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EP-4736530-A1 - SYSTEMS AND METHODS FOR PREDICTED MEASUREMENTS FOR ARTIFICIAL INTELLIGENCE/MACHINE LEARNING BASED CONDITIONAL HANDOVER ENHANCEMENTS

EP4736530A1EP 4736530 A1EP4736530 A1EP 4736530A1EP-4736530-A1

Abstract

Systems and methods for the use of various artificial intelligence (AI) /machine learning (ML) models with respect to various mobility aspects are described herein. The generation and use of L3 beam-level measurement predictions, L3 cell-level measurement predictions, L1 measurement predictions, network-based timing advance (TA) value predictions, and UE-based TA value predictions using corresponding ML models are discussed. Various examples of the inputs that may be used with respect to these ML models are discussed. The use of various ones of these predictions within mobility contexts including Layer-3 based handover, Layer 1/Layer 2 triggered mobility (LTM), and conditional handover (CHO) are discussed.

Inventors

  • CHENG, PENG
  • HU, HAIJING
  • PALLE VENKATA, Naveen Kumar R
  • SIROTKIN, ALEXANDER
  • Gurumoorthy, Sethuraman
  • ROSSBACH, Ralf

Assignees

  • Apple Inc.

Dates

Publication Date
20260506
Application Date
20230901

Claims (20)

  1. A method of a source base station of a radio access network (RAN) , comprising: receiving, from a user equipment (UE) , first one or more predicted Layer 3 (L3) measurements corresponding to a first target cell of a first target base station; performing a first conditional handover (CHO) preparation with the first target base station for the first target cell based on the predicted L3 measurements corresponding to the first target cell; sending, to the UE, a CHO configuration comprising a first condition for performing a first handover to the first target cell and a first indication of whether the first condition can be evaluated using second one or more predicted L3 measurements corresponding to the first target cell; and receiving, from the UE, a CHO configuration response in response to the CHO configuration.
  2. The method of claim 1, wherein the CHO configuration includes a confidence level threshold for using the second one or more predicted L3 measurements to evaluate the first condition.
  3. The method of claim 1, further comprising: receiving, from the UE, third one or more predicted Layer 3 (L3) measurements corresponding to a second target cell of a second target base station; and performing a second CHO preparation with the second target base station for the second target cell based on the third one or more predicted L3 measurements corresponding to the second target cell; wherein the CHO configuration further comprises a second condition for performing a second handover to the second target cell and a second indication of whether the second condition can be evaluated using fourth one or more predicted L3 measurements corresponding to the second target cell.
  4. The method of claim 1, wherein the CHO configuration further comprises a second condition for performing the first handover to the first target cell and a second indication of whether the second condition can be evaluated using the second one or more predicted L3 measurements.
  5. The method of claim 1, wherein the CHO configuration further comprises a priority value of the first target cell.
  6. The method of claim 1, wherein the CHO configuration response includes a suggested change to a target cell list used by the RAN.
  7. The method of claim 1, wherein the CHO configuration response includes updates to the first one or more predicted L3 measurements corresponding to the first target cell.
  8. The method of claim 1, wherein the CHO configuration response includes a prediction error metric for the first one or more predicted L3 measurements.
  9. The method of claim 1, wherein the CHO configuration response includes a suggested change to the CHO configuration.
  10. The method of claim 9, wherein the suggested change to the CHO configuration comprises one or more of a suggested change to a CHO event type, a suggested change to a threshold for a CHO event, and a suggested change to a time to trigger (TTT) .
  11. The method of claim 1, wherein the CHO configuration response includes a suggested priority value change for a priority value of the first target cell.
  12. The method of claim 1, further comprising sending, to the UE, an update to the CHO configuration based on information received from the UE in the CHO configuration response.
  13. The method of claim 1, further comprising receiving, from the UE, a UE assistance information (UAI) message comprising a suggested change to a target cell list used by the RAN.
  14. The method of claim 1, further comprising receiving, from the UE, a UE assistance information (UAI) message comprising a suggested target cell for an execution of a non-conditional handover.
  15. The method of claim 1, further comprising receiving, from the UE, a UE assistance information (UAI) message comprising a suggested priority value change for a priority value of the first target cell.
  16. A method of a user equipment (UE) , comprising: receiving, from a source base station of a network, a first conditional handover (CHO) configuration comprising a first condition for performing a first handover to a first target cell of a first target base station and a first indication of whether the first condition can be evaluated using first one or more predicted Layer 3 (L3) measurements corresponding to the first target cell; sending, to the network, a CHO configuration response in response to the first CHO configuration; evaluating that the first condition of the first CHO configuration has been met; and initiating the first handover to the first target cell in response to the evaluating that the first condition of the first CHO configuration has been met.
  17. The method of claim 16, further comprising: generating second one or more predicted L3 measurements corresponding to the first target cell; and sending, to the network, the second one or more predicted L3 measurements corresponding to the first target cell prior to receiving the first CHO configuration from the network.
  18. The method of claim 17, wherein the CHO configuration response includes updates to the second one or more predicted L3 measurements corresponding to the first target cell.
  19. The method of claim 17, wherein the CHO configuration response includes a prediction error metric for the second one or more predicted L3 measurements.
  20. The method of claim 16, wherein the first CHO configuration includes a confidence level threshold for using the first one or more predicted L3 measurements to evaluate the first condition.

