Method and apparatus for measurement prediction in wireless communication system

Abstract

A method and apparatus for measurement prediction in a wireless communication system are provided. A wireless device receives a measurement configuration including a measurement object from a network. A wireless device derives a first prediction for a measurement object. The wireless device sends information about the first prediction to the network. The wireless device derives a second prediction for the measurement object. Based on a difference between the first prediction and the second prediction, the wireless device transmits information related to the difference to the network.

Description

Technical Field

[0001] The present disclosure relates to methods and apparatus for measurement prediction in a wireless communication system. Background Art

[0002] The 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) is a technology that allows high-speed packet communications. Many solutions have been proposed for the LTE goal, including those aimed at reducing user and supplier costs, improving service quality, and expanding and improving coverage and system capacity. As upper layer requirements, 3GPP LTE requires reduced cost per bit, increased service availability, flexible use of frequency bands, simple structure, open interfaces, and appropriate power consumption of terminals.

[0003] The International Telecommunication Union (ITU) and 3GPP have begun work on developing requirements and specifications for New Radio (NR) systems. 3GPP must identify and develop the technical components required for successful standardization of new RATs that will meet both urgent market needs and longer-term requirements set forth by the ITU Radiocommunication Sector (ITU-R) International Mobile Telecommunications (IMT)-2020 process in a timely manner. In addition, NR should be able to use any spectrum band up to at least the 100 GHz range that can be used for wireless communications even in the more distant future.

[0004] The goal of NR is to be a single technology framework that addresses all use cases, requirements and deployment scenarios, including enhanced mobile broadband (eMBB), massive machine type communications (mMTC), ultra-reliable and low latency communications (URLLC), etc. NR should be inherently forward compatible. Summary of the invention

[0005] Technical issues

[0006] The application of AI / ML to wireless communications has been studied to improve the overall network and UE operational performance and the ability to provide various services. Using AI / ML, both the network and the UE can predict mobility and share the results to improve performance.

[0007] In 6G, the THz band can be used for a large amount of available bandwidth to meet the 6G requirements of Tbps data rates. However, in this high-frequency coverage, the cell coverage will be reduced and a large number of handovers will occur more frequently. This may result in handovers too early, too late, or to the wrong cell.

[0008] To improve mobility performance, the network and UE can use the predicted measurement results to find the appropriate time and suitable cell for handover. When the network instructs the UE to report a predicted measurement report, the UE can send a predicted measurement report of the cell for a future time. However, if the gap between the actual measurement and the predicted measurement is large, the network may make an incorrect decision about mobility. In order to notify the network of the wrong prediction, the UE needs to report another measurement report.

[0009] Therefore, there is a need for research on measurement prediction in wireless communication systems.

[0010] Technical Solution

[0011] In one aspect, a method performed by a wireless device in a wireless communication system is provided. The wireless device receives a measurement configuration including a measurement object from a network. The wireless device derives a first prediction for the measurement object. The wireless device sends information about the first prediction to the network. The wireless device derives a second prediction for the measurement object. Based on a difference between the first prediction and the second prediction, the wireless device sends information related to the difference to the network.

[0012] In another aspect, a device for implementing the above method is provided.

[0013] Technical Effects

[0014] The present disclosure may have various beneficial effects.

[0015] According to some embodiments of the present disclosure, a wireless device may efficiently perform measurement prediction while taking prediction errors into consideration.

[0016] For example, the network can identify whether the prediction measurement is reliable and the accuracy of the machine learning model. The network then reconfigures the AI / ML configuration (e.g., model) and parameters for the AI / ML.

[0017] In other words, the network can evaluate the reliability of the predicted measurements and the accuracy of the machine learning model. Based on this evaluation, the network can then adjust the AI / ML configuration (including the model and its parameters).

[0018] For example, by providing predictive measurements to the network, handover failures can be reduced.

[0019] For example, when the predicted measurement results undergo significant changes, the wireless device may improve the reliability of the prediction by providing updated predicted measurement results to the network.

[0020] According to some embodiments of the present disclosure, a wireless network system may provide an efficient solution for measurement.

[0021] The advantageous effects that can be obtained by the specific embodiments of the present disclosure are not limited to the advantageous effects listed above. For example, there may be various technical effects that a person of ordinary skill in the relevant field can understand and / or derive from the present disclosure. Therefore, the specific effects of the present disclosure are not limited to those explicitly described herein, but may include various effects that can be understood or derived from the technical features of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 An example of a communication system to which an implementation of the present disclosure is applied is shown.

[0023] Figure 2 An example of a wireless device to which an implementation of the present disclosure is applied is shown.

[0024] Figure 3 An example of a wireless device to which an implementation of the present disclosure is applied is shown.

[0025] Figure 4 Another example of a wireless device to which an implementation of the present disclosure is applied is shown.

[0026] Figure 5 An example of a UE to which an implementation of the present disclosure is applied is shown.

[0027] Figure 6 and Figure 7 An example of a protocol stack in a 3GPP-based wireless communication system to which an implementation of the present disclosure is applied is shown.

[0028] Figure 8 The frame structure in a 3GPP-based wireless communication system to which the implementation of the present disclosure is applied is shown.

[0029] Fig. 9 An example of data flow in a 3GPP NR system to which an implementation of the present disclosure is applied is shown.

[0030] Fig.10 An example of a functional framework for RAN intelligence is shown.

[0031] Fig.11 Examples of AI / ML model training in OAM and AI / ML model inference in NG-RAN nodes are shown.

[0032] Fig.12 An example of model training and model inference, both located in RAN nodes, is shown.

[0033] Fig.13 and Fig.14 Examples of neurons and neural network architectures are shown.

[0034] Fig.15An example of AI / ML inference is shown.

[0035] Fig.16 An example of an MLP DNN model is shown.

[0036] Fig.17 An example of a CNN model is shown.

[0037] Fig.18 An example of an RNN model is shown.

[0038] Fig.19 An example of reinforcement learning is shown.

[0039] Fig. 20 An example of a measurement report is shown.

[0040] Fig.21 An example of a method for measurement prediction in a wireless communication system according to some embodiments of the present disclosure is shown.

[0041] Fig. 22 , Fig.23 and Fig.24 An example of a criterion for determining a prediction error is shown.

[0042] Fig.25 and Fig.26 An example of a second measurement report based on a prediction error (gap) between a first measurement result and a second measurement result for a specific future time is shown.

[0043] Fig. 27 and Fig.28 An example of a second measurement report based on a predicted error (gap) between a first measurement result and a second measurement result for some future time is shown.

[0044] Fig.29 An example of a second measurement report when the second predicted measurement result does not satisfy the reporting condition for a specific future time is shown.

[0045] Fig.30 An example of a second measurement report when the second predicted measurement result does not satisfy the reporting condition for a certain future time is shown. DETAILED DESCRIPTION

[0046] The following techniques, devices and systems can be applied to various wireless multiple access systems. Examples of multiple access systems include code division multiple access (CDMA) systems, frequency division multiple access (FDMA) systems, time division multiple access (TDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single carrier frequency division multiple access (SC-FDMA) systems, and multi-carrier frequency division multiple access (MC-FDMA) systems. CDMA can be implemented by radio technologies such as Universal Terrestrial Radio Access (UTRA) or CDMA2000. TDMA can be implemented by radio technologies such as Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), or Enhanced Data Rates for GSM Evolution (EDGE). OFDMA can be implemented by radio technologies such as Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE802.16 (WiMAX), IEEE 802.20, or Evolved UTRA (E-UTRA). UTRA is part of 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 adopts OFDMA in DL and SC-FDMA in UL. LTE-Advanced (LTE-A) is an evolved version of 3GPP LTE.

[0047] For ease of description, the implementation of the present disclosure is mainly described with respect 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 a 3GPP-based wireless communication system are applicable to other mobile communication systems.

[0048] For terms and techniques not specifically described in the terms and techniques adopted in the present disclosure, reference may be made to wireless communication standard documents issued prior to the present disclosure.

[0049] In the present disclosure, "A or B" may mean "only A", "only B", or "both A and B". In other words, in the present disclosure, "A or B" may be interpreted as "A and / or B". For example, in the present disclosure, "A, B or C" may mean "only A", "only B", "only C", or "any combination of A, B, and C".

[0050] In the present disclosure, a slash ( / ) or a comma (,) may mean "and / or". For example, "A / B" may mean "A and / or B". Thus, "A / B" may mean "only A", "only B", or "both A and B". For example, "A, B, C" may mean "A, B, or C".

[0051] 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 being the same as "at least one of A and B".

[0052] 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".

[0053] In addition, the brackets 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, in the present disclosure, "control information" is not limited to "PDCCH", and "PDCCH" may be proposed as an example of "control information". In addition, even when it is shown as "control information (ie, PDCCH)", "PDCCH" may be proposed as an example of "control information".

[0054] The technical features described separately in one figure in the present disclosure can be implemented separately or simultaneously.

[0055] Although not limited thereto, the various descriptions, functions, processes, suggestions, methods and / or operational flowcharts of the present disclosure disclosed herein may be applied to various fields requiring wireless communication and / or connection between devices (e.g., 5G).

[0056] Hereinafter, the present disclosure will be described in more detail with reference to the accompanying drawings. Unless otherwise specified, 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.

[0057] Figure 1 An example of a communication system to which an implementation of the present disclosure is applied is shown.

[0058] exist Figure 1 The 5G usage scenarios shown in the present disclosure are only exemplary, and the technical features of the present disclosure may be applied to Figure 1 Other 5G usage scenarios shown in .

[0059] The three main demand categories for 5G include: (1) enhanced mobile broadband (eMBB) category, (2) massive machine type communication (mMTC) category, and (3) ultra-reliable and low-latency communication (URLLC) category.

[0060] Some use cases may require multiple categories for optimization, and other use cases may focus on only one key performance indicator (KPI). 5G supports such various use cases using a flexible and reliable approach.

[0061] eMBB goes far beyond basic mobile Internet access and covers rich two-way work and media and entertainment applications in the cloud and augmented reality. Data is one of the core driving forces of 5G, and in the 5G era, dedicated voice services may not be provided for the first time. In 5G, it is expected that voice will be simply processed as an application using a data connection provided by the communication system. The main reason for increasing business capacity is due to the increase in content size and the increase in the number of applications requiring high data transfer rates. As more and more devices are connected to the Internet, streaming services (audio and video), conversational video, and mobile Internet access will be more widely used. Many of these applications require a connection that is always on in order to push real-time information and alerts to users. Cloud storage and applications are rapidly increasing in mobile communication platforms and can be applied to both work and entertainment. Cloud storage is a special use case that accelerates the growth of uplink data transfer rates. 5G is also used for remote work in the cloud. When using a tactile interface, 5G requires much lower end-to-end latency to maintain a good user experience. Entertainment, such as cloud gaming and video streaming, is another core element that increases the demand for mobile broadband capabilities. Entertainment is essential for smartphones and tablets anywhere including high mobility environments such as trains, vehicles and airplanes. Other use cases are augmented reality for entertainment and information search. In this case, augmented reality requires very low latency and instantaneous data capacity.

[0062] Additionally, one of the most anticipated 5G use cases involves the ability to smoothly connect embedded sensors in all fields, i.e., mMTC. It is expected that the number of potential Internet of Things (IoT) devices will reach 204 billion by 2020. Industrial IoT is one of the categories that performs a major role in enabling smart cities, asset tracking, smart utilities, agriculture, and security infrastructure through 5G.

[0063] URLLC includes remote control and ultra-reliable / available low-latency links over the main infrastructure that will transform new services in industry (such as autonomous vehicles). The level of reliability and latency is necessary to control smart grids, automate industry, enable robotics, and control and adjust drones.

[0064] 5G is a means of providing streams estimated at hundreds of megabits per second to gigabits per second, and can complement fiber to the home (FTTH) and cable-based broadband (or DOCSIS). Such fast speeds are needed to deliver TV at a 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. Specific applications may require special network configurations. For example, for VR games, game companies need to merge core servers into the network operator's edge network servers to minimize latency.

[0065] It is expected that cars, together with many use cases for mobile communications for vehicles, are new and important motivating forces in 5G. For example, the entertainment of passengers requires high simultaneous capacity and mobile broadband with high mobility. This is because future users continue to expect high-quality connections, regardless of their location and speed. Another use case in the automotive field is the AR dashboard. The AR dashboard enables the driver to identify objects in the dark in addition to the objects seen from the front window, and displays the distance to the object and the movement of the object by overlapping the information told by the driver. In the future, the wireless module realizes the communication between vehicles, the information exchange between the vehicle and the supporting infrastructure, and the information exchange between the vehicle and other connected devices (e.g., devices accompanied by pedestrians). The safety system guides the alternative route of the behavior so that the driver can drive more safely, thereby reducing the risk of accidents. The next stage will be remotely controlled or self-driving vehicles. This requires very high reliability and very fast communication between different self-driving vehicles and between vehicles and infrastructure. In the future, self-driving vehicles will perform all driving activities, and the driver will only focus on abnormal traffic that the vehicle cannot recognize. The technical requirements of self-driving vehicles require ultra-low latency and ultra-high reliability, so that traffic safety is increased to a level that cannot be achieved by humans.

[0066] Smart cities and smart homes / buildings, referred to as smart societies, will be embedded in high-density wireless sensor networks. Distributed networks of smart sensors will identify conditions for cost and energy efficient maintenance of cities or homes. Similar configurations can be performed for corresponding homes. All temperature sensors, window and heating controllers, burglar alarms and home appliances are wirelessly connected. Many of these sensors are typically low in data transfer rate, power and cost. However, certain types of devices may require real-time HD video to perform monitoring.

[0067] The consumption and distribution of energy, including heat or gas, is distributed at a higher level, making it necessary to automatically control the distribution sensor network. Smart grids collect information and use digital information and communication technologies to connect sensors to each other, so as to act according to the collected information. Since this information can include the behavior of supply companies and consumers, smart grids can improve the distribution of fuels such as electricity by methods with efficiency, reliability, economic feasibility, production sustainability and automation. Smart grids can also be considered as another sensor network with low latency.

[0068] Mission-critical applications (e.g., e-health) are one of the 5G use cases. The health section includes many applications that can enjoy the benefits of mobile communications. Communication systems can support telemedicine that provides clinical treatment in remote locations. Telemedicine can help reduce barriers to distance and improve access to medical services that are not continuously available in remote rural areas. Telemedicine is also used to perform important treatments and save lives in emergency situations. Wireless sensor networks based on mobile communications can provide remote monitoring and sensors for parameters such as heart rate and blood pressure.

[0069] Wireless and mobile communications are becoming increasingly important in the field of industrial applications. Cabling is expensive in terms of installation and maintenance. Therefore, the possibility of replacing cables with reconfigurable wireless links is an attractive opportunity in many industrial fields. However, in order to achieve this replacement, wireless connections need to be established with latency, reliability and capacity similar to cables, and the management of wireless connections needs to be simplified. When it comes to connecting to 5G, low latency and very low error probability are new requirements.

[0070] Logistics and freight tracking are important use cases for mobile communications, which allow inventory and packages to be tracked anywhere using location-based information systems. The use cases for logistics and freight tracking typically require low data rates but require location information with wide range and reliability.

[0071] Reference Figure 1 , the communication system 1 includes wireless devices 100a to 100f, a base station (BS) 200, and a network 300. Figure 1 A 5G network is illustrated as an example of a network of the communication system 1, but implementations of the present disclosure are not limited to the 5G system and may be applied to future communication systems other than the 5G system.

[0072] BS 200 and network 300 may be implemented as wireless devices, and a specific wireless device may operate as a BS / network node relative to other wireless devices.

[0073] The wireless devices 100a to 100f represent devices that perform communication using a radio access technology (RAT) (e.g., 5G new RAT (NR) or LTE), and may be referred to as communication / wireless / 5G devices. The wireless devices 100a to 100f may include, but are not limited to, a robot 100a, vehicles 100b-1 and 100b-2, an extended reality (XR) device 100c, a handheld device 100d, a home appliance 100e, an IoT device 100f, and an artificial intelligence (AI) device / server 400. For example, a vehicle may include a vehicle with a wireless communication function, an autonomous vehicle, and a vehicle capable of performing communication between vehicles. A vehicle 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) installed in a vehicle, a television, a smart phone, a computer, a wearable device, a home appliance device, a digital signage, a vehicle, a robot, and the like. Handheld devices may include smart phones, smart tablets, wearable devices (e.g., smart watches or smart glasses), and computers (e.g., notebooks). Home appliances may include TVs, refrigerators, and washing machines. IoT devices may include sensors and smart meters.

[0074] In the present disclosure, the wireless devices 100a to 100f may be referred to as user equipment (UE). For example, the UE may include a cellular phone, a smart phone, a laptop computer, a digital broadcast terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation system, a plate-shaped personal computer (PC), a tablet PC, an ultrabook, a vehicle, a vehicle with an autonomous driving function, a connected car, a 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 / environmental device, a device related to 5G services, or a device related to the fourth industrial evolution field.

[0075] A UAV may be, for example, an aerial vehicle that is piloted by wireless control signals without a human on board.

