Assistance in model monitoring at user equipment side

Abstract

Example embodiments of the present disclosure are directed to apparatuses, methods, and computer readable storage media for assistance in model monitoring at user equipment side. In a solution, a first apparatus determines monitoring condition information. The monitoring condition information indicates at least one monitoring condition expected by the first apparatus for a model monitoring process. Then, the first apparatus transmits the monitoring condition information to a second apparatus. Furthermore, the first apparatus receives, from the second apparatus, monitoring assistance data for performing the model monitoring process. The monitoring assistance data corresponds to the monitoring condition information. Thereby, the proposed solution can advantageously improve the reliability of an outcome of the model monitoring process.

Description

ASSISTANCE IN MODEL MONITORING AT USER EQUIPMENT SIDECROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority from, and the benefit of US Provisional Application No. 63 / 717741, filed November 7, 2024, which is hereby incorporated by reference in its entirety.FIELD

[0002] Various example embodiments of the present disclosure generally relate to the field of telecommunication and in particular, to methods, devices, apparatuses and computer readable storage medium for assistance in model monitoring at user equipment side.BACKGROUND

[0003] In some communication systems such as the next cellular systems, artificial intelligence (Al) and / or machine learning (ML) technology is proposed to be used in order to improve the communication performance. An AI / ML model may be applied in the new radio (NR) radio interface to assist model functionalities or communication-related functions, such as, channel state information (CSI) overhead reduction, beam management (BM), positioning, and the like. However, the reliability of an outcome of a model monitoring process is generally expected to be further improved.SUMMARY

[0004] In a first aspect of the present disclosure, there is provided a first apparatus. The first apparatus includes at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus at least to: determine monitoring condition information, wherein the monitoring condition information indicates at least one monitoring condition expected by the first apparatus for a model monitoring process; transmit the monitoring condition information to a second apparatus; and receive, from the second apparatus, monitoring assistance data for performing the model monitoring process, wherein the monitoring assistance data corresponds to the monitoring condition information.

[0005] In a second aspect of the present disclosure, there is provided a second apparatus. The second apparatus includes at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus at least to: receive, from a first apparatus, monitoring condition information indicating at least one monitoring condition expected by the first apparatus for a model monitoring process;determine monitoring assistance data based on the monitoring condition information and a set of conditions for generating a set of candidate assistance data, wherein the monitoring assistance data corresponds to the monitoring condition information; and transmit, to the first apparatus, the monitoring assistance data for performing the model monitoring process.

[0006] In a third aspect of the present disclosure, there is provided a method. The method includes: determining monitoring condition information, wherein the monitoring condition information indicates at least one monitoring condition expected by the first apparatus for a model monitoring process; transmitting the monitoring condition information to a second apparatus; and receiving, from the second apparatus, monitoring assistance data for performing the model monitoring process, wherein the monitoring assistance data corresponds to the monitoring condition information.

[0007] In a fourth aspect of the present disclosure, there is provided a method. The method includes: receiving, from a first apparatus, monitoring condition information indicating at least one monitoring condition expected by the first apparatus for a model monitoring process; determining monitoring assistance data based on the monitoring condition information and a set of conditions for generating a set of candidate assistance data, wherein the monitoring assistance data corresponds to the monitoring condition information; and transmitting, to the first apparatus, the monitoring assistance data for performing the model monitoring process.

[0008] In a fifth aspect of the present disclosure, there is provided a first apparatus. The first apparatus includes means for determining monitoring condition information, wherein the monitoring condition information indicates at least one monitoring condition expected by the first apparatus for a model monitoring process; means for transmitting the monitoring condition information to a second apparatus; and means for receiving, from the second apparatus, monitoring assistance data for performing the model monitoring process, wherein the monitoring assistance data corresponds to the monitoring condition information.

[0009] In a sixth aspect of the present disclosure, there is provided a second apparatus. The second apparatus includes means for receiving, from a first apparatus, monitoring condition information indicating at least one monitoring condition expected by the first apparatus for a model monitoring process; means for determining monitoring assistance data based on the monitoring condition information and a set of conditions for generating a set of candidate assistance data, wherein the monitoring assistance data corresponds to the monitoring condition information; and means for transmitting, to the first apparatus, the monitoring assistance data for performing the model monitoring process.

[0010] In a seventh aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium includes instructions stored thereon for causing anapparatus to perform at least the method according to the third aspect.

[0011] In an eighth aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium includes instructions stored thereon for causing an apparatus to perform at least the method according to the fourth aspect.

[0012] It is to be understood that the Summary section is not intended to identify key or essential features of embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become easily comprehensible through the following description.BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Some example embodiments will now be described with reference to the accompanying drawings, where:

[0014] FIG. 1 illustrates an example communication environment in which example embodiments of the present disclosure can be implemented;

[0015] FIG. 2 illustrates a signaling chart for model monitoring assistance according to some example embodiments of the present disclosure;

[0016] FIG. 3 illustrates a further signaling chart for model monitoring assistance according to some example embodiments of the present disclosure;

[0017] FIG. 4 illustrates a flowchart of a method implemented at a first apparatus in accordance with some example embodiments of the present disclosure;

[0018] FIG. 5 illustrates a flowchart of a method implemented at a second apparatus in accordance with some example embodiments of the present disclosure;

[0019] FIG. 6 illustrates a simplified block diagram of a device that is suitable for implementing example embodiments of the present disclosure; and

[0020] FIG. 7 illustrates a block diagram of an example computer readable medium in accordance with some example embodiments of the present disclosure.

[0021] Throughout the drawings, the same or similar reference numerals represent the same or similar element.DETAILED DESCRIPTION

[0022] Principle of the present disclosure will now be described with reference to some example embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitation as to the scope of the disclosure. Embodiments described herein can be implemented in various manners other than the ones described below.

[0023] In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure belongs.

[0024] References in the present disclosure to “one embodiment,” “an embodiment,” “an example embodiment,” and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

[0025] It shall be understood that although the terms “first,” “second,”..., etc. in front of noun(s) and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another and they do not limit the order of the noun(s). For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term “and / or” includes any and all combinations of one or more of the listed terms.

[0026] As used herein, “at least one of the following: ” and “at least one of ” and similar wording, where the list of two or more elements are joined by “and” or “or”, mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements.

[0027] As used herein, unless stated explicitly, performing a step “in response to A” does not indicate that the step is performed immediately after “A” occurs and one or more intervening steps may be included.

[0028] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “has”, “having”, “includes” and / or “including”, when used herein, specify the presence of stated features, elements, and / or components etc., but do not preclude the presence or addition of one or more other features, elements, components and / or combinations thereof.

[0029] As used in this application, the term “circuitry” may refer to one or more or all of the following:(a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry) and(b) combinations of hardware circuits and software, such as (as applicable):(i) a combination of analog and / or digital hardware circuit(s) with software / firmware and(ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and(c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.

[0030] This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0031] As used herein, the term “communication network” refers to a network following any suitable communication standards, such as New Radio (NR), Long Term Evolution (LTE), LTE- Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), Narrow Band Internet of Things (NB-IoT) and so on. Furthermore, the communications between a terminal device and a network device in the communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G), the second generation (2G), 2.5G, 2.75G, the third generation (3G), the fourth generation (4G), 4.5G, the fifth generation (5G), 5.5G, the sixth generation (6G) communication protocols, and / or any other protocols either currently known or to be developed in the future. Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will of course also be future type communication technologies and systems with which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned system.

