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WO2025208420A1 - Prédiction de mesure de modèle basée sur l'intelligence artificielle côté réseau - Google Patents

Prédiction de mesure de modèle basée sur l'intelligence artificielle côté réseau

Info

Publication number
WO2025208420A1
WO2025208420A1 PCT/CN2024/085872 CN2024085872W WO2025208420A1 WO 2025208420 A1 WO2025208420 A1 WO 2025208420A1 CN 2024085872 W CN2024085872 W CN 2024085872W WO 2025208420 A1 WO2025208420 A1 WO 2025208420A1
Authority
WO
WIPO (PCT)
Prior art keywords
network
training
measurement
dedicated
events
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/085872
Other languages
English (en)
Inventor
Fangli Xu
Alexander Sirotkin
Haijing Hu
Peng Cheng
Ping-Heng Kuo
Ralf ROSSBACH
Yuqin Chen
Zhibin Wu
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Apple Inc
Original Assignee
Apple Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Apple Inc filed Critical Apple Inc
Priority to PCT/CN2024/085872 priority Critical patent/WO2025208420A1/fr
Publication of WO2025208420A1 publication Critical patent/WO2025208420A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports

Definitions

  • LTE Long Term Evolution
  • 5G NR Fifth Generation New Radio
  • 5G-NR also simply referred to as NR
  • NR provides, as compared to LTE, a higher capacity for a higher density of mobile broadband users, while also supporting device-to-device, ultra-reliable, and massive machine type communications with lower latency and/or lower battery consumption.
  • NR may allow for more flexible UE scheduling as compared to current LTE. Consequently, efforts are being made in ongoing developments of 5G-NR to take advantage of higher throughputs possible at higher frequencies.
  • Embodiments relate to wireless communications, and more particularly to apparatuses, systems, and methods for an apparatus of a user equipment (UE) , the apparatus comprising one or more processors, coupled to a memory, configured to: decode, from a network, configuration information for dedicated training measurement events, wherein the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models; perform, by the UE, one or more measurements based on the dedicated training measurement events; and encode, for transmission to the network, one or more measurement reports based on the dedicated training measurement events, wherein the measurement reports are used for training the one or more network-side AI based models.
  • UE user equipment
  • UAVs unmanned aerial vehicles
  • UACs unmanned aerial controllers
  • base stations access points
  • cellular phones tablet computers
  • wearable computing devices portable media players, and any of various other computing devices.
  • FIG. 1 B illustrates an example of a base station and an access point in communication with a user equipment (UE) device, according to some embodiments.
  • UE user equipment
  • FIG. 2 illustrates an example block diagram of a base station, according to some embodiments.
  • FIG. 3 illustrates an example block diagram of a server according to some embodiments.
  • FIG. 4 illustrates an example block diagram of a UE according to some embodiments.
  • FIG. 6 illustrates an example of a baseband processor architecture for a UE, according to some embodiments.
  • FIG. 7 illustrates an example block diagram of an interface of baseband circuitry according to some embodiments.
  • FIG. 8 illustrates an example of a control plane protocol stack in accordance with some embodiments.
  • FIG. 9 illustrates an example timing diagram signaling between a user equipment (UE) and base station (e.g., a base station (base station) ) for enabling network-side artificial intelligence measurement prediction according to some embodiments.
  • UE user equipment
  • base station e.g., a base station (base station)
  • FIG. 11 illustrates an example flow chart of a method of enabling network-side artificial intelligence based model inference measurement at a base station, according to some embodiments.
  • Memory Medium Any of various types of non-transitory memory devices or storage devices.
  • the term “memory medium” is intended to include an installation medium, e.g., a CD-ROM, floppy disks, or tape device; a computer system memory or random-access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc. ; a non-volatile memory such as a Flash, magnetic media, e.g., a hard drive, or optical storage; registers, or other similar types of memory elements, etc.
  • the memory medium may include other types of non-transitory memory as well or combinations thereof.
  • the memory medium may be located in a first computer system in which the programs are executed or may be located in a second different computer system which connects to the first computer system over a network, such as the Internet. In the latter instance, the second computer system may provide program instructions to the first computer for execution.
  • the term “memory medium” may include two or more memory mediums which may reside in different locations, e.g., in different computer systems that are connected over a network.
  • the memory medium may store program instructions (e.g., embodied as computer programs) that may be executed by one or more processors.
  • Carrier Medium a memory medium as described above, as well as a physical transmission medium, such as a bus, network, and/or other physical transmission medium that conveys signals such as electrical, electromagnetic, or digital signals.
  • a physical transmission medium such as a bus, network, and/or other physical transmission medium that conveys signals such as electrical, electromagnetic, or digital signals.
  • Programmable Hardware Element includes various hardware devices comprising multiple programmable function blocks connected via a programmable interconnect. Examples include FPGAs (Field Programmable Gate Arrays) , PLDs (Programmable Logic Devices) , FPOAs (Field Programmable Object Arrays) , and CPLDs (Complex PLDs) .
  • the programmable function blocks may range from fine grained (combinatorial logic or look up tables) to coarse grained (arithmetic logic units or processor cores) .
  • a programmable hardware element may also be referred to as "reconfigurable logic” .
  • Base Station has the full breadth of its ordinary meaning, and at least includes a wireless communication station installed at a fixed location and used to communicate as part of a wireless telephone system or radio system.
  • Processing Element refers to various elements or combinations of elements that are capable of performing a function in a device, such as a user equipment or a cellular network device.
  • Processing elements may include, for example: processors and associated memory, portions or circuits of individual processor cores, entire processor cores, processor arrays, circuits such as an ASIC (Application Specific Integrated Circuit) , programmable hardware elements such as a field programmable gate array (FPGA) , as well any of various combinations of the above.