[0076] VR devices may include, for example, devices for realizing objects or backgrounds of a virtual world. AR devices may include, for example, devices realized by connecting objects or backgrounds of a virtual world to objects or backgrounds of a real world. MR devices may include, for example, devices realized by merging objects or backgrounds of a virtual world into objects or backgrounds of a real world. Hologram devices may include, for example, devices for realizing a 360-degree stereoscopic image by recording and reproducing stereoscopic information, which uses the interference phenomenon of light generated when two lasers meet, which is called holographic imaging.

[0077] Public safety devices may include, for example, image relay devices or image devices wearable on a user's body.

[0078] MTC devices and IoT devices may be devices that do not require direct human intervention or manipulation, for example. For example, MTC devices and IoT devices may include smart meters, vending machines, thermometers, smart light bulbs, door locks, or various sensors.

[0079] The medical device may be, for example, a device for the purpose of diagnosing, treating, alleviating, curing or preventing a disease. For example, the medical device may be a device for the purpose of diagnosing, treating, alleviating or correcting an injury or harm. For example, the medical device may be a device for the purpose of inspecting, replacing or modifying a structure or function. For example, the medical device may be a device for the purpose of regulating 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 surgery.

[0080] The safety device may be, for example, a device installed to prevent possible danger and maintain safety. For example, the safety device may be a camera, a closed-circuit TV (CCTV), a recorder, or a black box.

[0081] Fintech devices may be devices that can provide financial services such as mobile payments, for example. For example, Fintech devices may include payment devices or point-of-sale (POS) systems.

[0082] Weather / environmental devices may include, for example, devices for monitoring or predicting weather / environmental conditions.

[0083] The wireless devices 100a to 100f may be connected to the network 300 via the BS 200. 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 super 5G network. Although the wireless devices 100a to 100f may communicate with each other via the BS 200 / network 300, the wireless devices 100a to 100f may perform direct communication (e.g., side link communication) with each other without passing through the BS 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). An IoT device (e.g., a sensor) may perform direct communication with other IoT devices (e.g., a sensor) or other wireless devices 100a to 100f.

[0084] Wireless communication / connection 150a, 150b, and 150c may be established between wireless devices 100a to 100f and / or between wireless devices 100a to 100f and BS200 and / or between BS200. In this document, wireless communication / connection 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. Wireless devices 100a to 100f and BS200 / wireless devices 100a to 100f may send / receive radio signals to each other through wireless communication / connection 150a, 150b, and 150c. For example, wireless communication / connection 150a, 150b, and 150c may send / receive signals through various physical channels. To this end, various configuration information configuration processes for sending / receiving radio signals, various signal processing processes (e.g., channel coding / decoding, modulation / demodulation, and resource mapping / demapping), and at least a portion of the resource allocation process can be performed based on various proposals of the present disclosure.

[0085] Here, the radio communication technology implemented in the wireless device 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, which may be implemented in specifications such as LTE Cat NB1 and / or LTE Cat NB2, and may not be limited to the above names. Additionally and / or alternatively, the radio communication technology implemented in the wireless device in the present disclosure may communicate based on LTE-M technology. For example, LTE-M technology may be an example of LPWAN technology, and may be referred to as various names such as enhanced machine type communication (eMTC). For example, LTE-M technology may be implemented in at least one of 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 names. Additionally and / or alternatively, the radio communication technology implemented in the wireless device in the present disclosure may include at least one of ZigBee, Bluetooth, and / or LPWAN considered as low-power communication, and may not be limited to the above names. For example, ZigBee technology may generate a personal area network (PAN) associated with small / low-power digital communication based on various specifications such as IEEE 802.15.4, and may be referred to as various names.

[0086] Figure 2An example of a wireless device to which an implementation of the present disclosure is applied is shown.

[0087] Reference Figure 2 , the first wireless device 100 and the second wireless device 200 can transmit / receive radio signals to / from an external device through various RATs (eg, LTE and NR). Figure 2 In the example, {the first wireless device 100 and the second wireless device 200} may correspond to the attached Figure 1 At least one of {wireless devices 100a to 100f and BS200}, {wireless devices 100a to 100f and wireless devices 100a to 100f} and / or {BS200 and BS200}.

[0088] The first wireless device 100 may include one or more processors 102 and one or more memories 104, and further include one or more transceivers 106 and / or one or more antennas 108. The processor 102 may control the memory 104 and / or the transceiver 106, and may be configured to implement the descriptions, functions, processes, suggestions, methods, and / or operational flowcharts described in the present disclosure. For example, the processor 102 may process the information in the memory 104 to generate first information / signals, and then transmit a radio signal including the first information / signals through the transceiver 106. The processor 102 may receive a radio signal including second information / signals through the transceiver 106, and then store information obtained by processing the second information / signals in the memory 104. The memory 104 may be connected to the processor 102 and may store various information related to the operation of the processor 102. For example, the memory 104 may store software codes including commands for executing part or all of the processes controlled by the processor 102 or for executing the descriptions, functions, processes, suggestions, methods, and / or operational flowcharts described in the present disclosure. In this document, the processor 102 and the memory 104 may be part of a communication modem / circuit / chip designed to implement a RAT (e.g., LTE or NR). The transceiver 106 may be connected to the processor 102 and transmit and / or receive radio signals through one or more antennas 108. Each of the transceivers 106 may include a transmitter and / or a receiver. The transceiver 106 may be used interchangeably with a radio frequency (RF) unit. In the present disclosure, the first wireless device 100 may represent a communication modem / circuit / chip.

[0089] The second wireless device 200 may include one or more processors 202 and one or more memories 204, and further include one or more transceivers 206 and / or one or more antennas 208. The processor 202 may control the memory 204 and / or the transceiver 206, and may be configured to implement the descriptions, functions, processes, suggestions, methods, and / or operation flowcharts described in the present disclosure. For example, the processor 202 may process the information in the memory 204 to generate third information / signals, and then transmit a radio signal including the third information / signals through the transceiver 206. The processor 202 may receive a radio signal including fourth information / signals through the transceiver 106, and then store information obtained by processing the fourth information / signals in the memory 204. The memory 204 may be connected to the processor 202 and may store various information related to the operation of the processor 202. For example, the memory 204 may store software codes including commands for executing part or all of the processes controlled by the processor 202 or for executing the descriptions, functions, processes, suggestions, methods, and / or operation flowcharts described in the present disclosure. Herein, the processor 202 and the memory 204 may be part of a communication modem / circuit / chip designed to implement a RAT (e.g., LTE or NR). The transceiver 206 may be connected to the processor 202 and transmit and / or receive radio signals through one or more antennas 208. Each of the transceivers 206 may include a transmitter and / or a receiver. The transceiver 206 may be used interchangeably with an RF unit. In the present disclosure, the second wireless device 200 may represent a communication modem / circuit / chip.

[0090] In the following, the hardware elements of the wireless devices 100 and 200 will be described in more detail. One or more protocol layers may be implemented by, but not limited to, one or more processors 102 and 202. For example, one or more processors 102 and 202 may implement one or more layers (e.g., functional layers such as a physical (PHY) layer, a medium access control (MAC) layer, a radio link control (RLC) layer, a packet data convergence protocol (PDCP) layer, a radio resource control (RRC) layer, and a service data adaptation protocol (SDAP) layer). According to the description, function, process, suggestion, method, and / or operation flowchart disclosed in the present disclosure, one or more processors 102 and 202 may generate one or more protocol data units (PDUs) and / or one or more service data units (SDUs). One or more processors 102 and 202 may generate messages, control information, data, or information according to the description, function, process, suggestion, method, and / or operation flowchart disclosed in the present disclosure. One or more processors 102 and 202 may generate a signal (e.g., a baseband signal) including a PDU, SDU, message, control information, data, or information according to the description, function, process, suggestion, method, and / or operational flowchart disclosed in the present disclosure, and provide the generated signal to one or more transceivers 106 and 206. One or more processors 102 and 202 may receive a signal (e.g., a baseband signal) from one or more transceivers 106 and 206, and obtain the PDU, SDU, message, control information, data, or information according to the description, function, process, suggestion, method, and / or operational flowchart disclosed in the present disclosure.

[0091] One or more processors 102 and 202 may be referred to as controllers, microcontrollers, microprocessors, or microcomputers. 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 one or more processors 102 and 202. The descriptions, functions, processes, suggestions, methods, and / or operational flow charts disclosed in the present disclosure may be implemented using firmware or software, and the firmware or software may be configured to include modules, processes, or functions. Firmware or software configured to execute the descriptions, functions, processes, suggestions, methods, and / or operational flow charts disclosed in the present disclosure may be included in one or more processors 102 and 202, or stored in one or more memories 104 and 204, thereby being driven by one or more processors 102 and 202. The descriptions, functions, processes, suggestions, methods and / or operational flowcharts disclosed in the present disclosure may be implemented using firmware or software in the form of codes, commands and / or command sets.

[0092] One or more memories 104 and 204 may be connected to one or more processors 102 and 202 and store various types of data, signals, messages, information, programs, codes, instructions, and / or commands. One or more memories 104 and 204 may be configured by read-only memory (ROM), random access memory (RAM), electrically erasable programmable read-only memory (EPROM), flash memory, hard drive, register, cache memory, computer-readable storage medium, and / or a combination thereof. One or more memories 104 and 204 may be located inside and / or outside of one or more processors 102 and 202. One or more memories 104 and 204 may be connected to one or more processors 102 and 202 by various technologies such as wired or wireless connections.

[0093] One or more transceivers 106 and 206 can send user data, control information and / or radio signals / channels mentioned in the description, function, process, suggestion, method and / or operation flow chart disclosed in the present disclosure to one or more other devices. One or more transceivers 106 and 206 can receive user data, control information and / or radio signals / channels mentioned in the description, function, process, suggestion, method and / or operation flow chart disclosed in the present disclosure from one or more other devices. For example, one or more transceivers 106 and 206 can be connected to one or more processors 102 and 202 and send and receive radio signals. For example, one or more processors 102 and 202 can perform control so that one or more transceivers 106 and 206 can send user data, control information or radio signals to one or more other devices. One or more processors 102 and 202 can perform control so that one or more transceivers 106 and 206 can receive user data, control information or radio signals from one or more other devices.

[0094] One or more transceivers 106 and 206 may be connected to one or more antennas 108 and 208, and 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, processes, suggestions, methods, and / or operational flow charts disclosed in the present disclosure through one or more antennas 108 and 208. In the present disclosure, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports).

[0095] One or more transceivers 106 and 206 can convert the received radio signal / channel, etc. from the RF band signal to the baseband signal in order to process the received user data, control information, radio signal / channel, etc. One or more transceivers 106 and 206 can convert the user data, control information, radio signal / channel, etc. processed by one or more processors 102 and 202 from the baseband signal to the RF band signal. To this end, one or more transceivers 106 and 206 may include (analog) oscillators and / or filters. For example, transceivers 106 and 206 can up-convert the OFDM baseband signal to the carrier frequency through their (analog) oscillators and / or filters under the control of processors 102 and 202, and send the up-converted OFDM signal of the carrier frequency. Transceivers 106 and 206 can receive the OFDM signal of the carrier frequency, and down-convert the OFDM signal to the OFDM baseband signal through their (analog) oscillators and / or filters under the control of processors 102 and 202.

[0096] In an implementation of the present disclosure, a UE may operate as a transmitting device in an uplink (UL) and as a receiving device in a downlink (DL). In an implementation of the present disclosure, a BS may operate as a receiving device in an UL and as a transmitting device in a DL. Hereinafter, for the convenience of description, it is mainly assumed that the first wireless device 100 acts as a UE and the second wireless device 200 acts as a BS. For example, a processor 102 connected to the first wireless device 100, installed on the first wireless device 100, or started in the first wireless device 100 may be configured to perform UE behavior according to an implementation of the present disclosure, or control the transceiver 106 to perform UE behavior according to an implementation of the present disclosure. A processor 202 connected to the second wireless device 200, installed on the second wireless device 200, or started in the second wireless device 200 may be configured to perform BS behavior according to an implementation of the present disclosure, or control the transceiver 206 to perform BS behavior according to an implementation of the present disclosure.

[0097] In the present disclosure, the BS is also referred to as a Node B (NB), an eNodeB (eNB) or a gNB.

[0098] Figure 3 An example of a wireless device to which an implementation of the present disclosure is applied is shown.

[0099] The wireless device may be implemented in various forms depending on the use case / service (see Figure 1 ).

[0100] Reference Figure 3 , the wireless devices 100 and 200 may correspond to Figure 2 The wireless devices 100 and 200 of the present invention may be configured by various elements, components, units / parts 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 an additional component 140. The communication unit 110 may include a communication circuit 112 and a transceiver 114. For example, the communication circuit 112 may include Figure 2 One or more processors 102 and 202 and / or Figure 2 One or more memories 104 and 204. For example, the transceiver 114 may include Figure 2 One or more transceivers 106 and 206 and / or Figure 2The control unit 120 is electrically connected to the communication unit 110, the memory unit 130, and the additional components 140, and controls the overall operation of each of the wireless devices 100 and 200. For example, the control unit 120 may control the electrical / mechanical operation of each of the wireless devices 100 and 200 based on the program / code / command / information stored in the memory unit 130. The control unit 120 may transmit information stored in the memory unit 130 to the outside (e.g., other communication devices) through a wireless / wired interface via the communication unit 110, or store information received from the outside (e.g., other communication devices) through a wireless / wired interface in the memory unit 130 via the communication unit 110.

[0101] The additional component 140 may be configured differently depending on the type of the wireless device 100 and 200. For example, the additional component 140 may include at least one of a power supply unit / battery, an input / output (I / O) unit (e.g., an audio I / O port, a video I / O port), a driving unit, and a computing unit. The wireless device 100 and 200 may be in the form of, but not limited to, a robot ( Figure 1 100a), vehicles ( Figure 1 100b-1 and 100b-2), XR devices ( Figure 1 100c), handheld device ( Figure 1 100d), household appliances ( Figure 1 100e), IoT devices ( Figure 1 100f), digital broadcast terminal, hologram device, public safety device, MTC device, medical device, Fintech device (or financial device), security device, climate / environmental device, AI server / device ( Figure 1 400), BSS( Figure 1 The wireless devices 100 and 200 may be implemented in the form of a wireless device 200 , a network node, etc. The wireless devices 100 and 200 may be used in a mobile or fixed location depending on the usage example / service.

[0102] exist Figure 3In the wireless devices 100 and 200, the entirety of the various elements, components, units / parts and / or modules in the wireless devices 100 and 200 may be connected to each other through a wired interface, or at least a portion 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 the first unit (e.g., 130 and 140) may be wirelessly connected through the communication unit 110. Each element, component, unit / part and / or module within the wireless devices 100 and 200 may also include one or more elements. For example, the control unit 120 may be configured by a group of one or more processors. As an example, the control unit 120 may be configured by a group of communication control processors, application processors (APs), electronic control units (ECUs), graphics processing units, and memory control processors. As another example, the memory unit 130 may be configured by RAM, DRAM, ROM, flash memory, volatile memory, non-volatile memory, and / or a combination thereof.

[0103] Figure 4 Another example of a wireless device to which an implementation of the present disclosure is applied is shown.

[0104] Reference Figure 4 , the wireless devices 100 and 200 may correspond to Figure 2 The wireless devices 100 and 200 may be configured by various elements, components, units / portions and / or modules.

[0105] 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 as a processor 102 and at least one memory such as a memory 104. The memory 104 may be operably connected to the processor 102. The memory 104 may store various types of information and / or instructions. The memory 104 may store software code 105, which implements instructions for executing the description, function, process, suggestion, method and / or operation flow chart disclosed in the present disclosure when executed by the processor 102. For example, the software code 105 may implement instructions for executing the description, function, process, suggestion, method and / or operation flow chart disclosed in the present disclosure when executed by the processor 102. For example, the software code 105 may control the processor 102 to execute one or more protocols. For example, the software code 105 may control the processor 102 to execute one or more layers of the radio interface protocol.

[0106] The second wireless device 200 may include at least one transceiver such as the transceiver 206 and at least one processing chip such as the processing chip 201. The processing chip 201 may include at least one processor such as the processor 202 and at least one memory such as the memory 204. The memory 204 may be operably connected to the processor 202. The memory 204 may store various types of information and / or instructions. The memory 204 may store software code 205, which implements instructions for executing the description, function, process, suggestion, method and / or operation flow chart disclosed in the present disclosure when executed by the processor 202. For example, the software code 205 may implement instructions for executing the description, function, process, suggestion, method and / or operation flow chart disclosed in the present disclosure when executed by the processor 202. For example, the software code 205 may control the processor 202 to execute one or more protocols. For example, the software code 205 may control the processor 202 to execute one or more layers of the wireless interface protocol.

[0107] Figure 5 An example of a UE to which an implementation of the present disclosure is applied is shown.

[0108] Reference Figure 5 , UE 100 may correspond to the attached Figure 2 The first wireless device 100 and / or Figure 4 A first wireless device 100 is provided.

[0109] 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 identity module (SIM) card 118 , a speaker 120 , and a microphone 122 .

