Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W36/00—Hand-off or reselection arrangements
  • H04W36/0005—Control or signalling for completing the hand-off
  • H04W36/0055—Transmission or use of information for re-establishing the radio link
  • H04W36/0061—Transmission or use of information for re-establishing the radio link of neighbour cell information
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W36/00—Hand-off or reselection arrangements
  • H04W36/0005—Control or signalling for completing the hand-off
  • H04W36/0055—Transmission or use of information for re-establishing the radio link
  • H04W36/0072—Transmission or use of information for re-establishing the radio link of resource information of target access point
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W36/00—Hand-off or reselection arrangements
  • H04W36/02—Buffering or recovering information during reselection ; Modification of the traffic flow during hand-off
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W36/00—Hand-off or reselection arrangements
  • H04W36/24—Reselection being triggered by specific parameters
  • H04W36/32—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data
  • H04W36/324—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data by mobility data, e.g. speed data
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W36/00—Hand-off or reselection arrangements
  • H04W36/0005—Control or signalling for completing the hand-off
  • H04W36/0055—Transmission or use of information for re-establishing the radio link
  • H04W36/0069—Transmission or use of information for re-establishing the radio link in case of dual connectivity, e.g. decoupled uplink/downlink
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W36/00—Hand-off or reselection arrangements
  • H04W36/0005—Control or signalling for completing the hand-off
  • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists

Definitions

• the present disclosure relates to a method and apparatus for performing AI based procedure for dual connectivity in a wireless communication system.
• 3rd generation partnership project (3GPP) long-term evolution (LTE) is a technology for enabling high-speed packet communications.
• 3GPP 3rd generation partnership project
• LTE long-term evolution
• Many schemes have been proposed for the LTE objective including those that aim to reduce user and provider costs, improve service quality, and expand and improve coverage and system capacity.
• the 3GPP LTE requires reduced cost per bit, increased service availability, flexible use of a frequency band, a simple structure, an open interface, and adequate power consumption of a terminal as an upper-level requirement.
• ITU international telecommunication union
• NR new radio
• 3GPP has to identify and develop the technology components needed for successfully standardizing the new RAT timely satisfying both the urgent market needs, and the more long-term requirements set forth by the ITU radio communication sector (ITU-R) international mobile telecommunications (IMT)-2020 process.
• ITU-R ITU radio communication sector
• IMT international mobile telecommunications
• the NR should be able to use any spectrum band ranging at least up to 100 GHz that may be made available for wireless communications even in a more distant future.
• the NR targets a single technical framework addressing all usage scenarios, requirements and deployment scenarios including enhanced mobile broadband (eMBB), massive machine-type-communications (mMTC), ultra-reliable and low latency communications (URLLC), etc.
• eMBB enhanced mobile broadband
• mMTC massive machine-type-communications
• URLLC ultra-reliable and low latency communications
• the NR shall be inherently forward compatible.
• AI Artificial Intelligence
• ML machine learning
• a RAN may decide a target node based on measurement report of the signaling quality of the neighbor.
• a problem such as, radio link failure, ping-pang may happen.
• a method performed by a master node (MN) in a wireless communication system receives, from a source secondary node (SN), a SN change required message including (i) information on a candidate target SN and (ii) information informing that the candidate target SN is decided by the source SN using an AI model.
• the MN performs a SN addition procedure with the candidate target SN without using an AI model.
• an apparatus for implementing the above method is provided.
• the present disclosure may have various advantageous effects.
• UE's dual connectivity performance could be enhanced by using AI model.
• a RAN node could select and/or decide a target secondary node more accurately.
• a dual connectivity problem for example, a dual connectivity failure or SN change ping-pang
• a dual connectivity failure or SN change ping-pang could be avoided as much as possible. Then, UE's service could be guaranteed without interruption.
• a RAN node could efficiently perform AI based procedure for Dual Connectivity in a wireless communication system.
• a RAN node could efficiently use AI model for a procedure related to a dual connectivity by using an indication informing whether an AI based information has been considered.
• a RAN node could acquire information for an AI based procedure for a dual connectivity.
• FIG. 1 shows an example of a communication system to which implementations of the present disclosure is applied.
• FIG. 2 shows an example of wireless devices to which implementations of the present disclosure is applied.
• FIG. 3 shows an example of a wireless device to which implementations of the present disclosure is applied.
• FIG. 4 shows an example of UE to which implementations of the present disclosure is applied.
• FIGS. 5 and 6 show an example of protocol stacks in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.
• FIG. 7 shows an example of the overall architecture of an NG-RAN to which technical features of the present disclosure can be applied.
• FIG. 8 shows an interface protocol structure for F1-C to which technical features of the present disclosure can be applied.
• FIG. 9 shows an example of a functional framework for RAN Intelligence to which implementations of the present disclosure is applied.
• FIG. 10 shows a Secondary Node Addition procedure to which implementations of the present disclosure is applied.
• FIG. 11 shows a SN initiated SN Change procedure to which implementations of the present disclosure is applied.
• FIG. 12 shows an example of a method for performing AI based procedure for dual connectivity in a wireless communication system, according to some embodiments of the present disclosure.
• FIG. 13 shows an example of a method for an AI based SN Addition procedure for dual connectivity in a wireless communication system.
• FIG. 14 shows an example of a method for an AI based SN change procedure for dual connectivity in a wireless communication system.
• CDMA code division multiple access
• FDMA frequency division multiple access
• TDMA time division multiple access
• OFDMA orthogonal frequency division multiple access
• SC-FDMA single carrier frequency division multiple access
• MC-FDMA multicarrier frequency division multiple access
• CDMA may be embodied through radio technology such as universal terrestrial radio access (UTRA) or CDMA2000.
• TDMA may be embodied through radio technology such as global system for mobile communications (GSM), general packet radio service (GPRS), or enhanced data rates for GSM evolution (EDGE).
• GSM global system for mobile communications
• GPRS general packet radio service
• EDGE enhanced data rates for GSM evolution
• OFDMA may be embodied through radio technology such as institute of electrical and electronics engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, or evolved UTRA (E-UTRA).
• IEEE institute of electrical and electronics engineers
• Wi-Fi Wi-Fi
• WiMAX IEEE 802.16
• E-UTRA evolved UTRA
• UTRA is a part of a universal mobile telecommunications system (UMTS).
• 3rd generation partnership project (3GPP) long term evolution (LTE) is a part of evolved UMTS (E-UMTS) using E-UTRA.
• 3GPP LTE employs OFDMA in DL and SC-FDMA in UL.
• LTE-advanced (LTE-A) is an evolved version of 3GPP LTE.
• implementations of the present disclosure are mainly described in regards to a 3GPP based wireless communication system.
• the technical features of the present disclosure are not limited thereto.
• the following detailed description is given based on a mobile communication system corresponding to a 3GPP based wireless communication system, aspects of the present disclosure that are not limited to 3GPP based wireless communication system are applicable to other mobile communication systems.
• a or B may mean “only A”, “only B”, or “both A and B”.
• a or B in the present disclosure may be interpreted as “A and/or B”.
• A, B or C in the present disclosure may mean “only A”, “only B”, “only C”, or “any combination of A, B and C”.
• slash (/) or comma (,) may mean “and/or”.
• A/B may mean “A and/or B”. Accordingly, “A/B” may mean “only A”, “only B”, or “both A and B”.
• A, B, C may mean “A, B or C”.
• “at least one of A and B” may mean “only A”, “only B” or “both A and B”.
• the expression “at least one of A or B” or “at least one of A and/or B” in the present disclosure may be interpreted as same as “at least one of A and B”.
• “at least one of A, B and C” may mean “only A”, “only B”, “only C”, or “any combination of A, B and C”.
• “at least one of A, B or C” or “at least one of A, B and/or C” may mean “at least one of A, B and C”.
• parentheses used in the present disclosure may mean “for example”.
• control information PDCCH
• PDCCH control information
• PDCCH control information
• PDCCH control information
• FIG. 1 shows an example of a communication system to which implementations of the present disclosure is applied.
• the 5G usage scenarios shown in FIG. 1 are only exemplary, and the technical features of the present disclosure can be applied to other 5G usage scenarios which are not shown in FIG. 1.
• Three main requirement categories for 5G include (1) a category of enhanced mobile broadband (eMBB), (2) a category of massive machine type communication (mMTC), and (3) a category of ultra-reliable and low latency communications (URLLC).
• eMBB enhanced mobile broadband
• mMTC massive machine type communication
• URLLC ultra-reliable and low latency communications
• Partial use cases may require a plurality of categories for optimization and other use cases may focus only upon one key performance indicator (KPI).
• KPI key performance indicator
• eMBB far surpasses basic mobile Internet access and covers abundant bidirectional work and media and entertainment applications in cloud and augmented reality.
• Data is one of 5G core motive forces and, in a 5G era, a dedicated voice service may not be provided for the first time.
• voice will be simply processed as an application program using data connection provided by a communication system.
• Main causes for increased traffic volume are due to an increase in the size of content and an increase in the number of applications requiring high data transmission rate.
• a streaming service (of audio and video), conversational video, and mobile Internet access will be more widely used as more devices are connected to the Internet.
• Cloud storage and applications are rapidly increasing in a mobile communication platform and may be applied to both work and entertainment.
• the cloud storage is a special use case which accelerates growth of uplink data transmission rate.
• 5G is also used for remote work of cloud. When a tactile interface is used, 5G demands much lower end-to-end latency to maintain user good experience.
• Entertainment for example, cloud gaming and video streaming, is another core element which increases demand for mobile broadband capability. Entertainment is essential for a smartphone and a tablet in any place including high mobility environments such as a train, a vehicle, and an airplane.
