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KR-20260065895-A - Methods and devices for AI/ML-based BFR enhancement

KR20260065895AKR 20260065895 AKR20260065895 AKR 20260065895AKR-20260065895-A

Abstract

Systems and methods for using various artificial intelligence (AI)/machine learning (ML) models regarding block error rate (BLER) are described herein. The generation and use of BLER predictions and the prediction of Layer 1 (L1) reference signal received power (RSRP) of candidate reference signals are discussed. Various examples of inputs that can be used for these ML models are discussed. The predicted BFD can be reported to network nodes prior to the actual BFD based on AI/ML models.

Inventors

  • 쳉, 펭
  • 수, 팡리
  • 첸, 유칭
  • 후, 하이징
  • 벤카타, 나빈 쿠마르 알 팔레
  • 쿠오, 핑-헹
  • 로스바흐, 랄프
  • 시롯킨, 알렉산더
  • 우, 지빈

Assignees

  • 애플 인크.

Dates

Publication Date
20260511
Application Date
20231031

Claims (20)

  1. As a method of user equipment (UE), A step of transmitting a message to a network node indicating that the UE supports prediction of the block error rate (BLER); A step of receiving an activation message from the network node that identifies a prediction model to be used in the UE to predict the BLER; A step of determining one or more predicted beam failure indications (BFIs) using the above prediction model; A step of determining potential future beam failure detection (BFD) based on one or more predicted BFIs; and A method comprising the step of transmitting a beam failure recovery (BFR) message to the network node before the potential future BFD occurs.
  2. A method according to claim 1, wherein the BFR message includes the predicted Layer 1 Reference Signal Received Power (RSRP) of candidate reference signals at a future time.
  3. A method according to claim 1, wherein the potential future BFD is predicted before the actual BFI is detected, and the BFR message is transmitted to a primary cell (PCell) via a medium access control control element (MAC-CE) or radio resource control (RRC) message.
  4. A method according to claim 1, wherein the potential future BFD is predicted after one or more actual BFIs are detected, and the BFR message is transmitted to a secondary cell (SCell) via a Media Access Control Element (MAC-CE) or Radio Resource Control (RRC) message.
  5. A method according to claim 1, further comprising the step of transmitting a notification message identifying one or more prediction models available in the UE.
  6. A method according to claim 1, wherein the step of determining the potential future BFD comprises the step of predicting a BLER for a configured BFD-reference signal (RS) or a transmission configuration indication (TCI) of an activated physical downlink control channel (PDCCH) based on historical BLER samples.
  7. A method according to claim 1, wherein the step of determining the potential future BFD includes the step of predicting the BLER of the first reference signal based on BLER samples of the second reference signal.
  8. A method according to claim 1, wherein the step of determining one or more predicted BFIs using the prediction model comprises the step of performing continuous prediction starting at the reception of the configuration until the timer expires.
  9. A method according to claim 1, wherein the step of determining one or more predicted BFIs using the prediction model comprises the step of performing periodic prediction.
  10. A method according to claim 1, wherein the step of determining one or more predicted BFIs using the prediction model comprises the step of performing an event trigger prediction.
  11. A method according to claim 10, wherein the events triggering the event trigger prediction include when a media access control (MAC) layer detects a threshold number of consecutive BFIs, when the BLER of the configured BFD-reference signals (RS) is greater than a threshold percentage, or when the BLER of a set number of the BFD-RSs is greater than the threshold percentage.
  12. A method according to claim 10, further comprising the step of monitoring the performance of the above-mentioned prediction model.
  13. In claim 1, the method further includes the step of transmitting auxiliary information to the network node, wherein the auxiliary information is Proposed optimal evaluation interval of Qout_LR; Proposed optimal value for beamFailureInstanceMaxCount; Proposed BFD-Reference Signal (RS) set; or Proposed BFD-RS for actual measurements A method comprising at least one of the following.
  14. In claim 1, the method further includes the step of receiving auxiliary information from the network node, wherein the auxiliary information is Nearby network node placement geometry; Long-term statistics of time correlation; or Long-term statistics of cell-to-cell correlation or beam-to-beam correlation A method comprising at least one of the following.
  15. As a method of network nodes, A step of receiving a message from a user device (UE) indicating that the UE supports prediction of the block error rate (BLER); A step of transmitting to the above UE an activation message identifying a prediction model to be used by the UE to predict the BLER; and A method comprising the step of receiving a beam failure recovery (BFR) message before a potential future BFD occurs, wherein the BFR message is based on the prediction model.
  16. In paragraph 15, the method wherein the BFR message includes predicted Layer 1 (L1) reference signal received power (RSRP) values of candidate reference signals at future times.
  17. A method according to claim 16, further comprising the step of switching beams based on the predicted L1 RSRP values.
  18. A method according to paragraph 15, wherein the potential future BFD is predicted by the UE before the actual BFI is detected, and the BFR message is transmitted to the primary cell (PCell) via a Media Access Control Element (MAC-CE) or Radio Resource Control (RRC) message.
  19. A method according to paragraph 15, wherein the potential future BFD is predicted by the UE after one or more actual BFIs are detected, and the BFR message is transmitted to a secondary cell (SCell) via a Media Access Control Element (MAC-CE) or Radio Resource Control (RRC) message.
  20. A method according to claim 15, further comprising the step of receiving a notification message identifying one or more prediction models available in the UE.

