Measurement collection and reporting

Abstract

The embodiment of the invention relates to a device and method for data collection and a computer readable storage medium. A first device prepares a measurement collection configuration including a list for Minimization of Drive Test (MDT) measurement collection and a measurement granularity. The list includes at least one second device and a plurality of cells. The first device transmits a measurement collection configuration to the at least one second device for triggering an MDT measurement activation for the terminal device trajectory. The first device receives at least one MDT measurement report from at least one second device, the at least one MDT measurement report comprising terminal device trajectory measurements collected based on the measurement collection configuration.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of Indian Provisional Application No. 202341065559, filed on September 29, 2023, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0002] Various example embodiments of the present disclosure relate generally to the field of telecommunications, and in particular to methods, devices, apparatus, and computer-readable storage media for measurement collection and reporting. Background Art

[0003] Artificial Intelligence (AI) / Machine Learning (ML)-based algorithms and models have been widely used to improve network performance in the following aspects: energy saving, load balancing, mobility optimization, traffic steering, positioning, etc. When deploying AI / ML models, model training, monitoring, selection, updating, reasoning, etc. may be necessary for basic lifecycle management (LCM) processes.

[0004] The network node (e.g., gNB) collects measurements from terminal devices (e.g., UE) in network coverage, and the measurements can be used as training data for AI / ML model training and / or inference data for AI / ML model inference. In some scenarios, AI / ML model training is deployed at Operation Maintenance Management (OAM), while AI / ML model inference functions reside within the Radio Access Network (RAN). Summary of the invention

[0005] In a first aspect of the present disclosure, a first device is provided. The first device includes at least one processor; and at least one memory storing instructions, which, when executed by the at least one processor, cause the first device to at least: prepare a measurement collection configuration, the measurement collection configuration including a list and measurement granularity for minimizing drive test (MDT) measurement collection, the list including at least one second device and multiple cells; transmit the measurement collection configuration to at least one second device to trigger MDT measurement activation for a terminal device trajectory; and receive at least one MDT measurement report from the at least one second device, the at least one MDT measurement report including terminal device trajectory measurements collected based on the measurement collection configuration.

[0006] In a second aspect of the present disclosure, a second device is provided. The second device includes: at least one processor; and at least one memory storing instructions, which, when executed by the at least one processor, cause the second device to at least: receive a measurement collection configuration from a first device, the measurement collection configuration including a list and a measurement granularity for minimization of drive test (MDT) measurement collection, the list including at least a second device and a plurality of cells; based on the measurement collection configuration, collect terminal device track measurements from at least one terminal device in at least a portion of the plurality of cells; and transmit an MDT measurement report to the first device, the MDT measurement report including the terminal device track measurements.

[0007] In a third aspect of the present disclosure, a method is provided. The method includes: preparing a measurement collection configuration at a first device, the measurement collection configuration including a list and a measurement granularity for minimizing drive test (MDT) measurement collection, the list including at least one second device and a plurality of cells; transmitting the measurement collection configuration to at least one second device for triggering MDT measurement activation for a terminal device trajectory; and receiving at least one MDT measurement report from at least one second device, the at least one MDT measurement report including terminal device trajectory measurements collected based on the measurement collection configuration.

[0008] In a fourth aspect of the present disclosure, a method is provided. The method includes: receiving a measurement collection configuration at a second device and from a first device, the measurement collection configuration including a list and a measurement granularity for minimization of drive test (MDT) measurement collection, the list including at least the second device and a plurality of cells; based on the measurement collection configuration, collecting terminal device trace measurements from at least one terminal device in at least a portion of the plurality of cells; and transmitting an MDT measurement report to the first device, the MDT measurement report including the terminal device trace measurements.

[0009] In a fifth aspect of the present disclosure, a first device is provided. The first device includes: a component for preparing a measurement collection configuration, the measurement collection configuration including a list and a measurement granularity for minimizing drive test (MDT) measurement collection, the list including at least one second device and a plurality of cells; a component for transmitting the measurement collection configuration to the at least one second device for triggering MDT measurement activation for a terminal device trajectory; and a component for receiving at least one MDT measurement report from the at least one second device, the at least one MDT measurement report including terminal device trajectory measurements collected based on the measurement collection configuration.