• Other use cases are augmented reality for entertainment and information search. In this case, the augmented reality requires very low latency and instantaneous data volume.
• one of the most expected 5G use cases relates a function capable of smoothly connecting embedded sensors in all fields, i.e., mMTC. It is expected that the number of potential Internet-of-things (IoT) devices will reach 204 hundred million up to the year of 2020.
• An industrial IoT is one of categories of performing a main role enabling a smart city, asset tracking, smart utility, agriculture, and security infrastructure through 5G.
• URLLC includes a new service that will change industry through remote control of main infrastructure and an ultra-reliable/available low-latency link such as a self-driving vehicle.
• a level of reliability and latency is essential to control a smart grid, automatize industry, achieve robotics, and control and adjust a drone.
• 5G is a means of providing streaming evaluated as a few hundred megabits per second to gigabits per second and may complement fiber-to-the-home (FTTH) and cable-based broadband (or DOCSIS). Such fast speed is needed to deliver TV in resolution of 4K or more (6K, 8K, and more), as well as virtual reality and augmented reality.
• Virtual reality (VR) and augmented reality (AR) applications include almost immersive sports games.
• a specific application program may require a special network configuration. For example, for VR games, gaming companies need to incorporate a core server into an edge network server of a network operator in order to minimize latency.
• Automotive is expected to be a new important motivated force in 5G together with many use cases for mobile communication for vehicles. For example, entertainment for passengers requires high simultaneous capacity and mobile broadband with high mobility. This is because future users continue to expect connection of high quality regardless of their locations and speeds.
• Another use case of an automotive field is an AR dashboard.
• the AR dashboard causes a driver to identify an object in the dark in addition to an object seen from a front window and displays a distance from the object and a movement of the object by overlapping information talking to the driver.
• a wireless module enables communication between vehicles, information exchange between a vehicle and supporting infrastructure, and information exchange between a vehicle and other connected devices (e.g., devices accompanied by a pedestrian).
• a safety system guides alternative courses of a behavior so that a driver may drive more safely drive, thereby lowering the danger of an accident.
• the next stage will be a remotely controlled or self-driven vehicle. This requires very high reliability and very fast communication between different self-driven vehicles and between a vehicle and infrastructure. In the future, a self-driven vehicle will perform all driving activities and a driver will focus only upon abnormal traffic that the vehicle cannot identify.
• Technical requirements of a self-driven vehicle demand ultra-low latency and ultra-high reliability so that traffic safety is increased to a level that cannot be achieved by human being.
• a smart city and a smart home/building mentioned as a smart society will be embedded in a high-density wireless sensor network.
• a distributed network of an intelligent sensor will identify conditions for costs and energy-efficient maintenance of a city or a home. Similar configurations may be performed for respective households. All of temperature sensors, window and heating controllers, burglar alarms, and home appliances are wirelessly connected. Many of these sensors are typically low in data transmission rate, power, and cost. However, real-time HD video may be demanded by a specific type of device to perform monitoring.
• the smart grid collects information and connects the sensors to each other using digital information and communication technology so as to act according to the collected information. Since this information may include behaviors of a supply company and a consumer, the smart grid may improve distribution of fuels such as electricity by a method having efficiency, reliability, economic feasibility, production sustainability, and automation.
• the smart grid may also be regarded as another sensor network having low latency.
• Mission critical application is one of 5G use scenarios.
• a health part contains many application programs capable of enjoying benefit of mobile communication.
• a communication system may support remote treatment that provides clinical treatment in a faraway place. Remote treatment may aid in reducing a barrier against distance and improve access to medical services that cannot be continuously available in a faraway rural area. Remote treatment is also used to perform important treatment and save lives in an emergency situation.
• the wireless sensor network based on mobile communication may provide remote monitoring and sensors for parameters such as heart rate and blood pressure.
• Wireless and mobile communication gradually becomes important in the field of an industrial application.
• Wiring is high in installation and maintenance cost. Therefore, a possibility of replacing a cable with reconstructible wireless links is an attractive opportunity in many industrial fields.
• it is necessary for wireless connection to be established with latency, reliability, and capacity similar to those of the cable and management of wireless connection needs to be simplified. Low latency and a very low error probability are new requirements when connection to 5G is needed.
• Logistics and freight tracking are important use cases for mobile communication that enables inventory and package tracking anywhere using a location-based information system.
• the use cases of logistics and freight typically demand low data rate but require location information with a wide range and reliability.
• the communication system 1 includes wireless devices 100a to 100f, base stations (BSs) 200, and a network 300.
• FIG. 1 illustrates a 5G network as an example of the network of the communication system 1, the implementations of the present disclosure are not limited to the 5G system, and can be applied to the future communication system beyond the 5G system.
• the BSs 200 and the network 300 may be implemented as wireless devices and a specific wireless device may operate as a BS/network node with respect to other wireless devices.
• the wireless devices 100a to 100f represent devices performing communication using radio access technology (RAT) (e.g., 5G new RAT (NR)) or LTE) and may be referred to as communication/radio/5G devices.
• RAT radio access technology
• the wireless devices 100a to 100f may include, without being limited to, a robot 100a, vehicles 100b-1 and 100b-2, an extended reality (XR) device 100c, a hand-held device 100d, a home appliance 100e, an IoT device 100f, and an artificial intelligence (AI) device/server 400.
• the vehicles may include a vehicle having a wireless communication function, an autonomous driving vehicle, and a vehicle capable of performing communication between vehicles.
• the vehicles may include an unmanned aerial vehicle (UAV) (e.g., a drone).
• UAV unmanned aerial vehicle
• the XR device may include an AR/VR/Mixed Reality (MR) device and may be implemented in the form of a head-mounted device (HMD), a head-up display (HUD) mounted in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance device, a digital signage, a vehicle, a robot, etc.
• the hand-held device may include a smartphone, a smartpad, a wearable device (e.g., a smartwatch or a smartglasses), and a computer (e.g., a notebook).
• the home appliance may include a TV, a refrigerator, and a washing machine.
• the IoT device may include a sensor and a smartmeter.
• the wireless devices 100a to 100f may be called user equipments (UEs).
• a UE may include, for example, a cellular phone, a smartphone, a laptop computer, a digital broadcast terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation system, a slate personal computer (PC), a tablet PC, an ultrabook, a vehicle, a vehicle having an autonomous traveling function, a connected car, an UAV, an AI module, a robot, an AR device, a VR device, an MR device, a hologram device, a public safety device, an MTC device, an IoT device, a medical device, a FinTech device (or a financial device), a security device, a weather/environment device, a device related to a 5G service, or a device related to a fourth industrial revolution field.
• PDA personal digital assistant
• PMP portable multimedia player
• PC slate personal computer
• tablet PC a tablet PC
• ultrabook a vehicle, a vehicle having an autonomous
• the UAV may be, for example, an aircraft aviated by a wireless control signal without a human being onboard.
• the VR device may include, for example, a device for implementing an object or a background of the virtual world.
• the AR device may include, for example, a device implemented by connecting an object or a background of the virtual world to an object or a background of the real world.
• the MR device may include, for example, a device implemented by merging an object or a background of the virtual world into an object or a background of the real world.
• the hologram device may include, for example, a device for implementing a stereoscopic image of 360 degrees by recording and reproducing stereoscopic information, using an interference phenomenon of light generated when two laser lights called holography meet.
• the public safety device may include, for example, an image relay device or an image device that is wearable on the body of a user.
• the MTC device and the IoT device may be, for example, devices that do not require direct human intervention or manipulation.
• the MTC device and the IoT device may include smartmeters, vending machines, thermometers, smartbulbs, door locks, or various sensors.
• the medical device may be, for example, a device used for the purpose of diagnosing, treating, relieving, curing, or preventing disease.
• the medical device may be a device used for the purpose of diagnosing, treating, relieving, or correcting injury or impairment.
• the medical device may be a device used for the purpose of inspecting, replacing, or modifying a structure or a function.
• the medical device may be a device used for the purpose of adjusting pregnancy.
• the medical device may include a device for treatment, a device for operation, a device for (in vitro) diagnosis, a hearing aid, or a device for procedure.
• the security device may be, for example, a device installed to prevent a danger that may arise and to maintain safety.
• the security device may be a camera, a closed-circuit TV (CCTV), a recorder, or a black box.
• CCTV closed-circuit TV
• the FinTech device may be, for example, a device capable of providing a financial service such as mobile payment.
• the FinTech device may include a payment device or a point of sales (POS) system.
• POS point of sales
• the weather/environment device may include, for example, a device for monitoring or predicting a weather/environment.
• the wireless devices 100a to 100f may be connected to the network 300 via the BSs 200.
• An AI technology may be applied to the wireless devices 100a to 100f and the wireless devices 100a to 100f may be connected to the AI server 400 via the network 300.
• the network 300 may be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, and a beyond-5G network.
• the wireless devices 100a to 100f may communicate with each other through the BSs 200/network 300, the wireless devices 100a to 100f may perform direct communication (e.g., sidelink communication) with each other without passing through the BSs 200/network 300.
• the vehicles 100b-1 and 100b-2 may perform direct communication (e.g., vehicle-to-vehicle (V2V)/vehicle-to-everything (V2X) communication).
• the IoT device e.g., a sensor
• the IoT device may perform direct communication with other IoT devices (e.g., sensors) or other wireless devices 100a to 100f.
• Wireless communication/connections 150a, 150b and 150c may be established between the wireless devices 100a to 100f and/or between wireless device 100a to 100f and BS 200 and/or between BSs 200.
• the wireless communication/connections may be established through various RATs (e.g., 5G NR) such as uplink/downlink communication 150a, sidelink communication (or device-to-device (D2D) communication) 150b, inter-base station communication 150c (e.g., relay, integrated access and backhaul (IAB)), etc.