Description

SYSTEMS AND METHODS FOR PREDICTED MEASUREMENTS FOR ARTIFICIAL INTELLIGENCE/MACHINE LEARNING BASED CONDITIONAL HANDOVER ENHANCEMENTS TECHNICAL FIELD This application relates generally to wireless communication systems, including wireless communication systems capable of performing measurement and/or timing advance (TA) predictions. BACKGROUND Wireless mobile communication technology uses various standards and protocols to transmit data between a base station and a wireless communication device. Wireless communication system standards and protocols can include, for example, 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) (e.g., 4G) , 3GPP New Radio (NR) (e.g., 5G) , and Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard for Wireless Local Area Networks (WLAN) (commonly known to industry groups as ) . As contemplated by the 3GPP, different wireless communication systems' standards and protocols can use various radio access networks (RANs) for communicating between a base station of the RAN (which may also sometimes be referred to generally as a RAN node, a network node, or simply a node) and a wireless communication device known as a user equipment (UE) . 3GPP RANs can include, for example, Global System for Mobile communications (GSM) , Enhanced Data Rates for GSM Evolution (EDGE) RAN (GERAN) , Universal Terrestrial Radio Access Network (UTRAN) , Evolved Universal Terrestrial Radio Access Network (E-UTRAN) , and/or Next-Generation Radio Access Network (NG-RAN) . Each RAN may use one or more radio access technologies (RATs) to perform communication between the base station and the UE. For example, the GERAN implements GSM and/or EDGE RAT, the UTRAN implements Universal Mobile Telecommunication System (UMTS) RAT or other 3GPP RAT, the E-UTRAN implements LTE RAT (sometimes simply referred to as LTE) , and NG-RAN implements NR RAT (sometimes referred to herein as 5G RAT, 5G NR RAT, or simply NR) . In  certain deployments, the E-UTRAN may also implement NR RAT. In certain deployments, NG-RAN may also implement LTE RAT. A base station used by a RAN may correspond to that RAN. One example of an E-UTRAN base station is an Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Node B (also commonly denoted as evolved Node B, enhanced Node B, eNodeB, or eNB) . One example of an NG-RAN base station is a next generation Node B (also sometimes referred to as a g Node B or gNB) . A RAN provides its communication services with external entities through its connection to a core network (CN) . For example, E-UTRAN may utilize an Evolved Packet Core (EPC) while NG-RAN may utilize a 5G Core Network (5GC) . BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced. FIG. 1 illustrates an example framework for the use of AI and/or ML in the context of a wireless communication system. FIG. 2 illustrates an L3 measurement framework that may be used in a wireless communication system, according to embodiments discussed herein. FIG. 3 illustrates a flow diagram for a UE-sided procedure for L3 measurement prediction as between a UE and a network according to embodiments herein. FIG. 4 illustrates a flow diagram for a two-sided procedure for L3 measurement prediction as between a UE and a network according to embodiments herein. FIG. 5A illustrates a first mechanism for making temporal predictions of L3 cell-level measurements, according to embodiments discussed herein. FIG. 5B illustrates a second mechanism for making temporal predictions of L3 cell-level measurements, according to embodiments discussed herein. FIG. 5C illustrates a third mechanism for making temporal predictions of L3 cell-level measurements, according to embodiments discussed herein. FIG. 5D illustrates a fourth mechanism for making temporal predictions of L3 cell-level measurements, according to embodiments discussed herein. FIG. 6A illustrates a first mechanism for making temporal predictions of L3 beam-level measurements, according to embodiments discussed herein. FIG. 6B illustrates a second mechanism for making temporal predictions of L3 beam-level measurements, according to embodiments discussed herein. FIG. 6C illustrates a third mechanism for making temporal predictions of L3 beam-level measurements, according to embodiments discussed herein. FIG. 7 illustrates a mechanism for making spatial predictions of L3 beam-level measurements, according to embodiments discussed herein. FIG. 8 illustrates a flow diagram of an LTM procedure between a UE and a base station of a network, according to embodiments discussed herein. FIG. 9 illustrates a diagram showing the operation of an RSTD-based TA mechanism as between a UE, a source cell, and a target cell, according to embodiments discussed herein. FIG. 10 illustrates a flow