  • ASIC Application Specific Integrated Circuit
  • FPGA field programmable gate array
  • channel widths may be variable (e.g., depending on device capability, band conditions, etc. ) .
  • LTE may support scalable channel bandwidths from 1.4 MHz to 20MHz.
  • 5G NR can support scalable channel bandwidths from 5 MHz to 100 MHz in Frequency Range 1 (FR1) and up to 400 MHz in FR2.
  • WLAN channels may be 22 MHz wide while Bluetooth channels may be 1 MHz wide.
  • Other protocols and standards may include different definitions of channels.
  • some standards may define and use multiple types of channels, e.g., different channels for uplink or downlink and/or different channels for different uses such as data, control information, etc.
  • band has the full breadth of its ordinary meaning, and at least includes a section of spectrum (e.g., radio frequency spectrum) in which channels are used or set aside for the same purpose.
  • spectrum e.g., radio frequency spectrum
  • the example embodiments are also described with regard to a fifth generation (5G) New Radio (NR) network that may configure a UE to control the UE side for enabling network-side artificial intelligence based model measurement prediction.
  • 5G fifth generation
  • NR New Radio
  • reference to a 5G NR network is merely provided for illustrative purposes.
  • the example embodiments may be utilized with any appropriate type of network.
  • a user equipment comprising one or more processors, coupled to a memory, may be configured to: decode, from a network, configuration information for dedicated training measurement events, wherein the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models; perform, by the UE, one or more measurements based on the dedicated training measurement events; and encode, for transmission to the network, one or more measurement reports based on the dedicated training measurement events, wherein the measurement reports are used for training the one or more network-side AI based models.
  • UE user equipment
  • FIGs. 1A and 1B Communication Systems
  • FIG. 1A illustrates a simplified example wireless communication system, according to some embodiments. It is noted that the system of FIG. 1A is merely one example of a possible system, and that features of this disclosure may be implemented in any of various systems, as desired.
  • the base station (BS) 102A may be a base transceiver station (BTS) or cell site (a “cellular base station” ) and may include hardware that enables wireless communication with the UEs 106A through 106N.
  • BTS base transceiver station
  • cellular base station a “cellular base station”
  • the base station 102A is implemented in the context of LTE, also referred to as the Evolved Universal Terrestrial Radio Access Network (E-UTRAN, it may alternately be referred to as an 'eNodeB' or ‘eNB’ .
  • E-UTRAN Evolved Universal Terrestrial Radio Access Network
  • eNB Evolved Universal Terrestrial Radio Access Network
  • the base station 102A is implemented in the context of 5G NR, it may alternately be referred to as ‘gNodeB’ or ‘base station’ .
  • the base station 102A may also be equipped to communicate with a network 100 (e.g., a core network of a cellular service provider, a telecommunication network such as a public switched telephone network (PSTN) , and/or the Internet, among various possibilities) .
  • a network 100 e.g., a core network of a cellular service provider, a telecommunication network such as a public switched telephone network (PSTN) , and/or the Internet, among various possibilities
  • PSTN public switched telephone network
  • the base station 102A may facilitate communication between the user devices and/or between the user devices and the network 100.
  • the cellular base station 102A may provide UEs 106 with various telecommunication capabilities, such as voice, SMS and/or data services.
  • Base station 102A and other similar base stations (such as base stations 102B...102N) operating according to the same or a different cellular communication standard may thus be provided as a network of cells, which may provide continuous or nearly continuous overlapping service to UEs 106A-N and similar devices over a geographic area via one or more cellular communication standards.
  • each UE 106 may also be capable of receiving signals from (and possibly within communication range of) one or more other cells (which might be provided by base stations 102B-N and/or any other base stations) , which may be referred to as “neighboring cells” .
  • Such cells may also be capable of facilitating communication between user devices and/or between user devices and the network 100.
  • Such cells may include “macro” cells, “micro” cells, “pico” cells, and/or cells which provide any of various other granularities of service area size.
  • base stations 102A-B illustrated in FIG. 1A might be macro cells, while base station 102N might be a micro cell. Other configurations are also possible.
  • a UE 106 may be capable of communicating using multiple wireless communication standards.
  • the UE 106 may be configured to communicate using a wireless networking (e.g., Wi-Fi) and/or peer-to-peer wireless communication protocol (e.g., Bluetooth, Wi-Fi peer-to-peer, etc. ) in addition to at least one cellular communication protocol (e.g., GSM, UMTS (associated with, for example, WCDMA or TD-SCDMA air interfaces) , LTE, LTE-A, 5G NR, HSPA, 3GPP2 CDMA2000 (e.g., 1xRTT, 1xEV-DO, HRPD, eHRPD) , etc. ) .
  • GSM Global System for Mobile communications
  • UMTS associated with, for example, WCDMA or TD-SCDMA air interfaces
  • LTE Long Term Evolution
  • LTE-A Long Term Evolution
  • 5G NR Fifth Generation
  • HSPA High Speed Packet Access
  • the UE 106 may also or alternatively be configured to communicate using one or more global navigational satellite systems (GNSS, e.g., GPS or GLONASS) , one or more mobile television broadcasting standards (e.g., ATSC-M/H or DVB-H) , and/or any other wireless communication protocol, if desired.
  • GNSS global navigational satellite systems
  • mobile television broadcasting standards e.g., ATSC-M/H or DVB-H
  • any other wireless communication protocol if desired.
  • Other combinations of wireless communication standards including more than two wireless communication standards are also possible.
  • the UE 106 may include one or more antennas for communicating using one or more wireless communication protocols or technologies.