[0110] The processor 102 may be configured to implement the descriptions, functions, processes, suggestions, methods and / or operational flow charts 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, processes, suggestions, methods and / or operational flow charts disclosed in the present disclosure. The layers of the radio interface protocol may be implemented in the processor 102. The processor 102 may include an ASIC, other chipsets, logic circuits and / or data processing devices. 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). Examples of the processor 102 can be found at MANUFACTURED BY SNAPDRAGON TM Series processors, Manufactured by EXYNOSTM Series processors, A series processors manufactured by Made by HELIO TM Series processors, ATOM manufactured TM series processors or corresponding to the next generation processors.

[0111] The memory 104 is operatively coupled to 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 devices. When the embodiment is implemented in software, the techniques described herein may be implemented with modules (e.g., processes, functions, etc.) that execute the descriptions, functions, processes, suggestions, methods, and / or operational flow charts disclosed in the present disclosure. The modules may be stored in the memory 104 and executed by the processor 102. The memory 104 may be implemented within the processor 102 or outside the processor 102, in which case the memory 104 may be communicatively coupled to the processor 102 via various means known in the art.

[0112] The transceiver 106 is operatively coupled to the processor 102 and transmits and / or receives radio signals. The transceiver 106 includes a transmitter and a receiver. The transceiver 106 may include a baseband circuit for processing radio frequency signals. The transceiver 106 controls one or more antennas 108 to transmit and / or receive radio signals.

[0113] The power management module 110 manages power to the processor 102 and / or the transceiver 106. The battery 112 provides power to the power management module 110.

[0114] The display 114 outputs the result processed by the processor 102. The keypad 116 receives input to be used by the processor 102. The keypad 116 may be displayed on the display 114.

[0115] The SIM card 118 is an integrated circuit designed to securely store an International Mobile Subscriber Identity (IMSI) number and its associated keys, which are used to identify and authenticate subscribers on mobile telephony devices such as mobile phones and computers. Contact information can also be stored on many SIM cards.

[0116] Speaker 120 outputs sound related results processed by processor 102. Microphone 122 receives sound related input to be used by processor 102.

[0117] Figure 6 and Figure 7 An example of a protocol stack in a 3GPP-based wireless communication system to which an implementation of the present disclosure is applied is shown.

[0118] Specifically, Figure 6 An example of a radio interface user plane protocol stack between a UE and a BS is illustrated, and Figure 7 An example of a radio interface control plane protocol stack between a UE and a BS is illustrated. The control plane refers to a path through which control messages for managing calls made between a UE and a network are transmitted. The user plane refers to a path through which data generated in an application layer (e.g., voice data or Internet packet data) is transmitted. Figure 6 , the user plane protocol stack can be divided into layer 1 (ie, PHY layer) and layer 2. Figure 7 , the control plane protocol stack can be divided into layer 1 (ie, PHY layer), layer 2, layer 3 (eg, RRC layer), and non-access stratum (NAS) layer. Layer 1, layer 2, and layer 3 are called access stratum (AS).

[0119] In the 3GPP LTE system, Layer 2 is separated into the following sublayers: MAC, RLC, and PDCP. In the 3GPP NR system, Layer 2 is separated into the following sublayers: MAC, RLC, PDCP, and SDAP. The PHY layer provides transport channels to the MAC sublayer, the MAC sublayer provides logical channels to the RLC sublayer, the RLC sublayer provides RLC channels to the PDCP sublayer, and the PDCP sublayer provides radio bearers to the SDAP sublayer. The SDAP sublayer provides Quality of Service (QoS) flows to the 5G core network.

[0120] In the 3GPP NR system, the main services and functions of the MAC sublayer include: mapping between logical channels and transport channels; multiplexing / demultiplexing MAC SDUs belonging to one or different logical channels to / from transport blocks (TBs) on transport channels for delivery to / from the physical layer; scheduling information reporting; error correction through hybrid automatic repeat request (HARQ) (one HARQ entity per cell in the case of carrier aggregation (CA); priority handling between UEs through dynamic scheduling; priority handling between logical channels of one UE through logical channel prioritization; padding. A single MAC entity can support multiple parameter sets, transmission timings, and cells. The mapping restrictions in the logical channel prioritization control which parameter set(s), cell, and transmission timing can be used by the logical channel.

[0121] MAC provides different kinds of data transmission services. In order to accommodate different kinds of data transmission services, multiple types of logical channels are defined, that is, each logical channel supports the transmission of a specific type of information. Each logical channel type is defined by what type of information is transmitted. Logical channels are divided into two groups: control channels and traffic channels. Control channels are used only for the transmission of control plane information, and traffic channels are used only for the transmission of user plane information. The broadcast control channel (BCCH) is a downlink logical channel for broadcasting system control information, the paging control channel (PCCH) is a downlink logical channel for transmitting paging information, system information change notifications, and indications of ongoing public warning services (PWS) broadcasts, the common control channel (CCCH) is a logical channel used to send control information between the UE and the network and is used by UEs that do not have an RRC connection with the network, and the dedicated control channel (DCCH) is a point-to-point bidirectional logical channel used by UEs with RRC connections to send dedicated control information between the UE and the network. The dedicated traffic channel (DTCH) is a point-to-point logical channel dedicated to one UE for transmitting user information. DTCH can exist in both the uplink and downlink. In the downlink, there are the following connections between logical channels and transport channels: BCCH can be mapped to the broadcast channel (BCH); BCCH can be mapped to the downlink shared channel (DL-SCH); PCCH can be mapped to the 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 the uplink, there are the following connections between logical channels and transport channels: CCCH can be mapped to the uplink shared channel (UL-SCH); DCCH can be mapped to UL-SCH; and DTCH can be mapped to UL-SCH.

[0122] The RLC sublayer supports three transmission modes: transparent mode (TM), unacknowledged mode (UM) and acknowledged mode (AM). The RLC configuration is per logical channel without dependency on parameter sets and / or transmission duration. In 3GPP NR systems, the main services and functions of the RLC sublayer depend on the transmission mode and include: delivery of upper layer PDUs; sequence numbering independent of sequence numbering 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 SDUs (AM and UM); duplicate detection (AM only); RLC SDU discard (AM and UM); RLC re-establishment; protocol error detection (AM).

[0123] 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); transmission of user data; reordering and duplicate detection; in-sequence delivery; PDCP PDU routing (in the case of split bearers); retransmission of PDCP SDU; ciphering, deciphering and integrity protection; PDCP SDU discard; PDCP re-establishment and data recovery for RLC AM; PDCP status reporting for RLC AM; PDCP PDU duplication 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; transmission of control plane data; reordering and duplicate detection; in-sequence delivery; PDCP PDU duplication and duplicate discard indication to lower layers.

[0124] In 3GPP NR system, the main services and functions of SDAP include: mapping between QoS flows and data radio bearers; marking QoS flow ID (QFI) in both DL and UL packets. A single protocol entity of SDAP is configured for each individual PDU session.

[0125] 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 RRC connection between 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 reporting; detection and recovery of radio link failures; NAS message transmission from UE to NAS / from NAS to UE.

[0126] Figure 8 The frame structure in a 3GPP-based wireless communication system to which the implementation of the present disclosure is applied is shown.

[0127] Figure 8The frame structure shown in is merely exemplary, and the number of subframes, the number of time slots, and / or the number of symbols in a frame may vary. In a 3GPP-based wireless communication system, OFDM parameter sets (e.g., subcarrier spacing (SCS), transmission time interval (TTI) duration) may be configured differently between multiple cells aggregated for one UE. For example, if the UE is configured with different SCSs for cells aggregated for the cell, the (absolute time) duration of time resources (e.g., subframes, time slots, or TTIs) including the same number of symbols may be different among the aggregated cells. In this article, the symbol may include an OFDM symbol (or CP-OFDM symbol), an SC-FDMA symbol (or a discrete Fourier transform-spread-OFDM (DFT-s-OFDM) symbol).

[0128] Reference Figure 8 , downlink and uplink transmissions are organized into frames. Each frame has T f =10m s Each frame is divided into two half-frames, each half-frame has 5 ms Duration. Each half frame consists of 5 subframes, where the duration of each subframe is T sf is 1 ms. Each subframe is divided into slots, and the number of slots in a subframe depends on the 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 parameter set is based on an exponentially scalable subcarrier spacing Δf=2 u *15kHz.

[0129] Table 1 shows the subcarrier spacing according to Δf=2 u *N is the number of OFDM symbols per time slot of 15kHz slot symb , the number of time slots per frame N frame,u slot , and the number of time slots N per subframe for normal CP subframe,u slot .

[0130] [Table 1]

[0131] u <![CDATA[N slot symb ]]> <![CDATA[N frame,u slot ]]> <![CDATA[N subframe，u slot ]]> 0 14 10 1 1 14 20 2 2 14 40 4 3 14 80 8 4 14 160 16

[0132] Table 2 shows the subcarrier spacing Δf=2 u *N is the number of OFDM symbols per time slot of 15kHz slot symb , the number of time slots per frame N frame,uslot , and the number of time slots N per subframe for the extended CP subframe,u slot .

[0133] [Table 2]

[0134] u <![CDATA[N slot symb ]]> <![CDATA[N frame，u slot ]]> <![CDATA[N subframe,u slot ]]> 2 12 40 4

[0135] A slot includes a plurality of symbols (e.g., 14 or 12 symbols) in the time domain. For each parameter set (e.g., subcarrier spacing) and carrier, a common resource block (CRB) N indicated by higher layer signaling (e.g., RRC signaling) is allocated. start,u grid To begin, we define N size,u grid,x *N RB sc subcarriers and N subframe,u symb OFDM symbol resource grid, where N size,u grid,x is the number of resource blocks (RBs) in the resource grid, and the subscript x is DL for the downlink and UL for the uplink. N RB sc is the number of subcarriers per RB. In 3GPP-based wireless communication systems, N RB sc Typically 12. For a given antenna port p, subcarrier spacing configuration u, and transmission direction (DL or UL), there is one resource grid. The carrier bandwidth N for subcarrier spacing configuration u size,u grid Given by high-level parameters (e.g., RRC parameters). Each element in the resource grid for antenna port p and subcarrier spacing configuration u is called a resource element (RE), and one complex symbol can be mapped to each RE. Each RE in the resource grid is uniquely identified by an index k in the frequency domain and an index l representing the symbol position relative to a reference point in the time domain.

[0136] In 3GPP-based wireless communication systems, an RB is defined by 12 consecutive subcarriers in the frequency domain. In 3GPP NR systems, RBs are classified into CRBs and physical resource blocks (PRBs). CRBs are numbered upwards from 0 in the frequency domain for subcarrier spacing configuration u. The center of subcarrier 0 of CRB 0 for subcarrier spacing configuration u coincides with "point A" used as a common reference point for the resource block grid. In 3GPP NR systems, PRBs are defined within bandwidth parts (BWPs) and are numbered from 0 to N. size BWP,i -1 numbering, where i is the number of the bandwidth part. Physical resource block n in bandwidth part i PRBWith common resource block n CRB The relationship between is as follows: PRB =n CRB +N size BWP,i , where N size BWP,i It is a common resource block where the bandwidth part starts relative to CRB 0. A BWP includes multiple consecutive RBs. A carrier can include up to N (e.g., 5) BWPs. A UE can be configured with one or more BWPs on a given component carrier. Only one BWP can be activated at a time among the BWPs configured for a UE. The active BWP defines the operating bandwidth of the UE within the operating bandwidth of the cell.

[0137] The NR frequency band may be defined as two types of frequency ranges, namely, FR1 and FR2. The numerical values ​​of the frequency ranges may vary. For example, the two types of frequency ranges (FR1 and FR2) may be as shown in Table 3 below. For ease of explanation, in the frequency range 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).

[0138] [Table 3]

[0139] Frequency range specification Corresponding frequency range Subcarrier spacing FR1 450MHz-6000MHz 15,30,60kHz FR2 24250MHz-52600MHz 60,120,240kHz

[0140] As described above, the numerical value of the frequency range of the NR system can be changed. For example, FR1 may include a frequency band of 410 MHz to 7125 MHz, as shown in Table 4 below. That is, FR1 may include a frequency band of 6 GHz (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 frequency band. The unlicensed frequency band may be used for various purposes, such as for communication of vehicles (e.g., autonomous driving).

[0141] [Table 4]

[0142] Frequency range specification Corresponding frequency range Subcarrier spacing FR1 410MHz-7125MHz 15,30,60kHz FR2 24250MHz-52600MHz 60,120,240kHz

[0143] In the present disclosure, the term "cell" may refer to a geographical area where one or more nodes provide a communication system or to a radio resource. "Cell" as a geographical area can be understood as a coverage range in which a node can provide services using a carrier, and "cell" as a radio resource (e.g., time-frequency resource) is associated with a bandwidth, which is a frequency range configured by a carrier. A "cell" associated with a radio resource is defined by a combination of downlink resources and uplink resources (e.g., a combination of DL component carriers (CCs) and UL CCs). A cell may be configured only by downlink resources, or may be configured by downlink resources and uplink resources. Since the DL coverage (which is the range in which a node can send a valid signal) and the UL coverage (which is the range in which a node can receive a valid signal from a UE) depend on the carrier that carries the signal, the coverage of a node may be associated with the coverage of a "cell" of a radio resource used by the node. Therefore, the term "cell" may be used to sometimes represent the service coverage of a node, to represent a radio resource at other times, or to represent the range in which a signal using a radio resource can reach with effective strength at other times.

[0144] In CA, two or more CCs are aggregated. The UE can receive or transmit on one or more CCs simultaneously according to its capabilities. CA is supported for both continuous CCs and non-contiguous CCs. When CA is configured, the UE has only one RRC connection with the network. When the RRC connection is established / reestablished / switched, one serving cell provides NAS mobility information, and when the RRC connection is reestablished / switched, one serving cell provides security input. This cell is called the primary cell (PCell). PCell is a cell operating on the primary frequency, where the UE performs an initial connection establishment process or initiates a connection reestablishment process. Depending on the UE capabilities, the secondary cell (SCell) can be configured to form a set of serving cells together with the PCell. SCell is a cell that provides additional radio resources on top of a special cell (PCell). Therefore, the set of configured serving cells for the UE always consists of one PCell and one or more SCells. For dual connection (DC) operation, the term "PCell" refers to the PCell of the primary cell group (MCG) or the primary SCell (PSCell) of the secondary cell group (SCG). SpCell supports PUCCH transmission and contention-based random access and is always activated. MCG is a set of serving cells associated with the master node, which includes SpCell (PCell) and optionally one or more SCells. For UEs configured with DC, SCG is a subset of serving cells associated with the secondary node, which includes PSCell and zero or more SCells. For UEs in RRC_CONNECTED that are not configured with CA / DC, there is only one serving cell consisting of PCell. For UEs in RRC_CONNECTED that are configured with CA / DC, the term "serving cell" is used to refer to the set of cells consisting of SpCell and all SCells. In DC, two MAC entities are configured in the UE: one for MCG and one for SCG.

[0145] Fig. 9 An example of data flow in a 3GPP NR system to which an implementation of the present disclosure is applied is shown.

[0146] Reference Fig. 9 , "RB" means radio bearer, and "H" means header. Radio bearers are classified into two groups: DRB for user plane data and SRB for control plane data. MAC PDU is transmitted / received to / from an external device through the PHY layer using radio resources. MAC PDU arrives at the PHY layer in the form of a transport block.

[0147] In the PHY layer, uplink transport channels UL-SCH and RACH are mapped to their physical channels PUSCH and PRACH, respectively, and 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 physical PUCCH, and downlink control information (DCI) is mapped to PDCCH. MAC PDU related to UL-SCH is sent by UE via PUSCH based on UL grant, and MAC PDU related to DL-SCH is sent by BS via PDSCH based on DL assignment.

[0148] In the following, technical features related to AI / ML are described.

[0149] So far, the application of AI / ML to wireless communications has been limited to implementation-based approaches on both the network and UE sides. The study on enhancements to data collection for NR and ENDC (FS_NR_ENDC_data_collect) has examined the functional framework for RAN intelligence enabled by further enhancing data collection through use cases, examples, etc., and identified potential standardization impacts on current NG-RAN nodes and interfaces. In SA WG2 AI / ML related studies, the network function NWDAF (Network Data Analysis Function) was introduced in Rel-15 and enhanced in Rel-16 and Rel-17.

[0150] In this study, we explore the benefits of enhancing the air interface with features that enable improved support for AI / ML based algorithms to enhance performance and / or reduce complexity / overhead. The enhanced performance here depends on the use case considered and can be, for example, improved throughput, robustness, accuracy or reliability, etc.

[0151] By studying a few carefully selected use cases, evaluating their performance compared to traditional approaches and the associated potential regulatory impact of solutions implementing them, this SI will serve as the foundation for future air interface use cases leveraging AI / ML technologies.

[0152] The goal is to consider enough use cases to enable the identification of common AI / ML frameworks, including functional requirements for AI / ML architectures, which can be used in subsequent projects. The study should also identify areas where AI / ML can improve the performance of air interface functions.

[0153] This study will be used to identify what is needed for adequate AI / ML model characterization and description, thereby establishing relevant notations for discussion and subsequent evaluation. Identify and consider various levels of collaboration between gNB and UE.

[0154] An assessment of the achievable gains from applying AI / ML based techniques for the considered use cases will be performed with the corresponding identification of the KPIs, with the goal of better understanding the achievable gains and the associated complexity requirements.

[0155] Finally, the regulatory impact will be evaluated in order to improve the overall understanding required to enable AI / ML technologies for air interfaces.