• the wireless devices 100a to 100f and the BSs 200/the wireless devices 100a to 100f may transmit/receive radio signals to/from each other through the wireless communication/connections 150a, 150b and 150c.
• the wireless communication/connections 150a, 150b and 150c may transmit/receive signals through various physical channels.
• various configuration information configuring processes e.g., channel encoding/decoding, modulation/demodulation, and resource mapping/de-mapping
• resource allocating processes for transmitting/receiving radio signals, may be performed based on the various proposals of the present disclosure.
• AI refers to the field of studying artificial intelligence or the methodology that can create it
• machine learning refers to the field of defining various problems addressed in the field of AI and the field of methodology to solve them.
• Machine learning is also defined as an algorithm that increases the performance of a task through steady experience on a task.
• Robot means a machine that automatically processes or operates a given task by its own ability.
• robots with the ability to recognize the environment and make self-determination to perform actions can be called intelligent robots.
• Robots can be classified as industrial, medical, home, military, etc., depending on the purpose or area of use.
• the robot can perform a variety of physical operations, such as moving the robot joints with actuators or motors.
• the movable robot also includes wheels, brakes, propellers, etc., on the drive, allowing it to drive on the ground or fly in the air.
• Autonomous driving means a technology that drives on its own, and autonomous vehicles mean vehicles that drive without user's control or with minimal user's control.
• autonomous driving may include maintaining lanes in motion, automatically adjusting speed such as adaptive cruise control, automatic driving along a set route, and automatically setting a route when a destination is set.
• the vehicle covers vehicles equipped with internal combustion engines, hybrid vehicles equipped with internal combustion engines and electric motors, and electric vehicles equipped with electric motors, and may include trains, motorcycles, etc., as well as cars.
• Autonomous vehicles can be seen as robots with autonomous driving functions.
• VR technology provides objects and backgrounds of real world only through computer graphic (CG) images.
• AR technology provides a virtual CG image on top of a real object image.
• MR technology is a CG technology that combines and combines virtual objects into the real world.
• MR technology is similar to AR technology in that they show real and virtual objects together. However, there is a difference in that in AR technology, virtual objects are used as complementary forms to real objects, while in MR technology, virtual objects and real objects are used as equal personalities.
• NR supports multiples numerologies (and/or multiple subcarrier spacings (SCS)) to support various 5G services. For example, if SCS is 15 kHz, wide area can be supported in traditional cellular bands, and if SCS is 30 kHz/60 kHz, dense-urban, lower latency, and wider carrier bandwidth can be supported. If SCS is 60 kHz or higher, bandwidths greater than 24.25 GHz can be supported to overcome phase noise.
• numerologies and/or multiple subcarrier spacings (SCS)
• the NR frequency band may be defined as two types of frequency range, i.e., FR1 and FR2.
• the numerical value of the frequency range may be changed.
• the frequency ranges of the two types may be as shown in Table 1 below.
• FR1 may mean “sub 6 GHz range”
• FR2 may mean “above 6 GHz range,” and may be referred to as millimeter wave (mmW).
• mmW millimeter wave
• FR1 may include a frequency band of 410MHz to 7125MHz as shown in Table 2 below. That is, FR1 may include a frequency band of 6GHz (or 5850, 5900, 5925 MHz, etc.) or more. For example, a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or more included in FR1 may include an unlicensed band. Unlicensed bands may be used for a variety of purposes, for example for communication for vehicles (e.g., autonomous driving).
• the radio communication technologies implemented in the wireless devices in the present disclosure may include narrowband internet-of-things (NB-IoT) technology for low-power communication as well as LTE, NR and 6G.
• NB-IoT technology may be an example of low power wide area network (LPWAN) technology, may be implemented in specifications such as LTE Cat NB1 and/or LTE Cat NB2, and may not be limited to the above-mentioned names.
• LPWAN low power wide area network
• the radio communication technologies implemented in the wireless devices in the present disclosure may communicate based on LTE-M technology.
• LTE-M technology may be an example of LPWAN technology and be called by various names such as enhanced machine type communication (eMTC).
• eMTC enhanced machine type communication
• LTE-M technology may be implemented in at least one of the various specifications, such as 1) LTE Cat 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-bandwidth limited (non-BL), 5) LTE-MTC, 6) LTE Machine Type Communication, and/or 7) LTE M, and may not be limited to the above-mentioned names.
• the radio communication technologies implemented in the wireless devices in the present disclosure may include at least one of ZigBee, Bluetooth, and/or LPWAN which take into account low-power communication, and may not be limited to the above-mentioned names.
• ZigBee technology may generate personal area networks (PANs) associated with small/low-power digital communication based on various specifications such as IEEE 802.15.4 and may be called various names.
• PANs personal area networks
• FIG. 2 shows an example of wireless devices to which implementations of the present disclosure is applied.
• a first wireless device 100 and a second wireless device 200 may transmit/receive radio signals to/from an external device through a variety of RATs (e.g., LTE and NR).
• RATs e.g., LTE and NR
• ⁇ the first wireless device 100 and the second wireless device 200 ⁇ may correspond to at least one of ⁇ the wireless device 100a to 100f and the BS 200 ⁇ , ⁇ the wireless device 100a to 100f and the wireless device 100a to 100f ⁇ and/or ⁇ the BS 200 and the BS 200 ⁇ of FIG. 1.
• the first wireless device 100 may include at least one transceiver, such as a transceiver 106, at least one processing chip, such as a processing chip 101, and/or one or more antennas 108.
• a transceiver such as a transceiver 106
• a processing chip such as a processing chip 101
• antennas 108 one or more antennas 108.
• the processing chip 101 may include at least one processor, such a processor 102, and at least one memory, such as a memory 104. It is exemplarily shown in FIG. 2 that the memory 104 is included in the processing chip 101. Additional and/or alternatively, the memory 104 may be placed outside of the processing chip 101.
• the processor 102 may control the memory 104 and/or the transceiver 106 and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts described in the present disclosure. For example, the processor 102 may process information within the memory 104 to generate first information/signals and then transmit radio signals including the first information/signals through the transceiver 106. The processor 102 may receive radio signals 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 operably connectable to the processor 102.
• the memory 104 may store various types of information and/or instructions.
• the memory 104 may store a software code 105 which implements instructions that, when executed by the processor 102, perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
• the software code 105 may implement instructions that, when executed by the processor 102, perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
• the software code 105 may control the processor 102 to perform one or more protocols.
• the software code 105 may control the processor 102 to perform one or more layers of the radio interface protocol.
• the processor 102 and the memory 104 may be a part of a communication modem/circuit/chip designed to implement 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 transceiver 106 may include a transmitter and/or a receiver.
• the transceiver 106 may be interchangeably used with radio frequency (RF) unit(s).
• the first wireless device 100 may represent a communication modem/circuit/chip.
• the second wireless device 200 may include at least one transceiver, such as a transceiver 206, at least one processing chip, such as a processing chip 201, and/or one or more antennas 208.
• the processing chip 201 may include at least one processor, such a processor 202, and at least one memory, such as a memory 204. It is exemplarily shown in FIG. 2 that the memory 204 is included in the processing chip 201. Additional and/or alternatively, the memory 204 may be placed outside of the processing chip 201.
• the processor 202 may control the memory 204 and/or the transceiver 206 and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts described in the present disclosure. For example, the processor 202 may process information within the memory 204 to generate third information/signals and then transmit radio signals including the third information/signals through the transceiver 206. The processor 202 may receive radio signals 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 operably connectable to the processor 202.
• the memory 204 may store various types of information and/or instructions.
• the memory 204 may store a software code 205 which implements instructions that, when executed by the processor 202, perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
• the software code 205 may implement instructions that, when executed by the processor 202, perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
• the software code 205 may control the processor 202 to perform one or more protocols.
• the software code 205 may control the processor 202 to perform one or more layers of the radio interface protocol.
• the processor 202 and the memory 204 may be a part of a communication modem/circuit/chip designed to implement 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 transceiver 206 may include a transmitter and/or a receiver.
• the transceiver 206 may be interchangeably used with RF unit.
• the second wireless device 200 may represent a communication modem/circuit/chip.
• One or more protocol layers may be implemented by, without being limited to, one or more processors 102 and 202.
• the one or more processors 102 and 202 may implement one or more layers (e.g., functional layers such as physical (PHY) layer, media access control (MAC) layer, radio link control (RLC) layer, packet data convergence protocol (PDCP) layer, radio resource control (RRC) layer, and service data adaptation protocol (SDAP) layer).
• layers e.g., functional layers such as physical (PHY) layer, media access control (MAC) layer, radio link control (RLC) layer, packet data convergence protocol (PDCP) layer, radio resource control (RRC) layer, and service data adaptation protocol (SDAP) layer).
• PHY physical
• MAC media access control
• RLC radio link control
• PDCP packet data convergence protocol
• RRC radio resource control
• SDAP service data adaptation protocol
• the one or more processors 102 and 202 may generate one or more protocol data units (PDUs) and/or one or more service data unit (SDUs) according to the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
• the one or more processors 102 and 202 may generate messages, control information, data, or information according to the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
• the one or more processors 102 and 202 may generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure and provide the generated signals to the one or more transceivers 106 and 206.
• the one or more processors 102 and 202 may receive the signals (e.g., baseband signals) from the one or more transceivers 106 and 206 and acquire the PDUs, SDUs, messages, control information, data, or information according to the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
• the one or more processors 102 and 202 may be referred to as controllers, microcontrollers, microprocessors, or microcomputers.
• the one or more processors 102 and 202 may be implemented by hardware, firmware, software, or a combination thereof.