  • the UE 106 may be configured to communicate using, for example, CDMA2000 (1xRTT /1xEV-DO /HRPD /eHRPD) , LTE/LTE-Advanced, or 5G NR using a single shared radio and/or GSM, LTE, LTE-Advanced, or 5G NR using the single shared radio.
  • the shared radio may couple to a single antenna, or may couple to multiple antennas (e.g., for MIMO) for performing wireless communications.
  • a radio may include any combination of a baseband processor, analog RF signal processing circuitry (e.g., including filters, mixers, oscillators, amplifiers, etc. ) , or digital processing circuitry (e.g., for digital modulation as well as other digital processing) .
  • the radio may implement one or more receive and transmit chains using the aforementioned hardware.
  • the UE 106 may share one or more parts of a receive and/or transmit chain between multiple wireless communication technologies, such as those discussed above.
  • the UE 106 may include separate transmit and/or receive chains (e.g., including separate antennas and other radio components) for each wireless communication protocol with which it is configured to communicate.
  • the UE 106 may include one or more radios which are shared between multiple wireless communication protocols, and one or more radios which are used exclusively by a single wireless communication protocol.
  • the UE 106 might include a shared radio for communicating using either LTE or 5G NR (or LTE or 1xRTTor LTE or GSM) , and separate radios for communicating using each of Wi-Fi and Bluetooth. Other configurations are also possible.
  • FIG. 2 Block Diagram of a Base Station
  • FIG. 2 illustrates an example block diagram of a base station 102, according to some embodiments. It is noted that the base station of FIG. 2 is merely one example of a possible base station. As shown, the base station 102 may include processor (s) 204 which may execute program instructions for the base station 102. The processor (s) 204 may also be coupled to memory management unit (MMU) 240, which may be configured to receive addresses from the processor (s) 204 and translate those addresses to locations in memory (e.g., memory 260 and read only memory (ROM) 250) or to other circuits or devices.
  • MMU memory management unit
  • base station 102 may be a next generation base station, e.g., a 5G New Radio (5G NR) base station, or “base station” .
  • base station 102 may be connected to a legacy evolved packet core (EPC) network and/or to a NR core (NRC) network.
  • EPC legacy evolved packet core
  • NRC NR core
  • base station 102 may be considered a 5G NR cell and may include one or more transition and reception points (TRPs) .
  • TRPs transition and reception points
  • a UE capable of operating according to 5G NR may be connected to one or more TRPs within one or more base stations.
  • the base station 102 may include at least one antenna 234, and possibly multiple antennas.
  • the at least one antenna 234 may be configured to operate as a wireless transceiver and may be further configured to communicate with UE devices 106 via radio 230.
  • the antenna 234 communicates with the radio 230 via communication chain 232.
  • Communication chain 232 may be a receive chain, a transmit chain or both.
  • the radio 230 may be configured to communicate via various wireless communication standards, including, but not limited to, 5G NR, LTE, LTE-A, GSM, UMTS, CDMA2000, Wi-Fi, etc.
  • the base station 102 may be configured to communicate wirelessly using multiple wireless communication standards.
  • the base station 102 may include multiple radios, which may enable the base station 102 to communicate according to multiple wireless communication technologies.
  • the base station 102 may include an LTE radio for performing communication according to LTE as well as a 5G NR radio for performing communication according to 5G NR.
  • the base station 102 may be capable of operating as both an LTE base station and a 5G NR base station.
  • the base station 102 may include a multi-mode radio which is capable of performing communications according to any of multiple wireless communication technologies (e.g., 5G NR and Wi-Fi, LTE and Wi-Fi, LTE and UMTS, LTE and CDMA2000, UMTS and GSM, etc. ) .
  • multiple wireless communication technologies e.g., 5G NR and Wi-Fi, LTE and Wi-Fi, LTE and UMTS, LTE and CDMA2000, UMTS and GSM, etc.
  • the BS 102 may include hardware and software components for implementing or supporting implementation of features described herein.
  • the processor 204 of the base station 102 may be configured to implement or support implementation of part or all of the methods described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium) .
  • the processor 204 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array) , or as an ASIC (Application Specific Integrated Circuit) , or a combination thereof.
  • processor 204 of the BS 102 in conjunction with one or more of the other components 230, 232, 234, 240, 250, 260, 270 may be configured to implement or support implementation of part or all of the features described herein.
  • processor (s) 204 may be comprised of one or more processing elements. In other words, one or more processing elements may be included in processor (s) 204. Thus, processor (s) 204 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor (s) 204. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of processor (s) 204.
  • circuitry e.g., first circuitry, second circuitry, etc.
  • radio 230 may be comprised of one or more processing elements.
  • one or more processing elements may be included in radio 230.
  • radio 230 may include one or more integrated circuits (ICs) that are configured to perform the functions of radio 230.
  • each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of radio 230.
  • FIG. 3 Block Diagram of a Server
  • FIG. 3 illustrates an example block diagram of a server 104, according to some embodiments. It is noted that the server of FIG. 3 is merely one example of a possible server. As shown, the server 104 may include processor (s) 344 which may execute program instructions for the server 104. The processor (s) 344 may also be coupled to memory management unit (MMU) 374, which may be configured to receive addresses from the processor (s) 344 and translate those addresses to locations in memory (e.g., memory 364 and read only memory (ROM) 354) or to other circuits or devices.
  • MMU memory management unit
  • the server 104 may be configured to provide a plurality of devices, such as base station 102, and UE devices 106 access to network functions, e.g., as further described herein.
  • processor (s) 344 may be comprised of one or more processing elements. In other words, one or more processing elements may be included in processor (s) 344.
  • processor (s) 344 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor (s) 344.