[0032] As used herein, the term “network device” refers to a node in a communication network via which a terminal device accesses the network and receives services therefrom. The network device may refer to a base station (BS) or an access point (AP), for example, a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), an NR NB (also referred to as a gNB), a Remote Radio Unit (RRU), a radio header (RH), a remote radio head (RRH), a relay, an Integrated Access and Backhaul (IAB) node, a low power node such as a femto, a pico, a non-terrestrial network (NTN) or non-ground network device such as a satellite network device, a low earth orbit (LEO) satellite and a geosynchronous earth orbit (GEO) satellite, an aircraft network device, and so forth, depending on the applied terminology and technology. In some example embodiments, radio access network (RAN) split architecture includes a Centralized Unit (CU) and a Distributed Unit (DU) at an IAB donor node. An IAB node includes a Mobile Terminal (IAB-MT) part that behaves like a UE toward the parent node, and a DU part of an IAB node behaves like a base station toward the next-hop IAB node.

[0033] The term “terminal device” refers to any end device that may be capable of wireless communication. By way of example rather than limitation, a terminal device may also be referred to as a communication device, user equipment (UE), a Subscriber Station (SS), a Portable Subscriber Station, a Mobile Station (MS), or an Access Terminal (AT). The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones, a tablet, a wearable terminal device, a personal digital assistant (PDA), portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), USB dongles, smart devices, wireless customer-premises equipment (CPE), an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. The terminal device may also correspond to a Mobile Termination (MT) part of an IAB node (e.g., a relay node). In the following description, the terms “terminal device”, “communication device”, “terminal”, “user equipment” and “UE” may be used interchangeably.

[0034] As used herein, the term “resource,” “transmission resource,” “resource block,” “physical resource block” (PRB), “uplink resource,” or “downlink resource” may refer toany resource for performing a communication, for example, a communication between a terminal device and a network device, such as a resource in time domain, a resource in frequency domain, a resource in space domain, a resource in code domain, or any other combination of the time, frequency, space and / or code domain resource enabling a communication, and the like. In the following, unless explicitly stated, a resource in both frequency domain and time domain will be used as an example of a transmission resource for describing some example embodiments of the present disclosure. It is noted that example embodiments of the present disclosure are equally applicable to other resources in other domains.

[0035] FIG. 1 illustrates an example communication environment 100 in which example embodiments of the present disclosure can be implemented. In the communication environment 100, there are a plurality of communication devices, for example, a first apparatus 110 and a second apparatus 120. These apparatuses can communicate with each other.

[0036] It is to be understood that the number of devices and their connections shown in FIG.1 are only for the purpose of illustration without suggesting any limitation. The communication environment 100 may include any suitable number of devices configured to implementing example embodiments of the present disclosure. By way of example rather than limitation, in some example embodiments, the communication environment 100 may further includes one or more apparatuses (not shown in FIG. 1).

[0037] Communications in the communication environment 100 may be implemented according to any proper communication protocol(s), including, but not limited to, cellular communication protocols, wireless local network communication protocols such as Institute for Electrical and Electronics Engineers (IEEE) 802.11 and the like, and / or any other protocols currently known or to be developed in the future. Moreover, the communication may utilize any proper wireless communication technology, including but not limited to: Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Frequency Division Duplex (FDD), Time Division Duplex (TDD), Multiple-Input Multiple-Output (MIMO), Orthogonal Frequency Division Multiple (OFDM), Discrete Fourier Transform spread OFDM (DFT-s-OFDM) and / or any other technologies currently known or to be developed in the future.

[0038] The application of AI / ML to wireless communications has been thus far limited to implementation-based approaches, both, at the network and the UE sides. However, augmenting the air-interface with features enabling improved support of AI / ML based algorithms may potentially offer enhanced performance e.g., improved throughput,robustness, accuracy or reliability, etc. depending on the use cases as well as reduced complexity / overhead. To this end, AI / ML for air interface has been studied for various use cases.

[0039] For example, an example use case may be channel state information (CSI) feedback enhancement, such as spatial-frequency domain CSI compression using two-sided Al model, or time domain CSI prediction using UE sided model. A further example use case may be beam management, such as spatial-domain downlink beam prediction for Set A of beams based on measurement results of Set B of beams, or temporal downlink beam prediction for Set A of beams based on the historic measurement results of Set B of beams. A still further example use case may be positioning accuracy enhancements, such as direct AI / ML positioning, or AI / ML assisted positioning.

[0040] By way of example, the use case of direct AI / ML positioning may involve one or more of the following aspects: UE-based positioning with UE-side model (Case 1), UE- assisted / location management function (LMF) -based positioning with LMF-side model (Case 2b), Next Generation Radio Access Network (NG-RAN) node assisted positioning with LMF-side model (Case 3b), or the like. For direct AI / ML positioning, an output of the AI / ML model may include a UE location, e.g., fingerprinting based on channel observation as the input of AI / ML model.

[0041] Moreover, the use case of AI / ML assisted positioning may involve one or more of the following aspects: UE-assisted / LMF-based positioning with UE-side model (Case 2a), NG-RAN node assisted positioning with gNB-side model (Case 3a), or the like. For AI / ML assisted positioning, an output of the AI / ML model may include new measurement and / or an enhancement of existing measurement, e.g., Line of Sight (LOS) / Non-Line of Sight (NLOS) identification, timing and / or angle of measurement, likelihood of measurement.

[0042] As used herein, AI / ML model performance monitoring (also referred to as model monitoring or monitoring for short) may refer to a procedure that monitors the inference performance of the AI / ML model. The model may reside in a UE, an LMF or a gNB.

[0043] For a UE-side model, the performance of the model may be monitored at the UE or at the LMF. Furthermore, the monitoring may be based on a label-based method(s), where ground-truth label (or its approximation) is provided for monitoring the accuracy of model output. Additionally or alternatively, the monitoring may be based on a label-free method(s), where model monitoring does not require ground-truth label (or its approximation).

[0044] In addition, the monitoring may be based on a model output, e.g., an estimated UE location corresponding to model output for direct AI / ML positioning, an estimatedintermediate parameter(s) corresponding to model output for AI / ML assisted positioning, a ground-truth label corresponding to model inference output for both direct and AI / ML assisted positioning. Additionally or alternatively, the monitoring may be based on a model input, e.g., measurement corresponding to model inference input.

[0045] The model performance monitoring aspects for AI / ML positioning have been studied, which includes the entities for generating the training data, the content of the training data, the time stamp associated with the collected data, etc. In particular, it is proposed to reuse Release- 18 assistance data transfer framework from LMF to the target UE, where the positioning reference unit (PRU) measurement (e.g., legacy measurement) and the corresponding PRU location are sent via LMF to the target UE. However, among the pairs of PRU measurement and the corresponding PRU location, which pair is to be sent to the UE is not yet studied.

[0046] Furthermore, the assistance signaling from the LMF to the UE for UE-side model monitoring may be required. In the present disclosure, the assistance data provided by the LMF to the UE to aid in model monitoring is referred to as the monitoring assistance data.