[0156] For research on AI / ML for air interfaces, the basic framework and principles for the FS_NR_ENDC_data_collect convention should be considered for possible applicability.

[0157] Study the 3GPP framework for AI / ML over the air interface corresponding to each target use case with regard to aspects such as performance, complexity, and potential specification impact.

[0158] Use cases to focus on:

[0159] 1>The initial set of use cases includes:

[0160] a) CSI feedback enhancements, e.g., reduced overhead, improved accuracy, prediction [RAN1]

[0161] b) Beam management, e.g., beam prediction in the time and / or spatial domain for overhead and latency reduction, beam selection accuracy improvement [RAN1]

[0162] c) Enhanced positioning accuracy for different scenarios, including, for example, scenarios with extensive NLOS conditions [RAN1]

[0163] 2>Finally determine representative sub-use cases for each use case for characterization and baseline performance evaluation

[0164] a) AI / ML approaches for selected sub-use cases need to be diverse enough to support various requirements regarding gNB-UE collaboration levels

[0165] - The selection of use cases for this study is intended only for the development of a framework to apply AI / ML to the air interface for these and other use cases. The selection itself is not intended to provide any indication of the prospects of any future specification project.

[0166] AI / ML models, terms, and descriptions used to identify common and specific features for the Framework investigation:

[0167] 3> Characterize the limited stages and associated complexity of AI / ML related algorithms:

[0168] a) Model generation, e.g., model training (including input / output, pre-processing / post-processing, online / offline if applicable), model validation, model testing, if applicable

[0169] b) Inference operations, e.g., input / output, pre-processing / post-processing, if applicable

[0170] 4> Identify the various levels of collaboration between UE and gNB relevant to the selected use case, e.g.

[0171] a) No collaboration: AI / ML algorithms based only on implementation, without information exchange [for comparison purposes]

[0172] b) Various levels of UE / gNB collaboration targeting either standalone or joint ML operation.

[0173] 5> Characterize the lifecycle management of AI / ML models: for example, model training, model deployment, model inference, model monitoring, model update

[0174] 6>Datasets for training, validation, testing, and inference

[0175] 7> Identify common symbols and terminology for AI / ML related functions, processes, and interfaces

[0176] 8> Note: Consider work done against FS_NR_ENDC_data_collect where appropriate

[0177] For the considered use case:

[0178] - Evaluate the performance benefits of AI / ML based algorithms for a final representative set of agreed use cases:

[0179] a) Statistical model-based approach for link and system level simulation.

[0180] i. Extensions of 3GPP evaluation methods should be considered as needed to better suit AI / ML-based technologies.

[0181] ii. Whether field data are optionally needed to further estimate the performance and robustness in real-world environments should be discussed as part of the study.

[0182] iii. Common assumptions in dataset construction are required for training, validation, and testing for the chosen use case.

[0183] iv. Consider appropriate model training strategies, collaboration levels, and associated implications

[0184] v. Consider convention-based AI models for calibration

[0185] vi. Description of the AI ​​model and training methodology used for evaluation should be reported for information and cross-checking purposes.

[0186] b) KPIs: Identify common KPIs and corresponding requirements for AI / ML operations. Identify use case specific KPIs and benchmarks for selected use cases.

[0187] i. The performance, inference latency, and computational complexity of AI / ML-based algorithms should be compared with the performance, inference latency, and computational complexity of existing technology baselines.

[0188] ii. The overhead, power consumption (including computation), memory storage and hardware requirements (including for a given processing latency) and generalization capabilities associated with enabling the corresponding AI / ML solutions should be considered.

[0189] - Estimate potential normative impact, especially for agreed use cases and common frameworks in the final representative set:

[0190] c) PHY layer, for example (RAN1)

[0191] i. Consider aspects related to, for example, potential specifications for AI model lifecycle management and dataset construction for training, validation, and testing for the chosen use case.

[0192] ii. Use case and collaboration level specific specification impacts, such as new signaling, means for training and validation data assistance, auxiliary information, measurements and feedback

[0193] d) Protocol wise, e.g. (RAN2)-RAN2 starts work only after there is sufficient progress in use case studies in RAN1.

[0194] i. Consider aspects related to, for example, capability indication per RAN1 input, configuration and control processes (training / inference), and management of data and AI / ML models.

[0195] ii. Collaboration level specific specification impact per use case

[0196] e) Interoperability and testability aspects, e.g. (RAN4) - RAN4 will only start work after there is sufficient progress in use case studies in RAN1 and RAN2.

[0197] i. Requirements and test framework for validating AI / ML based performance enhancements and ensuring that UEs and gNBs with AI / ML meet or exceed existing minimum requirements, where applicable

[0198] ii. Consider the need and implications of defining AI / ML processing capabilities

[0199] -Specific AI / ML models are not expected to be specified and depend on the implementation. User data privacy needs to be protected.

[0200] - Research on AI / ML for air interfaces is based on the current RAN architecture and no new interfaces should be introduced.

[0201] Fig.10 An example of a functional framework for RAN intelligence is shown.

[0202] >Data collection is the function that provides input data to the model training and model inference functions. AI / ML algorithm-specific data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) is not performed in the data collection function.

[0203] Examples of input data may include measurements from UEs or different network entities, feedback from actors, outputs from AI / ML models.

[0204] >> Training Data: Data required as input for AI / ML model training functions.

[0205] >>Inference data: Data required as input for the inference function of the AI / ML model.

[0206] >Model training is the function that performs AI / ML model training, validation, and testing, and can generate model performance metrics as part of the model testing process. If necessary, the model training function is also responsible for data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) based on the training data delivered by the data collection function.

[0207] >>Model deployment / update: used to initially deploy trained, validated, and tested AI / ML models to the model inference function or to pass updated models to the model inference function.

[0208] >Model inference is a function that provides AI / ML model inference output (e.g., prediction or decision). The model inference function can provide model performance feedback to the model training function when applicable. If necessary, the model inference function is also responsible for data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) based on the inference data passed by the data collection function.

[0209] >>Output: The inference output of the AI / ML model produced by the model inference function.

[0210] >>>Note: The details of the inference output are use case specific.

[0211] >>Model performance feedback: When available, it can be used to monitor the performance of AI / ML models.

[0212] >An actor is a function that receives output from the model inference function and triggers or performs corresponding actions. An actor can trigger actions for other entities or itself.

[0213] >> Feedback: Information that may be needed to derive training data, inference data, or monitor the performance of AI / ML models and their impact on the network by updating KPIs and performance counters.

[0214] In the following, technical features related to mobility optimization are described.

[0215] Mobility management is a solution that ensures service continuity during mobility by minimizing dropped calls, RLF, unnecessary handovers, and ping-pong. For future high-frequency networks, as the coverage of a single node decreases, the frequency of UE switching between nodes becomes high, especially for high-mobility UEs. In addition, for applications characterized by strict QoS requirements such as reliability, latency, etc., QoE is sensitive to handover performance, so that mobility management should avoid unsuccessful handovers and reduce the latency during the handover process. However, for conventional methods, it is difficult to achieve almost zero-failure handover based on trial and error schemes. Unsuccessful handover situations are the main cause of packet drops or additional delays during mobility periods, which is undesirable for packet drop intolerance and low-latency applications. In addition, due to the randomness and inconstancy of the transmission environment, the effectiveness of feedback-based adjustments may be weak. In addition to the baseline case of mobility, areas for optimization of mobility include dual connectivity, CHO, and DAPS, each of which has additional aspects to be processed in the optimization of mobility.

[0216] Mobility aspects of SON that can be enhanced by using AI / ML include

[0217] -Reduction in the probability of unexpected events

[0218] -UE location / mobility / performance prediction

[0219] -Business Orientation

[0220] Reduce the probability of unexpected events associated with mobility.

[0221] Examples of such unexpected events are:

[0222] - Intra-system late handover: Radio link failure (RLF) occurs after the UE has stayed in a cell for a long period of time; the UE attempts to re-establish the radio link connection in a different cell.

[0223] - Intra-system premature handover: 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.

[0224] - Intra-system handover to wrong cell: 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.

[0225] -Successful switch: During a successful switch, there was a potential problem.

[0226] RAN intelligence can observe multiple HO events with associated parameters, use this information to train its ML model and try to identify parameter sets that lead to successful HOs and those that lead to unexpected events.

[0227] UE location / mobility / performance prediction

[0228] Predicting the location of the UE is a key part of optimization for mobility, as many RRM actions related to mobility (e.g., selecting a handover target cell) can benefit from the predicted UE location / trajectory. UE mobility prediction is also a key factor in optimizing early data forwarding, especially for CHO. Prediction of UE performance when the UE is served by a specific cell is a key factor in determining which is the best mobility target to maximize efficiency and performance.

[0229] Business Orientation

[0230] Efficient resource handling can be achieved by adjusting the switching trigger point and selecting the best combination of Pcell / PSCell / Scell ​​to serve users.

[0231] Existing traffic steering may also be improved by providing mobility or dual connectivity related information to the RAN node.

[0232] For example, before initiating a handover, the source gNB may use feedback on UE performance collected for successful handovers that occurred in the past and received from neighboring gNBs.

[0233] Similarly, for the dual connectivity case, before triggering the addition of a secondary gNB or triggering an SN change, the eNB can use the information (feedback) received from the gNB in ​​the past to successfully complete the SN addition or SN change process.

[0234] In both reporting examples, the source RAN node of the mobility event or the RAN node acting as the master node (eNB for EN-DC, gNB for NR-DC) can use the feedback received from another RAN node as input to AI / ML functions supporting service-related decisions (e.g., selection of the target cell in the case of mobility, selection of PSCell / Scell ​​in the other case) so that future decisions can be optimized.

[0235] Location for AI / ML model training and AI / ML model inference

[0236] Considering the location of AI / ML model training and AI / ML model inference for mobility solutions, consider the following two options:

[0237] -AI / ML model training functions are deployed in OAM, while model inference functions reside in RAN nodes

[0238] - Both AI / ML model training functions and AI / ML model inference functions reside within the RAN node

[0239] In addition, for CU-DU split scenarios, the following options are possible:

[0240] -AI / ML model training is located in the CU-CP or OAM, and the AI / ML model inference function is located in the CU-CP.

[0241] It also allows the gNB to continue model training based on the AI / ML model trained in OAM.

[0242] Fig.11 Examples of AI / ML model training in OAM and AI / ML model inference in NG-RAN nodes are shown.

[0243] Step 0. It is assumed that the NG-RAN node 2 optionally has an AI / ML model that can generate the required inputs (e.g., resource status and utilization prediction / assessment, etc.).

[0244] Step 1. The NG-RAN node configures measurement information on the UE side and sends a configuration message including the configuration information to the UE.

[0245] Step 2. The UE collects indicated measurements (eg, UE measurement results related to RSRP, RSRQ, SINR of neighboring cells and serving cells).

[0246] Step 3. The UE sends a measurement report message including the required measurements to NG-RAN node 1.

[0247] Step 4. The NG-RAN node 1 sends input data for training to the OAM, where the input data for training includes required input information from the NG-RAN node 1 and measurements from the UE.

[0248] Step 5. The NG-RAN node 2 sends input data for training to the OAM, wherein the input data for training includes 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 may include corresponding inference results from the NG-RAN node 2.

[0249] Step 6. Model training. Use the required measurements to train the AI / ML model optimized for UE mobility.

[0250] Step 7. OAM sends an AI / ML model deployment message to deploy the trained / updated AI / ML model into the NG-RAN node. The NG-RAN node can also continue model training based on the AI / ML model received from OAM.

[0251] Note: This step is outside the scope of RAN3 Rel-17.

[0252] Step 8. NG-RAN node 1 obtains the measurement report as inference data for UE mobility optimization.

[0253] Step 9. The NG-RAN node 1 obtains input data for inference from the NG-RAN node 2 for UE mobility optimization, wherein the input data for inference includes 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 may include a corresponding inference result from the NG-RAN node 2.

[0254] Step 10. Model inference: Utilize the required measurements into model inference to output predictions (e.g., UE trajectory prediction, target cell prediction, target NG-RAN node prediction, etc.).

[0255] Step 11. NG-RAN1 sends model performance feedback to OAM (if applicable).

[0256] Note: This step is outside the scope of RAN3.

[0257] Step 12: Based on the prediction, recommended action or configuration, NG-RAN node 1, the target NG-RAN node (represented by NG-RAN node 2 in this step in the flowchart) and the UE perform a mobility optimization / handover procedure to handover the UE from NG-RAN node 1 to the target NG-RAN node.

[0258] Step 13. NG-RAN node 1 sends feedback information to OAM.

[0259] Step 14. NG-RAN node 2 sends feedback information to OAM.

[0260] Fig.12 An example of model training and model inference, both located in RAN nodes, is shown.

[0261] Step 0. It is assumed that the NG-RAN node 2 optionally has an AI / ML model that can generate the required inputs (e.g., resource status and utilization prediction / assessment, etc.).

[0262] Step 1. NG-RAN node 1 configures measurement information on the UE side and sends a configuration message including the configuration information to the UE.

[0263] Step 2. The UE collects indicated measurements (eg, UE measurements related to RSRP, RSRQ, SINR of neighboring cells and serving cells).

[0264] Step 3. The UE sends a measurement report message including the required measurements to NG-RAN node 1.

[0265] Step 4. The NG-RAN node 1 obtains input data for training from the NG-RAN node 2, wherein the input data for training includes 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 may include the corresponding inference result from the NG-RAN node 2.

[0266] Step 5. Model training. Use the required measurements to train the AI / ML model for mobility optimization.

[0267] Step 6. NG-RAN node 1 obtains the measurement report as inference data for real-time UE mobility optimization.

[0268] Step 7. The NG-RAN node 1 obtains input data for inference from the NG-RAN node 2 for UE mobility optimization, wherein the input data for inference includes 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 may include a corresponding inference result from the NG-RAN node 2.

[0269] Step 8. Model inference. The required measurements are utilized into model inference to output predictions, including, for example, UE trajectory prediction, target cell prediction, target NG-RAN node prediction, etc.

[0270] Step 9: Based on the prediction, recommended action or configuration, NG-RAN node 1, the target NG-RAN node (represented by NG-RAN node 2 in this step in the flowchart) and the UE perform a mobility optimization / handover procedure to handover the UE from NG-RAN node 1 to the target NG-RAN node.

[0271] Step 10. NG-RAN node 2 sends feedback information after the mobility optimization action to NG-RAN node 1.

[0272] For example, UE mobility information for training purposes is only sent to the gNB that requests such information or when triggered.

[0273] Inputs to AI / ML-based mobility optimization

[0274] The following data are required as input data for mobility optimization.

[0275] From UE:

[0276] -UE location information (e.g., coordinates, serving cell ID, moving speed) interpreted by the gNB implementation when available.

[0277] - Radio measurements related to the serving cell and neighboring cells (e.g. RSRP, RSRQ, SINR) associated with the UE location information.

[0278] -UE mobility history information.

[0279] From neighboring RAN nodes:

[0280] - Historical information of UE from neighbors

[0281] - Location, QoS parameters and performance information (e.g., loss rate, delay, etc.) of historical HO UEs

[0282] - Current / forecasted resource status

[0283] - Successful and unsuccessful handovers (including premature handovers, late handovers or handovers to wrong (suboptimal) cells) in the past by the UE based on the existing SON / RLF reporting mechanism.

[0284] From a local node:

[0285] -UE trajectory prediction

[0286] - Current / forecasted resource status

[0287] - Current / forecasted UE traffic

[0288] Output of AI / ML-based mobility optimization

[0289] AI / ML-based mobility optimization can generate the following information as output:

[0290] -UE trajectory prediction (UE latitude, longitude, altitude, cell ID in the future time period)

[0291] NOTE: Whether the UE trajectory prediction is an external output to the node hosting the model inference functionality should be discussed during the specification work phase.

[0292] -Estimated arrival probability in CHO and associated confidence intervals

[0293] - The predicted handover target node (candidate cell in CHO) may be together with the confidence of the prediction.

[0294] - Priority, switching execution timing, and predicted resource reservation time window for CHO.

[0295] -UE traffic prediction (will be used internally by RAN nodes and details depend on specification work phase)

[0296] - Model output validity time will be discussed during R18 specification work for each inference output.

[0297] Feedback on mobility optimization based on AI / ML

[0298] The following data is required as feedback data for mobility optimization.

[0299] - QoS parameters (eg throughput, packet delay for switched UEs, etc.).

[0300] - Resource status information update from the target NG-RAN.

[0301] - Capability information from the target NG-RAN. The details of the performance information will be discussed during the specification work phase.

[0302] Standard Impact

[0303] To improve mobility decisions at the gNB (gNB-CU), the gNB may request mobility feedback from neighboring nodes. The details of the procedure will be determined during the specification phase.

[0304] If the gNB requires existing UE measurements for AI / ML based mobility optimization, RAN3 should reuse the existing framework (including MDT and RRM measurement results). Based on the use case description, whether new UE measurement results are required depends on the specification stage.

[0305] MDT process enhancements should be discussed during the specification phase.

[0306] Potential Xn interface impact:

[0307] - Predicted resource status information and performance information from candidate target NG-RAN nodes to source NG-RAN nodes

[0308] - A new signalling procedure or an existing procedure to retrieve input information via the Xn interface.