  • each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of processor (s) 344.
  • FIG. 4 illustrates an example simplified block diagram of a communication device 106, according to some embodiments. It is noted that the block diagram of the communication device of FIG. 4 is only one example of a possible communication device.
  • communication device 106 may be a user equipment (UE) device, a mobile device or mobile station, a wireless device or wireless station, a desktop computer or computing device, a mobile computing device (e.g., a laptop, notebook, or portable computing device) , a tablet, an unmanned aerial vehicle (UAV) , a UAV controller (UAC) and/or a combination of devices, among other devices.
  • the communication device 106 may include a set of components 400 configured to perform core functions.
  • this set of components may be implemented as a system on chip (SOC) , which may include portions for various purposes.
  • SOC system on chip
  • this set of components 400 may be implemented as separate components or groups of components for the various purposes.
  • the set of components 400 may be coupled (e.g., communicatively; directly or indirectly) to various other circuits of the communication device 106.
  • the communication device 106 may include various types of memory (e.g., including NAND flash 410) , an input/output interface such as connector I/F 420 (e.g., for connecting to a computer system; dock; charging station; input devices, such as a microphone, camera, keyboard; output devices, such as speakers; etc. ) , the display 460, which may be integrated with or external to the communication device 106, and cellular communication circuitry 430 such as for 5G NR, LTE, GSM, etc., and short to medium range wireless communication circuitry 429 (e.g., Bluetooth TM and WLAN circuitry) .
  • communication device 106 may include wired communication circuitry (not shown) , such as a network interface card, e.g., for Ethernet.
  • the cellular communication circuitry 430 may couple (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 435 and 436 as shown.
  • the short to medium range wireless communication circuitry 429 may also couple (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 437 and 438 as shown.
  • the short to medium range wireless communication circuitry 429 may couple (e.g., communicatively; directly or indirectly) to the antennas 435 and 436 in addition to, or instead of, coupling (e.g., communicatively; directly or indirectly) to the antennas 437 and 438.
  • the short to medium range wireless communication circuitry 429 and/or cellular communication circuitry 430 may include multiple receive chains and/or multiple transmit chains for receiving and/or transmitting multiple spatial streams, such as in a multiple-input multiple output (MIMO) configuration.
  • MIMO multiple-input multiple output
  • cellular communication circuitry 430 may include dedicated receive chains (including and/or coupled to, e.g., communicatively; directly or indirectly. dedicated processors and/or radios) for multiple RATs (e.g., a first receive chain for LTE and a second receive chain for 5G NR) .
  • cellular communication circuitry 430 may include a single transmit chain that may be switched between radios dedicated to specific RATs.
  • a first radio may be dedicated to a first RAT, e.g., LTE, and may be in communication with a dedicated receive chain and a transmit chain shared with an additional radio, e.g., a second radio that may be dedicated to a second RAT, e.g., 5G NR, and may be in communication with a dedicated receive chain and the shared transmit chain.
  • a first RAT e.g., LTE
  • a second radio may be dedicated to a second RAT, e.g., 5G NR, and may be in communication with a dedicated receive chain and the shared transmit chain.
  • the communication device 106 may also include and/or be configured for use with one or more user interface elements.
  • the user interface elements may include any of various elements, such as display 460 (which may be a touchscreen display) , a keyboard (which may be a discrete keyboard or may be implemented as part of a touchscreen display) , a mouse, a microphone and/or speakers, one or more cameras, one or more buttons, and/or any of various other elements capable of providing information to a user and/or receiving or interpreting user input.
  • the communication device 106 may further include one or more smart cards 445 that include SIM (Subscriber Identity Module) functionality, such as one or more UICC (s) (Universal Integrated Circuit Card (s) ) cards 445.
  • SIM Subscriber Identity Module
  • UICC Universal Integrated Circuit Card
  • SIM entity is intended to include any of various types of SIM implementations or SIM functionality, such as the one or more UICC (s) cards 445, one or more eUICCs, one or more eSIMs, either removable or embedded, etc.
  • the UE 106 may include at least two SIMs. Each SIM may execute one or more SIM applications and/or otherwise implement SIM functionality.
  • each SIM may be a single smart card that may be embedded, e.g., may be soldered onto a circuit board in the UE 106, or each SIM 410 may be implemented as a removable smart card.
  • the SIM (s) may be one or more removable smart cards (such as UICC cards, which are sometimes referred to as “SIM cards” )
  • the SIMs 410 may be one or more embedded cards (such as embedded UICCs (eUICCs) , which are sometimes referred to as “eSIMs” or “eSIM cards” ) .
  • one or more of the SIM (s) may implement embedded SIM (eSIM) functionality; in such an embodiment, a single one of the SIM (s) may execute multiple SIM applications.
  • Each of the SIMs may include components such as a processor and/or a memory; instructions for performing SIM/eSIM functionality may be stored in the memory and executed by the processor.
  • the UE 106 may include a combination of removable smart cards and fixed/non-removable smart cards (such as one or more eUICC cards that implement eSIM functionality) , as desired.
  • the UE 106 may comprise two embedded SIMs, two removable SIMs, or a combination of one embedded SIMs and one removable SIMs.
  • Various other SIM configurations are also contemplated.
  • the SOC 400 may include processor (s) 402, which may execute program instructions for the communication device 106 and display circuitry 404, which may perform graphics processing and provide display signals to the display 460.
  • the processor (s) 402 may also be coupled to memory management unit (MMU) 440, which may be configured to receive addresses from the processor (s) 402 and translate those addresses to locations in memory (e.g., memory 406, read only memory (ROM) 450, NAND flash memory 410) and/or to other circuits or devices, such as the display circuitry 404, short to medium range wireless communication circuitry 429, cellular communication circuitry 430, connector I/F 420, and/or display 460.