[0047] For ease of discussion, some example embodiments of the present disclosure described below may focus on UE-side model models, in particular, Case 1, with the monitoring being by the UE itself. Furthermore, the model output-based monitoring is considered with the label-based monitoring method. However, it should be understood that the proposed solutions may also be applicable to any other suitable considerations (e.g., Case 2a and / or the like) that are not explicitly discussed herein.

[0048] To support label-based UE-side AI / ML model performance monitoring at the UE itself for AI / ML positioning Case 1, one possible option is to reuse Release- 18 Long term evolution Positioning Protocol (LPP) assistance data transfer framework from LMF to the target UE where the PRU measurement and the corresponding PRU location are sent from LMF to the target UE.

[0049] As mentioned above, the data (e.g., PRU measurements and the corresponding PRU location) provided by the LMF to the UE for the model monitoring purpose may be referred to as the monitoring assistance data. Here, from the monitoring assistance data received from the LMF, the UE may use the PRU measurement (either as it is or with further processing / filtering) as an input to the AI / ML model and compare the output of the model (i.e., location estimate) to the ‘true’ PRU location sent by the LMF for monitoring.

[0050] However, it is to be understood that the model life circle management (LCM) (incl. model training, model selection) may be left to the UE implementation. And the UE may make use of different models for different conditions, e.g., different geographical areas(referred to as areas for short). This is due to the fact that the UE may have generated and maintained different suitable models for different conditions (e.g., areas). For example, models model# 1 and model #2 are used by the UE during its mobility for area#l and area#2, respectively. Hence, for a given condition at the UE (area#l), the monitoring assistance data must be associated with the condition at the UE.

[0051] For example, when the condition includes a geographical area (as mentioned above), the location information in the monitoring assistance data should be contained in the same area, say area#l, where the UE is performing the model monitoring. Otherwise, i.e., if the location info is contained in another area, say area#2, this will lead to a false alarm at the UE on the model performance. Because the model being used at the area#l is tuned for area#l, and hence it may not show good performance for the monitoring assistance data associated with area#2 anyway.

[0052] The LMF may not be aware of the current condition of the UE in which the UE is (or intends to be) when performing the model monitoring. Consequently, the monitoring assistance data provided by the LMF to UE may not be of much help if it corresponds to a different condition than the condition required by the UE (e.g., different area). For example, a model that is actually performing well at a given condition may be deemed as not suitable since the model monitoring based on the monitoring assistance data that correspond to a different condition is used for the model monitoring.

[0053] Several solutions are proposed herein to at least address the above-mentioned problem. In the proposed solutions, for UE-side model monitoring, one or more of the following features may be employed to obtain the monitoring assistance data (e.g., PRU measurement and the corresponding PRU location) from the LMF that is relevant for the condition at the UE during model monitoring:

[0054] 1) The UE sends its model monitoring condition information to the LMF. For example, the model monitoring condition (e.g., geographical area, observed positioning reference signal (PRS) characteristics) may refer to the condition anticipated by the UE during the model monitoring process.

[0055] 2) The LMF selects the monitoring assistance data based on the monitoring condition indication by the UE and the condition in which the monitoring assistance data was generated (e.g., at a PRU). The selection involves comparing the monitoring condition and the condition during the data generation, and selecting the data, i.e., PRU measurement and the corresponding PRU location, such that the condition in which the data was generated has high similarity to the indicated monitoring condition. In other words, monitoring assistance data that is representative of the monitoring condition is selected.

[0056] 3) The LMF provides the selected monitoring assistance data to the UE, and it may indicate the quality of the monitoring assistance data to the UE.

[0057] In aid of the above-mentioned features, the proposed solutions can advantageously enable the LMF to selectively provide the monitoring assistance data that is associated with the conditions (to be-) observed at the UE during the inference of the model. Thereby, the proposed solutions can avoid false alarms on the model performance (or misleading model performance outcome) of a certain UE-side model when the model monitoring is performed at the UE based on the monitoring assistance data provided by the LMF.

[0058] In some example embodiments, a model monitoring node may be a terminal device, such as a UE or the like, and a model monitoring assistant node may be a network device, such as an LMF or the like. The model monitoring assistant node may refer to a node that assists the model monitoring node. The model monitoring node may receive data from the model monitoring assistant node, and in this case, it may also be referred to as a data receiving node. The model, such as the AI / ML model, may be hosted by a model hosting node. The model hosting node may be separated with the model monitoring node. Alternatively, the model monitoring node and the model hosting node may be implemented in an integrated way or may be considered to be the same. It should be understood that the possible implementation of the model monitoring node, the model monitoring assistant node, and the model hosting node described here are merely illustrative and therefore should not be construed as limiting the present disclosure in any way. By way of example, the model monitoring node and the model hosting node may also be implemented separately.

[0059] For ease of understanding, the behaviors of the model monitoring node and the model monitoring assistant node will be described separately here.

[0060] The model monitoring node (such as a UE or the like) is configured to perform at least a part of the following operations:

[0061] 1) determining monitoring condition information. The monitoring conditions include one or more conditions anticipated (or expected) by the UE for the model monitoring process, and the monitoring condition information will be described in detail below;

[0062] 2) sending the monitoring condition information to the model monitoring assistant node;

[0063] 3) in response to sending the monitoring condition information, receiving monitoring assistance data from the model monitoring assistant node;

[0064] 4) receiving quality information on the monitoring assistance data. For example, the quality information may be based on a similarity measure between the monitoring condition information and the condition in which the monitoring assistance data wasgenerated;

[0065] 5) performing model performance monitoring by using the monitoring assistance data. For example, based on the quality information on the monitoring assistance data, the model monitoring node may determine whether or not to use the received monitoring assistance data in model monitoring.

[0066] In some example embodiments, the monitoring condition information may include at least one or more of the following: (i) coarse location information which may include an estimated coarse location of the UE during monitoring process, and may be a direct positioning model inference output, the serving cell or beam; (ii) timing information which may indicate a time frame (e.g., certain hours of the day) in which the first apparatus is estimated to perform the model monitoring process, which may be a time frame in which the UE would like to perform monitoring; (iii) monitoring time information which may include an estimated time that UE would like to perform monitoring so that the monitoring assistance data is timely provided by a model monitoring assistant node to the model monitoring node; (iv) reference signal (e.g., PRS) information which may include information on the PRS (e.g., PRS resource ID) that will be used at the UE for model input data generation. It is to be understood that the monitoring condition information may also include any other suitable information, such as a quality indicator for the estimated components in the monitoring condition information, e.g., quality of estimated coarse location relating to its uncertainty. The scope of the present disclosure is not limited in this respect.

[0067] The model monitoring assistant node (such as an LMF or the like) is configured to perform at least a part of the following operations:

[0068] 1) collecting monitoring assistance data from data generating entities and conditions in which the data was generated;

[0069] 2) receiving monitoring condition information from the model monitoring node;

[0070] 3) selecting monitoring assistance data at least based on the received monitoring condition information and the condition in which the monitoring assistance data was generated;

[0071] 4) sending monitoring assistance data to the model monitoring node;

[0072] 5) determining the quality of the selected monitoring assistance data at least based on the received monitoring condition information and the condition in which the selected monitoring assistance data was generated;

[0073] 6) sending quality information on the monitoring assistance data to the model monitoring node.