• ASICs application specific integrated circuits
• DSPs digital signal processors
• DSPDs digital signal processing devices
• PLDs programmable logic devices
• FPGAs field programmable gate arrays
• the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure may be implemented using firmware or software and the firmware or software may be configured to include the modules, procedures, or functions.
• Firmware or software configured to perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure may be included in the one or more processors 102 and 202 or stored in the one or more memories 104 and 204 so as to be driven by the one or more processors 102 and 202.
• the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure may be implemented using firmware or software in the form of code, commands, and/or a set of commands.
• the one or more memories 104 and 204 may be connected to the one or more processors 102 and 202 and store various types of data, signals, messages, information, programs, code, instructions, and/or commands.
• the one or more memories 104 and 204 may be configured by read-only memories (ROMs), random access memories (RAMs), electrically erasable programmable read-only memories (EPROMs), flash memories, hard drives, registers, cash memories, computer-readable storage media, and/or combinations thereof.
• the one or more memories 104 and 204 may be located at the interior and/or exterior of the one or more processors 102 and 202.
• the one or more memories 104 and 204 may be connected to the one or more processors 102 and 202 through various technologies such as wired or wireless connection.
• the one or more transceivers 106 and 206 may transmit user data, control information, and/or radio signals/channels, mentioned in the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure, to one or more other devices.
• the one or more transceivers 106 and 206 may receive user data, control information, and/or radio signals/channels, mentioned in the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure, from one or more other devices.
• the one or more transceivers 106 and 206 may be connected to the one or more processors 102 and 202 and transmit and receive radio signals.
• the one or more processors 102 and 202 may perform control so that the one or more transceivers 106 and 206 may transmit user data, control information, or radio signals to one or more other devices.
• the one or more processors 102 and 202 may perform control so that the one or more transceivers 106 and 206 may receive user data, control information, or radio signals from one or more other devices.
• the one or more transceivers 106 and 206 may be connected to the one or more antennas 108 and 208 and the one or more transceivers 106 and 206 may be configured to transmit and receive user data, control information, and/or radio signals/channels, mentioned in the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure, through the one or more antennas 108 and 208.
• the one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports).
• the one or more transceivers 106 and 206 may convert received radio signals/channels, etc., from RF band signals into baseband signals in order to process received user data, control information, radio signals/channels, etc., using the one or more processors 102 and 202.
• the one or more transceivers 106 and 206 may convert the user data, control information, radio signals/channels, etc., processed using the one or more processors 102 and 202 from the base band signals into the RF band signals.
• the one or more transceivers 106 and 206 may include (analog) oscillators and/or filters.
• the transceivers 106 and 206 can up-convert OFDM baseband signals to a carrier frequency by their (analog) oscillators and/or filters under the control of the processors 102 and 202 and transmit the up-converted OFDM signals at the carrier frequency.
• the transceivers 106 and 206 may receive OFDM signals at a carrier frequency and down-convert the OFDM signals into OFDM baseband signals by their (analog) oscillators and/or filters under the control of the transceivers 102 and 202.
• a UE may operate as a transmitting device in uplink (UL) and as a receiving device in downlink (DL).
• a BS may operate as a receiving device in UL and as a transmitting device in DL.
• the first wireless device 100 acts as the UE
• the second wireless device 200 acts as the BS.
• the processor(s) 102 connected to, mounted on or launched in the first wireless device 100 may be configured to perform the UE behavior according to an implementation of the present disclosure or control the transceiver(s) 106 to perform the UE behavior according to an implementation of the present disclosure.
• the processor(s) 202 connected to, mounted on or launched in the second wireless device 200 may be configured to perform the BS behavior according to an implementation of the present disclosure or control the transceiver(s) 206 to perform the BS behavior according to an implementation of the present disclosure.
• a BS is also referred to as a node B (NB), an eNode B (eNB), or a gNB.
• NB node B
• eNB eNode B
• gNB gNode B
• FIG. 3 shows an example of a wireless device to which implementations of the present disclosure is applied.
• the wireless device may be implemented in various forms according to a use-case/service (refer to FIG. 1).
• wireless devices 100 and 200 may correspond to the wireless devices 100 and 200 of FIG. 2 and may be configured by various elements, components, units/portions, and/or modules.
• each of the wireless devices 100 and 200 may include a communication unit 110, a control unit 120, a memory unit 130, and additional components 140.
• the communication unit 110 may include a communication circuit 112 and transceiver(s) 114.
• the communication circuit 112 may include the one or more processors 102 and 202 of FIG. 2 and/or the one or more memories 104 and 204 of FIG. 2.
• the transceiver(s) 114 may include the one or more transceivers 106 and 206 of FIG.
• the control unit 120 is electrically connected to the communication unit 110, the memory 130, and the additional components 140 and controls overall operation of each of the wireless devices 100 and 200. For example, the control unit 120 may control an electric/mechanical operation of each of the wireless devices 100 and 200 based on programs/code/commands/information stored in the memory unit 130.
• the control unit 120 may transmit the information stored in the memory unit 130 to the exterior (e.g., other communication devices) via the communication unit 110 through a wireless/wired interface or store, in the memory unit 130, information received through the wireless/wired interface from the exterior (e.g., other communication devices) via the communication unit 110.
• the additional components 140 may be variously configured according to types of the wireless devices 100 and 200.
• the additional components 140 may include at least one of a power unit/battery, input/output (I/O) unit (e.g., audio I/O port, video I/O port), a driving unit, and a computing unit.
• I/O input/output
• the wireless devices 100 and 200 may be implemented in the form of, without being limited to, the robot (100a of FIG. 1), the vehicles (100b-1 and 100b-2 of FIG. 1), the XR device (100c of FIG. 1), the hand-held device (100d of FIG. 1), the home appliance (100e of FIG. 1), the IoT device (100f of FIG.
• the wireless devices 100 and 200 may be used in a mobile or fixed place according to a use-example/service.
• the entirety of the various elements, components, units/portions, and/or modules in the wireless devices 100 and 200 may be connected to each other through a wired interface or at least a part thereof may be wirelessly connected through the communication unit 110.
• the control unit 120 and the communication unit 110 may be connected by wire and the control unit 120 and first units (e.g., 130 and 140) may be wirelessly connected through the communication unit 110.
• Each element, component, unit/portion, and/or module within the wireless devices 100 and 200 may further include one or more elements.
• the control unit 120 may be configured by a set of one or more processors.
• control unit 120 may be configured by a set of a communication control processor, an application processor (AP), an electronic control unit (ECU), a graphical processing unit, and a memory control processor.
• the memory 130 may be configured by a RAM, a DRAM, a ROM, a flash memory, a volatile memory, a non-volatile memory, and/or a combination thereof.
• FIG. 4 shows an example of UE to which implementations of the present disclosure is applied.
• a UE 100 may correspond to the first wireless device 100 of FIG. 2 and/or the wireless device 100 or 200 of FIG. 3.
• a UE 100 includes a processor 102, a memory 104, a transceiver 106, one or more antennas 108, a power management module 110, a battery 1112, a display 114, a keypad 116, a subscriber identification module (SIM) card 118, a speaker 120, and a microphone 122.
• SIM subscriber identification module
• the processor 102 may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
• the processor 102 may be configured to control one or more other components of the UE 100 to implement the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure.
• Layers of the radio interface protocol may be implemented in the processor 102.
• the processor 102 may include ASIC, other chipset, logic circuit and/or data processing device.
• the processor 102 may be an application processor.
• the processor 102 may include at least one of a digital signal processor (DSP), a central processing unit (CPU), a graphics processing unit (GPU), a modem (modulator and demodulator).
• DSP digital signal processor
• CPU central processing unit
• GPU graphics processing unit
• modem modulator and demodulator
• processor 102 may be found in SNAPDRAGON TM series of processors made by Qualcomm ® , EXYNOS TM series of processors made by Samsung ® , A series of processors made by Apple ® , HELIO TM series of processors made by MediaTek ® , ATOM TM series of processors made by Intel ® or a corresponding next generation processor.
• the memory 104 is operatively coupled with the processor 102 and stores a variety of information to operate the processor 102.
• the memory 104 may include ROM, RAM, flash memory, memory card, storage medium and/or other storage device.
• modules e.g., procedures, functions, etc.
• the modules can be stored in the memory 104 and executed by the processor 102.
• the memory 104 can be implemented within the processor 102 or external to the processor 102 in which case those can be communicatively coupled to the processor 102 via various means as is known in the art.
• the transceiver 106 is operatively coupled with the processor 102, and transmits and/or receives a radio signal.
• the transceiver 106 includes a transmitter and a receiver.
• the transceiver 106 may include baseband circuitry to process radio frequency signals.
• the transceiver 106 controls the one or more antennas 108 to transmit and/or receive a radio signal.
• the power management module 110 manages power for the processor 102 and/or the transceiver 106.
• the battery 112 supplies power to the power management module 110.
• the display 114 outputs results processed by the processor 102.
• the keypad 116 receives inputs to be used by the processor 102.
• the keypad 16 may be shown on the display 114.
• the SIM card 118 is an integrated circuit that is intended to securely store the international mobile subscriber identity (IMSI) number and its related key, which are used to identify and authenticate subscribers on mobile telephony devices (such as mobile phones and computers). It is also possible to store contact information on many SIM cards.
• IMSI international mobile subscriber identity
• the speaker 120 outputs sound-related results processed by the processor 102.
• the microphone 122 receives sound-related inputs to be used by the processor 102.
• FIGS. 5 and 6 show an example of protocol stacks in a 3GPP based wireless communication system to which implementations of the present disclosure is applied.
• FIG. 5 illustrates an example of a radio interface user plane protocol stack between a UE and a BS
• FIG. 6 illustrates an example of a radio interface control plane protocol stack between a UE and a BS.