  • the MMU 440 may be configured to perform memory protection and page table translation or set up. In some embodiments, the MMU 440 may be included as a portion of the processor (s) 402.
  • processor 402 may include one or more processing elements.
  • processor 402 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor 402.
  • each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of processor (s) 402.
  • the short to medium range wireless communication circuitry 429 may include one or more ICs that are configured to perform the functions of short to medium range wireless communication circuitry 429.
  • each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of short to medium range wireless communication circuitry 429.
  • FIG. 5 illustrates an example simplified block diagram of cellular communication circuitry, according to some embodiments. It is noted that the block diagram of the cellular communication circuitry of FIG. 5 is only one example of a possible cellular communication circuit.
  • cellular communication circuitry 530 which may be cellular communication circuitry 430, may be included in a communication device, such as communication device 106 described above.
  • communication device 106 may be a user equipment (UE) device, a mobile device or mobile station, a wireless device or wireless station, a desktop computer or computing device, a mobile computing device (e.g., a laptop, notebook, or portable computing device) , a tablet and/or a combination of devices, among other devices.
  • UE user equipment
  • Modem 510 may be configured for communications according to a first RAT, e.g., such as LTE or LTE-A, and modem 520 may be configured for communications according to a second RAT, e.g., such as 5G NR.
  • a first RAT e.g., such as LTE or LTE-A
  • modem 520 may be configured for communications according to a second RAT, e.g., such as 5G NR.
  • processors 512 may include one or more processing elements.
  • processors 512 may include one or more integrated circuits (ICs) that are configured to perform the functions of processors 512.
  • each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of processors 512.
  • FIG. 6 Block Diagram of a Baseband Processor Architecture for a UE
  • FIG. 6 illustrates example components of a device 600 in accordance with some embodiments. It is noted that the device of FIG. 6 is merely one example of a possible system, and that features of this disclosure may be implemented in any of various UEs, as desired.
  • the baseband circuitry 604 may include a third generation (3G) baseband processor 604A, a fourth generation (4G) baseband processor 604B, a fifth generation (5G) baseband processor 604C, or other baseband processor (s) 604D for other existing generations, generations in development or to be developed in the future (e.g., second generation (2G) , sixth generation (6G) , etc. ) .
  • the baseband circuitry 604 e.g., one or more of baseband processors 604A-D
  • the baseband circuitry 604 may include one or more audio digital signal processor (s) (DSP) 604F.
  • the audio DSP (s) 604F may be include elements for compression/decompression and echo cancellation and may include other suitable processing elements in other embodiments.
  • Components of the baseband circuitry may be suitably combined in a single chip, a single chipset, or disposed on a same circuit board in some embodiments.
  • some or all of the constituent components of the baseband circuitry 604 and the application circuitry 602 may be implemented together such as, for example, on a system on a chip (SOC) .
  • SOC system on a chip
  • the amplifier circuitry 606b may be configured to amplify the down-converted signals and the filter circuitry 606c may be a low-pass filter (LPF) or band-pass filter (BPF) configured to remove unwanted signals from the down-converted signals to generate output baseband signals.
  • Output baseband signals may be provided to the baseband circuitry 604 for further processing.
  • the output baseband signals may be zero-frequency baseband signals, although this is not a necessity.
  • mixer circuitry 606a of the receive signal path may comprise passive mixers, although the scope of the embodiments is not limited in this respect.
  • the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may include two or more mixers and may be arranged for quadrature downconversion and upconversion, respectively.
  • the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may include two or more mixers and may be arranged for image rejection (e.g., Hartley image rejection) .
  • the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a may be arranged for direct downconversion and direct upconversion, respectively.
  • the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may be configured for super-heterodyne operation.
  • the output baseband signals, and the input baseband signals may be analog baseband signals, although the scope of the embodiments is not limited in this respect.
  • the output baseband signals, and the input baseband signals may be digital baseband signals.
  • the RF circuitry 606 may include analog-to-digital converter (ADC) and digital-to-analog converter (DAC) circuitry and the baseband circuitry 604 may include a digital baseband interface to communicate with the RF circuitry 606.
  • ADC analog-to-digital converter
  • DAC digital-to-analog converter
  • a separate radio IC circuitry may be provided for processing signals for each spectrum, although the scope of the embodiments is not limited in this respect.
  • the synthesizer circuitry 606d may be a fractional-N synthesizer or a fractional N/N+1 synthesizer, although the scope of the embodiments is not limited in this respect as other types of frequency synthesizers may be suitable.
  • synthesizer circuitry 606d may be a delta-sigma synthesizer, a frequency multiplier, or a synthesizer comprising a phase-locked loop with a frequency divider.
  • the synthesizer circuitry 606d may be configured to synthesize an output frequency for use by the mixer circuitry 606a of the RF circuitry 606 based on a frequency input and a divider control input. In some embodiments, the synthesizer circuitry 606d may be a fractional N/N+1 synthesizer.
  • frequency input may be provided by a voltage controlled oscillator (VCO) , although that is not a necessity.
  • VCO voltage controlled oscillator
  • Divider control input may be provided by either the baseband circuitry 604 or the applications processor 602 depending on the desired output frequency.
  • a divider control input (e.g., N) may be determined from a look-up table based on a channel indicated by the applications processor 602.
  • Synthesizer circuitry 606d of the RF circuitry 606 may include a divider, a delay-locked loop (DLL) , a multiplexer and a phase accumulator.
  • the divider may be a dual modulus divider (DMD) and the phase accumulator may be a digital phase accumulator (DPA) .