[0074] Example embodiments of the present disclosure will be described in detail belowwith reference to the accompanying drawings.

[0075] FIG. 2 illustrates a signaling chart 200 for model monitoring assistance according to some example embodiments of the present disclosure. For the purposes of discussion, the signaling chart 200 will be discussed with reference to FIG. 1, for example, by using the first apparatus 110 and the second apparatus 120. In some example embodiments, the first apparatus 110 may include a terminal device, such as a UE or the like, and may be configured to implement the above-mentioned model monitoring node. The second apparatus 120 may include a network device, such as an LMF or the like, and may be configured to implement the above-mentioned model monitoring assistant node.

[0076] In the signaling chart 300, the first apparatus 110 determines 210 monitoring condition information. The monitoring condition information indicates at least one monitoring condition expected by the first apparatus 110 for a model monitoring process. For example, the monitoring condition information may include a variety of monitoring conditions. For instance, the monitoring condition information may include, but not limited to, coarse location information of the first apparatus 110 during the model monitoring process, timing information indicating a time frame in which the first apparatus is estimated to perform the model monitoring process, monitoring time information, reference signal information, and / or the like.

[0077] In some example embodiments, the coarse location information may include one or more locations estimated for the first apparatus 110. For example, the one or more locations may include a location estimated by the model that is to be monitored. In addition, a location in the coarse location information may be represented by a location coordinate, a serving cell identification (ID), a global cell ID (GCI), a serving beam ID, a serving node ID, a network area ID, and / or the like.

[0078] In some example embodiments, the timing information may indicate the time frame in which the first apparatus 110 is estimated to perform the model monitoring process, which may be a coarse time for performing the model monitoring process, for example, certain hours of a day or a certain time range of a day. The first apparatus 110 may perform the model monitoring process at a certain time point or in a certain time period within the indicated time frame or time range.

[0079] In some example embodiments, the monitoring time information may indicate a first time point or a first time period at which the first apparatus 110 is estimated to perform the model monitoring process. In addition, or alternatively, the monitoring time information may indicate a second time point or a second time period at which the second apparatus 120 is to provide the monitoring assistance data.

[0080] In some example embodiments, the reference signal information may indicate information on a reference signal that is to be used at the first apparatus 110 for generation of input data to a model. For example, the reference signal information may include information about a resource for the reference signal, bandwidth of the reference signal, transmission reception point (TRP) antenna information, and / or the like. By way of example rather than limitation, in a use case for positioning accuracy enhancements, the reference signal may be a positioning reference signal (PRS).

[0081] It is to be understood that the possible implementations of the monitoring condition information described above are merely illustrative. The monitoring condition information may also include any other suitable information. For example, the monitoring condition information may further include a quality indicator, and the quality indicator may indicate a quality of at least one monitoring condition included in the monitoring condition information, such as a quality of the coarse location information or a quality of the reference signal information. The scope of the present disclosure is not limited in this respect.

[0082] Furthermore, the first apparatus 110 transmits 220 the monitoring condition information to the second apparatus 120. Correspondingly, the second apparatus 120 receives 230 the monitoring condition information from the first apparatus 110. The second apparatus 120 determines 240 monitoring assistance data based on the monitoring condition information and a set of conditions for generating a set of candidate assistance data. The monitoring assistance data corresponds to the monitoring condition information.

[0083] In some example embodiments, the monitoring assistance data may include a reference signal measurement, such as a positioning reference signal measurement at a positioning reference unit (PRU) or the like. Additionally or alternatively, the monitoring assistance data may include ground truth information corresponding to the reference signal measurement, such as a location of the PRU or the like. It should be understood that the possible implementations of the monitoring assistance data described here are merely illustrative and therefore should not be construed as limiting the present disclosure in any way.

[0084] In some example embodiments, the second apparatus 120 may determine a similarity of a monitoring condition included in the monitoring condition information and a first condition in the set of conditions for generating first candidate assistance data. If the similarity exceeds a similarity threshold, the second apparatus 120 may determine the first candidate assistance data as the monitoring assistance data.

[0085] By way of example, in a case where the monitoring condition information includes the coarse location information, the second apparatus 120 may select, from a set of candidateassistance data, a pair of a PRU measurement and a PRU location as the monitoring assistance data. A distance between the PRU location in the pair and a location indicated in the coarse location information is below a distance threshold.

[0086] Moreover, the second apparatus 120 transmits 250 the monitoring assistance data to the first apparatus 110. Correspondingly, the first apparatus 110 receives 260, from the second apparatus 120, monitoring assistance data for performing the model monitoring process.

[0087] In some additional example embodiments, the second apparatus 120 may further determine quality information on the monitoring assistance data, and transmit the quality information to the first apparatus 110. The quality information may indicate at least one quality determined based on a similarity between the monitoring condition information and a condition in which the monitoring assistance data was generated. After receiving the quality information, the first apparatus 110 may determine, based on the quality information, whether or not to use the monitoring assistance data in the model monitoring process. By way of example rather than limitation, if the similarity indicated by the quality information is lower than a predetermined threshold, the first apparatus 110 may determine to performing the model monitoring process without using the monitoring assistance data.

[0088] In view of the foregoing, the proposed solutions can advantageously enable the second apparatus 120 to selectively provide the monitoring assistance data that is associated with the conditions (to be-) observed at the first apparatus 110 during the inference of the model. Thereby, the proposed solutions can avoid false alarms on the model performance or misleading model performance outcome when the model monitoring is performed at the first apparatus 110 based on the monitoring assistance data provided by the second apparatus 120. That is, the reliability of an outcome of the model monitoring process can be improved.

[0089] The solutions presented in FIG. 2 will be described in more details below with reference to FIG. 3, which illustrates a further signaling chart 300 for model monitoring assistance according to some example embodiments of the present disclosure.

[0090] In example embodiments discussed with respect to FIG. 3, the model monitoring node, which may be a UE, is denoted by UE 301, and the model monitoring assistance node, which may be an LMF, is denoted by LMF 302. For example, the UE 301 may be an example implementation of the first apparatus 110 in FIG. 1, and the LMF 302 may be an example implementation of the second apparatus 120 in FIG. 1. It is to be understood that the model monitoring node and the model monitoring assistance node may also be implemented in any other suitable manner. The scope of the present disclosure is not limited in this respect.

[0091] At 310, the UE 301 may determine the monitoring condition information. For example, the monitoring conditions may include one or more conditions anticipated (or expected) by the UE 301 for the model monitoring process.

[0092] In some example embodiments, the monitoring condition information may include coarse location information. The coarse location information may include an estimated coarse location of the UE 301 during monitoring process. It is to be understood that the coarse location may be different from the UE 301’s current location. For example, the coarse location may be an expected location during monitoring if UE 301 is expected to be mobile until monitoring.

[0093] In one example embodiment, the coarse location may be a location estimation generated by the model that is to be monitored. In a further example embodiment, the UE 301 may provide a list of location information where the UE 301, based on its mobility, anticipates the locations through which it may travel. In this case, the LMF 302 is expected to provide multiple monitoring assistance data corresponding each of the listed locations at 350.

[0094] In addition, a coarse location may be expressed as location coordinates, serving cell ID (Physical cell IDs (PCIs), global cell IDs (GCIs)), serving beam ID, gNB ID, network area / zone ID, etc. Moreover, the monitoring condition information may include coarse location information together with quality information, e.g., relating to uncertainty of the location coordinates. This will be described in detail below.