Description

Methods and devices for AI/ML-based BFR enhancement The present application generally relates to wireless communication systems, including wireless communication systems capable of performing block error rate predictions. Wireless mobile communication technology uses various standards and protocols to transmit data between base stations and wireless communication devices. Wireless communication system standards and protocols may include, for example, the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) (e.g., 4G), 3GPP New Radio (NR) (e.g., 5G), and the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard for Wireless Local Area Networks (WLANs) (commonly known to industry groups as Wi-Fi® ) . As considered by 3GPP, different radio communication system standards and protocols may use various RANs for communication between a base station of a radio access network (RAN) (which may also, sometimes, generally be referred to as a RAN node, network node, or simply node) and a radio communication device known as user equipment (UE). 3GPP RANs may include, for example, GSM (global system for mobile communications), GERAN (enhanced data rates for GSM evolution (EDGE) RAN), UTRAN (Universal Terrestrial Radio Access Network), E-UTRAN (Evolved Universal Terrestrial Radio Access Network), and/or NG-RAN (Next-Generation Radio Access Network). Each RAN may perform communication between a base station and a UE using one or more radio access technologies (RATs). For example, a GERAN implements GSM and/or EDGE RATs, a UTRAN implements UMTS (Universal Mobile Telecommunication System) RATs or other 3GPP RATs, an E-UTRAN implements LTE RATs (sometimes referred to simply as LTE), and an NG-RAN implements NR RATs (sometimes referred to herein as 5G RATs, 5G NR RATs, or simply NR). In certain deployments, an E-UTRAN may also implement NR RATs. In certain deployments, an NG-RAN may also implement LTE RATs. Base stations used by a RAN can correspond to that RAN. An example of an E-UTRAN base station is an E-UTRAN (Evolved Universal Terrestrial Radio Access Network) Node B (also commonly referred to as Evolved Node B, Enhanced Node B, eNodeB, or eNB). An example of an NG-RAN base station is a Next Generation Node B (sometimes also referred to as g Node B or gNB). A RAN provides its communication services to external entities through its connection to a core network (CN). For example, E-UTRAN can utilize the Evolved Packet Core (EPC), whereas NG-RAN can utilize the 5G Core Network (5GC). Frequency bands for 5G NR can be separated into two or more different frequency ranges. For example, Frequency Range 1 (FR1) may include frequency bands operating at sub-6 GHz frequencies, some of which are bands available under previous standards and may be extended to potentially cover new spectrum offerings from 410 megahertz (MHz) to 7125 MHz. Frequency Range 2 (FR2) may include frequency bands from 24.25 GHz to 52.6 GHz. Note that in some systems, FR2 may also include frequency bands from 52.6 GHz to 71 GHz (or above). Bands within the millimeter wave (mmWave) range of FR2 have smaller coverage than the bands within FR1 but may potentially have higher available bandwidth. Those skilled in the art will recognize that these frequency ranges provided as examples may change over time or by area. To facilitate the identification of discussions regarding any specific element or action, the top digits or numbers within a drawing number refer to the drawing number in which the element was first introduced. FIG. 1 illustrates an exemplary framework for the use of AI and/or ML in the context of a wireless communication system according to some embodiments. FIG. 2a illustrates two timelines for a beam failure detection procedure according to some embodiments. FIG. 2b illustrates a table showing configurations for different frequency ranges. FIG. 3a illustrates a flowchart for BFR for a primary cell (PCell) according to some embodiments. FIG. 3b illustrates a flowchart for BFR for a secondary cell (SCell) according to some embodiments. FIG. 4a illustrates an example of a legacy implementation of BFR according to some embodiments. FIG. 4b illustrates an example in which, according to some embodiments, a UE can use AI/ML to predict BFD before it occurs. FIG. 4c illustrates an example in which a UE can use AI/ML to predict some BFIs after one or more actual BFIs have been detected according to some embodiments. FIG. 5 illustrates an example of a flowchart of a UE-side procedure for BFD prediction between a UE and a network according to embodiments of the present specification. Figure 6a illustrates an example of a UE configured to perform BLER prediction at a future time. Figure 6b illustrates an example of a UE configured to predict the BLER value of RS based on samples from other BFD-RS. FIG. 6c illustrates an example of a UE configured to predict the BLER value of a BFD-RS set for one TRP point based on the actual value