[0010] In a sixth aspect of the present disclosure, a second device is provided. The second device includes: a component for receiving a measurement collection configuration from a first device, the measurement collection configuration including a list and a measurement granularity for minimization of drive test (MDT) measurement collection, the list including at least a second device and a plurality of cells; a component for collecting terminal device trace measurements from at least one terminal device in at least a portion of the plurality of cells based on the measurement collection configuration; and a component for transmitting an MDT measurement report to the first device, the MDT measurement report including the terminal device trace measurements.

[0011] In a seventh aspect of the present disclosure, a computer-readable medium is provided, wherein the computer-readable medium includes instructions stored thereon, and the instructions are used to cause a device to at least execute the method according to the third aspect.

[0012] In an eighth aspect of the present disclosure, a computer-readable medium is provided, wherein the computer-readable medium includes instructions stored thereon, and the instructions are used to cause a device to at least execute the method according to the fourth aspect.

[0013] It should be understood that the invention summary is not intended to identify the key or essential features of the 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 readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Some example embodiments will now be described with reference to the accompanying drawings, in which:

[0015] Figure 1 illustrates an example communication environment in which example embodiments of the present disclosure may be implemented;

[0016] Figure 2 A schematic diagram of a functional framework for RAN intelligence is illustrated;

[0017] Figure 3 illustrates an example flow for AI / ML model training in OAM and AI / ML model inference in a RAN node;

[0018] Figure 4 illustrates an example mechanism for measurement configuration and reporting between a RAN node and an OAM according to some example embodiments of the present disclosure;

[0019] Figure 5 illustrates a signaling diagram for a measurement configuration and reporting procedure according to some example embodiments of the present disclosure;

[0020] Fig. 6A illustrates a schematic diagram of beam level measurements exposed to OAM from a gNB-DU according to some example embodiments of the present disclosure;

[0021] Figure 6BA schematic diagram illustrating cell level measurements exposed to OAM from a gNB-CU according to some example embodiments of the present disclosure is illustrated;

[0022] Figure 6C illustrates a schematic diagram of beam-level and cell-level measurements of exposure to OAM from a gNB-CU according to some example embodiments of the present disclosure;

[0023] Figure 7 illustrates a signaling diagram of a measurement configuration and reporting procedure for cell-level measurement and beam-level measurement according to some example embodiments of the present disclosure;

[0024] Figure 8 A schematic diagram illustrating the distribution of terminal device trajectories in a cell according to some example embodiments of the present disclosure;

[0025] Fig. 9 illustrates a flow chart of a method implemented at a device according to some example embodiments of the present disclosure;

[0026] Fig.10 illustrates a flow chart of a method implemented at a device according to some example embodiments of the present disclosure;

[0027] Fig.11 illustrates a simplified block diagram of a device suitable for implementing an example embodiment of the present disclosure; and

[0028] Fig.12 A block diagram of an example computer-readable medium according to some example embodiments of the present disclosure is illustrated.

[0029] Throughout the drawings, the same or similar reference numerals refer to the same or similar elements. DETAILED DESCRIPTION

[0030] The principles of the present disclosure will now be described with reference to some example embodiments. It should be understood that these embodiments are only for illustrative purposes and are helpful for those skilled in the art to understand and implement the present disclosure without implying any limitation on the scope of the present disclosure. The embodiments described herein can be implemented in various ways except for the way described below.

[0031] 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 skill in the art to which this disclosure belongs.

[0032] References in this disclosure to "one embodiment," "an embodiment," "an example embodiment," etc. indicate that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment necessarily includes the particular feature, structure, or characteristic. Furthermore, such phrases do not necessarily refer to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in conjunction with an embodiment, whether or not explicitly described, it is considered within the knowledge of those skilled in the art that such feature, structure, or characteristic may be affected in conjunction with other embodiments.

[0033] It should be understood that although the terms "first", "second", etc. 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 nouns. For example, without departing from the scope of the exemplary embodiment, the first element may be referred to as the second element, and similarly, the second element may be referred to as the first element. As used herein, the term "and / or" includes any and all combinations of one or more of the listed terms.