[0095] In some additional or alternative example embodiments, the monitoring condition information may include timing information. The timing information may indicate a time frame in which the first apparatus is estimated to perform the model monitoring process. By way of example rather than limitation, the timing information may be time(s) of the day, date(s) of the day, in (range of) timestamps, as well as periodicity (e.g., of measurements, optionally with starting and / or ending time) for performing the model monitoring process.

[0096] Additionally or alternatively, the monitoring condition information may include monitoring time. The monitoring time may include an estimated time that the UE 301 would like to perform monitoring or receiving related monitoring assistance so that the monitoring assistance data is timely provided by the LMF 302 to the UE 301. For example, the UE 301 may indicate a specific time, a min and / or max time, together with duration for performing monitoring, or a list of them for multiple monitoring occasions in the future. In addition, or alternatively, the UE 301 may indicate the time for the LMF 302 to provide the assistance data for monitoring, before performing monitoring.

[0097] In some additional or alternative example embodiments, the monitoring conditioninformation may include PRS information. The PRS information may include information on the PRS (e.g., PRS resource ID) that will be used at the UE 301 for model input data generation. This information may be applicable only when more than one PRSs are being transmitted (from a gNB) to the UE 301. For example, some information of PRS may be indicated explicitly, e.g., PRS bandwidth, whereas some (e.g., TRP antenna information) may be indicated implicitly, e.g., via associated IDs pre-determined / configured by the network.

[0098] Furthermore, the monitoring condition information may further include a quality indicator(s) for one or more estimated components (e.g., coarse location information, timing information, monitoring time information, PRS information, and / or the like) in the monitoring condition information. For example, as briefly mentioned above, the monitoring condition information may include a quality indicator of coarse location information. The quality indicator may include the quality information perceived by the UE 301 on its coarse location estimate in the coarse location information.

[0099] This perceived quality information can advantageously help the LMF 302 in deciding whether or not to select (at 330) and provide (at 350) multiple monitoring assistance data. For example, if the perceived quality is poor (e.g., a large variance is expected in the indicated location estimate), the LMF 302 may provide multiple monitoring assistance data that are not only suitable for the indicated location, but also to the nearby neighboring areas, since the LMF 302 is not sure which area the UE 301 may be in.

[0100] It should be understood that the possible implementations of the monitoring condition information here are merely illustrative and therefore should not be construed as limiting the present disclosure in any way. The monitoring condition information may also include any other suitable information.

[0101] At 320, the UE 301 may send the monitoring condition information that is prepared at 310 to the LMF 302. At 330, the LMF 302 may select monitoring assistance data at least based on the received monitoring condition information and the condition in which the monitoring assistance data was generated.

[0102] For ease of illustration, data collection at the LMF 302 will be briefly describe at first. The LMF 302 may request the data generating entities to report the condition (e.g., location, PRS configuration, etc.) in which the data is generated. Furthermore, the data generating entities may report the condition in which the data was generated. It is to be understood that the data generation condition may or may not be reported explicitly by the data generation entity. For example, if the collected monitoring assistance data includes PRU location, the condition geographical area (location in which the data was generated)need not be reported separately along with the monitoring assistance data. In addition, the condition may also include the capability with which the data generating entity generated the data. It should be understood that the collection of the potential monitoring assistance data (e.g., measurements and / or location) at the LMF 302 from the data generating entities such as PRUs is not shown in FIG. 3. This may happen, for example, well before 330 or right after 320.

[0103] Turn to details regarding selection of data monitoring assistance data. The LMF 302 may select the monitoring assistance data based on the monitoring condition indicated by the UE 301 and the condition in which the monitoring assistance data was generated (e.g., at a PRU). In practice, the conditions during data collection may deviate from the ones indicated by the UE 301 for monitoring. As such, the selection may involve comparing the monitoring condition and the condition during the data generation, and selecting the data, e.g., PRU measurement and the corresponding PRU location, such that the condition in which the data was generated has high similarity to the indicated monitoring condition.

[0104] By way of example, when the coarse location information is indicated by the UE 301 as a part of the monitoring condition information indicated in 320, the LMF 302 may select a particular pair of PRU measurement and PRU location from the all the available pairs to be the monitoring assistance data, where the PRU location of the selected data is closest to the indicated coarse location.

[0105] At 340, the LMF 302 may determine the quality of the selected monitoring assistance data at least based on the received monitoring condition information and the condition in which the selected monitoring assistance data was generated.

[0106] In some example embodiments, the quality may be based on a similarity measure between the received monitoring condition information and the condition in which the selected monitoring assistance data was generated. By way of example rather than limitation, a quality value is determined as a function of the distance between the location of the PRU, from which monitoring data is collected, and the location indicated by the UE 301 for monitoring. It is to be understood that step 340 may also be omitted in some example embodiments of the present disclosure.

[0107] At 350, the LMF 302 may send monitoring assistance data to the UE 301. In some example embodiments, the LMF 302 may additionally include the quality information on the monitoring assistance data determined at 340 along with the monitoring assistance data.

[0108] At 360, the UE 301 may perform the model performance monitoring by using the monitoring assistance data received from the LMF 302. In some example embodiments, based on the quality information on the monitoring assistance data, the UE 301 maydetermine whether or not to use the received monitoring assistance data in model monitoring. For example, if the quality of the monitoring assistance data is poor, the UE 301 may skip monitoring based on the monitoring assistance data. This may also cause the UE 301 to fall back to fall back positioning methods, e.g., legacy NR positioning methods.

[0109] In view of the above, the model performance outcome may give accurate information on the suitability of the model to a certain condition. The proposed solutions enable the LMF to provide model monitoring assistance corresponding to a condition (e.g., geographical area) similar to that of the UE during monitoring. Thereby, the reliability of the monitoring outcome can be improved.

[0110] FIG. 4 shows a flowchart of an example method 400 implemented at a first apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 400 will be described from the perspective of the first apparatus 110 in FIG. 1.[OHl] At block 410, the first apparatus 110 determines monitoring condition information. The monitoring condition information indicates at least one monitoring condition expected by the first apparatus for a model monitoring process.

[0112] At block 420, the first apparatus 110 transmits the monitoring condition information to a second apparatus.

[0113] At block 430, the first apparatus 110 receives, from the second apparatus, monitoring assistance data for performing the model monitoring process. The monitoring assistance data corresponds to the monitoring condition information.

[0114] In some example embodiments, the monitoring condition information includes at least one of the following monitoring conditions: coarse location information of the first apparatus during the model monitoring process, timing information indicating a time frame in which the first apparatus is estimated to perform the model monitoring process, monitoring time information indicating at least one of a first time point or a first time period at which the first apparatus is estimated to perform the model monitoring process, or a second time point or a second time period at which the second apparatus is to provide the monitoring assistance data, or reference signal information indicating information on a reference signal that is to be used at the first apparatus for generation of input data to a model.

[0115] In some example embodiments, the coarse location information includes one or more locations estimated for the first apparatus.

[0116] In some example embodiments, the one or more locations includes a location estimated by the model that is to be monitored.