  • the DMD may be configured to divide the input signal by either N or N+1 (e.g., based on a carry out) to provide a fractional division ratio.
  • the DLL may include a set of cascaded, tunable, delay elements, a phase detector, a charge pump and a D-type flip-flop.
  • the delay elements may be configured to break a VCO period up into Nd equal packets of phase, where Nd is the number of delay elements in the delay line.
  • Nd is the number of delay elements in the delay line.
  • synthesizer circuitry 606d may be configured to generate a carrier frequency as the output frequency, while in other embodiments, the output frequency may be a multiple of the carrier frequency (e.g., twice the carrier frequency, four times the carrier frequency) and used in conjunction with quadrature generator and divider circuitry to generate multiple signals at the carrier frequency with multiple different phases with respect to each other.
  • the output frequency may be a LO frequency (fLO) .
  • the RF circuitry 606 may include an IQ/polar converter.
  • the FEM circuitry 608 may include a TX/RX switch to switch between transmit mode and receive mode operation.
  • the FEM circuitry may include a receive signal path and a transmit signal path.
  • the receive signal path of the FEM circuitry may include an LNA to amplify received RF signals and provide the amplified received RF signals as an output (e.g., to the RF circuitry 606) .
  • the transmit signal path of the FEM circuitry 608 may include a power amplifier (PA) to amplify input RF signals (e.g., provided by RF circuitry 606) , and one or more filters to generate RF signals for subsequent transmission (e.g., by one or more of the one or more antennas 610) .
  • PA power amplifier
  • the PMC 612 may manage power provided to the baseband circuitry 604.
  • the PMC 612 may control power-source selection, voltage scaling, battery charging, or DC-to-DC conversion.
  • the PMC 612 may often be included when the device 600 is capable of being powered by a battery, for example, when the device is included in a UE.
  • the PMC 612 may increase the power conversion efficiency while providing desirable implementation size and heat dissipation characteristics.
  • the PMC 612 may control, or otherwise be part of, various power saving mechanisms of the device 600. For example, if the device 600 is in a radio resource control_Connected (RRC_Connected) state, where it is still connected to the RAN node as it expects to receive traffic shortly, then it may enter a state known as Discontinuous Reception Mode (DRX) after a period of inactivity. During this state, the device 600 may power down for brief intervals of time and thus save power.
  • RRC_Connected radio resource control_Connected
  • DRX Discontinuous Reception Mode
  • the baseband circuitry 604 may further include one or more interfaces to communicatively couple to other circuitries/devices, such as a memory interface 712 (e.g., an interface to send/receive data to/from memory external to the baseband circuitry 604) , an application circuitry interface 714 (e.g., an interface to send/receive data to/from the application circuitry 602 of FIG. 6) , an RF circuitry interface 716 (e.g., an interface to send/receive data to/from RF circuitry 606 of FIG.
  • a memory interface 712 e.g., an interface to send/receive data to/from memory external to the baseband circuitry 604
  • an application circuitry interface 714 e.g., an interface to send/receive data to/from the application circuitry 602 of FIG.
  • an RF circuitry interface 716 e.g., an interface to send/receive data to/from RF circuitry 606 of FIG.
  • FIG. 8 Control Plane Protocol Stack
  • the MAC layer 802 may perform mapping between logical channels and transport channels, multiplexing of MAC service data units (SDUs) from one or more logical channels onto transport blocks (TB) to be delivered to PHY via transport channels, de-multiplexing MAC SDUs to one or more logical channels from transport blocks (TB) delivered from the PHY via transport channels, multiplexing MAC SDUs onto TBs, scheduling information reporting, error correction through hybrid automatic repeat request (HARQ) , and logical channel prioritization.
  • SDUs MAC service data units
  • TB transport blocks
  • HARQ hybrid automatic repeat request
  • the RLC layer 803 may operate in a plurality of modes of operation, including: Transparent Mode (TM) , Unacknowledged Mode (UM) , and Acknowledged Mode (AM) .
  • the RLC layer 803 may execute transfer of upper layer protocol data units (PDUs) , error correction through automatic repeat request (ARQ) for AM data transfers, and concatenation, segmentation and reassembly of RLC SDUs for UM and AM data transfers.
  • PDUs protocol data units
  • ARQ automatic repeat request
  • the RLC layer 803 may also execute re-segmentation of RLC data PDUs for AM data transfers, reorder RLC data PDUs for UM and AM data transfers, detect duplicate data for UM and AM data transfers, discard RLC SDUs for UM and AM data transfers, detect protocol errors for AM data transfers, and perform RLC re-establishment.
  • the PDCP layer 804 may execute header compression and decompression of IP data, maintain PDCP Sequence Numbers (SNs) , perform in-sequence delivery of upper layer PDUs at re-establishment of lower layers, eliminate duplicates of lower layer SDUs at re-establishment of lower layers for radio bearers mapped on RLC AM, cipher and decipher control plane data, perform integrity protection and integrity verification of control plane data, control timer-based discard of data, and perform security operations (e.g., ciphering, deciphering, integrity protection, integrity verification, etc. ) .
  • security operations e.g., ciphering, deciphering, integrity protection, integrity verification, etc.
  • the non-access stratum (NAS) protocols 806 form the highest stratum of the control plane between the UE 601 and the MME 621.
  • the NAS protocols 806 support the mobility of the UE 601 and the session management procedures to establish and maintain IP connectivity between the UE 601 and the P-GW 623.
  • the S1 Application Protocol (S1-AP) layer 815 may support the functions of the S1 interface and comprise Elementary Procedures (EPs) .
  • An EP is a unit of interaction between the RAN node 102A and the CN 100.