• the control plane refers to a path through which control messages used to manage call by a UE and a network are transported.
• the user plane refers to a path through which data generated in an application layer, for example, voice data or Internet packet data are transported.
• the user plane protocol stack may be divided into Layer 1 (i.e., a PHY layer) and Layer 2.
• the control plane protocol stack may be divided into Layer 1 (i.e., a PHY layer), Layer 2, Layer 3 (e.g., an RRC layer), and a non-access stratum (NAS) layer.
• Layer 1 i.e., a PHY layer
• Layer 2 e.g., an RRC layer
• NAS non-access stratum
• Layer 1 Layer 2 and Layer 3 are referred to as an access stratum (AS).
• the Layer 2 is split into the following sublayers: MAC, RLC, and PDCP.
• the Layer 2 is split into the following sublayers: MAC, RLC, PDCP and SDAP.
• the PHY layer offers to the MAC sublayer transport channels, the MAC sublayer offers to the RLC sublayer logical channels, the RLC sublayer offers to the PDCP sublayer RLC channels, the PDCP sublayer offers to the SDAP sublayer radio bearers.
• the SDAP sublayer offers to 5G core network quality of service (QoS) flows.
• QoS quality of service
• the main services and functions of the MAC sublayer include: mapping between logical channels and transport channels; multiplexing/de-multiplexing of MAC SDUs belonging to one or different logical channels into/from transport blocks (TB) delivered to/from the physical layer on transport channels; scheduling information reporting; error correction through hybrid automatic repeat request (HARQ) (one HARQ entity per cell in case of carrier aggregation (CA)); priority handling between UEs by means of dynamic scheduling; priority handling between logical channels of one UE by means of logical channel prioritization; padding.
• HARQ hybrid automatic repeat request
• a single MAC entity may support multiple numerologies, transmission timings and cells. Mapping restrictions in logical channel prioritization control which numerology(ies), cell(s), and transmission timing(s) a logical channel can use.
• MAC Different kinds of data transfer services are offered by MAC.
• multiple types of logical channels are defined, i.e., each supporting transfer of a particular type of information.
• Each logical channel type is defined by what type of information is transferred.
• Logical channels are classified into two groups: control channels and traffic channels. Control channels are used for the transfer of control plane information only, and traffic channels are used for the transfer of user plane information only.
• Broadcast control channel is a downlink logical channel for broadcasting system control information
• PCCH paging control channel
• PCCH is a downlink logical channel that transfers paging information
• common control channel CCCH
• DCCH dedicated control channel
• DTCH Dedicated traffic channel
• a DTCH can exist in both uplink and downlink.
• BCCH can be mapped to broadcast channel (BCH); BCCH can be mapped to downlink shared channel (DL-SCH); PCCH can be mapped to paging channel (PCH); CCCH can be mapped to DL-SCH; DCCH can be mapped to DL-SCH; and DTCH can be mapped to DL-SCH.
• PCCH downlink shared channel
• CCCH can be mapped to DL-SCH
• DCCH can be mapped to DL-SCH
• DTCH can be mapped to DL-SCH.
• the RLC sublayer supports three transmission modes: transparent mode (TM), unacknowledged mode (UM), and acknowledged node (AM).
• the RLC configuration is per logical channel with no dependency on numerologies and/or transmission durations.
• the main services and functions of the RLC sublayer depend on the transmission mode and include: transfer of upper layer PDUs; sequence numbering independent of the one in PDCP (UM and AM); error correction through ARQ (AM only); segmentation (AM and UM) and re-segmentation (AM only) of RLC SDUs; reassembly of SDU (AM and UM); duplicate detection (AM only); RLC SDU discard (AM and UM); RLC re-establishment; protocol error detection (AM only).
• the main services and functions of the PDCP sublayer for the user plane include: sequence numbering; header compression and decompression using robust header compression (ROHC); transfer of user data; reordering and duplicate detection; in-order delivery; PDCP PDU routing (in case of split bearers); retransmission of PDCP SDUs; ciphering, deciphering and integrity protection; PDCP SDU discard; PDCP re-establishment and data recovery for RLC AM; PDCP status reporting for RLC AM; duplication of PDCP PDUs and duplicate discard indication to lower layers.
• ROIHC robust header compression
• the main services and functions of the PDCP sublayer for the control plane include: sequence numbering; ciphering, deciphering and integrity protection; transfer of control plane data; reordering and duplicate detection; in-order delivery; duplication of PDCP PDUs and duplicate discard indication to lower layers.
• the main services and functions of SDAP include: mapping between a QoS flow and a data radio bearer; marking QoS flow ID (QFI) in both DL and UL packets.
• QFI QoS flow ID
• a single protocol entity of SDAP is configured for each individual PDU session.
• the main services and functions of the RRC sublayer include: broadcast of system information related to AS and NAS; paging initiated by 5GC or NG-RAN; establishment, maintenance and release of an RRC connection between the UE and NG-RAN; security functions including key management; establishment, configuration, maintenance and release of signaling radio bearers (SRBs) and data radio bearers (DRBs); mobility functions (including: handover and context transfer, UE cell selection and reselection and control of cell selection and reselection, inter-RAT mobility); QoS management functions; UE measurement reporting and control of the reporting; detection of and recovery from radio link failure; NAS message transfer to/from NAS from/to UE.
• SRBs signaling radio bearers
• DRBs data radio bearers
• mobility functions including: handover and context transfer, UE cell selection and reselection and control of cell selection and reselection, inter-RAT mobility
• QoS management functions UE measurement reporting and control of the reporting; detection of and recovery from radio link failure; NAS
• FIG. 7 shows an example of the overall architecture of an NG-RAN to which technical features of the present disclosure can be applied.
• a gNB may include a gNB-CU (hereinafter, gNB-CU may be simply referred to as CU) and at least one gNB-DU (hereinafter, gNB-DU may be simply referred to as DU).
• gNB-CU may be simply referred to as CU
• gNB-DU may be simply referred to as DU
• the gNB-CU is a logical node hosting RRC, SDAP and PDCP protocols of the gNB or an RRC and PDCP protocols of the en-gNB.
• the gNB-CU controls the operation of the at least one gNB-DU.
• the gNB-DU is a logical node hosting RLC, MAC, and physical layers of the gNB or the en-gNB.
• the operation of the gNB-DU is partly controlled by the gNB-CU.
• One gNB-DU supports one or multiple cells.
• One cell is supported by only one gNB-DU.
• the gNB-CU and gNB-DU are connected via an F1 interface.
• the gNB-CU terminates the F1 interface connected to the gNB-DU.
• the gNB-DU terminates the F1 interface connected to the gNB-CU.
• One gNB-DU is connected to only one gNB-CU. However, the gNB-DU may be connected to multiple gNB-CUs by appropriate implementation.
• the F1 interface is a logical interface. For NG-RAN, the NG and Xn-C interfaces for a gNB consisting of a gNB-CU and gNB-DUs, terminate in the gNB-CU.
• the S1-U and X2-C interfaces for a gNB consisting of a gNB-CU and gNB-DUs, terminate in the gNB-CU.
• the gNB-CU and connected gNB-DUs are only visible to other gNBs and the 5GC as a gNB.
• FIG. 8 shows an interface protocol structure for F1-C to which technical features of the present disclosure can be applied.
• a transport network layer is based on Internet protocol (IP) transport, comprising a stream control transmission protocol (SCTP) layer on top of the IP layer.
• IP Internet protocol
• SCTP stream control transmission protocol
• An application layer signaling protocol is referred to as an F1 application protocol (E1AP).
• Section 3.1 of 3GPP TS 37.817 v0.1.0 may be referred.
• Data collection Data collected from the network nodes, management entity or UE, as a basis for ML model training, data analytics and inference.
• ML Model A data driven algorithm by applying machine learning techniques that generates a set of outputs consisting of predicted information, based on a set of inputs
• ML Training An online or offline process to train an ML model by learning features and patterns that best present data and get the trained ML model for inference.
• ML Inference A process of using a trained ML model to make a prediction or guide the decision based on collected data and ML model.
• FIG. 9 shows an example of a functional framework for RAN Intelligence to which implementations of the present disclosure is applied.
• a model training host may receive, from a data sources, training data.
• the model training host may provide, to a model inference host, model deployment and/or update.
• the model training host may receive, from the model inference host, model performance feedback.
• the model inference host may receive, from data sources, inference data and transmit, to an actor, output.
• the actor may transmit, to one or more subjects of action, an action.
• the data sources may receive, from the one or more subjects of action, performance feedback.
• actor and subject of action could be in one box or separate.
• the feedback from action to Model training host may be needed.
• the feedback from subject of action to the data sources may be Performance feedback or Model performance feedback and other possible refinement.
• FIG. 10 shows a Secondary Node Addition procedure to which implementations of the present disclosure is applied.
• the Secondary Node (SN) Addition procedure is initiated by the MN and is used to establish a UE context at the SN in order to provide resources from the SN to the UE. For bearers requiring SCG radio resources, this procedure is used to add at least the initial SCG serving cell of the SCG. This procedure can also be used to configure an SN terminated MCG bearer (where no SCG configuration is needed).
• step S1001 the MN decides to request the target SN to allocate resources for one or more specific PDU Sessions/QoS Flows, indicating QoS Flows characteristics (QoS Flow Level QoS parameters, PDU session level TNL address information, and PDU session level Network Slice info).
• QoS Flows characteristics QoS Flow Level QoS parameters, PDU session level TNL address information, and PDU session level Network Slice info.
• MN indicates the requested SCG configuration information, including the entire UE capabilities and the UE capability coordination result.
• the MN also provides the latest measurement results for SN to choose and configure the SCG cell(s).
• the MN may request the SN to allocate radio resources for split SRB operation.