[0117] In some example embodiments, a location in the coarse location information isrepresented by at least one of: a location coordinate, a serving cell identification (ID), a global cell ID (GCI), a serving beam ID, a serving node ID, or a network area ID.

[0118] In some example embodiments, the reference signal information includes at least one of: information about a resource for the reference signal, bandwidth of the reference signal, or transmission reception point (TRP) antenna information.

[0119] In some example embodiments, the reference signal includes a positioning reference signal.

[0120] In some example embodiments, the monitoring condition information further includes a quality indicator, wherein the quality indicator indicates a quality of the at least one monitoring condition included in the monitoring condition information.

[0121] In some example embodiments, the monitoring assistance data includes at least one of: a reference signal measurement, or ground truth information corresponding to the reference signal measurement.

[0122] In some example embodiments, the reference signal measurement includes a positioning reference signal measurement at a positioning reference unit (PRU), and / or wherein the ground truth information includes a location of the PRU.

[0123] In some example embodiments, the method 400 further includes: receiving quality information on the monitoring assistance data, the quality information indicating at least one quality determined based on a similarity between the monitoring condition information and a condition in which the monitoring assistance data was generated.

[0124] In some example embodiments, the method 400 further includes: determining whether or not to use the monitoring assistance data in the model monitoring process; and performing the model monitoring process based on the determination.

[0125] In some example embodiments, the first apparatus includes a terminal device, and the second apparatus includes a network device.

[0126] FIG. 5 shows a flowchart of an example method 500 implemented at a second apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 500 will be described from the perspective of the second apparatus 120 in FIG. 1.

[0127] At block 510, the second apparatus 120 receives, from a first apparatus, monitoring condition information indicating at least one monitoring condition expected by the first apparatus for a model monitoring process.

[0128] At block 520, the second apparatus 120 determines monitoring assistance data based on the monitoring condition information and a set of conditions for generating a set of candidate assistance data. The monitoring assistance data corresponds to the monitoringcondition information.

[0129] At block 530, the second apparatus 120 transmits, to the first apparatus, the monitoring assistance data for performing the model monitoring process.

[0130] In some example embodiments, the monitoring condition information includes at least one of the following monitoring conditions: coarse location information of the first apparatus during the model monitoring process, timing information indicating a time frame in which the first apparatus is estimated to perform the model monitoring process, monitoring time information indicating at least one of a first time point or a first time period at which the first apparatus is estimated to perform the model monitoring process, or a second time point or a second time period at which the second apparatus is to provide the monitoring assistance data, or reference signal information indicating information on a reference signal that is to be used at the first apparatus for generation of input data to a model.

[0131] In some example embodiments, the coarse location information includes one or more locations estimated for the first apparatus.

[0132] In some example embodiments, the one or more locations includes a location estimated by the model that is to be monitored.

[0133] In some example embodiments, a location in the coarse location information is represented by at least one of a location coordinate, a serving cell identification (ID), a global cell ID (GCI), a serving beam ID, a serving node ID, or a network area ID.

[0134] In some example embodiments, the reference signal information includes at least one of information about a resource for the reference signal, bandwidth of the reference signal, or transmission reception point (TRP) antenna information.

[0135] In some example embodiments, the reference signal includes a positioning reference signal.

[0136] In some example embodiments, the monitoring condition information further includes a quality indicator, wherein the quality indicator indicates a quality of the at least one monitoring condition included in the monitoring condition information.

[0137] In some example embodiments, the monitoring assistance data includes at least one of a reference signal measurement, or ground truth information corresponding to the reference signal measurement.

[0138] In some example embodiments, the reference signal measurement includes a positioning reference signal measurement at a positioning reference unit (PRU), and / or wherein the ground truth information includes a location of the PRU.

[0139] In some example embodiments, the method 500 further includes: determining a similarity of a monitoring condition included in the monitoring condition information and afirst condition in the set of conditions for generating first candidate assistance data; and in accordance with a determination that the similarity exceeds a similarity threshold, determining the first candidate assistance data as the monitoring assistance data.

[0140] In some example embodiments, the method 500 further includes: selecting, from a set of candidate assistance data, a pair of a PRU measurement and a PRU location as the monitoring assistance data, wherein a distance between the PRU location in the pair and a location indicated in the coarse location information is below a distance threshold.

[0141] In some example embodiments, the method 500 further includes: determining quality information on the monitoring assistance data, the quality information indicating at least one quality determined based on a similarity between the monitoring condition information and a condition in which the monitoring assistance data was generated; and transmitting the quality information to the first apparatus.

[0142] In some example embodiments, the first apparatus includes a terminal device, and the second apparatus includes a network device.

[0143] In some example embodiments, a first apparatus capable of performing any of the method 400 (for example, the first apparatus 110 in FIG. 1) may include means for performing the respective operations of the method 400. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The first apparatus may be implemented as or included in the first apparatus 110 in FIG. 1.

[0144] In some example embodiments, the first apparatus includes means for determining monitoring condition information, wherein the monitoring condition information indicates at least one monitoring condition expected by the first apparatus for a model monitoring process; means for transmitting the monitoring condition information to a second apparatus; and means for receiving, from the second apparatus, monitoring assistance data for performing the model monitoring process, wherein the monitoring assistance data corresponds to the monitoring condition information.

[0145] In some example embodiments, the monitoring condition information includes at least one of the following monitoring conditions: coarse location information of the first apparatus during the model monitoring process, timing information indicating a time frame in which the first apparatus is estimated to perform the model monitoring process, monitoring time information indicating at least one of: a first time point or a first time period at which the first apparatus is estimated to perform the model monitoring process, or a second time point or a second time period at which the second apparatus is to provide the monitoring assistance data, or reference signal information indicating information on a reference signalthat is to be used at the first apparatus for generation of input data to a model.

[0146] In some example embodiments, the coarse location information includes one or more locations estimated for the first apparatus.

[0147] In some example embodiments, the one or more locations includes a location estimated by the model that is to be monitored.

[0148] In some example embodiments, a location in the coarse location information is represented by at least one of a location coordinate, a serving cell identification (ID), a global cell ID (GCI), a serving beam ID, a serving node ID, or a network area ID.

[0149] In some example embodiments, the reference signal information includes at least one of information about a resource for the reference signal, bandwidth of the reference signal, or transmission reception point (TRP) antenna information.

[0150] In some example embodiments, the reference signal includes a positioning reference signal.

[0151] In some example embodiments, the monitoring condition information further includes a quality indicator, wherein the quality indicator indicates a quality of the at least one monitoring condition included in the monitoring condition information.

[0152] In some example embodiments, the monitoring assistance data includes at least one of a reference signal measurement, or ground truth information corresponding to the reference signal measurement.

[0153] In some example embodiments, the reference signal measurement includes a positioning reference signal measurement at a positioning reference unit (PRU), and / or wherein the ground truth information includes a location of the PRU.

[0154] In some example embodiments, the first apparatus further includes: means for receiving quality information on the monitoring assistance data, the quality information indicating at least one quality determined based on a similarity between the monitoring condition information and a condition in which the monitoring assistance data was generated.

[0155] In some example embodiments, the first apparatus further includes: means for determining whether or not to use the monitoring assistance data in the model monitoring process; and means for performing the model monitoring process based on the determination.

[0156] In some example embodiments, the first apparatus includes a terminal device, and the second apparatus includes a network device.

