EP-4736511-A1 - SYSTEMS AND METHODS FOR PREDICTIVE MEASUREMENTS FOR ARTIFICIAL INTELLIGENCE/MACHINE LEARNING BASED MOBILITY ENHANCEMENT

EP4736511A1EP 4736511 A1EP4736511 A1EP 4736511A1EP-4736511-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
PALLE VENKATA, Naveen Kumar R
CHEN, YUQIN
HU, HAIJING
ROSSBACH, Ralf
XU, FANGLI
SIROTKIN, ALEXANDER
KUO, PING-HENG
WU, ZHIBIN

Assignees

Apple Inc.

Dates

Publication Date: 20260506
Application Date: 20230901

Claims (20)

A method of a user equipment (UE) , comprising: sending, to a network, a notification message identifying one or more measurement prediction models at the UE; receiving, from the network, an activation message identifying a first measurement prediction model for use from the one or more measurement prediction models at the UE; generating one or more actual measurements of one or more reference signals received at the UE from a cell of the network; generating, using the first measurement prediction model, one or more predicted measurements based the one or more actual measurements; and sending a first measurement report comprising the one or more predicted measurements to the network.
The method of claim 1, wherein the one or more predicted measurements comprise one or more predicted Layer 3 (L3) cell-level measurements.
The method of claim 2, wherein: the one or more actual measurements comprise one or more actual L3 cell-level measurements for the one or more reference signals received at the UE; and the one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by providing the one or more actual L3 cell-level measurements to the first measurement prediction model.
The method of claim 2, wherein: the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by: providing the one or more actual L1 beam-level measurements to the first measurement prediction model to generate one or more predicted L1 beam-level measurements; and performing linear averaging and L3 filtering over the one or more actual L1 beam-level measurements and the one or more predicted L1 beam level-measurements.
The method of claim 4, wherein L3 filter coefficients used for the L3 filtering are generated by the measurement prediction model.
The method of claim 2, wherein: the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by: performing linear averaging over the one or more actual L1 beam-level measurements; and providing one or more actual linear averaging results of the linear averaging and a set of configured L3 filter coefficients to the first measurement prediction model.
The method of claim 2, wherein: the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by providing the one or more actual L1 beam level measurements and a set of configured L3 filter coefficients to the first measurement prediction model.
The method of claim 2, wherein: the one or more predicted L3 cell-level measurements are for a neighbor cell to the cell; and the generating, using the first measurement prediction model, one or more predicted L3 cell-level measurements is further based on correlation information for the neighbor cell.
The method of claim 2, further comprising receiving, from the network, configuration information identifying the cell, and wherein the one or more predicted L3 cell-level measurements are for the cell.
The method of claim 2, further comprising receiving, from the network, configuration information identifying a frequency, and wherein the one or more predicted L3 cell-level measurements are for the frequency.
The method of claim 2, further comprising receiving, from the network, configuration information identifying a condition for generating the one or more predicted L3 cell-level measurements, and wherein the one or more predicted L3 cell-level measurements are generated in response to determining, at the UE, that the condition has been met.
The method of claim 11, wherein the condition comprises whether a prior L3 measurement is less than a threshold.
The method of claim 11, wherein the condition comprises determining that the one or more predicted L3 cell-level measurements would correspond to inter-frequency measurements.
The method of claim 2, further comprising receiving, from the network, configuration information identifying the cell, and wherein the UE selects to generate the one or more predicted L3 cell-level measurements based on the identification of the cell in the configuration information.
The method of claim 1, wherein the one or more predicted measurements comprise one or more predicted Layer 3 (L3) beam-level measurements.
The method of claim 15, wherein: the one or more actual measurements comprise one or more actual L3 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 beam-level measurements are generated using the first measurement prediction model by providing the one or more actual L3 beam-level measurements to the first measurement prediction model.
The method of claim 15, wherein: the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 beam-level measurements are generated using the first measurement prediction model by: providing the one or more actual L1 beam-level measurements to the first measurement prediction model to generate one or more predicted L1 beam-level measurements; and performing L3 filtering over the one or more actual L1 beam-level measurements and the one or more predicted L1 beam level-measurements.
The method of claim 15, wherein: the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 beam-level measurements are generated using the first measurement prediction model by providing the one or more actual L1 beam-level measurements to the first measurement prediction model.
The method of claim 15, wherein: the one or more reference signals are received on one or more beams; the one or more predicted L3 beam-level measurements are for a neighbor beam to the one or more beams that is not part of the one or more beams; and the generating, using the first measurement prediction model, one or more predicted L3 beam-level measurements is further based on statistical information of a channel between the UE and the cell.
The method of claim 15, further comprising receiving, from the network, configuration information identifying a beam, and wherein the one or more predicted L3 beam-level measurements are for the beam.

Description

SYSTEMS AND METHODS FOR PREDICTIVE MEASUREMENTS 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 for