[0309] - New signalling procedure or existing procedure to retrieve feedback information via the Xn interface.

[0310] In the following, technical features related to AI and ML are described.

[0311] Artificial intelligence (AI) / machine learning (ML) is being used in a range of application areas across industrial sectors, achieving significant productivity gains. Specifically, in mobile communication systems, mobile devices (e.g., smart phones, smart vehicles, UAVs, mobile robots) are increasingly replacing conventional algorithms (e.g., speech recognition, machine translation, image recognition, video processing, user behavior prediction) with AI / ML models to enable applications such as enhanced photography, smart personal assistants, VR / AR, video games, video analysis, personalized shopping recommendations, autonomous driving / navigation, smart home appliances, mobile robots, mobile healthcare, and mobile finance.

[0312] Artificial intelligence (AI) is the science and engineering of building intelligent machines that can perform tasks like humans.

[0313] Deep Neural Networks

[0314] Fig.13 and Fig.14 Examples of neurons and neural network architectures are shown.

[0315] Within the field of ML, there is an area generally referred to as brain-inspired computing, which is a program that aims to simulate some aspects of how we understand the brain to work. Since the main computing element of the human brain is believed to be 86 billion neurons, both sub-areas of brain-inspired computing are inspired by the neuronal architecture, such as Fig.13 shown.

[0316] Compared to the spike computing approach, a more popular ML approach uses "neural networks" as models. Neural networks (NNs) derive their inspiration from the notion that the computation of a neuron involves a weighted sum of input values. However, instead of simply outputting a weighted sum, the NN applies a nonlinear function to generate an output only when the input exceeds a certain threshold, such as Fig.13 shown.

[0317] Fig.14A schematic diagram of a computational neural network is shown. Neurons in the input layer receive some values ​​and propagate them to neurons in the middle layers of the network (which are also called "hidden layers"). The weighted sums from one or more hidden layers are ultimately propagated to the output layer, which presents the final output of the network.

[0318] A neural network with more than three layers (i.e., more than one hidden layer) is called a deep neural network (DNN). Compared with the traditional shallow structured NN architecture, DNN (also known as deep learning) has made amazing breakthroughs in many necessary application fields since the 2010s because they can achieve human-level accuracy or even exceed human accuracy. Deep learning technology uses supervised and / or unsupervised strategies to automatically learn hierarchical representations in deep architectures for classification. In the case of a large number of hidden layers, the excellent performance of DNN comes from its ability to extract high-level features from raw sensory data to obtain an effective representation of the input space after using statistical learning through a large amount of data. In recent years, due to the big data obtained from the real world, the rapidly increasing computing power and the continuously evolving algorithms, DNN has become the most popular ML model for many AI applications.

[0319] Training and Inference

[0320] Training is the process by which an AI / ML model learns to perform its given task, more specifically, by optimizing the values ​​of the weights in a DNN. A DNN is trained by feeding it a training set, which is usually correctly labeled training samples. Taking image classification as an example, 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 output and the output calculated 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, a DNN with high accuracy is obtained.

[0321] There are many ways to train a network for different goals. The above is supervised learning, which uses labeled training samples to find the correct output for a task. Unsupervised learning uses unlabeled training samples to find structures or clusters in the data. Reinforcement learning can be used to output what actions the agent should take next to maximize the expected reward. Transfer learning is to adjust previously trained weights (e.g., weights in a global model) using a new training set, which is used for faster or more accurate training for a personalized model.

[0322] Fig.15 An example of AI / ML inference is shown.

[0323] 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 called inference. During model inference, inputs from the real world are passed through the DNN. Fig.15 As shown, the network then outputs a prediction for the task. For example, the input could be the pixels of an image, the sampled amplitude of an audio wave, or a numerical representation of the state of some system or game. Correspondingly, the network's output could be the 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 a proposed action that should be taken.

[0324] The performance of DNNs is obtained at the cost of high computational complexity. Therefore, more efficient computing engines (e.g., graphics processing units (GPUs) and network processing units (NPUs)) are usually used. Compared with inference, which only involves the feedforward process, training usually requires more computing and storage resources because it also involves the backpropagation process.

[0325] Widely used DNN models and algorithms

[0326] Fig.16 An example of an MLP DNN model is shown.

[0327] Many DNN models have been developed over the past two decades. Each of these models has a different "network architecture" in terms of the number of layers, layer types, layer shapes (i.e., filter sizes, number of channels and filters), and connections between layers. Fig.16 Three popular structures of DNN are presented: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). The Multilayer Perceptron (MLP) model is the most basic DNN, which consists of a series of fully connected layers. In a fully connected layer, all outputs are connected to all inputs, such as Fig.16 As shown in Figure 2, MLP requires a lot of storage and computation.

[0328] Fig.17 An example of a CNN model is shown.

[0329] One way to limit the number of weights that contribute to the output is to compute the output using only functions of a fixed-size input window. A very popular window-based DNN model uses convolution operations to structure the computation, hence the name Convolutional Neural Network (CNN). Fig.17 As shown in Figure 1, CNN consists of multiple convolutional layers. By applying various convolutional filters, the CNN model can capture high-level representations of the input data, making it popular in image classification and speech recognition tasks.

[0330] Fig.18 An example of an RNN model is shown.

[0331] Recurrent Neural Network (RNN) models are another type of DNN that uses sequential data feeds. The input to an RNN consists of the current input and previous samples. Each neuron in an RNN has an internal memory that holds information from the calculations of previous samples. Fig.18 As shown, the basic unit of RNN is called a unit, and in addition, each unit is composed of layers, and a series of units enables the RNN model to be processed sequentially. RNN models have been widely used in natural language processing tasks (e.g., language modeling, machine translation, question answering, word embedding, and document classification) on mobile devices.

[0332] Fig.19 An example of reinforcement learning is shown.

[0333] Deep reinforcement learning (DRL) is not another DNN model. It is composed of DNN and reinforcement learning. Fig.19 As shown in Figure 1, the goal of DRL is to create intelligent agents that can execute efficient strategies to maximize the rewards of long-term tasks with controllable actions. Typical applications of DRL are to solve various scheduling problems such as decision-making problems in games and rate selection for video transmission.

[0334] In the following, technical features related to measurement reporting are described. Reference may be made to section 5.5.4 and part of section 5.5.5 of 3GPP TS 38.331 v17.0.0.

[0335] Measurement report trigger

[0336] If AS security has been successfully activated, the UE shall:

[0337] 1>For each measId included in measIdList in VarMeasConfig:

[0338] 2> If the corresponding reportConfig includes a reportType set to eventTriggered or periodical:

[0339] 3>If the corresponding measObject involves NR:

[0340] 4> If the corresponding reportConfig includes measRSSI-ReportConfig:

[0341] 5> The resources indicated by rmtc-Config on the associated frequency are considered applicable;

[0342] 4>If eventA1 or eventA2 is configured in the corresponding reportConfig:

[0343] 5> It is considered that only the service cell is applicable;

[0344] 4>If eventA3 or eventA5 is configured in the corresponding reportConfig:

[0345] 5> If a serving cell is associated with a measObjectNR and a neighbor is associated with another measObjectNR, any serving cell associated with the other measObjectNR is considered to be also a neighboring cell;

[0346] 4> If the corresponding reportConfig includes a reportType set to periodical; or

[0347] 4>For measurement events other than eventA1 or eventA2:

[0348] 5>If useAllowedCellList is set to true:

[0349] 6> When the relevant cell is included in the allowedCellsToAddModList defined in the VarMeasConfig for this measId, any neighboring cell detected based on the parameters in the associated measObjectNR is considered applicable;

[0350] 5> Otherwise:

[0351] 6> When the relevant cell is not included in the excludedCellsToAddModList defined in the VarMeasConfig for this measId, any neighboring cell detected based on the parameters in the associated measObjectNR is considered applicable;

[0352] 3> Otherwise, if the corresponding measObject involves E-UTRA:

[0353] 4>If eventB1 or eventB2 is configured in the corresponding reportConfig:

[0354] 5> Treat the serving cell (if any) on the associated E-UTRA frequency as a neighbor cell;

[0355] 4> When the relevant cell is not included in the excludedCellsToAddModListEUTRAN defined in VarMeasConfig for this measId, any neighboring cell detected on the associated frequency is considered applicable;

[0356] 3> Otherwise, if the corresponding measObject involves UTRA-FDD:

[0357] 4> If eventB1-UTRA-FDD or eventB2-UTRA-FDD is configured in the corresponding reportConfig; or

[0358] 4>If the corresponding reportConfig includes a reportType set to periodical:

[0359] 5> When the relevant cell is included in the cellsToAddModList defined in the VarMeasConfig for this measId, the neighboring cell on the associated frequency is considered applicable;

[0360] 3> Otherwise, if the corresponding measObject involves L2 U2N relay UE:

[0361] 4>If eventY1-Relay is configured in the corresponding reportConfig; or

[0362] 4>If the corresponding reportConfig includes a reportType set to periodical:

[0363] 5> Any L2 U2N relay UE detected on the associated frequency is considered to be applicable to this measId;

[0364] 2> Otherwise, if the corresponding reportConfig includes a reportType set to reportCGI:

[0365] 3> Cells detected on the associated measObject with a physical cell identity matching the value of cellForWhichToReportCGI included in the corresponding reportConfig within VarMeasConfig are considered applicable;

[0366] 2> Otherwise, if the corresponding reportConfig includes reportType set to reportSFTD:

[0367] 3>If the corresponding measObject involves NR:

[0368] 4>If reportSFTD-meas is set to true:

[0369] 5>NR PSCell is considered applicable;

[0370] 4> Otherwise, if reportSFTD-NeighMeas is included:

[0371] 5>If cellsForWhichToReportSFTD is configured in the corresponding reportConfig:

[0372] 6> Any NR neighbor cell detected on the associated measObjectNR with the same physical cell identity as included in cellsForWhichToReportSFTD is considered applicable;

[0373] 5> Otherwise:

[0374] 6> When the relevant cell is not included in the excludedCellsToAddModList defined in the VarMeasConfig for this measId, up to 3 strongest NR neighboring cells detected based on the parameters in the associated measObjectNR are considered applicable;

[0375] 3> Otherwise, if the corresponding measObject involves E-UTRA:

[0376] 4>If reportSFTD-Meas is set to true:

[0377] 5> E-UTRA PSCell is considered applicable;

[0378] 2> Otherwise, if the corresponding reportConfig includes a reportType set to cli-Periodical or cli-EventTriggered:

[0379] 3> All CLI measurement resources included in the corresponding measObject are considered applicable;

[0380] 2> Otherwise, if the corresponding reportConfig includes reportType set to rxTxPeriodical:

[0381] 3> All Rx-Tx time difference measurement resources included in the corresponding measObject are considered applicable;

[0382] 2> If the corresponding reportConfig involves a report for NR sidelink communication (i.e., reportConfigNR-SL):

[0383] 3> The transmission resource pool indicated by tx-PoolMeasToAddModList defined in VarMeasConfig for this measId is considered applicable;

[0384] 2> If reportType is set to eventTriggered, and if the entry conditions applicable to the event are met for one or more applicable cells for all measurements after layer 3 filtering made during timeToTrigger defined for the event in VarMeasConfig (i.e., the event corresponding to the eventId of the corresponding reportConfig in VarMeasConfig), and VarMeasReportList does not include a measurement report entry for that measId (the first cell triggers the event):

[0385] 3> Include the measurement report entry in the VarMeasReportList for the measId;

[0386] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0387] 3> Include the relevant cells in the cellsTriggeredList defined in the VarMeasReportList for the measId;

[0388] 3> If useT312 is set to true in reportConfig for the event:

[0389] 4> If the T310 for the corresponding SpCell is running; and

[0390] 4> If T312 is not running for the corresponding SpCell:

[0391] 5>Start timer T312 for the corresponding SpCell, wherein the value of T312 is configured in the corresponding measObjectNR;

[0392] 3>Initiate measurement report process;

[0393] 2> Otherwise, if reportType is set to eventTriggered, and if the entry conditions applicable to the event (i.e., the events corresponding to the eventId of the corresponding reportConfig in VarMeasConfig) are met for all measurements after layer 3 filtering made during timeToTrigger defined for the event in VarMeasConfig for one or more applicable cells not included in cellsTriggeredList (subsequent cells trigger the event):

[0394] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0395] 3> Include the relevant cells in the cellsTriggeredList defined in the VarMeasReportList for the measId;

[0396] 3> If useT312 is set to true in reportConfig for the event:

[0397] 4> If the T310 for the corresponding SpCell is running; and

[0398] 4> If T312 is not running for the corresponding SpCell:

[0399] 5>Start timer T312 for the corresponding SpCell, wherein the value of T312 is configured in the corresponding measObjectNR;

[0400] 3>Initiate measurement report process;

[0401] 2> Otherwise, if reportType is set to eventTriggered, and if the leaving condition applicable to the event is met for all measurements after layer 3 filtering made during timeToTrigger defined in VarMeasConfig for the event for one or more cells included in cellsTriggeredList defined in VarMeasReportList for the measId:

[0402] 3>Remove the relevant cells in cellsTriggeredList defined in VarMeasReportList for the measId;

[0403] 3> If reportOnLeave is set to true for the corresponding report configuration:

[0404] 4>Initiate measurement report process;

[0405] 3> If the cellsTriggeredList defined in the VarMeasReportList for the measId is empty:

[0406] 4> Remove the measurement report entry in VarMeasReportList for the measId;

[0407] 4> If it is running, stop the periodic report timer for the measId;

[0408] 2> If reportType is set to eventTriggered, and if the entry conditions applicable to the event are met for one or more applicable L2 U2N relay UEs for all measurements after layer 3 filtering made during timeToTrigger defined for the event in VarMeasConfig (i.e. the event corresponding to the eventId of the corresponding reportConfig in VarMeasConfig), and VarMeasReportList does not include a measurement report entry for that measId (first L2 U2N relay UE triggering event):

[0409] 3> Include the measurement report entry in the VarMeasReportList for the measId;

[0410] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0411] 3> Include the relevant L2 U2N relay UE in the relaysTriggeredList defined in the VarMeasReportList for this measId;

[0412] 3>Initiate measurement report process;

[0413] 2> Otherwise, if reportType is set to eventTriggered, and if the entry conditions applicable to the event are met for all measurements after layer 3 filtering made during timeToTrigger defined for this event in VarMeasConfig for one or more applicable L2 U2N relay UEs not included in relaysTriggeredList (i.e., events corresponding to eventId of corresponding reportConfig in VarMeasConfig) (subsequent L2 U2N relay UE triggered event):

[0414] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0415] 3> Include the relevant L2 U2N relay UE in the relaysTriggeredList defined in the VarMeasReportList for this measId;

[0416] 3>Initiate measurement report process;

[0417] 2> Otherwise, if reportType is set to eventTriggered, and if the leaving condition applicable to the event is met for all measurements after layer 3 filtering made during timeToTrigger defined in VarMeasConfig for the event for one or more of the L2U2N relay UEs included in the relaysTriggeredList defined in VarMeasReportList for the measId:

[0418] 3> Remove the relevant L2 U2N relay UE in the relaysTriggeredList defined in the VarMeasReportList for the measId;

[0419] 3> If reportOnLeave is set to true for the corresponding report configuration:

[0420] 4>Initiate measurement report process;

[0421] 3> If the relaysTriggeredList defined in the VarMeasReportList for the measId is empty:

[0422] 4> Remove the measurement report entry in VarMeasReportList for the measId;

[0423] 4> If it is running, stop the periodic report timer for the measId;

[0424] 2> Otherwise, if reportType is set to eventTriggered, and if the entry conditions applicable to the event are met for all measurements made during timeToTrigger defined for the event in VarMeasConfig for one or more applicable transport resource pools (i.e., events corresponding to eventId of the corresponding reportConfig in VarMeasConfig), and VarMeasReportList does not include a measurement report entry for that measId (first transport resource pool triggering event):

[0425] 3> Include the measurement report entry in the VarMeasReportList for the measId;

[0426] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0427] 3> Include the relevant transmission resource pools in the poolsTriggeredList defined in the VarMeasReportList for the measId;

[0428] 3>Initiate measurement report process;

[0429] 2> Otherwise, if reportType is set to eventTriggered, and if the entry conditions applicable to the event are met for all measurements made during timeToTrigger defined for this event in VarMeasConfig for one or more applicable transport resource pools not included in poolsTriggeredList (i.e., events corresponding to eventId of the corresponding reportConfig in VarMeasConfig) (subsequent transport resource pool triggering event):

[0430] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0431] 3> Include the relevant transmission resource pools in the poolsTriggeredList defined in the VarMeasReportList for the measId;

[0432] 3>Initiate measurement report process;

[0433] 2> Otherwise, if reportType is set to eventTriggered, and if the leaving condition applicable to the event is met for all measurements made during timeToTrigger defined in VarMeasConfig for the event for one or more applicable transport resource pools included in poolsTriggeredList defined in VarMeasReportList for the measId:

[0434] 3> Remove the relevant transmission resource pools in poolsTriggeredList defined in VarMeasReportList for the measId;

[0435] 3> If the poolsTriggeredList defined in the VarMeasReportList for the measId is empty:

[0436] 4> Remove the measurement report entry in VarMeasReportList for the measId;

[0437] 4> If it is running, stop the periodic report timer for the measId;

[0438] 2> Otherwise, if reportType is set to eventTriggered, and if eventId is set to eventD1, and if the entry condition applicable to the event (i.e., the event corresponding to the eventId of the corresponding reportConfig in VarMeasConfig) is met during the timeToTrigger defined in the VarMeasConfig for the event:

[0439] 3> Include the measurement report entry in the VarMeasReportList for the measId;

[0440] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0441] 3>Initiate measurement report process;

[0442] 2> If reportType is set to periodical and if the (first) measurement result is available:

[0443] 3> Include the measurement report entry in the VarMeasReportList for the measId;

[0444] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0445] 3> If the corresponding reportConfig includes measRSSI-ReportConfig:

[0446] 4> When the RSSI sample value is reported by the physical layer after the first L1 measurement duration, the measurement reporting process is immediately initiated;

[0447] 3> Otherwise, if the corresponding reportConfig includes ul-DelayValueConfig:

[0448] 4> Initiate the measurement reporting procedure immediately after the first measurement result is provided from the lower layer associated with the DRB identity;

[0449] 3> Otherwise, if the corresponding reportConfig includes ul-ExcessDelayConfig:

[0450] 4> Initiate the measurement reporting procedure immediately after the first measurement result is provided from the lower layer associated with the DRB identity according to the configured threshold per DRB identity;

[0451] 3> Otherwise, if reportAmount exceeds 1:

[0452] 4> Initiate the measurement reporting procedure immediately after the quantity to be reported becomes available for the NR SpCell or available for serving the L2 U2N relay UE (if the UE is a L2U2N remote UE);

[0453] 3> Otherwise (that is, reportAmount is equal to 1):

[0454] 4> Initiate the measurement reporting procedure immediately after the quantity to be reported becomes available for the NR SpCell and for the strongest cell among the applicable cells, or available for the NR SpCell and for the strongest L2 U2N relay UE among the applicable L2 U2N relay UEs; or initiate the measurement reporting procedure immediately after the quantity to be reported becomes available for serving the L2 U2N relay UE and for the strongest cell among the applicable cells (if the UE is a L2 U2N remote UE);

[0455] 2> If, in the case where the corresponding reportConfig relates to a report for NR sidelink communication, reportType is set to periodical and if the (first) measurement result is available:

[0456] 3> Include the measurement report entry in the VarMeasReportList for the measId;

[0457] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0458] 3> Initiate the measurement reporting procedure immediately after the quantity to be reported becomes available for the NR SpCell and the CBR measurement results become available;

[0459] 2> If reportType is set to cli-EventTriggered, and if the entry conditions applicable to the event are met for one or more applicable CLI measurement resources for all measurements after layer 3 filtering made during timeToTrigger defined for the event in VarMeasConfig (i.e., events corresponding to eventId of the corresponding reportConfig in VarMeasConfig), and VarMeasReportList does not include a measurement report entry for that measId (first CLI measurement resource triggering event):

[0460] 3> Include the measurement report entry in the VarMeasReportList for the measId;

[0461] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0462] 3> Include the relevant CLI measurement resources in the cli-TriggeredList defined in the VarMeasReportList for this measId;

[0463] 3>Initiate measurement report process;

[0464] 2> Otherwise, if reportType is set to cli-EventTriggered, and if the entry conditions applicable to the event are met for all measurements after layer 3 filtering made during timeToTrigger defined for this event in VarMeasConfig for one or more CLI measurement resources not included in cli-TriggeredList (i.e., events corresponding to eventId of the corresponding reportConfig in VarMeasConfig) (subsequent CLI measurement resource triggering event):

[0465] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0466] 3> Include the relevant CLI measurement resources in the cli-TriggeredList defined in the VarMeasReportList for this measId;

[0467] 3>Initiate measurement report process;

[0468] 2> Otherwise, if reportType is set to cli-EventTriggered, and if the leaving conditions applicable to the event are met for all measurements after layer 3 filtering made during timeToTrigger defined in VarMeasConfig for the event for one or more of the CLI measurement resources included in the cli-TriggeredList defined in VarMeasReportList for the measId:

[0469] 3> Remove the relevant CLI measurement resources in the cli-TriggeredList defined in the VarMeasReportList for the measId;

[0470] 3> If reportOnLeave is set to true for the corresponding report configuration:

[0471] 4>Initiate measurement report process;

[0472] 3> If the cli-TriggeredList defined in the VarMeasReportList for the measId is empty:

[0473] 4> Remove the measurement report entry in VarMeasReportList for the measId;

[0474] 4> If it is running, stop the periodic report timer for the measId;

[0475] 2> If reportType is set to cli-Periodical and if the (first) measurement result is available:

[0476] 3> Include the measurement report entry in the VarMeasReportList for the measId;

[0477] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0478] 3> Initiate the measurement reporting procedure immediately after the quantity to be reported becomes available for at least one CLI measurement resource;

[0479] 2> If reportType is set to rxTxPeriodical and if the (first) measurement result is available:

[0480] 3> Include the measurement report entry in the VarMeasReportList for the measId;

[0481] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0482] 3>Initiate measurement report process;

[0483] 2> When the periodic reporting timer for this measId expires:

[0484] 3>Initiate measurement reporting process.

[0485] 2> If the corresponding reportConfig includes reportType is set to reportSFTD:

[0486] 3>If the corresponding measObject involves NR:

[0487] 4> If drx-SFTD-NeighMeas is included:

[0488] 5> If the quantity to be reported becomes available for each requested PCell and NR cell pair:

[0489] 6> Stop timer T322;

[0490] 6>Initiate measurement report process;

[0491] otherwise

[0492] 5> Initiate the measurement reporting procedure immediately after the quantity to be reported becomes available for each requested PCell and NR cell pair or after the maximum measurement reporting delay as specified in TS 38.133

[14] ;

[0493] 3> Otherwise, if the corresponding measObject involves E-UTRA:

[0494] 4> the measurement reporting procedure is initiated immediately after the quantity to be reported becomes available for a pair of PCell and E-UTRA PSCell or after the maximum measurement reporting delay as specified in TS 38.133

[14] ;

[0495] 2>If reportType is set to reportCGI:

[0496] 3> If the UE obtains SIB1 or SystemInformationBlockType1 for the requested cell; or

[0497] 3> If the UE detects that the requested NR cell does not send SIB1 (see TS 38.213

[13] , clause 13):

[0498] 4> Stop timer T321;

[0499] 4> Include the measurement report entry in the VarMeasReportList for the measId;

[0500] 4> Set numberOfReportsSent defined in VarMeasReportList for this measId to 0;

[0501] 4>Initiate measurement report process;

[0502] 2> When T321 for this measId expires:

[0503] 3> Include the measurement report entry in the VarMeasReportList for the measId;

[0504] 3> Set numberOfReportsSent defined in VarMeasReportList for the measId to 0;

[0505] 3>Initiate measurement report process;

[0506] 2> When T322 for this measId expires:

[0507] 3>Initiate measurement reporting process.

[0508] The events are as follows:

[0509] - Event A1: Service becomes better than threshold

[0510] - Event A2: Service becomes worse than threshold

[0511] - Event A3: Neighbor becomes better offset than SpCell

[0512] - Event A4: Neighbor becomes better than threshold

[0513] - Event A5: SpCell becomes worse than threshold 1, and the neighbor becomes better than threshold 2

[0514] - Event A6: Neighbor becomes better offset than SCell

[0515] - Event B1: Inter-RAT neighbor becomes better than the threshold

[0516] - Event B2: PCell becomes worse than threshold 1, and inter-RAT neighbors become better than threshold 2

[0517] - Event I1: Interference becomes above the threshold

[0518] - Event C1: NR side link channel busy rate is higher than the threshold

[0519] - Event C2: NR side link channel busy rate is lower than the threshold

[0520] - Event X1: The serving L2 U2N relay UE becomes worse than threshold 1, and the NR cell becomes better than threshold 2

[0521] - Event X2: Serving L2 U2N relay UE becomes worse than threshold

[0522] - Event Y1: PCell becomes worse than threshold 1, and candidate L2 U2N relay UE becomes better than threshold 2

[0523] - Event Y2: Candidate L2 U2N relay UE becomes better than the threshold

[0524] Fig. 20 An example of a measurement report is shown.

[0525] The purpose of this procedure is to transfer measurement results from the UE to the network. The UE shall initiate this procedure only after successful AS security activation.

[0526] For the measId for which the measurement reporting procedure is triggered, the UE sets the measResults in the MeasurementReport message as follows:

[0527] 1> Set measId to the measurement identifier that triggers the measurement report;

[0528] 1> For each serving cell configured with servingCellMO:

[0529] 2> If the reportConfig associated with the measId that triggered the measurement report includes rsType:

[0530] 3> If serving cell measurement based on rsType included in reportConfig that triggers the measurement report is available:

[0531] 4> Set measResultServingCell in measResultServingMOList to include the RSRP, RSRQ and available SINR of the serving cell derived based on the rsType included in reportConfig that triggered the measurement report;

[0532] 2> Otherwise:

[0533] 3> If SSB-based serving cell measurement is available:

[0534] 4> Set measResultServingCell in measResultServingMOList to include RSRP, RSRQ and available SINR of the serving cell derived based on SSB;

[0535] 3> Otherwise, if CSI-RS based serving cell measurement is available:

[0536] 4> Set measResultServingCell in measResultServingMOList to include the RSRP, RSRQ and available SINR of the serving cell derived based on CSI-RS;

[0537] 1> Set servCellId in measResultServingMOList to include each NR serving cell configured with servingCellMO (if any);

[0538] 1> If the reportConfig associated with the measId that triggers the measurement report includes reportQuantityRS-Indexes and maxNrofRS-IndexesToReport:

[0539] 2> For each serving cell configured with servingCellMO, include beam measurement information according to the associated reportConfig;

[0540] 1> If the reportConfig associated with the measId that triggered the measurement report includes reportAddNeighMeas:

[0541] 2> For each measObjectId referenced in measIdList that is also referenced by servingCellMO, except for the measObjectId corresponding to the measId that triggered the measurement report:

[0542] 3> If the measObjectNR indicated by servingCellMO includes the RS resource configuration corresponding to the rsType indicated in reportConfig:

[0543] 4> If the RSRP measurement result is available for the cell corresponding to the relevant measObjectNR with the highest measured RSRP, then the measResultBestNeighCell in measResultServingMOList is set to include the physCellId and the available measurement quantity based on the reportQuantityCell and rsType indicated in the reportConfig of the non-serving cell corresponding to the measObjectNR, otherwise if the RSRQ measurement result is available for the cell corresponding to the measObjectNR, it will be the cell with the highest measured RSRQ, otherwise it will be the cell with the highest measured SINR;

[0544] 4> If the reportConfig associated with the measId that triggers the measurement report includes reportQuantityRS-Indexes and maxNrofRS-IndexesToReport:

[0545] 5> For each best non-serving cell included in the measurement report:

[0546] 6> Includes beam measurement information according to the associated reportConfig;

[0547] Reporting of beam measurement information

[0548] For the beam measurement information to be included in the measurement report, the UE shall:

[0549] 1>If reportType is set to eventTriggered:

[0550] 2> If available, the trigger quantity is considered as the ranking quantity, otherwise, if available, RSRP is considered as the ranking quantity, otherwise, if available, RSRQ is considered as the ranking quantity, otherwise, SINR is considered as the ranking quantity;

[0551] 1>If reportType is set to periodical:

[0552] 2>If single report quantity is set to true in reportQuantityRS-Indexes;

[0553] 3> The configured single quantity is considered as the sorted quantity;

[0554] 2> Otherwise:

[0555] 3>If rsrp is set to true;

[0556] 4> RSRP is considered as the ranking quantity;

[0557] 3> Otherwise:

[0558] 4> RSRQ is considered as the ranking quantity;

[0559] 1> Set rsIndexResults to include up to maxNrofRS-IndexesToReport SS / PBCH block indices or CSI-RS indices in order of decreasing sort amount as follows:

[0560] 2> If the measurement information to be included is based on SS / PBCH blocks:

[0561] 3> Include in resultsSSB-Indexes the index associated with the best beam for this SS / PBCH block ranking amount, and if absThreshSS-BlocksConsolidation is included in the VarMeasConfig for the measObject associated with the cell to report the beam, the remaining beams with a ranking amount higher than absThreshSS-BlocksConsolidation;

[0562] 3> If includeBeamMeasurements is set to true, include the SS / PBCH-based measurements for quantity in reportQuantityRS-Indexes for each SS / PBCH block index;

[0563] 2> Otherwise, if the beam measurement information to be included is based on CSI-RS:

[0564] 3> include in resultsCSI-RS-Indexes the index associated with the best beam for that CSI-RS ranking amount, and if absThreshCSI-RS-Consolidation is included in the VarMeasConfig for the measObject associated with the cell for which the beam is to be reported, the remaining beams with a ranking amount higher than absThreshCSI-RS-Consolidation;

[0565] 3> If includeBeamMeasurements is set to true, the CSI-RS based measurements for quantity are included in reportQuantityRS-Indexes for each CSI-RS index.

[0566] In addition, the application of AI / ML to wireless communications has been studied to improve the overall network and UE operating performance and the ability to provide various services. Using AI / ML, both the network and the UE can predict mobility and share the results to improve performance.

[0567] In 6G, the THz band can be used for a large amount of available bandwidth to meet the 6G requirements of Tbps data rates. However, in this high-frequency coverage, the cell coverage will be reduced and a large number of handovers will occur more frequently. This may result in handovers too early, too late, or to the wrong cell.

[0568] To improve mobility performance, the network and UE can use the predicted measurement results to find the appropriate time and suitable cell for handover. When the network instructs the UE to report a predicted measurement report, the UE can send a predicted measurement report of the cell for a future time. However, if the gap between the actual measurement and the predicted measurement is large, the network may make an incorrect decision about mobility. In order to notify the network of the wrong prediction, the UE needs to report another measurement report.

[0569] Therefore, there is a need for research on measurement prediction in wireless communication systems.

[0570] Hereinafter, a method for measurement prediction in a wireless communication system according to some embodiments of the present disclosure will be described with reference to the following drawings.

[0571] The following figures are created to explain the specific embodiments of the present disclosure. The names of the specific devices or the names of the specific signals / messages / fields shown in the figures are provided by way of example, and therefore the technical features of the present disclosure are not limited to the specific names used in the following figures. In this article, a wireless device may be referred to as a user equipment (UE).

[0572] Fig.21 An example of a method for measurement prediction in a wireless communication system according to some embodiments of the present disclosure is shown.

[0573] Specifically, Fig.21 An example of a method performed by a wireless device in a wireless communication system is shown.

[0574] In step S2101 , the wireless device may receive a measurement configuration including a measurement object from a network.

[0575] For example, the measurement object may include information about at least one cell. For example, the measurement object may include information about at least one reference signal and / or at least one synchronization signal block (SSB). For example, at least one reference signal may include a channel state information reference signal (CSI-RS).

[0576] For example, a measurement configuration may include reporting conditions.

[0577] For example, the reporting conditions may include information about at least one reporting event.

[0578] For example, the at least one report may include at least one of the following:

[0579] - Event A1: Service becomes better than threshold

[0580] - Event A2: Service becomes worse than threshold

[0581] - Event A3: Neighbor becomes better offset than SpCell

[0582] - Event A4: Neighbor becomes better than threshold

[0583] - Event A5: SpCell becomes worse than threshold 1, and the neighbor becomes better than threshold 2

[0584] - Event A6: Neighbor becomes better offset than SCell

[0585] - Event B1: Inter-RAT neighbor becomes better than the threshold

[0586] - Event B2: PCell becomes worse than threshold 1, and inter-RAT neighbors become better than threshold 2

[0587] - Event I1: Interference becomes above the threshold

[0588] - Event C1: NR side link channel busy rate is higher than the threshold

[0589] - Event C2: NR side link channel busy rate is lower than the threshold

[0590] - Event X1: The serving L2 U2N relay UE becomes worse than threshold 1, and the NR cell becomes better than threshold 2

[0591] - Event X2: Serving L2 U2N relay UE becomes worse than threshold

[0592] - Event Y1: PCell becomes worse than threshold 1, and candidate L2 U2N relay UE becomes better than threshold 2

[0593] - Event Y2: Candidate L2 U2N relay UE becomes better than the threshold

[0594] In step S2102, the wireless device may derive a first prediction for a measurement object.

[0595] For example, the wireless device may configure a first prediction window. At a first time point, the wireless device may start measurement prediction within the first prediction window. The wireless device may derive one or more measurement results for the measurement object within the first prediction window.

[0596] In step S2103, the wireless device may send information about the first prediction to the network.

[0597] In step S2104, the wireless device may derive a second prediction for the measurement object.

[0598] For example, the wireless device may configure a second prediction window. For example, the second prediction window may have the same duration as the first prediction window. At a second time point, the wireless device may start measurement prediction within the second prediction window. The wireless device may derive one or more measurement results for the measurement object within the second prediction window.

[0599] For example, a first prediction for the measurement object may be derived based on satisfying the reporting condition. A second prediction for the measurement object may be derived based on satisfying the reporting condition.