• NGEN-DC and NR-DC the MN always provides all the needed security information to the SN (even if no SN terminated bearers are setup) to enable SRB3 to be setup based on SN decision.
• the MN For MN terminated bearer options that require Xn-U resources between the MN and the SN, the MN provides Xn-U UL TNL address information. For SN terminated bearers, the MN provides a list of available DRB IDs. The S-NG-RAN node shall store this information and use it when establishing SN terminated bearers. The SN may reject the request.
• the MN For SN terminated bearer options that require Xn-U resources between the MN and the SN, the MN provides in step S1001 a list of QoS flows per PDU Sessions for which SCG resources are requested to be setup upon which the SN decides how to map QoS flows to DRB.
• MCG and SCG resources may be requested of such an amount, that the QoS for the respective QoS Flow is guaranteed by the exact sum of resources provided by the MCG and the SCG together, or even more.
• MN decision is reflected in step S1001 by the QoS Flow parameters signalled to the SN, which may differ from QoS Flow parameters received over NG.
• the MN may request the direct establishment of SCG and/or split bearers, i.e., without first having to establish MCG bearers. It is also allowed that all QoS flows can be mapped to SN terminated bearers, i.e., there is no QoS flow mapped to an MN terminated bearer.
• step S1002 if the RRM entity in the SN is able to admit the resource request, it allocates respective radio resources and, dependent on the bearer type options, respective transport network resources. For bearers requiring SCG radio resources the SN triggers UE Random Access so that synchronisation of the SN radio resource configuration can be performed. The SN decides for the PSCell and other SCG SCells and provides the new SCG radio resource configuration to the MN within an SN RRC configuration message contained in the SN Addition Request Acknowledge message.
• the SN In case of bearer options that require Xn-U resources between the MN and the SN, the SN provides Xn-U TNL address information for the respective DRB, Xn-U UL TNL address information for SN terminated bearers, Xn-U DL TNL address information for MN terminated bearers. For SN terminated bearers, the SN provides the NG-U DL TNL address information for the respective PDU Session and security algorithm. If SCG radio resources have been requested, the SCG radio resource configuration is provided.
• transmission of user plane data may take place after step S1002.
• the MN For MN terminated bearers for which PDCP duplication with CA is configured in NR SCG side, the MN allocates up to 4 separate Xn-U bearers and the SN provides a logical channel ID for primary or split secondary path to the MN.
• the SN allocates up to 4 separate Xn-U bearers and the MN provides a logical channel ID for primary or split secondary path to the SN via an additional MN-initiated SN modification procedure.
• step S1002a for SN terminated bearers using MCG resources, the MN provides Xn-U DL TNL address information in the Xn -U Address Indication message.
• step S1003 the MN sends the MN RRC reconfiguration message to the UE including the SN RRC configuration message, without modifying it.
• step S1004 the UE applies the new configuration and replies to MN with MN RRC reconfiguration complete message, including an SN RRC response message for SN, if needed. In case the UE is unable to comply with (part of) the configuration included in the MN RRC reconfiguration message, it performs the reconfiguration failure procedure.
• step S1005 the MN informs the SN that the UE has completed the reconfiguration procedure successfully via SN Reconfiguration Complete message, including the SN RRC response message, if received from the UE.
• step S1006 if configured with bearers requiring SCG radio resources, the UE performs synchronisation towards the PSCell configured by the SN.
• the order the UE sends the MN RRC reconfiguration complete message and performs the Random Access procedure towards the SCG is not defined.
• the successful RA procedure towards the SCG is not required for a successful completion of the RRC Connection Reconfiguration procedure.
• step S1007 if PDCP termination point is changed to the SN for bearers using RLC AM, and when RRC full configuration is not used, the MN sends the SN Status Transfer.
• the MN may take actions to minimise service interruption due to activation of MR-DC (Data forwarding).
• steps S1009-S1012 if applicable, the update of the UP path towards the 5GC is performed via a PDU Session Path Update procedure .
• FIG. 11 shows a SN initiated SN Change procedure to which implementations of the present disclosure is applied.
• the SN initiated SN change procedure is used to transfer a UE context from the source SN to a target SN and to change the SCG configuration in UE from one SN to another.
• step S1101 the source SN initiates the SN change procedure by sending the SN Change Required message, which contains a candidate target node ID and may include the SCG configuration (to support delta configuration) and measurement results related to the target SN.
• the MN requests the target SN to allocate resources for the UE by means of the SN Addition procedure, including the measurement results related to the target SN received from the source SN. If data forwarding is needed, the target SN provides data forwarding addresses to the MN.
• the target SN includes the indication of the full or delta RRC configuration.
• the MN triggers the UE to apply the new configuration.
• the MN indicates the new configuration to the UE in the MN RRC reconfiguration message including the SN RRC reconfiguration message generated by the target SN.
• the UE applies the new configuration and sends the MN RRC reconfiguration complete message, including the SN RRC response message for the target SN, if needed.
• the UE is unable to comply with (part of) the configuration included in the MN RRC reconfiguration message, it performs the reconfiguration failure procedure.
• step S1106 if the allocation of target SN resources was successful, the MN confirms the change of the source SN. If data forwarding is needed the MN provides data forwarding addresses to the source SN. If direct data forwarding is used for SN terminated bearers, the MN provides data forwarding addresses as received from the target SN to source SN. Reception of the SN Change Confirm message triggers the source SN to stop providing user data to the UE and, if applicable, to start data forwarding.
• step S1107 if the RRC connection reconfiguration procedure was successful, the MN informs the target SN via SN Reconfiguration Complete message with the included SN RRC response message for the target SN, if received from the UE.
• step S1108 the UE synchronizes to the target SN.
• step S1109 if PDCP termination point is changed for bearers using RLC AM, the source SN sends the SN Status Transfer, which the MN sends then to the target SN, if needed.
• step S1110 if applicable, data forwarding from the source SN takes place. It may be initiated as early as the source SN receives the SN Change Confirm message from the MN.
• step S1111 the source SN sends the Secondary RAT Data Usage Report message to the MN and includes the data volumes delivered to and received from the UE.
• the order the SN sends the Secondary RAT Data Usage Report message and performs data forwarding with MN/target SN is not defined.
• the SN may send the report when the transmission of the related QoS flow is stopped.
• steps S1112-S1116 if applicable, a PDU Session path update procedure is triggered by the MN.
• step S1117 upon reception of the UE Context Release message, the source SN releases radio and C-plane related resources associated to the UE context. Any ongoing data forwarding may continue.
• AI Artificial Intelligence
• ML machine learning
• the signalling support for AI deserves study of the training and the execution involved in AI schemes, the data required by the AI algorithms (potentially reported by the UE or collected from different parts of the network), and outputs generated by the algorithms to be delivered to other network nodes or Network Functions (NFs) in RAN, CN, or Operations, Administration and Maintenance (OAM)/Change Management (CHM).
• NFs Network Functions
• CHM Operations, Administration and Maintenance
• a RAN may decide a target node based on measurement report of the signaling quality of the neighbor.
• a problem such as, radio link failure, ping-pang may happen.
• FIG. 12 shows an example of a method for performing AI based procedure for dual connectivity in a wireless communication system, according to some embodiments of the present disclosure.
• FIG. 12 shows an example of a method performed by a master node (MN) in a wireless communication system.
• MN master node
• a MN may receive, from a source secondary node (SN), a SN change required message including (i) information on a candidate target SN and (ii) information informing that the candidate target SN is decided by the source SN using an AI model.
• SN source secondary node
• the SN change required message may also include information on the AI model used by the source SN.
• the MN and the SN may establish a dual connectivity for a specific UE.
• the source SN may use the AI model for deciding the candidate target SN based on (i) mobility information and/or (ii) location information related to a specific UE and/or other UEs.
• the source SN may acquire (i) mobility information and/or (ii) location information for the specific UE and/or other UEs and store the information for using the AI model.
• the MN may transmit, to the source SN, (i) a mobility information for a specific UE and (ii) a location information for the specific UE, before receiving the SN change required message.
• the MN may transmit, to the source SN, a SN addition request message for a SN addition procedure before receiving the SN change required message.
• the SN addition request message may include (i) a mobility information for a specific UE and (ii) a location information for the specific UE .
• the MN may acquire the mobility information from a UE context for the specific UE.
• the mobility information may be provided by the AI function in a core network (CN).
• CN core network
• the mobility information may include (i) mobility statistics for the specific UE and/or (ii) mobility predictions for the specific UE.
• the information on mobility statistics may include at least one of (i) UE group ID or UE ID (for example, Subscription Permanent Identifier (SUPI) and/or (ii) a time slot entry.
• the time slot entry may include list of time slots during the analytics target period.
• the time slot entry may include (i) time slot start (time slot start within the Analytics target period), (ii) Duration (Duration of the time slot (average and variance)), and/or (iii) information on UE location (Observed location statistics).
• information on UE location may include (i) UE location (TA and/or cells which the UE stays) and/or (ii) Ratio (Percentage of UEs in the group (in the case of an UE group)).
• the information on mobility prediction may include at least one of (i) UE group ID or UE ID (for example, Subscription Permanent Identifier (SUPI) and/or (ii) a time slot entry.
• the time slot entry may include list of predicted time slots.
• the time slot entry may include (i) time slot start (time slot start within the Analytics target period), (ii) Duration (Duration of the time slot (average and variance)), and/or (iii) information on UE location (predicted location prediction during the analytics target period).
• information on UE location may include (i) UE location (TA or cells where the UE or UE group may move into), (ii) confidence of this prediction, and/or (iii) Ratio (Percentage of UEs in the group (in the case of an UE group)).
• the location information may include (i) information on a current location for the specific UE and/or (ii) information on a past location for the specific UE.
