: considering that the inference function is unstable based on that the prediction accuracy of the inference function do not satisfy a first performance KPI.

[0740] For example, the operations further comprise: considering that the inference function is stable based on that the prediction accuracy of the inference function satisfy a second performance KPI.

[0741] For example, the operations further comprise: increasing number of measurements for the at least one measurement object within a certain time period, based on considering that the inference function is unstable.

[0742] For example, the operations further comprise: decreasing number of measurements for the at least one measurement object within a certain time period, based on considering that the inference function is stable.

[0743] For example, the operations further comprise: transmitting, to the network, a message including information related to the adjusted resources for the monitoring operations.

[0744] For example, the information related to the adjusted resources for the monitoring operations includes information related to adjusted measurements frequency of the monitoring operations.

[0745] For example, the message includes information informing whether the inference function is considered as stable or unstable.

[0746] For example, the measurement configuration includes information related to measurements number and / or measurements frequency for monitoring operations for deriving the prediction accuracy of the inference function.

[0747] 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.

[0748] Hereinafter, a non-transitory computer-readable medium has stored thereon a plurality of instructions for monitoring operations for inference function based on prediction accuracy, according to some embodiments of the present disclosure, will be described.

[0749] 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.

[0750] 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.

[0751] The computer-readable medium may include a tangible and non-transitory computer-readable storage medium.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] The stored plurality of instructions may cause the wireless device to perform operations. The operations comprise: receiving, from a network, a measurement configuration including information related to at least one measurement object; performing predictions related to the at least one measurement object based on an inference function; performing monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object; and adjusting resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object.

[0756] For example, the operations further comprise: decreasing measurements frequency of the monitoring operations based on that the measurement results for the at least one measurement object are equal to or greater than a first threshold value.

[0757] For example, the operations further comprise: increasing measurements frequency of the monitoring operations based on that the measurement results for the at least one measurement object are less than a second threshold value.

[0758] For example, the operations further comprise: increasing measurements frequency of the monitoring operations based on that the prediction accuracy of the inference function do not satisfy a first performance key performance indicator (KPI).

[0759] For example, the operations further comprise: decreasing measurements frequency of the monitoring operations based on that the prediction accuracy of the inference function satisfy a second KPI.

[0760] For example, the operations further comprise: considering that the inference function is unstable based on that the prediction accuracy of the inference function do not satisfy a first performance KPI.

[0761] For example, the operations further comprise: considering that the inference function is stable based on that the prediction accuracy of the inference function satisfy a second performance KPI.

[0762] For example, the operations further comprise: increasing number of measurements for the at least one measurement object within a certain time period, based on considering that the inference function is unstable.

[0763] For example, the operations further comprise: decreasing number of measurements for the at least one measurement object within a certain time period, based on considering that the inference function is stable.

[0764] For example, the operations further comprise: transmitting, to the network, a message including information related to the adjusted resources for the monitoring operations.

[0765] For example, the information related to the adjusted resources for the monitoring operations includes information related to adjusted measurements frequency of the monitoring operations.

[0766] For example, the message includes information informing whether the inference function is considered as stable or unstable.

[0767] For example, the measurement configuration includes information related to measurements number and / or measurements frequency for monitoring operations for deriving the prediction accuracy of the inference function.

[0768] 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.

[0769] Hereinafter, a method performed by a base station (BS) for monitoring operations for inference function based on prediction accuracy, according to some embodiments of the present disclosure, will be described.

[0770] The method comprises: transmitting, by a base station to a wireless device, a measurement configuration including information related to at least one measurement object, wherein the wireless device performs predictions related to the at least one measurement object based on an inference function, wherein the wireless device performs monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object, and wherein the wireless device adjusts resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object; and receiving, by the base station from the wireless device, a message including information related to the adjusted resources for the monitoring operations.

[0771] Hereinafter, a base station (BS) for monitoring operations for inference function based on prediction accuracy, according to some embodiments of the present disclosure, will be described.

[0772] The BS may include a transceiver, a memory, and a processor operatively coupled to the transceiver and the memory.

[0773] The processor may be configured to perform operations. The operations comprise: transmitting, to a wireless device, a measurement configuration including information related to at least one measurement object, wherein the wireless device performs predictions related to the at least one measurement object based on an inference function, wherein the wireless device performs monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object, and wherein the wireless device adjusts resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object; and receiving, from the wireless device, a message including information related to the adjusted resources for the monitoring operations.

[0774] The present disclosure can have various advantageous effects.

[0775] According to some embodiments of the present disclosure, a wireless device could efficiently perform monitoring operations for inference function based on prediction accuracy.

[0776] For example, measurement resources can be dynamically adopted by considering the model monitoring periodicity according to model accuracy. It can reduce resource waste and have a positive impact on data performance.

[0777] In other words, the wireless device can save resources for monitoring operations for inference model.

[0778] According to some embodiments of the present disclosure, the wireless communication system could provide an efficient solution for monitoring operations for inference function based on prediction accuracy.

[0779] 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.

[0780] Claims in the present disclosure can be combined in a various way. For instance, technical features in method claims of the present disclosure can be combined to be implemented or performed in an apparatus, and technical features in apparatus claims can be combined to be implemented or performed in a method. Further, technical features in method claim(s) and apparatus claim(s) can be combined to be implemented or performed in an apparatus. Further, technical features in method claim(s) and apparatus claim(s) can be combined to be implemented or performed in a method. Other implementations are within the scope of the following claims.