[0034] As used herein, “at least one of: ” and “at least one of ” and similar expressions, where a list of two or more elements is combined 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 of the elements.

[0035] As used herein, unless explicitly stated otherwise, performing a step "in response to A" does not indicate immediate execution after "A" occurs, and may include one or more intermediate steps.

[0036] The terms used herein are used only to describe specific embodiments and are not intended to limit the example embodiments. As used herein, the singular forms "a", "an", and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will be further understood that the terms "comprise", "including", "having", "having", "including" and / or "comprising", when used herein, specify the presence of the features, elements and / or components, etc., but do not exclude the presence or addition of one or more other features, elements, components and / or combinations thereof.

[0037] As used in this application, the term "circuitry" may refer to one or more or all of the following: (a) hardware circuit implementation only (such as implementation only in analog and / or digital circuits) and (b) a combination of hardware circuitry and software such as (where applicable): (i) a combination of analog and / or digital hardware circuits and software / firmware and (ii) any portion of a hardware processor (including a digital signal processor) with software, software and memory that work together to enable a device such as a mobile phone or server to perform various functions) and (c) A hardware circuit and / or processor that requires software (eg, firmware) for operation, such as a microprocessor or portion of a microprocessor, but the software may not be present when not required for operation.

[0038] The definition of circuitry applies to all uses of the 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 only a hardware circuit or processor (or multiple processors) or a 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 a particular claim element), a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in a server, cellular network device, or other computing or network device.

[0039] As used herein, the term "communication network" refers to a network that complies with any suitable communication standard, such as New Radio (NR), Long Term Evolution (LTE), Advanced LTE (LTE-A), Wideband Code Division Multiple Access (WCDMA), High Speed ​​Packet Access (HSPA), Narrowband Internet of Things (NB-IoT), etc. In addition, the communication between the terminal device and the network device in the communication network can be performed according to any suitable generation communication protocol, including but not limited to the first generation (1G), second generation (2G), 2.5G, 2.75G, third generation (3G), fourth generation (4G), 4.5G, fifth generation (5G), sixth generation (6G) communication protocol and / or any other protocol currently known or developed in the future. The embodiments of the present disclosure can be applied to various communication systems. In view of the rapid development of communication, there are of course communication technologies and systems of future types that can implement the present disclosure. It should not be considered that the scope of the present disclosure is limited to the above-mentioned system.

[0040] 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 from it. A network device may refer to a base station (BS) or an access point (AP), for example, a Node B (Node B or NB), an evolved NodeB (e Node B or eNB), a NR NB (also referred to as a gNB), a remote radio unit (RRU), a radio head (RH), a remote radio head (RRH), a relay, an integrated access and backhaul (IAB) node, a low-power node such as a femto, a micro, a non-terrestrial network (NTN) or a non-terrestrial 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, etc., depending on the terminology and technology applied. In some example embodiments, a 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) portion that behaves like a UE toward a parent node, and the DU portion of the IAB node behaves like a base station toward a next-hop IAB node.

[0041] The term "terminal device" refers to any terminal device capable of wireless communication. As an example and not limitation, a terminal device may also be referred to as a communication device, a user equipment (UE), a user station (SS), a portable user station, a mobile station (MS) or an access terminal (AT). The terminal device may include, but is not limited to, a mobile phone, a cellular phone, a smart phone, a voice over IP (VoIP) phone, a wireless local loop phone, a tablet computer, a wearable terminal device, a personal digital assistant (PDA), a portable computer, a desktop computer, an image capture terminal device (such as a digital camera, a game terminal device, a music storage and playback device), a vehicle-mounted wireless terminal device, a wireless endpoint, a mobile station, a laptop embedded device (LEE), a laptop device (LME), a USB dongle, a smart device, a wireless customer premises equipment (CPE), an Internet of Things (IoT) device, a watch or other wearable device, a head mounted display (HMD), a vehicle, a drone, medical equipment and applications (e.g., remote surgery), industrial equipment and applications (e.g., robots and / or other wireless devices operating in the context of an industrial and / or automated processing chain), consumer electronic devices, equipment operating on a commercial and / or industrial wireless network, etc. The terminal device may also correspond to the mobile terminal (MT) part of an IAB node (eg, a relay node).In the following description, the terms "terminal device", "communication device", "terminal", "user equipment" and "UE" may be used interchangeably.