• the MN may receive the location information from the specific UE.
• the location information may include information on a predicted location for the specific UE.
• the predicted location may include output of an AI model of the specific UE.
• the predicted location for the specific UE may include (i) information on Global Positioning System (GPS), Global Navigation Satellite System (GNSS), Tracking Area (TA) and/or a cell where the specific UE will move into and/or (ii) information on confidence of the predicted location.
• GPS Global Positioning System
• GNSS Global Navigation Satellite System
• TA Tracking Area
• the location information may not include a predicted location for the specific UE, based on that the specific UE does not include an AI model.
• a MN may perform a SN addition procedure with the candidate target SN node without using an AI model.
• the MN may determine whether to use the AI model for deciding a target SN upon receiving the SN change required message.
• the MN may determine not to use the AI model for deciding the target SN. Otherwise, if the SN change required message does not include information informing that the candidate target SN is decided by the source SN using an AI model, the MN may determine to use the AI model for deciding the target SN.
• the MN may decide the candidate target SN as the target SN based on determining not to use the AI model. Therefore, the MN may perform the SN addition procedure with the candidate target SN as the target SN.
• the MN may transmits, to the candidate target SN, a SN addition request message.
• the SN addition request message may include (i) information informing that the candidate target SN is decided by the source SN using an AI model and (ii) information on the AI model used by the source SN.
• the MN may transmit, to the target SN, (i) mobility information and/or (ii) location information related to the specific UE and/or other UEs. Therefore, the target SN may use the received information for using AI model to decide the next target SN.
• the MN may receive, from the candidate target SN, a SN addition request acknowledge message.
• the SN Addition procedure may be performed for a specific UE.
• the SN change required message may include a global ID, for example, a Subscription Permanent Identifier (SUPI) for the specific UE.
• SUPI Subscription Permanent Identifier
• the MN may store information on the candidate target SN as a target SN for the specific UE, upon performing the SN addition procedure with the candidate target SN.
• the MN may update information on the candidate target SN during of serving under this SN for the specific UE, upon receiving a SN release message for the specific UE from the candidate target SN.
• the MN may transmit, to the specific UE, a Radio Resource Control (RRC) connection reconfiguration message.
• RRC Radio Resource Control
• the RRC connection reconfiguration message may include (i) information on the candidate target SN, (ii) information informing that the candidate target SN is decided by the source SN using an AI model, and (iii) information on the AI model used by the source SN.
• the RRC connection reconfiguration message may include mobility information for the specific UE and/or other UEs.
• the MN may receive, from the specific UE, an RRC connection reconfiguration complete message.
• the specific UE may be in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the specific UE.
• FIG. 13 shows an example of a method for an AI based SN Addition procedure for dual connectivity in a wireless communication system.
• FIG. 13 illustrates a diagram for AI based SN Addition procedure for a specific UE.
• the UE context within the master node gNB may contain information regarding roaming and access restrictions which were provided either at connection establishment or at the last TA update.
• the following information may be also included for AI based dual connectivity, which is from the analytics results provided by the AI function in CN:
• SUPI Subscription Permanent Identifier
• Duration of the time slot (average and variance)
• UE location TA or cells which the UE stays
• Duration of the time slot (average and variance)
• UE location TA or cells where the UE or UE group may move into
• the number of time slots and UE locations may be limited by the maximum number of objects provided as part of Analytics Reporting Information
• the time slots may be provided by order of time, possibly overlapping.
• the locations may be provided by decreasing value of ratio for a given time slot.
• the sum of all ratios on a given time slot may be equal or less than 100%.
• the least probable locations on a given Analytics target period may not be provided.
• the master node may configure the UE measurement procedures (including the indication of requesting to report UE's location information (current and past), and/or the indication of reporting UE's AI model training result on the location) and the UE may report according to the measurement configuration and request:
• UE may report the current location information and the past location information, for example, GPS, GNSS, it can also be TA or cells which the UE stays and the duration in which UE stays, universal time can be referred.
• the current location information and the past location information for example, GPS, GNSS, it can also be TA or cells which the UE stays and the duration in which UE stays, universal time can be referred.
• UE may also have an AI based training model on its locations in the past time duration.
• the input may be, for example, GPS, GNSS, it can also be TA or cells which the UE stays and the duration in which UE stays, universal time can be referred.
• the output may be Predicted location prediction during the Analytics target period, for example, GPS, GNSS, TA or cells where the UE or UE group may move into, the confidence of this prediction.
• step S1302 (1) the information received from CN in step S1300, (2) UE's location information received from UE in step S1301, and (3) also the UE history information and UE's past mobility pattern, will be the input to AI model for training in RAN side (eNB or gNB, or a master node).
• AI model for training in RAN side (eNB or gNB, or a master node).
• the proper algorithm may be applied.
• the predication result of UE received in step S1301 can be the direct reference for RAN to decide the secondary node as an option.
• the output of this training model in RAN or master node for example, predicated target cell IDs/gNB IDs, target secondary node IDs (also Confidence of this prediction) together with the information of RSRP/RSRP received before will be the factors for the source gNB/ master node to decide the target node / target secondary node(s).
• the AI training model here may locate in this gNB/eNB/Master node or other central node, which may manage many RAN nodes.
• the information above received in step S1300 and S1301 may pass to the central node and the result is passed back to this RAN/gNB/master node. It is also possible to locate the AI training model in a specific CU.
• the master node may decide to add a secondary node for the UE, in which the secondary node is selected based on the information above.
• the MN may decide to request the selected target SN to allocate resources for one or more specific PDU Sessions/QoS Flows, indicating QoS Flows characteristics (QoS Flow Level QoS parameters, PDU session level TNL address information, and PDU session level Network Slice info).
• QoS Flows characteristics QoS Flow Level QoS parameters, PDU session level TNL address information, and PDU session level Network Slice info.
• MN may indicate the requested SCG configuration information, including the entire UE capabilities and the UE capability coordination result.
• the MN may also provide the latest measurement results for SN to choose and configure the SCG cell(s).
• the MN may request the SN to allocate radio resources for split SRB operation.
• the MN may always provide all the needed security information to the SN (even if no SN terminated bearers are setup) to enable SRB3 to be setup based on SN decision.
• the information received from CN in step S1300 may be also included for further dual connectivity to consider.
• Duration of the time slot (average and variance)
• UE location TA or cells which the UE stays
• Duration of the time slot (average and variance)
• UE location TA or cells where the UE or UE group may move into
• the information from UE in step S1301 is also included:
• - UE's reported current location information and the past location information for example, GPS, GNSS, it can also be TA or cells which the UE stays and the duration in which UE stays, universal time can be referred.
• the output information of the AI training model in step S1302 for example, the predicated further target cell IDs/gNB IDs/target secondary nodes (also Confidence of this prediction) may be also included for further mobility to consider.
• step S1304 decision may be made by the Secondary node.
• the information received in step S1303 is stored to use for further mobility/dual connectivity or for next input of AI training model.
• step S1305 if the RRM entity in the SN is able to admit the resource request, it may allocate respective radio resources and, dependent on the bearer type options, respective transport network resources.
• the SN addition ACK message may be sent to MN.
• the Master node may send the RRC reconfiguration message to the UE including the SN RRC configuration message.
• the information of the finally selected cell or secondary node may be also included for UE as a update input / reference of its AI training model, for further mobility behaviour of this UE (for example, UE global ID (for example, SUPI) can be included) or other UE.
• UE global ID for example, SUPI
• FIG. 14 shows an example of a method for an AI based SN change procedure for dual connectivity in a wireless communication system.
• FIG. 14 illustrates a diagram for AI based SN change procedure for a specific UE.
• the SN change procedure in FIG. 14 may be performed after the SN addition procedure in FIG. 13.
• the source SN node may take the AI based UE information received before during the SN addition procedure into account and decide the candidate target SN node (s) (one or more).
• the source SN may initiate the SN change procedure by sending the SN Change Required message, which contains the AI based selected candidate target node ID(s) and may include the SCG configuration (to support delta configuration) and measurement results related to the target SN.
• the SN Change Required message may also include an indication informing that AI based UE information has been considered, i.e., selected target ID (s) are the outcome of considering AI model applied/received before.
• step S1402 if the indication/AI considered target SN information is received in step S1401, the MN may request directly the target SN to allocate resources for the UE by means of the SN Addition procedure, including the measurement results related to the target SN received from the source SN.
• the MN could use its AI based UE information stored in step S1302 of FIG. 13 to decide finally the target SN for this UE:
• the finally decided target SN may be based on the AI based UE information in MN.
• the finally decided target SN may be based on the AI based UE information in MN together with the radio information from UE.
• the finally decided target SN may be based on both AI based UE information in MN and also the reported potential SN from source SN together with the radio information from UE.
• the target SN may give a response message to MN. If data forwarding is needed, the target SN may provide data forwarding addresses to the MN.
• the target SN may include the indication of the full or delta RRC configuration.
• the MN update the information of this UE, for example, target SN node ID (successful addition) for further AI model training and UE's future mobility/dual connectivity.
• the MN could be updated on the information of this UE, for example, target SN node ID (duration of serving under this SN) for further AI model training and UE's future mobility/dual connectivity.
• the information can be transmitted from SN to MN when this SN is released, for example, by the SN release message.
• the MN may trigger the UE to apply the new configuration.
• the MN may indicate the new configuration to the UE in the MN RRC reconfiguration message including the SN RRC reconfiguration message generated by the target SN.
• the UE may apply the new configuration and send the MN RRC reconfiguration complete message, including the SN RRC response message for the target SN, if needed.
• the UE may perform the reconfiguration failure procedure.
• the information of the finally selected cell or secondary node may be also included for UE as a update input / reference of its AI training model, for further mobility behaviour of this UE (UE global ID (for example, SUPI) can be included) or other UE.