  • the S1-AP layer services may comprise two groups: UE-associated services and non UE-associated services. These services perform functions including, but not limited to: E-UTRAN Radio Access Bearer (E-RAB) management, UE capability indication, mobility, NAS signaling transport, RAN Information Management (RIM) , and configuration transfer.
  • E-RAB E-UTRAN Radio Access Bearer
  • RIM RAN Information Management
  • the Stream Control Transmission Protocol (SCTP) layer (alternatively referred to as the SCTP/IP layer) 814 may ensure reliable delivery of signaling messages between the RAN node 102A and the MME 621 based, in part, on the IP protocol, supported by the IP layer 813.
  • the L2 layer 812 and the L1 layer 811 may refer to communication links (e.g., wired or wireless) used by the RAN node and the MME to exchange information.
  • Wireless communication systems provide mobility by enabling user equipment (UEs) to move between cells via a process referred to as handover.
  • Handover occurs when a mobile UE switches from one cell to another neighboring cell.
  • Mechanisms have been established to help ensure a smooth transition between cells.
  • NR supports different types of handover that were not supported in the previous 4G LTE specification.
  • the basic handover in NR has been based on LTE handover mechanisms in which the network controls UE mobility based on UE measurement reporting. This measurement reporting typically involves Layer 3 (L3) measurements of neighbor cells and reporting from the UE to the eNB.
  • L3 Layer 3
  • FIG. 9 Timing Diagram for enabling network-side artificial intelligence (AI) based model measurement prediction.
  • AI artificial intelligence
  • AI-based mobility involves a network using artificial intelligence (AI) /machine learning (ML) models to predict and optimize handover decisions, considering factors such as cell-level measurements, inter-cell beam-level measurements, and potential handover failures.
  • AI/ML-aided mobility for network-triggered layer 3 (L3) -based handover offers potential benefits and gains, particularly in aspects like AI/ML-based RRM measurement and event inference (e.g. prediction) .
  • the focus of the illustrated embodiments, as described herein, is on network-sided AI/ML model training for mobility.
  • Network-sided measurement and event prediction can be trained in the network using the existing measurement framework without any enhancements in the standard.
  • UE User Equipment
  • a new optional capability may be defined for these dedicated network AI/ML training measurements, and the network is only allowed to configure the dedicated network AI/ML model training measurements if user consent (e.g., via a user’s UE) has been provided.
  • new dedicated training measurement events are specifically designed for network-sided AI/ML model training. These new dedicated training measurement events are to be used for collecting measurements for training purposes of network based AI/ML models.
  • the existing or legacy measurement framework which is already used for mobility management and other network functions, will continue to operate as usual. In other words, the introduction of the new dedicated training measurement events will not replace or disrupt the existing measurement framework.
  • a flag or indicator can be included in the measurement configuration to explicitly mark the dedicated training measurements as being used for network-sided AI model training purposes. This flag would provide a clear distinction between training-related events and other measurement events.
  • the mechanisms of the illustrated embodiments provide the specific measurement events that can be used for network-sided AI/ML model training.
  • Event A3 Neighbor cell becomes offset better than the serving cell (SpCell) .
  • the new dedicated training measurement events AT1-AT6 are defined as follows:
  • the new dedicated training measurement events AT1-AT6 can be considered as a first set of new dedicated training measurement events AT1-AT6.
  • the mechanisms of the illustrated embodiments provide a dedicated set of measurements that can be used exclusively for training purposes. This separation allows for a clear distinction between measurements used for training and those used for other network functions, enabling more focused and efficient data collection for AI/ML model development and the capability to control the collection of data for network-sided AI/ML model training from the UE.
  • these new dedicated training measurement events are specifically designed to facilitate network-sided model training and provide the network with information about when measurements change above a configured threshold compared to the previously reported measurement.
  • Additional new dedicated training measurement events can be defined as:
  • the new dedicated training measurement events AT7-T10 can be considered as a second set of dedicated training measurement events
  • These dedicated training measurement events are designed to capture changes in measurements relative to the previously reported values. By comparing the current measurement to the measurement in a previous report, the network can identify significant changes or improvements in the serving cell or neighbor cells.
  • the mechanisms of the illustrated embodiments also provide for a simplified approach where existing measurement events (e.g., A1-A6) can be re-used for training and there are no new dedicated measurement events defined (e.g. AT1 –AT10) .
  • existing measurement events e.g., A1-A6
  • AT1 –AT10 new dedicated measurement events defined
  • the only change is the addition of a flag to a measurement object indicating that it is used for network sided model training and not for mobility.
  • the SON report may also be referred to as the "measurements for training report, ” which is specifically defined to collect measurements for network-sided training for transmission from the UE to the network via the base station.
  • the UE may store measurements for network-sided training in a new variable called "VarMeasTraining-Report. " To indicate the availability of the Measurements for Training Report to the network, the UE utilizes a standard SON mechanism.
  • the mechanisms of the illustrated embodiments provide for an enhanced minimization of drive tests (MDT) report for gathering the new dedicated training measurement for training the one or more network-side AI based models and then sending the enhanced MDT report to the network.
  • the enhanced MDT report includes the one or more new dedicated training measurements for training the one or more network-side AI based models.
  • the signaling may begin with a network (e.g., base station 102) transmitting 902, to a UE, such as UE 106, configuration information for dedicated training measurement events, where the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models (e.g., the AI-based model 920) .
  • a network e.g., base station 102
  • a UE such as UE 106
  • configuration information for dedicated training measurement events correspond to a set of defined measurement events used by the network
  • the dedicated training measurement events are used exclusively for training one or more network-side AI based models (e.g., the AI-based model 920) .
  • the dedicated training measurement events include a first set of events that corresponds to the set of defined measurement events.