[0600] For example, a first prediction may be derived at a first time point for a specific future time point. A second prediction may be derived at a second time point for the specific future time point.

[0601] For example, the first prediction may include at least one predicted measurement result at a future point in time. The second prediction may include at least one predicted measurement result at a future point in time.

[0602] For example, the first prediction may include at least one predicted time that satisfies the reporting condition. The second prediction may include at least one predicted time that satisfies the reporting condition.

[0603] In step S2105, based on the difference between the first prediction and the second prediction, the wireless device may send information related to the difference to the network.

[0604] For example, the wireless device may determine whether to send information related to the difference to the network. When the difference between the first prediction and the second prediction is equal to or greater than a threshold, the wireless device may determine to send information related to the difference to the network. When the difference between the first prediction and the second prediction is less than a threshold, the wireless device may determine not to send information related to the difference to the network.

[0605] In other words, information related to the difference is transmitted based on the difference between the first prediction and the second prediction being equal to or greater than a threshold value.

[0606] According to some embodiments of the present disclosure, a wireless device may communicate with at least one of a user device other than the wireless device, a network, or an autonomous vehicle.

[0607] In the following, some embodiments of a method for measurement prediction in a wireless communication system are described.

[0608] In the present disclosure, the UE may provide the network with predicted measurement results for future time to assist the network in pre-processing required operations. For mobility, the network may also use the predicted measurement results to predict appropriate mobility time and appropriate target cell for mobility.

[0609] However, the prediction results may be inaccurate. In a wireless channel, the prediction of channel quality necessarily introduces prediction errors. If the prediction task is intended to predict the far future, the prediction error tends to increase (i.e., the error at a longer prediction horizon is higher). In contrast, if the prediction task is intended to predict the near future, the prediction error tends to decrease (i.e., the error at a shorter prediction horizon is smaller). If the prediction results provided to the network are inaccurate, the network may make incorrect decisions for mobility and / or radio resource management.

[0610] To avoid misjudgment, if the UE detects that the previous predicted measurement report is inaccurate, the UE may send a supplementary measurement report.

[0611] The network may configure a measurement configuration including a measurement object and a measurement report condition for a measurement target (cell / beam / reference signal).

[0612] The network may configure the UE with measurement configuration for measurement reporting.

[0613] >The measurement configuration may include measurement objects and measurement reporting conditions.

[0614] >The measurement configuration can include prediction time information.

[0615] >> Report configuration can include forecast time information.

[0616] >>The predicted time information may include duration T.

[0617] The network can be configured with a threshold associated with the prediction error.

[0618] >The UE can use a threshold to detect erroneous predicted measurement results.

[0619] To perform predictive measurements, the UE may be configured with more prediction model configurations.

[0620] >The prediction model configuration may include prediction model structure information,

[0621] >>The network can configure the machine learning model to be used by the UE.

[0622] >>>Networks can include machine learning types such as reinforcement learning, supervised learning, or unsupervised learning.

[0623] >>>Networks can include machine learning models such as DNN, CNN, RNN, and DRL.

[0624] >>>The configured ML model can be a pre-trained ML model that has been trained a priori by a network.

[0625] >>>>Describe the configured ML model through model description information including model structure and parameters.

[0626] >>>>For example, a neural network-based model may include an input layer, an output layer, and a hidden layer, wherein each layer includes one or more neurons.

[0627] >>>>>Connect different layers based on the connections between neurons in different layers

[0628] >>>>>> Each connection between two different neurons in two different layers can be directional (e.g., neuron A to neuron B, meaning the output of neuron A is fed into neuron B)

[0629] >>>>>>Each neuron can provide input to one or more connected neurons (1 to N connections).

[0630] >>>>>>For a connection between two neurons (neuron A to neuron B), the output of one neuron (A) is scaled by the weight, and the other neuron takes the scaled output as its input.

[0631] >>>>>>Each neuron can take input from one or more connected neurons (N to 1 connection) and combines the inputs from connected neurons and produces an output based on an activation function.

[0632] >>>Configured ML model can be the ML model to be trained.

[0633] >>>>The configured ML model is described by the model description information, which includes the model structure and initial parameters to be trained.

[0634] >>>>When the network configures the ML model to be trained, it is also possible to configure the training parameters such as the optimization target and optimization-related configuration parameters.

[0635] >>>The network may include machine learning input parameters for the machine learning model (e.g., UE location information, radio measurements related to serving and neighboring cells, UE mobility history).

[0636] >>>The network can include machine learning outputs (e.g., UE trajectory prediction, predicted target cell, predicted switching time, and UE traffic prediction).

[0637] >>UE can perform machine learning model training, validation, and testing, which can generate model performance metrics based on predictive model configuration.

[0638] >>>UE can perform model training using machine learning input parameters.

[0639] >>UE can use the configured ML model to perform ML tasks such as prediction of measurements.

[0640] >>>UE can derive machine learning output.

[0641] >>>UE can make inferences based on the output and use the output as feedback to the machine learning model.

[0642] >> The UE can send feedback to the network regarding the results related to the machine learning output and the accuracy of the machine learning model.

[0643] >>>The network can update the machine learning model and parameters associated with the machine learning model.

[0644] At each time, the UE may derive measurement results based on the measurement configuration and the configured ML model.

[0645] >The UE may perform measurements and perform necessary operations to derive measurement results.

[0646] >UE can derive predicted measurement results for cells associated with relevant measurement objects for future times

[0647] >If the corresponding measurement object is associated with the predicted time information T1, the UE can derive the predicted measurement result.

[0648] >>For example, at the current time t, the UE can derive the predicted measurement results for t+T1.

[0649] >The UE may derive predicted measurement results for cells associated with the relevant measurement object for a certain future time.

[0650] >>The UE can derive the future time when the predicted measurement result meets the reporting condition within the prediction time window T2.

[0651] >To derive the predicted measurement results, the UE may apply the configured prediction model.

[0652] If the predicted measurement result of the relevant cell for the future time satisfies the measurement report condition, the UE may send a first measurement report for the future time.

[0653] The first measurement result may include at least one of the following:

[0654] >The time to trigger the first predicted measurement report, represented by t a express.

[0655] >Current time (t a ) is measured by M(t a )express.

[0656] >The time at which the predicted measurement result satisfies the reported event is denoted by t1.

[0657] >>According to an embodiment, t1 may be a time without considering TTT. That is, t1 is the time when the measurement result initially meets the reporting event.

[0658] >>According to an embodiment, t1 may be a time for which TTT is considered. That is, t1 is a time for which a measurement result keeps satisfying a reporting event within the duration of TTT.

[0659] If the prediction time window T1 is configured,

[0660] Condition: For the time period [t1-TTT, t a +T1] remains to meet reporting conditions.

[0661] If the prediction time window T2 is not configured,

[0662] Condition: The forecast measurements for the time period [t1-TTT, t1] remain to satisfy the reporting condition.

[0663] If the minimum prediction time T2 is configured min ,

[0664] Condition: The forecast measurement for the time period [t1-TTT, t1] remains to meet the reporting condition, and t1 is not within t a +T2 min Before.

[0665] If the maximum prediction time T2 is configured max ,

[0666] Condition: The forecast measurement for the time period [t1-TTT, t1] remains to meet the reporting condition, and t1 is within ta+T2 max Before.

[0667] >At current time t a The predicted measurement result for time t1 is derived from P t1 (t a )express.

[0668] >UE derives the predicted time information (T1) for reporting the predicted measurement results.

[0669] >UE derives the prediction time window (T2) for the predicted measurement results to be reported.

[0670] >One or more predicted measurement results, in units of, for example, RSRP, RSRQ or SINR.

[0671] For each time in the prediction window, a series of {time, predicted measurement result of time} may be included. The time interval of the entries may be configured by the network.

[0672] >Measurement ID to indicate the measurement object and reporting event associated with the measurement report.

[0673] The UE keeps performing / deriving predicted measurements of the relevant cells that triggered the first measurement report.

[0674] The UE may send a second measurement report based on the prediction error of the measurement target.

[0675] Fig. 22 , Fig.23 and Fig.24 An example of a criterion for determining a prediction error is shown.

[0676] Specifically, Fig. 22 The case where the prediction error is measured as the difference in measurement quality between predicted measurements derived at different times (ta and tb, respectively) for a certain future time t1 is illustrated.

[0677] For example, Fig. 22 As shown, the error is measured as a and t b ) is derived from the predicted measurement results, where the specific future time t1 is based on the measurement quality difference between the predicted measurement results at t a The predicted measurement results of the derived measurement target meet the time of the measurement report event. That is, based on the value P t1 (t b )-P t1 (t a ) derivation error, where P y (x) indicates the predicted measurement result of the measurement target derived at time x for the relevant future time y. That is,

[0678] >Alt1: Error = P t1 (t b )-P t1 (t a), that is, a positive error value may indicate that the prediction result in the second measurement report is better than the prediction result in the first measurement report. Equivalently, a negative error value of the error may indicate that the prediction result in the second measurement report is worse than the prediction result in the first measurement report.

[0679] >Alt2: Error = P t1 (t b )-P t1 (t a )

[0680] Specifically, Fig.23 The case where the error is measured as the time difference between the second time and the first time at which the reporting event is satisfied based on the predicted measurements derived at the corresponding times is illustrated.

[0681] For example, the error is measured as the time difference between a first time and a second time, such as Fig.23 As shown,

[0682] The first time (t1) is based on the a The time at which the predicted measurement results of the derived measurement target meet the reporting criteria.

[0683] The second time (t1') is based on the b The time at which the predicted measurement results of the derived measurement target meet the reporting criteria.

[0684] That is to say,

[0685] Alt1: Error = (t1' - t1), ie, a positive error value may indicate that a time included in the first measurement result is before a time included in the second measurement result by an amount of the error value.

[0686] Atl2: Error = (t1-t1'),

[0687] Specifically, Fig.24 The example shows that the error is measured as the error at different times (t a and t b ) in the case of poor measurement quality between the predicted measurement results derived.

[0688] For example, Fig.24 As shown, the error is measured as the difference between the values ​​of a and t b ) is the difference in measurement quality between the predicted measurement results derived from t2 (t b )-P t2 (t a ) derivation error, where P y(x) indicates the predicted measurement result of the measurement target derived at time x for future time y. That is,

[0689] >Alt1: Error = P t2 (t b )-P t2 (t a ), that is, a positive error value may indicate that the prediction result in the second measurement report is better than the prediction result in the first measurement report. Equivalently, a negative error value of the error may indicate that the prediction result in the second measurement report is worse than the prediction result in the first measurement report.

[0690] >Alt2: Error = P t2 (t a )-P t2 (t b )

[0691] Here, t2>t b (Therefore, considering t b >t a , t2>t a ).

[0692] The UE may send a measurement report based on the absolute value of the prediction error.

[0693] If the absolute value of the prediction error is greater than the threshold, the UE sends a second measurement report.

[0694] If the absolute value of the prediction error is smaller than the threshold, the UE does not send the second measurement report.

[0695] The second measurement report may include at least one of the following:

[0696] >t1': The time at which the most recent predicted measurement result of the measurement target satisfies the reporting event.

[0697] >Information used to indicate that the measurement report is a supplementary measurement report, which may include:

[0698] >>t1: The time when the predicted measurement result of the measurement target in the first measurement report meets the reporting event.

[0699] >>Measurement ID of the first measurement report.

[0700] >>Indicator indicating that the measurement report is a second (supplementary) measurement report associated with the first measurement report.

[0701] >t b : Time to trigger / send the second measurement report

[0702] >M(t b ): t bThe measurement results of the measurement target at the location.

[0703] >P t1’ (t b ): For time t1' at t b Predicted measurement results for derived measurement targets

[0704] >P t1 (t b ): For time t1 at t b The predicted measurement results of the derived measurement target, where t1 is the time when the reporting event is met in the first measurement report.

[0705] >If this error type is used to trigger a second measurement report, it is the error in the predicted quality.

[0706] >If this error type is used to trigger the second measurement report, it is the error in the predicted time.

[0707] >Accuracy information of the prediction model calculated on the UE side

[0708] >Thresholds configured by the network.

[0709] Fig.25 and Fig.26 An example of a second measurement report based on a prediction error (gap) between a first measurement result and a second measurement result for a specific future time is shown.

[0710] Specifically, Fig.25 A predicted measurement report for an A3 event for a future time ta+T1 is illustrated, wherein there is a large gap between a first predicted measurement result and a second predicted measurement result.

[0711] Specifically, Fig.26 A predicted measurement report for an A3 event for a future time ta+T1 is illustrated, wherein there is a small gap between a first predicted measurement result and a second predicted measurement result.

[0712] 1) The network configures the measurement configuration of the cell (including measurement objects and measurement report configuration), prediction time information T1 for the predicted measurement result, and a threshold for detecting erroneous predicted measurement results.

[0713] 2) UE for the future time t indicated in the time information a +T1 derivation time t a The first predicted measurement result of the cell.

[0714] 3) If the first measurement result for the future time meets the reporting condition, the UE sends a first measurement report of the cell.

[0715] > Fig.25and Fig.26 Of the two, in t a , the UE sends a first measurement report.

[0716] >The first measurement report includes the measurement results for time t a +T1 measurement results.

[0717] 4) For time t a +T1, UE at time t b A second predictive measurement of the cell is performed.

[0718] 5) UE determines the future time t a + The prediction error (gap) between the first and second measurements of T1.

[0719] 6-1) If at time t b For time t a +T1 gap is greater than the threshold, the UE sends a second measurement report for the cell. Fig.25 )

[0720] 6-2) If for time t a + If the T1 gap is smaller than the threshold, the UE does not send the second measurement report for the cell. Fig.26 )

[0721] For example, the technical features related to the A3 event are as follows.

[0722] Inequality A3-1 (entry condition)

[0723] Mn+Ofn+Ocn–Hys>Mp+Ofp+Ocp+Off

[0724] Inequality A3-2 (leave condition)

[0725] Mn+Ofn+Ocn+Hys <Mp+Ofp+Ocp+Off

[0726] Define the variables in the formula as follows:

[0727] Mn is the measurement result of the neighboring cells without considering any offset.

[0728] Ofn is the measurement object specific offset of the reference signal of the neighbor cell (ie, offsetMO defined in measObjectNR corresponding to the neighbor cell).

[0729] Ocn is the cell specific offset of the neighbor cell (ie, cellIndividualOffset defined within measObjectNR for the frequency corresponding to the neighbor cell) and is set to zero if not configured for the neighbor cell.

[0730] Mp is the measurement result of SpCell without considering any offset.

[0731] Ofp is the measurement object specific offset of the SpCell (ie, offsetMO defined in the measObjectNR corresponding to the SpCell).

[0732] Ocp is the cell-specific offset of the SpCell (ie, cellIndividualOffset defined in measObjectNR corresponding to the SpCell) and is set to zero if not configured for the SpCell.

[0733] Hys is the hysteresis parameter for this event (ie, the hysteresis defined in reportConfigNR for this event).

[0734] Off is the offset parameter for this event (ie, a3-Offset defined in reportConfigNR for this event).

[0735] Mn and Mp are expressed in dBm in the case of RSRP, or in dB in the case of RSRQ and RS-SINR.

[0736] Ofn, Ocn, Ofp, Ocp, Hys, and Off are expressed in dB.

[0737] - The definition of Event A3 also applies to CondEvent A3.

[0738] Fig. 27 and Fig.28 An example of a second measurement report based on a predicted error (gap) between a first measurement result and a second measurement result for some future time is shown.

[0739] Specifically, Fig. 27 A predicted measurement report for an A3 event for a future time t1 (= t0 + TTT) is illustrated, wherein there is a large gap between a first predicted measurement result and a second predicted measurement result.

[0740] Specifically, Fig.28 A predicted measurement report for an A3 event for a future time t1 (= t0 + TTT) is illustrated, wherein there is a small gap between a first predicted measurement result and a second predicted measurement result.

[0741] 1) The network configures the measurement configuration applicable to the cell (including measurement objects and measurement report configuration) and the threshold for detecting erroneous predicted measurement results.

[0742] 2) The UE derives a first measurement result of the cell for a future time when the predicted measurement result meets the reporting condition.

[0743] 3) If at the current time t a If the first measurement result is met, the UE sends a first measurement report of the cell.

[0744] > Fig. 27 and Fig.28 In t a , the UE sends a first measurement report.

[0745] >The first measurement report includes time information t1 and a measurement result for time t1.

[0746] 4) For time t1, UE at time t b A second predictive measurement of the cell is performed.

[0747] 5) The UE determines the prediction error (gap) between the first measurement result and the second measurement result for the future time t1.

[0748] 6-1) If at time t b If the prediction error (gap) is greater than the threshold, the UE sends a second measurement report for the cell. Fig. 27 )

[0749] 6-2) If the prediction error (gap) is less than the threshold, the UE does not send the second measurement report of the cell. Fig.28 )

[0750] Fig.29 An example of a second measurement report when the second predicted measurement result does not satisfy the reporting condition for a specific future time is shown.

[0751] Specifically, Fig.29 The predicted measurement report for the A3 event at the future time ta+T1 is illustrated when the second predicted measurement result does not satisfy the reporting condition.

