EP-4736512-A1 - SYSTEMS AND METHODS FOR PREDICTIVE TIMING ADVANCE FOR ARTIFICIAL INTELLIGENCE/MACHINE LEARNING BASED MOBILITY ENHANCEMENT

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
• CHEN, YUQIN

Assignees

• Apple Inc.

Dates

Publication Date

20260506

Application Date

20230901

Claims (20)

1. A method of a user equipment (UE) , comprising: sending, to a network, a notification message identifying one or more reference signal time difference (RSTD) -based timing advance (TA) prediction models at the UE; receiving, from the network, an activation message identifying a first RSTD-based TA prediction model for use from the one or more RSTD-based TA prediction models at the UE; generating one or more actual RSTD-based TA measurements based on one or more reference signals received at the UE from one or more target cells of the network and a TA value for a serving cell of the network; generating, using the first RSTD-based TA prediction model, one or more predicted RSTD-based TA measurements for a first target cell based on the one or more actual RSTD-based TA measurements for the one or more target cells; and sending, to the network, an RSTD-based TA prediction report comprising the one or more predicted RSTD-based TA measurements.
2. The method of claim 1, wherein the one or more reference signals are reference signals of the first target cell.
3. The method of claim 1, wherein the one or more reference signals are reference signals that were not transmitted by the first target cell.
4. The method of claim 1, wherein the RSTD-based TA prediction report further includes a validity time for the one or more predicted RSTD-based TA measurements for the first target cell.
5. The method of claim 1, wherein the RSTD-based TA prediction report further includes a confidence level for the one or more predicted RSTD-based TA measurements for the first target cell.
6. The method of claim 1, further comprising: receiving, from the network, configuration information indicating a predicted RSTD-based TA measurement periodicity and an actual RSTD-based TA measurement periodicity; and sending an actual RSTD-based TA report comprising the one or more actual RSTD-based TA measurements for the first target cell to the UE according to the actual RSTD-based TA measurement periodicity; wherein the RSTD-based TA prediction report comprising the one or more predicted RSTD-based TA measurements for the first target cell is sent according to the predicted RSTD-based TA measurement periodicity.
7. The method of claim 1, wherein the sending of the RSTD-based TA prediction report comprising the one or more predicted RSTD-based TA measurements to the network is triggered by a determination by the UE that a first predicted RSTD-based TA measurement of the one or more predicted RSTD-based TA measurements for the first target cell and a prior predicted RSTD-based TA measurement for the first target cell differ by at least threshold.
8. The method of claim 1, wherein the RSTD-based TA prediction report further comprises an indication that the one or more predicted RSTD-based TA measurements are predictive RSTD-based TA measurements.
9. The method of claim 1, wherein the RSTD-based TA prediction report further comprises the one or more actual measurements of the one or more reference signals.
10. The method of claim 1, wherein the one or more predicted RSTD-based TA measurements are sorted in the RSTD-based TA prediction report first based on Layer 3 (L3) measurements for corresponding cells, then based on confidence levels associated with the one or more predicted RSTD-based TA measurements.
11. A method of a radio access network (RAN) , comprising: receiving, from a user equipment (UE) , a reference signal time difference (RSTD) timing advance (TA) prediction model; sending, to the UE, one or more reference signals from one or more target cells; receiving, from the UE, one or more actual RSTD-based TA measurements of the one or more reference signals; and generating, using the RSTD-based TA prediction model, one or more predicted RSTD-based TA measurements for a first target cell based on the one or more actual RSTD-based TA measurements for the one or more target cells.
12. The method of claim 11, further comprising sending, to the UE, configuration information to be used by the UE to re-train the measurement prediction model.
13. The method of claim 11, further comprising receiving, from the UE, a timestamp at which the one or more actual RSTD-based TA measurements were generated.
14. The method of claim 11, further comprising receiving, from the UE, a position of the UE and a moving orientation of the UE.
15. The method of claim 11, further comprising receiving, from the UE, a change to a moving orientation of the UE.
16. A method of a user equipment (UE) , comprising: generating, using a reference signal time difference (RSTD) timing advance (TA) prediction model, one or more predicted RSTD-based TA measurements based on first reference signals received at the UE from one or more target cells of a network; generating one or more actual RSTD-based TA measurements corresponding to the one or more predicted RSTD-based TA measurements by measuring second reference signals received at the UE from the one or more target cells; calculating a confidence level using the one or more predicted RSTD-based TA measurements and the one or more actual RSTD-based TA measurements; and reporting the confidence level to network.
17. The method of claim 16, wherein the calculating the confidence level comprises determining a mean squared error (MSE) between the predicted RSTD-based TA measurements and the actual RSTD-based TA measurements.
18. The method of claim 16, further comprising receiving, from the network, an instructions to stop using the RSTD-based TA prediction model.
19. The method of claim 16, further comprising reporting, to the network, a first timestamp at which the predicted RSTD-based TA measurements were generated and a second timestamp at which the actual RSTD-based TA measurements were generated.
20. The method of claim 16, further comprising reporting, to the network, a position of the UE and a moving orientation of the UE.

Description

SYSTEMS AND METHODS FOR PREDICTIVE TIMING ADVANCE FOR ARTIFICIAL INTELLIGENCE/MACHINE LEARNING BASED MOBILITY ENHANCEMENT 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 diagram f