  • the dedicated training measurement events are associated with the UE having capability to support the dedicated training measurement events and require consent of the UE prior to configuring the UE to perform the dedicated training measurement events.
  • the dedicated training measurement events include one or more of 1) a first event that is triggered when a cell with a highest measurement quality changes and is associated with a similar measurement object or frequency with a previous cell, and/or 2) a second event that is triggered when cell with a highest measurement quality changes, but the change occurs with the cell.
  • the UE can send 906 to the network 1020 (e.g., via the base station 102) , one or more measurement reports based on the dedicated training measurement events, where the measurement reports are used for training the one or more network-side AI based models.
  • the one or more measurements e.g., dedicated training measurements
  • the signaling may also include the UE monitoring 908 performance of the one or more AI based models 920. Also, the signaling may include the base station 102 or network 1020 monitoring 910 performance of the one or more AI based models 920.
  • FIG. 10 Flow Chart for a Method of enabling network-side artificial intelligence based model measurement at a UE.
  • FIG. 10 illustrates an example flow chart of a method 1000 of enabling network-side artificial intelligence based model inference validation, at a UE, according to some embodiments.
  • the method shown in FIG. 10 may be used in conjunction with any of the systems, methods, or devices illustrated in the Figures, among other devices. In various embodiments, some of the method elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired.
  • a method 1000 for decoding, from a network, configuration information for dedicated training measurement events, wherein the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models, as in block 1010.
  • the method 100 may further comprise performing, by the UE, one or more measurements based on the dedicated training measurement events, as in block 1012.
  • the method 100 may further comprise encoding, for transmission to the network, one or more measurement reports based on the dedicated training measurement events, where the measurement reports are used for training the one or more network-side AI based model, as in block 1014.
  • the dedicated training measurement events include a first set of events that corresponds to the set of defined measurement events.
  • the dedicated training measurement events include one or more of: 1) a first event that is triggered when a cell with a highest measurement quality changes and is associated with a similar measurement object or frequency with a previous cell; and 2) a second event that is triggered when cell with a highest measurement quality changes, but the change occurs with the cell.
  • the dedicated SON report is a measurement for training Report.
  • the enhanced MDT report uses a LogMeasReport message that is in a UEInformationResponse message.
  • the enhanced MDT report is sent to a Trace Collection Entity (TCE) via a Trace Activation NG-AP message when the training the one or more network-side AI based models is performed in an Operations, Administration, and Maintenance (OAM) domain.
  • TCE Trace Collection Entity
  • OAM Operations, Administration, and Maintenance
  • the Trace Activation NG-AP message is enhanced to include a model training entity (MTE) IP address and an MTE URI.
  • MTE model training entity
  • the method 1000 further comprises indicating, by the UE, support for measurement reporting for training the one or more network-side AI based models using an optional UE capability.
  • the optional UE capability is defined for the dedicated training measurement events or all measurement events.
  • the method 1100 further comprises encoding, for transmission to the UE, configuration information for a second set of events that corresponds to the set of defined measurement events to facilitate the training the one or more network-side AI based models associated with the network, wherein the second set of events are triggered when the one or more measurements is greater than a configured threshold compared to a previously reported measurement.
  • the method 1100 further comprises encoding, for transmission to the UE, an initial value for each of the dedicated training measurement events; and encoding, for transmission to the network, the initial value even if the triggering conditions are not met.
  • the method 1100 further comprises decoding, from the UE, the dedicated SON report, wherein the dedicated SON report includes multiple measurements used for training the one or more network-side AI based models.
  • the method 1100 further comprises decoding, from the UE, a new availability indication information element (IE) referred to as UE-TrainingMeasurementsAvailable in one or more of a RRCReestablishmentComplete message, a RRCReconfigurationComplete message, a RRCResumeComplete message, and a RRCSetupComplete message to indicate the availability of the dedicated SON report.
  • IE new availability indication information element
  • the method 1100 further comprises decoding, from the UE, an enhanced UEInformationRequest message that indicates to the UE to provide measurements for training the one or more network-side AI based models.
  • the method 1100 further comprises decoding, from the UE, an enhanced UEInformationResponse message that carries the one or more measurements that are logged for training the one or more network-side AI based models; and the enhanced MDT report, wherein the enhanced MDT report includes the one or more measurements for training the one or more network-side AI based models.
  • an apparatus is disclosed that is configured to cause a user equipment (UE) to assist with performing any of the operations of the method 1100.
  • UE user equipment
  • a computer program product comprising computer instructions which, when executed by one or more processors, perform any of the operations described with respect to the method 1200.

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Abstract

L'invention concerne un procédé d'activation de mesures de modèle basées sur l'intelligence artificielle (IA) côté réseau par un équipement utilisateur (UE). Le procédé consiste à décoder, à partir d'un réseau, des informations de configuration pour des événements de mesure d'apprentissage dédiés, les événements de mesure d'apprentissage dédiés correspondant à un ensemble d'événements de mesure définis utilisés par le réseau, et les événements de mesure d'apprentissage dédiés étant utilisés exclusivement pour entraîner un ou plusieurs modèles basés sur l'IA côté réseau ; effectuer, par l'UE, une ou plusieurs mesures sur la base des événements de mesure d'apprentissage dédiés ; et coder, pour une transmission au réseau, un ou plusieurs rapports de mesure sur la base des événements de mesure d'apprentissage dédiés, les rapports de mesure étant utilisés pour entraîner le ou les modèles basés sur l'IA côté réseau.
PCT/CN2024/085872 2024-04-03 2024-04-03 Prédiction de mesure de modèle basée sur l'intelligence artificielle côté réseau Pending WO2025208420A1 (fr)

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