[0752] 1) The network configures the measurement configuration applicable to the cell (including measurement objects and measurement report configuration), prediction time information T1 for the predicted measurement result, and a threshold for detecting erroneous predicted measurement results.

[0753] 2) UE predicts the future time t indicated in the time information a +T1 derivation time t a The first measurement result of the cell.

[0754] 3) If the first measurement result meets the reporting condition, the UE sends a first measurement report of the cell.

[0755] >The first measurement report includes the measurement results for time ta +T1 measurement results

[0756] 4) At time t a Afterwards, the UE keeps performing predictive measurements of the cell.

[0757] 5) When the UE predicts that the measurement result does not meet the reporting condition for time ta+T1, the UE b ) sends a second measurement report of the cell.

[0758] Fig.30 An example of a second measurement report when the second predicted measurement result does not satisfy a reporting condition for a certain future time is shown.

[0759] Specifically, Fig.30 The example shows a predicted measurement report for the A3 event at the future time t1+TTT when the second predicted measurement result does not satisfy the reporting condition.

[0760] 1) The network configures the measurement configuration applicable to the cell (including measurement objects and measurement report configuration) and the threshold for detecting erroneous predicted measurement results.

[0761] 2) UE derives time t for a future time when the predicted measurement result meets the reporting condition a The first measurement result of the cell.

[0762] 3) If the first measurement result meets the reporting condition, the UE sends a first measurement report of the cell.

[0763] >The first measurement report includes time information t1 (t0+TTT) and the measurement result for time t1.

[0764] 4) UE at time t a Then keep performing the predicted measurement of the cell.

[0765] 5) When the UE predicts that the measurement result does not meet the reporting condition for time t1, the UE b ) sends a second measurement report of the cell.

[0766] According to some embodiments of the present disclosure, a wireless device may receive a measurement report configuration applicable to a cell from a network. For example, the configuration may include a first report configuration and a second report configuration. The first report configuration may include a report event applicable to the cell. The second report configuration may include a prediction error related configuration.

[0767] The wireless device may derive a first measurement result of a cell derived at a first time for a future time. The wireless device may send a first measurement report for the cell to the network based on the first reporting configuration. For example, the first measurement result may satisfy a reporting event included in the first reporting configuration.

[0768] The wireless device may derive a second measurement result of the cell derived at the second time for the future prediction time. The wireless device may send a second measurement report for the cell to the network based on the second reporting configuration. For example, the prediction error may be determined based on the first measurement, the second measurement, and the second reporting configuration.

[0769] Figures 21 to 30 Some of the detailed steps shown in the examples may not be necessary steps and may be omitted. Figures 21 to 30 In addition to the steps shown, other steps may be added, and the order of the steps may be changed. Some of the above steps may have their own technical meanings.

[0770] Hereinafter, a device for measurement prediction in a wireless communication system according to some embodiments of the present disclosure will be described. Here, the device may be Figure 2 , Figure 3 and Figure 5 A wireless device (100 or 200) in.

[0771] For example, the wireless device may perform the above method. Detailed descriptions overlapping with the above may be simplified or omitted.

[0772] Reference Figure 5 , the wireless device 100 may include a processor 102 , a memory 104 , and a transceiver 106 .

[0773] According to some embodiments of the present disclosure, the processor 102 may be configured to be operatively coupled with the memory 104 and the transceiver 106 .

[0774] The processor 102 may be configured to control the transceiver 106 to receive a measurement configuration including a measurement object from a network. The processor 102 may be configured to derive a first prediction for the measurement object. The processor 102 may be configured to control the transceiver 106 to send information about the first prediction to the network. The processor 102 may be configured to derive a second prediction for the measurement object. Based on a difference between the first prediction and the second prediction, the processor 102 may be configured to control the transceiver 106 to send information related to the difference to the network.

[0775] For example, the measurement object may include information about at least one cell.

[0776] For example, the measurement object may include information about at least one reference signal and / or at least one synchronization signal block (SSB).

[0777] For example, processor 102 may be configured to determine whether to send information related to the difference to the network.

[0778] For example, information related to the difference may be sent based on the difference between the first prediction and the second prediction being equal to or greater than a threshold.

[0779] For example, a measurement configuration may include reporting conditions.

[0780] For example, a first prediction for the measurement object may be derived based on satisfying the reporting condition.For example, a second prediction for the measurement object may be derived based on satisfying the reporting condition.

[0781] For example, the first prediction may be derived at a first time point for a specific future time point. For example, the second prediction may be derived at a second time point for a specific future time point.

[0782] For example, the first prediction may include at least one predicted measurement result at a future time point.For example, the second prediction may include at least one predicted measurement result at a future time point.

[0783] For example, the first prediction may include at least one predicted time that satisfies the reporting condition.For example, the second prediction may include at least one predicted time that satisfies the reporting condition.

[0784] For example, the processor 102 may be configured to control the transceiver 106 to communicate with at least one of a user device other than a wireless device, a network, or an autonomous vehicle.

[0785] Hereinafter, a processor of a wireless device for measurement prediction in a wireless communication system according to some embodiments of the present disclosure will be described.

[0786] The processor may be configured to control the wireless device to receive a measurement configuration including a measurement object from a network. The processor may be configured to control the wireless device to derive a first prediction for the measurement object. The processor may be configured to control the wireless device to send information about the first prediction to the network. The processor may be configured to control the wireless device to derive a second prediction for the measurement object. Based on a difference between the first prediction and the second prediction, the processor may be configured to control the wireless device to send information related to the difference to the network.

[0787] For example, the measurement object may include information about at least one cell.

[0788] For example, the measurement object may include information about at least one reference signal and / or at least one synchronization signal block (SSB).

[0789] For example, the processor may be configured to control the wireless device to determine whether to send information related to the difference to the network.

[0790] For example, information related to the difference may be sent based on the difference between the first prediction and the second prediction being equal to or greater than a threshold.

[0791] For example, a measurement configuration may include reporting conditions.

[0792] For example, a first prediction for the measurement object may be derived based on satisfying the reporting condition.For example, a second prediction for the measurement object may be derived based on satisfying the reporting condition.

[0793] For example, the first prediction may be derived at a first time point for a specific future time point. For example, the second prediction may be derived at a second time point for a specific future time point.

[0794] For example, the first prediction may include at least one predicted measurement result at a future time point.For example, the second prediction may include at least one predicted measurement result at a future time point.

[0795] For example, the first prediction may include at least one predicted time that satisfies the reporting condition.For example, the second prediction may include at least one predicted time that satisfies the reporting condition.

[0796] For example, the processor may be configured to control the wireless device to communicate with at least one of a user device other than the wireless device, a network, or an autonomous vehicle.

[0797] In the following, a non-transitory computer-readable medium having stored thereon a plurality of instructions for measurement prediction in a wireless communication system according to some embodiments of the present disclosure will be described.

[0798] According to some embodiments of the present disclosure, the technical features of the present disclosure may be directly implemented in hardware, in software executed by a processor, or in a combination of the two. For example, a method performed by a wireless device in wireless communication may be implemented in hardware, software, firmware, or any combination thereof. For example, the software may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other storage medium.

[0799] Some examples of storage media are coupled to a processor so that the processor can read information from the storage media. In an alternative, the storage medium can be integrated into the processor. The processor and the storage medium can reside in an ASIC. For another example, the processor and the storage medium can reside as discrete components.

[0800] The computer-readable medium may include tangible and non-transitory computer-readable storage media.

[0801] 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 foregoing.

[0802] Additionally, the methods described herein may be implemented 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.

[0803] According to some embodiments of the present disclosure, a plurality of instructions are stored on a non-transitory computer-readable medium, and the plurality of stored instructions may be executed by a processor of a wireless device.

[0804] The stored plurality of instructions may cause the wireless device to receive a measurement configuration including a measurement object from a network. The stored plurality of instructions may cause the wireless device to derive a first prediction for the measurement object. The stored plurality of instructions may cause the wireless device to send information about the first prediction to the network. The stored plurality of instructions may cause the wireless device to derive a second prediction for the measurement object. Based on a difference between the first prediction and the second prediction, the stored plurality of instructions may cause the wireless device to send information related to the difference to the network.

[0805] For example, the measurement object may include information about at least one cell.

[0806] For example, the measurement object may include information about at least one reference signal and / or at least one synchronization signal block (SSB).

[0807] For example, the stored plurality of instructions may cause the wireless device to determine whether to send information related to the difference to the network.

[0808] For example, information related to the difference may be sent based on the difference between the first prediction and the second prediction being equal to or greater than a threshold.

[0809] For example, a measurement configuration may include reporting conditions.

[0810] For example, a first prediction for the measurement object may be derived based on satisfying the reporting condition.For example, a second prediction for the measurement object may be derived based on satisfying the reporting condition.

[0811] For example, the first prediction may be derived at a first time point for a specific future time point. For example, the second prediction may be derived at a second time point for a specific future time point.

[0812] For example, the first prediction may include at least one predicted measurement result at a future time point.For example, the second prediction may include at least one predicted measurement result at a future time point.

[0813] For example, the first prediction may include at least one predicted time that satisfies the reporting condition.For example, the second prediction may include at least one predicted time that satisfies the reporting condition.

[0814] According to some embodiments of the present disclosure, the stored plurality of instructions may enable the wireless device to communicate with at least one of a user device other than the wireless device, a network, or an autonomous vehicle.

[0815] Hereinafter, a method for measurement prediction performed by a base station in a wireless communication system according to some embodiments of the present disclosure will be described.

[0816] The BS may send a measurement configuration including a measurement object to the wireless device. The BS may receive information about a first prediction from the wireless device, wherein the first prediction is derived from the wireless device at a first point in time. The BS may receive information related to a difference between the first prediction and the second prediction from the wireless device. For example, the second prediction may be derived from the wireless device at a second point in time that comes after the first point in time.

[0817] Hereinafter, a base station for measurement prediction in a wireless communication system according to some embodiments of the present disclosure will be described.

[0818] The BS may include a transceiver, a memory, and a processor operatively coupled to the transceiver and the memory.

[0819] The processor may be configured to control the transceiver to send a measurement configuration including a measurement object to the wireless device. The processor may be configured to control the transceiver to receive information about a first prediction from the wireless device, wherein the first prediction is derived from the wireless device at a first time point. The processor may be configured to control the transceiver to receive information related to a difference between the first prediction and the second prediction from the wireless device. For example, the second prediction may be derived from the wireless device at a second time point that comes after the first time point.

[0820] The present disclosure may have various beneficial effects.

[0821] According to some embodiments of the present disclosure, a wireless device may efficiently perform measurement prediction while taking prediction errors into consideration.

[0822] For example, the network can identify whether the prediction measurement is reliable and the accuracy of the machine learning model. The network then reconfigures the AI / ML configuration (e.g., model) and parameters for the AI / ML.

[0823] In other words, the network can evaluate the reliability of the predicted measurements and the accuracy of the machine learning model. Based on this evaluation, the network can then adjust the AI / ML configuration (including the model and its parameters).

[0824] For example, by providing predictive measurements to the network, handover failures can be reduced.

[0825] For example, when the predicted measurement results experience significant changes, the wireless device may improve the reliability of the prediction by providing updated predicted measurement results to the network.

[0826] According to some embodiments of the present disclosure, a wireless network system may provide an efficient solution for measurement.

[0827] The advantageous effects that can be obtained by the specific embodiments of the present disclosure are not limited to the advantageous effects listed above. For example, there may be various technical effects that can be understood by a person skilled in the relevant art and / or derived from the present disclosure. Therefore, the specific effects of the present disclosure are not limited to those explicitly described herein, but may include various effects that can be understood or derived from the technical features of the present disclosure.

[0828] The claims in this disclosure may be combined in various ways. For example, the technical features in the method claims of this disclosure may be combined to be implemented or performed in a device, and the technical features in the device claims may be combined to be implemented or performed in a method. In addition, the technical features in the method claims and the device claims may be combined to be implemented or performed in a device. In addition, the technical features in the method claims and the device claims may be combined to be implemented or performed in a method. Other implementations are within the scope of the appended claims.

Claims

1. A method performed by a wireless device in a wireless communication system, the method comprising the following steps: receiving a measurement configuration including a measurement object from a network; deriving a first prediction for the measurement object; sending information about the first prediction to the network; deriving a second prediction for the measurement object; as well as Based on the difference between the first prediction and the second prediction, information related to the difference is sent to the network.

2. The method according to claim 1, in, The measurement object includes information about at least one cell.

3. The method according to claim 1, in, The measurement object includes information about at least one reference signal and / or at least one synchronization signal block SSB.

4. The method according to claim 1, wherein: The method further comprises the following steps: A determination is made as to whether to send information related to the difference to the network.

5. The method according to claim 1, in, The information related to the difference is transmitted based on the difference between the first prediction and the second prediction being equal to or greater than a threshold.

6. The method according to claim 1, in, The measurement configuration includes reporting conditions.

7. The method according to claim 6, in, The first prediction for the measurement object is derived based on satisfying the reporting condition.

8. The method according to claim 6, in, The second prediction for the measurement object is derived based on satisfying the reporting condition.

9. The method according to claim 1, in, The first prediction is derived at a first time point for a specific future time point.

10. The method according to claim 9, in, The second prediction is derived at a second time point for the specific future time point.

11. The method according to claim 1, in, The first prediction comprises at least one predicted measurement result for a future point in time.

12. The method according to claim 11, in, The second prediction includes at least one predicted measurement result for the future point in time.

13. The method according to claim 1, in, The first prediction includes at least one predicted time that satisfies a reporting condition.

14. The method according to claim 13, in, The second prediction includes at least one predicted time that satisfies the reporting condition.

15. The method according to claim 1, in, The wireless device communicates with at least one of a user device other than the wireless device, a network, or an autonomous vehicle.

16. A wireless device in a wireless communication system, the wireless device comprising: Transceiver; Memory; as well as at least one processor operatively coupled to the transceiver and the memory and adapted to: controlling the transceiver to receive a measurement configuration including a measurement object from a network; deriving a first prediction for the measurement object; controlling the transceiver to send information about the first prediction to the network; deriving a second prediction for the measurement object; as well as Based on a difference between the first prediction and the second prediction, the transceiver is controlled to send information related to the difference to the network.

17. The wireless device according to claim 16, in, The measurement object includes information about at least one cell.

18. The wireless device according to claim 16, in, The measurement object includes information about at least one reference signal and / or at least one synchronization signal block SSB.

19. The wireless device of claim 16, wherein: The at least one processor is further adapted to: A determination is made as to whether to send information related to the difference to the network.

20. The wireless device according to claim 16, in, Information related to the difference is transmitted based on the difference between the first prediction and the second prediction being equal to or greater than a threshold.

21. The wireless device according to claim 16, in, The measurement configuration includes reporting conditions.

22. The wireless device according to claim 21, in, The first prediction for the measurement object is derived based on satisfying the reporting condition.

23. The wireless device according to claim 21, in, The second prediction for the measurement object is derived based on satisfying the reporting condition.

24. The wireless device according to claim 16, in, The first prediction is derived at a first time point for a specific future time point.

25. The wireless device according to claim 24, in, The second prediction is derived at a second time point for the specific future time point.

26. The wireless device according to claim 16, in, The first prediction comprises at least one predicted measurement result for a future point in time.

27. The wireless device according to claim 26, in, The second prediction includes at least one predicted measurement result for the future point in time.

28. The wireless device according to claim 16, in, The first prediction includes at least one predicted time that satisfies a reporting condition.

29. The wireless device according to claim 28, in, The second prediction includes at least one predicted time that satisfies the reporting condition.

30. The wireless device of claim 16, in, The wireless device communicates with at least one of a user device other than the wireless device, a network, or an autonomous vehicle.

31. A processor for a wireless device in a wireless communication system, wherein: The processor is configured to control the wireless device to perform operations including: receiving a measurement configuration including a measurement object from a network; deriving a first prediction for the measurement object; sending information about the first prediction to the network; deriving a second prediction for the measurement object; and Based on a difference between the first prediction and the second prediction, information related to the difference is sent to the network.

32. A non-transitory computer readable medium storing a plurality of instructions, the plurality of instructions, when executed by a processor of a wireless device, causing the wireless device to perform operations comprising: receiving a measurement configuration including a measurement object from a network; deriving a first prediction for the measurement object; sending information about the first prediction to the network; deriving a second prediction for the measurement object; as well as Based on a difference between the first prediction and the second prediction, information related to the difference is sent to the network.

33. A method performed by a base station in a wireless communication system, the method comprising the following steps: sending a measurement configuration including a measurement object to a wireless device; receiving information about a first prediction from the wireless device, wherein the first prediction is derived from the wireless device at a first point in time; and receiving, from the wireless device, information related to a difference between the first prediction and the second prediction, The second prediction is derived from the wireless device at a second time point that comes after the first time point.

34. A base station in a wireless communication system, the base station comprising: Transceiver; Memory; as well as a processor operatively coupled to the transceiver and the memory and adapted to: controlling the transceiver to send a measurement configuration including a measurement object to a wireless device; controlling the transceiver to receive information about a first prediction from the wireless device, wherein the first prediction is derived from the wireless device at a first point in time; and controlling the transceiver to receive information related to a difference between the first prediction and the second prediction from the wireless device, The second prediction is derived from the wireless device at a second time point that comes after the first time point.

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