Claims

1.A method, comprising:receiving, by a wireless device from a network, a measurement configuration including information related to at least one measurement object;performing, by the wireless device, predictions related to the at least one measurement object based on an inference function;performing, by the wireless device, monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object; andadjusting, by the wireless device, resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object.2.The method of claim 1, wherein the method further comprising:decreasing, by the wireless device, measurements frequency of the monitoring operations based on that the measurement results for the at least one measurement object are equal to or greater than a first threshold value.3.The method of claim 1, wherein the method further comprising:increasing, by the wireless device, measurements frequency of the monitoring operations based on that the measurement results for the at least one measurement object are less than a second threshold value.4.The method of claim 1, wherein the method further comprising:increasing, by the wireless device, measurements frequency of the monitoring operations based on that the prediction accuracy of the inference function do not satisfy a first performance key performance indicator (KPI).5.The method of claim 1, wherein the method further comprising:decreasing, by the wireless device, measurements frequency of the monitoring operations based on that the prediction accuracy of the inference function satisfy a second KPI.6.The method of claim 1, wherein the method further comprising:considering, by the wireless device, that the inference function is unstable based on that the prediction accuracy of the inference function do not satisfy a first performance KPI.7.The method of claim 1, wherein the method further comprising:considering, by the wireless device, that the inference function is stable based on that the prediction accuracy of the inference function satisfy a second performance KPI.8.The method of claim 1, wherein the method further comprising:increasing, by the wireless device, number of measurements for the at least one measurement object within a certain time period, based on considering that the inference function is unstable.9.The method of claim 1, wherein the method further comprising:decreasing, by the wireless device, number of measurements for the at least one measurement object within a certain time period, based on considering that the inference function is stable.10.The method of claim 1, wherein the method further comprising:transmitting, by the wireless device to the network, a message including information related to the adjusted resources for the monitoring operations.11.The method of claim 10,wherein the information related to the adjusted resources for the monitoring operations includes information related to adjusted measurements frequency of the monitoring operations.12.The method of claim 10,wherein the message includes information informing whether the inference function is considered as stable or unstable.13.The method of claim 1,wherein the measurement configuration includes information related to measurements number and / or measurements frequency for monitoring operations for deriving the prediction accuracy of the inference function.14.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.15.A wireless device, comprising:a transceiver;a memory; andat least one processor operatively coupled to the transceiver and the memory, and adapted to perform operations, the operations comprising:receiving, from a network, a measurement configuration including information related to at least one measurement object;performing predictions related to the at least one measurement object based on an inference function;performing monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object; andadjusting resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object.16.The wireless device of claim 15, wherein the operations further comprising:decreasing measurements frequency of the monitoring operations based on that the measurement results for the at least one measurement object are equal to or greater than a first threshold value.17.The wireless device of claim 15, wherein the operations further comprising:increasing measurements frequency of the monitoring operations based on that the measurement results for the at least one measurement object are less than a second threshold value.18.The wireless device of claim 15, wherein the operations further comprising:increasing measurements frequency of the monitoring operations based on that the prediction accuracy of the inference function do not satisfy a first performance key performance indicator (KPI).19.The wireless device of claim 15, wherein the operations further comprising:decreasing measurements frequency of the monitoring operations based on that the prediction accuracy of the inference function satisfy a second KPI.20.The wireless device of claim 15, wherein the operations further comprising:considering that the inference function is unstable based on that the prediction accuracy of the inference function do not satisfy a first performance KPI.21.The wireless device of claim 15, wherein the operations further comprising:considering that the inference function is stable based on that the prediction accuracy of the inference function satisfy a second performance KPI.22.The wireless device of claim 15, wherein the operations further comprising:increasing number of measurements for the at least one measurement object within a certain time period, based on considering that the inference function is unstable.23.The wireless device of claim 15, wherein the operations further comprising:decreasing number of measurements for the at least one measurement object within a certain time period, based on considering that the inference function is stable.24.The wireless device of claim 15, wherein the operations further comprising:transmitting, to the network, a message including information related to the adjusted resources for the monitoring operations.25.The wireless device of claim 24, wherein the operations further comprising:wherein the information related to the adjusted resources for the monitoring operations includes information related to adjusted measurements frequency of the monitoring operations.26.The wireless device of claim 24, wherein the operations further comprising:wherein the message includes information informing whether the inference function is considered as stable or unstable.27.The wireless device of claim 15, wherein the operations further comprising:wherein the measurement configuration includes information related to measurements number and / or measurements frequency for monitoring operations for deriving the prediction accuracy of the inference function.28.The wireless device of claim 15,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.29.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 measurement configuration including information related to at least one measurement object;performing predictions related to the at least one measurement object based on an inference function;performing monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object; andadjusting resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object.30.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 measurement configuration including information related to at least one measurement object;performing predictions related to the at least one measurement object based on an inference function;performing monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object; andadjusting resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object.31.A method, comprising,transmitting, by a base station to a wireless device, a measurement configuration including information related to at least one measurement object,wherein the wireless device performs predictions related to the at least one measurement object based on an inference function,wherein the wireless device performs monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object, andwherein the wireless device adjusts resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object; andreceiving, by the base station from the wireless device, a message including information related to the adjusted resources for the monitoring operations.32.A base station, comprising:a transceiver;a memory; anda processor operatively coupled to the transceiver and the memory, and adapted to perform operations, the operations comprising:transmitting, to a wireless device, a measurement configuration including information related to at least one measurement object,wherein the wireless device performs predictions related to the at least one measurement object based on an inference function,wherein the wireless device performs monitoring operations for deriving prediction accuracy of the inference function, based on i) measurement results for the at least one measurement object and ii) prediction results related to the at least one measurement object, andwherein the wireless device adjusts resources for the monitoring operations based on i) the prediction accuracy of the inference function or ii) the measurement results for the at least one measurement object; andreceiving, from the wireless device, a message including information related to the adjusted resources for the monitoring operations.

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