• UE global ID for example, SUPI
• steps shown in the example of FIGS. 12, 13, and 14 may not be essential steps and may be omitted.
• steps other than the steps shown in FIGS. 12, 13, and 14 may be added, and the order of the steps may vary. Some of the above steps may have their own technical meaning.
• a master node may include a processor, and a memory.
• the processor may be configured to be coupled operably with the memory.
• the processor may be configured to receive, from a source secondary node (SN), a SN change required message including (i) information on a candidate target SN and (ii) information informing that the candidate target SN is decided by the source SN using an AI model.
• the processor may be configured to perform a SN addition procedure with the candidate target SN without using an AI model.
• the processor may be configured to determine whether to use the AI model for deciding a target SN upon receiving the SN change required message.
• the processor may be configured to decide the candidate target SN as the target SN based on determining not to use the AI model.
• the processor may be configured to transmit, to the candidate target SN, a SN addition request message for the SN addition procedure.
• the processor may be configured to receive, from the candidate target SN, a SN addition request acknowledge message for the SN addition procedure.
• the SN addition request message may include (i) information informing that the candidate target SN is decided by the source SN using an AI model and (ii) information on the AI model used by the source SN.
• the SN addition procedure may be performed for a specific UE.
• the SN change required message may include a Subscription Permanent Identifier (SUPI) for the specific UE.
• SUPI Subscription Permanent Identifier
• the processor may be configured to store information on the candidate target SN as a target SN for the specific UE, upon performing the SN addition procedure with the candidate target SN.
• the processor may be configured to update information on the candidate target SN during of serving under this SN for the specific UE, upon receiving a SN release message for the specific UE from the candidate target SN.
• the processor may be configured to transmit, to the specific UE, a Radio Resource Control (RRC) connection reconfiguration message.
• the processor may be configured to receive, from the specific UE, a RRC connection reconfiguration complete message.
• the RRC connection reconfiguration message may include (i) information on the candidate target SN, (ii) information informing that the candidate target SN is decided by the source SN using an AI model, and (iii) information on the AI model used by the source SN.
• the processor may be configured to transmit, to the source SN, (i) a mobility information for a specific UE and (ii) a location information for the specific UE, before receiving the SN change required message.
• the mobility information may include (i) mobility statistics for the specific UE and/or (ii) mobility predictions for the specific UE.
• the mobility information may be acquired from a UE context for the specific UE.
• the location information may include (i) information on a current location for the specific UE and/or (ii) information on a past location for the specific UE.
• the location information may include information on a predicted location for the specific UE.
• the predicted location may include output of an AI model of the specific UE.
• the specific UE may be in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the specific UE.
• MN master node
• the processor may be configured to control the MN to receive, from a source secondary node (SN), a SN change required message including (i) information on a candidate target SN and (ii) information informing that the candidate target SN is decided by the source SN using an AI model.
• the processor may be configured to control the MN to perform a SN addition procedure with the candidate target SN without using an AI model.
• the processor may be configured to control the MN to determine whether to use the AI model for deciding a target SN upon receiving the SN change required message.
• the processor may be configured to control the MN to decide the candidate target SN as the target SN based on determining not to use the AI model.
• the processor may be configured to control the MN to transmit, to the candidate target SN, a SN addition request message for the SN addition procedure.
• the processor may be configured to control the MN to receive, from the candidate target SN, a SN addition request acknowledge message for the SN addition procedure.
• the SN addition request message may include (i) information informing that the candidate target SN is decided by the source SN using an AI model and (ii) information on the AI model used by the source SN.
• the SN addition procedure may be performed for a specific UE.
• the SN change required message may include a Subscription Permanent Identifier (SUPI) for the specific UE.
• SUPI Subscription Permanent Identifier
• the processor may be configured to control the MN to store information on the candidate target SN as a target SN for the specific UE, upon performing the SN addition procedure with the candidate target SN.
• the processor may be configured to control the MN to update information on the candidate target SN during of serving under this SN for the specific UE, upon receiving a SN release message for the specific UE from the candidate target SN.
• the processor may be configured to control the MN to transmit, to the specific UE, a Radio Resource Control (RRC) connection reconfiguration message.
• the processor may be configured to control the MN to receive, from the specific UE, a RRC connection reconfiguration complete message.
• the RRC connection reconfiguration message may include (i) information on the candidate target SN, (ii) information informing that the candidate target SN is decided by the source SN using an AI model, and (iii) information on the AI model used by the source SN.
• the processor may be configured to control the MN to transmit, to the source SN, (i) a mobility information for a specific UE and (ii) a location information for the specific UE, before receiving the SN change required message.
• the mobility information may include (i) mobility statistics for the specific UE and/or (ii) mobility predictions for the specific UE.
• the mobility information may be acquired from a UE context for the specific UE.
• the location information may include (i) information on a current location for the specific UE and/or (ii) information on a past location for the specific UE.
• the location information may include information on a predicted location for the specific UE.
• the predicted location may include output of an AI model of the specific UE.
• the specific UE may be in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the specific UE.
• a non-transitory computer-readable medium has stored thereon a plurality of instructions for performing AI based procedure for dual connectivity in a wireless communication system, according to some embodiments of the present disclosure, will be described.
• the technical features of the present disclosure could be embodied directly in hardware, in a software executed by a processor, or in a combination of the two.
• a method performed by a wireless device in a wireless communication may be implemented in hardware, software, firmware, or any combination thereof.
• a software may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other storage medium.
• storage medium is coupled to the processor such that the processor can read information from the storage medium.
• the storage medium may be integral to the processor.
• the processor and the storage medium may reside in an ASIC.
• the processor and the storage medium may reside as discrete components.
• the computer-readable medium may include a tangible and non-transitory computer-readable storage medium.
• non-transitory computer-readable media may include random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, or any other medium that can be used to store instructions or data structures.
• RAM random access memory
• SDRAM synchronous dynamic random access memory
• ROM read-only memory
• NVRAM non-volatile random access memory
• EEPROM electrically erasable programmable read-only memory
• FLASH memory magnetic or optical data storage media, or any other medium that can be used to store instructions or data structures.
• Non-transitory computer-readable media may also include combinations of the above.
• the method described herein may be realized at least in part by a computer-readable communication medium that carries or communicates code in the form of instructions or data structures and that can be accessed, read, and/or executed by a computer.
• a non-transitory computer-readable medium has stored thereon a plurality of instructions.
• the stored a plurality of instructions may be executed by a processor of a master node (MN).
• MN master node
• the stored a plurality of instructions may cause the MN to receive, from a source secondary node (SN), a SN change required message including (i) information on a candidate target SN and (ii) information informing that the candidate target SN is decided by the source SN using an AI model.
• the stored a plurality of instructions may cause the MN to perform a SN addition procedure with the candidate target SN without using an AI model.
• the stored a plurality of instructions may cause the MN to determine whether to use the AI model for deciding a target SN upon receiving the SN change required message.
• the stored a plurality of instructions may cause the MN to decide the candidate target SN as the target SN based on determining not to use the AI model.
• the stored a plurality of instructions may cause the MN to transmit, to the candidate target SN, a SN addition request message for the SN addition procedure.
• the stored a plurality of instructions may cause the MN to receive, from the candidate target SN, a SN addition request acknowledge message for the SN addition procedure.
• the SN addition request message may include (i) information informing that the candidate target SN is decided by the source SN using an AI model and (ii) information on the AI model used by the source SN.
• the SN addition procedure may be performed for a specific UE.
• the SN change required message may include a Subscription Permanent Identifier (SUPI) for the specific UE.
• SUPI Subscription Permanent Identifier
• the stored a plurality of instructions may cause the MN to store information on the candidate target SN as a target SN for the specific UE, upon performing the SN addition procedure with the candidate target SN.
• the stored a plurality of instructions may cause the MN to update information on the candidate target SN during of serving under this SN for the specific UE, upon receiving a SN release message for the specific UE from the candidate target SN.
• the stored a plurality of instructions may cause the MN to transmit, to the specific UE, a Radio Resource Control (RRC) connection reconfiguration message.
• the stored a plurality of instructions may cause the MN to receive, from the specific UE, a RRC connection reconfiguration complete message.
• the RRC connection reconfiguration message may include (i) information on the candidate target SN, (ii) information informing that the candidate target SN is decided by the source SN using an AI model, and (iii) information on the AI model used by the source SN.
• the stored a plurality of instructions may cause the MN to transmit, to the source SN, (i) a mobility information for a specific UE and (ii) a location information for the specific UE, before receiving the SN change required message.
• the mobility information may include (i) mobility statistics for the specific UE and/or (ii) mobility predictions for the specific UE.
• the mobility information may be acquired from a UE context for the specific UE.
• the location information may include (i) information on a current location for the specific UE and/or (ii) information on a past location for the specific UE.
• the location information may include information on a predicted location for the specific UE.
• the predicted location may include output of an AI model of the specific UE.
• the specific UE may be in communication with at least one of a user equipment, a network, or an autonomous vehicle other than the specific UE.
• the present disclosure may have various advantageous effects.
• UE's dual connectivity performance could be enhanced by using AI model.
• a RAN node could select and/or decide a target secondary node more accurately.
• a dual connectivity problem for example, a dual connectivity failure or SN change ping-pang
• a dual connectivity failure or SN change ping-pang could be avoided as much as possible. Then, UE's service could be guaranteed without interruption.
• a RAN node could efficiently perform AI based procedure for Dual Connectivity in a wireless communication system.
• a RAN node could efficiently use AI model for a procedure related to a dual connectivity by using an indication informing whether an AI based information has been considered.
• a RAN node could acquire information for an AI based procedure for a dual connectivity.

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