[0157] In some example embodiments, a second apparatus capable of performing any of the method 500 (for example, the second apparatus 120 in FIG. 1) may include means for performing the respective operations of the method 500. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or softwaremodule. The second apparatus may be implemented as or included in the second apparatus 120 in FIG. 1.

[0158] In some example embodiments, the second apparatus includes means for receiving, from a first apparatus, monitoring condition information indicating at least one monitoring condition expected by the first apparatus for a model monitoring process; means for determining monitoring assistance data based on the monitoring condition information and a set of conditions for generating a set of candidate assistance data, wherein the monitoring assistance data corresponds to the monitoring condition information; and means for transmitting, to the first apparatus, the monitoring assistance data for performing the model monitoring process.

[0159] In some example embodiments, the monitoring condition information includes at least one of the following monitoring conditions: coarse location information of the first apparatus during the model monitoring process, timing information indicating a time frame in which the first apparatus is estimated to perform the model monitoring process, monitoring time information indicating at least one of a first time point or a first time period at which the first apparatus is estimated to perform the model monitoring process, or a second time point or a second time period at which the second apparatus is to provide the monitoring assistance data, or reference signal information indicating information on a reference signal that is to be used at the first apparatus for generation of input data to a model.

[0160] In some example embodiments, the coarse location information includes one or more locations estimated for the first apparatus.

[0161] In some example embodiments, the one or more locations includes a location estimated by the model that is to be monitored.

[0162] In some example embodiments, a location in the coarse location information is represented by at least one of a location coordinate, a serving cell identification (ID), a global cell ID (GCI), a serving beam ID, a serving node ID, or a network area ID.

[0163] In some example embodiments, the reference signal information includes at least one of information about a resource for the reference signal, bandwidth of the reference signal, or transmission reception point (TRP) antenna information.

[0164] In some example embodiments, the reference signal includes a positioning reference signal.

[0165] In some example embodiments, the monitoring condition information further includes a quality indicator, wherein the quality indicator indicates a quality of the at least one monitoring condition included in the monitoring condition information.

[0166] In some example embodiments, the monitoring assistance data includes at least oneof: a reference signal measurement, or ground truth information corresponding to the reference signal measurement.

[0167] In some example embodiments, the reference signal measurement includes a positioning reference signal measurement at a positioning reference unit (PRU), and / or wherein the ground truth information includes a location of the PRU.

[0168] In some example embodiments, the second apparatus further includes: means for determining a similarity of a monitoring condition included in the monitoring condition information and a first condition in the set of conditions for generating first candidate assistance data; and means for in accordance with a determination that the similarity exceeds a similarity threshold, determining the first candidate assistance data as the monitoring assistance data.

[0169] In some example embodiments, the second apparatus further includes: means for selecting, from a set of candidate assistance data, a pair of a PRU measurement and a PRU location as the monitoring assistance data, wherein a distance between the PRU location in the pair and a location indicated in the coarse location information is below a distance threshold.

[0170] In some example embodiments, the second apparatus further includes: means for determining quality information on the monitoring assistance data, the quality information indicating at least one quality determined based on a similarity between the monitoring condition information and a condition in which the monitoring assistance data was generated; and means for transmitting the quality information to the first apparatus.

[0171] In some example embodiments, the first apparatus includes a terminal device, and the second apparatus includes a network device.

[0172] FIG. 6 is a simplified block diagram of a device 600 that is suitable for implementing example embodiments of the present disclosure. The device 600 may be provided to implement a communication device, for example, the first apparatus 110 or the second apparatus 120 as shown in FIG. 1. As shown, the device 600 includes one or more processors 610, one or more memories 620 coupled to the processor 610, and one or more communication modules 640 coupled to the processor 610.

[0173] The communication module 640 is for bidirectional communications. The communication module 640 has one or more communication interfaces to facilitate communication with one or more other modules or devices. The communication interfaces may represent any interface that is necessary for communication with other network elements. In some example embodiments, the communication module 640 may include at least one antenna.

[0174] The processor 610 may be of any type suitable to the local technical network and may include one or more of the following: general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples. The device 600 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.

[0175] The memory 620 may include one or more non-volatile memories and one or more volatile memories. Examples of the non-volatile memories include, but are not limited to, a Read Only Memory (ROM) 624, an electrically programmable read only memory (EPROM), a flash memory, a hard disk, a compact disc (CD), a digital video disk (DVD), an optical disk, a laser disk, and other magnetic storage and / or optical storage. Examples of the volatile memories include, but are not limited to, a random-access memory (RAM) 622 and other volatile memories that will not last in the power-down duration.

[0176] A computer program 630 includes computer executable instructions that are executed by the associated processor 610. The instructions of the program 630 may include instructions for performing operations / acts of some example embodiments of the present disclosure. The program 630 may be stored in the memory, e.g., the ROM 624. The processor 610 may perform any suitable actions and processing by loading the program 630 into the RAM 622.

[0177] The example embodiments of the present disclosure may be implemented by means of the program 630 so that the device 600 may perform any process of the disclosure as discussed with reference to FIG. 2 to FIG. 5. The example embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

[0178] In some example embodiments, the program 630 may be tangibly contained in a computer readable medium which may be included in the device 600 (such as in the memory 620) or other storage devices that are accessible by the device 600. The device 600 may load the program 630 from the computer readable medium to the RAM 622 for execution. In some example embodiments, the computer readable medium may include any types of non- transitory storage medium, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like. The term “non-transitory,” as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).

[0179] FIG. 7 shows an example of the computer readable medium 700 which may be in form of CD, DVD or other optical storage disk. The computer readable medium 700 has theprogram 630 stored thereon.

[0180] Generally, various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, and other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. Although various aspects of embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representations, it is to be understood that the block, apparatus, system, technique or method described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

[0181] Some example embodiments of the present disclosure also provide at least one computer program product tangibly stored on a computer readable medium, such as a non- transitory computer readable medium. The computer program product includes computerexecutable instructions, such as those included in program modules, being executed in a device on a target physical or virtual processor, to carry out any of the methods as described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.

[0182] Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. The program code may be provided to a processor or controller of a general-purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0183] In the context of the present disclosure, the computer program code or related data may be carried by any suitable carrier to enable the device, apparatus or processor to perform various processes and operations as described above. Examples of the carrier include a signal, computer readable medium, and the like.

[0184] The computer readable medium may be a computer readable signal medium or acomputer readable storage medium. A computer readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0185] Further, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, although several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the present disclosure, but rather as descriptions of features that may be specific to particular embodiments. Unless explicitly stated, certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, unless explicitly stated, various features that are described in the context of a single embodiment may also be implemented in a plurality of embodiments separately or in any suitable sub-combination.

[0186] Although the present disclosure has been described in languages specific to structural features and / or methodological acts, it is to be understood that the present disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

WHAT IS CLAIMED IS:

1. A first apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus at least to: determine monitoring condition information, wherein the monitoring condition information indicates at least one monitoring condition expected by the first apparatus for a model monitoring process; transmit the monitoring condition information to a second apparatus; and receive, from the second apparatus, monitoring assistance data for performing the model monitoring process, wherein the monitoring assistance data corresponds to the monitoring condition information.

2. The first apparatus of claim 1, wherein the monitoring condition information comprises at least one of the following monitoring conditions: coarse location information of the first apparatus during the model monitoring process, timing information indicating a time frame in which the first apparatus is estimated to perform the model monitoring process, monitoring time information indicating at least one of: a first time point or a first time period at which the first apparatus is estimated to perform the model monitoring process, or a second time point or a second time period at which the second apparatus is to provide the monitoring assistance data, or reference signal information indicating information on a reference signal that is to be used at the first apparatus for generation of input data to a model.

3. The first apparatus of claim 2, wherein the coarse location information comprises one or more locations estimated for the first apparatus.

4. The first apparatus of claim 3, wherein the one or more locations comprises a location estimated by the model that is to be monitored.

5. The first apparatus of claim 3, wherein a location in the coarse location information is represented by at least one of: a location coordinate, a serving cell identification (ID), a global cell ID (GCI), a serving beam ID, a serving node ID, or a network area ID.

6. The first apparatus of claim 2, wherein the reference signal information comprises at least one of information about a resource for the reference signal, bandwidth of the reference signal, or transmission reception point (TRP) antenna information.

7. The first apparatus of claim 2 or 6, wherein the reference signal comprises a positioning reference signal.

8. The first apparatus of claim 2, wherein the monitoring condition information further comprises a quality indicator, wherein the quality indicator indicates a quality of the at least one monitoring condition comprised in the monitoring condition information.

9. The first apparatus of any of claims 1 to 8, wherein the monitoring assistance data comprises at least one of a reference signal measurement, or ground truth information corresponding to the reference signal measurement.

10. The first apparatus of claim 9, wherein the reference signal measurement comprises a positioning reference signal measurement at a positioning reference unit (PRU), and / or wherein the ground truth information comprises a location of the PRU.

11. The first apparatus of any of claims 1 to 10, wherein the first apparatus is caused to: receive quality information on the monitoring assistance data, the quality information indicating at least one quality determined based on a similarity between the monitoring condition information and a condition in which the monitoring assistance data was generated.

12. The first apparatus of any of claims 1 to 11, wherein the first apparatus is caused to: determine whether or not to use the monitoring assistance data in the model monitoring process; and perform the model monitoring process based on the determination.

13. The first apparatus of any of claims 1 to 12, wherein the first apparatus comprises a terminal device, and the second apparatus comprises a network device.

14. A second apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus at least to: receive, from a first apparatus, monitoring condition information indicating at least one monitoring condition expected by the first apparatus for a model monitoring process; determine monitoring assistance data based on the monitoring condition information and a set of conditions for generating a set of candidate assistance data, wherein the monitoring assistance data corresponds to the monitoring condition information; and transmit, to the first apparatus, the monitoring assistance data for performing the model monitoring process.

15. The second apparatus of claim 14, wherein the monitoring condition information comprises at least one of the following monitoring conditions: coarse location information of the first apparatus during the model monitoring process, timing information indicating a time frame in which the first apparatus is estimated to perform the model monitoring process, monitoring time information indicating at least one of: a first time point or a first time period at which the first apparatus is estimated to perform the model monitoring process, or a second time point or a second time period at which the second apparatus is to provide the monitoring assistance data, or reference signal information indicating information on a reference signal that is to be used at the first apparatus for generation of input data to a model.

16. The second apparatus of claim 15, wherein the coarse location information comprises one or more locations estimated for the first apparatus.

17. The second apparatus of claim 16, wherein the one or more locations comprises a location estimated by the model that is to be monitored.

18. The second apparatus of claim 16, wherein a location in the coarse location information is represented by at least one of a location coordinate, a serving cell identification (ID), a global cell ID (GCI), a serving beam ID, a serving node ID, or a network area ID.

19. The second apparatus of claim 15, wherein the reference signal information comprises at least one of: information about a resource for the reference signal, bandwidth of the reference signal, or transmission reception point (TRP) antenna information.

20. The second apparatus of claim 15 or 19, wherein the reference signal comprises a positioning reference signal.

21. The second apparatus of claim 15, wherein the monitoring condition information further comprises a quality indicator, wherein the quality indicator indicates a quality of the at least one monitoring condition comprised in the monitoring condition information.

22. The second apparatus of any of claims 14 to 21, wherein the monitoring assistance data comprises at least one of: a reference signal measurement, or ground truth information corresponding to the reference signal measurement.

23. The second apparatus of claim 22, wherein the reference signal measurement comprises a positioning reference signal measurement at a positioning reference unit (PRU), and / or wherein the ground truth information comprises a location of the PRU.

24. The second apparatus of any of claims 14 to 23, wherein the second apparatus is caused to: determine a similarity of a monitoring condition comprised in the monitoring condition information and a first condition in the set of conditions for generating first candidate assistance data; and in accordance with a determination that the similarity exceeds a similarity threshold, determine the first candidate assistance data as the monitoring assistance data.

25. The second apparatus of claim 24, wherein the monitoring condition information comprises coarse location information of the first apparatus during the model monitoring process, and the second apparatus is caused to: select, from a set of candidate assistance data, a pair of a PRU measurement and a PRUlocation as the monitoring assistance data, wherein a distance between the PRU location in the pair and a location indicated in the coarse location information is below a distance threshold.

26. The second apparatus of any of claims 14 to 25, wherein the second apparatus is caused to: determine quality information on the monitoring assistance data, the quality information indicating at least one quality determined based on a similarity between the monitoring condition information and a condition in which the monitoring assistance data was generated; and transmit the quality information to the first apparatus.

27. The second apparatus of any of claims 14 to 26, wherein the first apparatus comprises a terminal device, and the second apparatus comprises a network device.

28. A method comprising: determining monitoring condition information, wherein the monitoring condition information indicates at least one monitoring condition expected by the first apparatus for a model monitoring process; transmitting the monitoring condition information to a second apparatus; and receiving, from the second apparatus, monitoring assistance data for performing the model monitoring process, wherein the monitoring assistance data corresponds to the monitoring condition information.

29. A method comprising: receiving, from a first apparatus, monitoring condition information indicating at least one monitoring condition expected by the first apparatus for a model monitoring process; determining monitoring assistance data based on the monitoring condition information and a set of conditions for generating a set of candidate assistance data, wherein the monitoring assistance data corresponds to the monitoring condition information; and transmitting, to the first apparatus, the monitoring assistance data for performing the model monitoring process.

30. A first apparatus comprising: means for determining monitoring condition information, wherein the monitoring condition information indicates at least one monitoring condition expected by the first apparatus for a model monitoring process;means for transmitting the monitoring condition information to a second apparatus; and means for receiving, from the second apparatus, monitoring assistance data for performing the model monitoring process, wherein the monitoring assistance data corresponds to the monitoring condition information.

31. A second apparatus comprising: means for receiving, from a first apparatus, monitoring condition information indicating at least one monitoring condition expected by the first apparatus for a model monitoring process; means for determining monitoring assistance data based on the monitoring condition information and a set of conditions for generating a set of candidate assistance data, wherein the monitoring assistance data corresponds to the monitoring condition information; and means for transmitting, to the first apparatus, the monitoring assistance data for performing the model monitoring process.

32. A computer readable medium comprising instructions stored thereon for causing an apparatus at least to perform the method of claim 28 or the method of claim 29.

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