[0042] As used herein, the terms "resource", "transmission resource", "resource block", "physical resource block" (PRB), "uplink resource" or "downlink resource" may refer to any resource used to perform communication, for example, communication between a terminal device and a network device, such as resources in the time domain, resources in the frequency domain, resources in the spatial domain, resources in the code domain, or any other combination of time, frequency, space and / or code domain resources to achieve communication. In the following, unless explicitly stated, resources in the frequency domain and the time domain will be used as examples of transmission resources for describing some example embodiments of the present disclosure. Note that the example embodiments of the present disclosure are equally applicable to other resources in other domains.

[0043] As used herein, a machine learning (ML) entity may be an ML model or may include an ML model and ML model related metadata. An ML entity may be managed as a single composite entity. In some example embodiments, an ML entity may be implemented as an MLApp.

[0044] To help understand the terminology, here is a RAN1 agreement on a list of terms used for AI / ML:

[0045] AI / ML Model: A data-driven algorithm that applies AI / ML techniques to generate a set of outputs based on a set of inputs.

[0046] AI / ML Model Delivery: A general term referring to the delivery of AI / ML models from one entity to another in any manner. Note: Entity can mean network node / function (e.g. gNB, LMF, etc.), UE, dedicated server, etc.

[0047] AI / ML model inference: The process of using a trained AI / ML model to produce a set of outputs based on a set of inputs.

[0048] AI / ML model testing: A subprocess of training to evaluate the performance of the final AI / ML model using a dataset different from that used for model training and validation. Unlike AI / ML model validation, testing does not assume subsequent adjustments to the model.

[0049] AI / ML model training: The process of training an AI / ML model in a data-driven manner [by learning input / output relationships] and obtaining a trained AI / ML model for inference.

[0050] AI / ML Model Delivery: Delivery of an AI / ML model over the air interface in a manner that is opaque to 3GPP signaling, either the parameters of the model structure known at the receiving end or a new model with parameters. The delivery may contain a complete model or a partial model.

[0051] AI / ML model validation: A subprocess of training to evaluate the quality of an AI / ML model using a dataset different from the one used for model training, which helps select model parameters that generalize beyond the dataset used for model training.

[0052] Data Collection: The process of collecting data by network nodes, management entities or UEs for the purpose of AI / ML model training, data analysis and inference.

[0053] Federated learning / federated training: A machine learning technique that trains AI / ML models across multiple distributed edge nodes (e.g., UE, gNB), each of which performs local model training using local data samples. This technique requires multiple interactions of the model but does not require the exchange of local data samples.

[0054] Function Identification: The process / method to identify AI / ML functions for common understanding between NW and UE. Note: Information about AI / ML functions can be shared during function identification. Where the AI / ML function resides depends on the specific use case and sub-use case.

[0055] Model activation: Enabling an AI / ML model for a specific function.

[0056] Model Deactivation: Disable AI / ML models used for specific features.

[0057] Model download: Model transfer from the network to the UE.

[0058] Model Identification: The process / method of identifying an AI / ML model for common understanding between NW and UE. Note: The process / method of model identification may or may not be applicable. Note: Information about the AI / ML model may be shared during model identification.

[0059] Model monitoring: The process of monitoring the inference performance of AI / ML models.

[0060] Model parameter update: The process of updating the model parameters of a model.

[0061] Model Selection: The process of selecting an AI / ML model to activate among multiple models of the same AI / ML enabled feature. Note: Model selection can be performed with or without model activation.

[0062] Model switching: Deactivate the currently active AI / ML model and activate a different AI / ML model for a specific function.

[0063] Model updating: The process of updating the model parameters and / or model structure of a model.

[0064] Model upload: Model transfer from UE to the network.

[0065] Network-side (AI / ML) model: an AI / ML model whose reasoning is completely executed at the network.

[0066] Offline field data: Data collected from the field and used for offline training of AI / ML models.

[0067] Offline Training: The AI / ML training process where a model is trained based on a collected dataset, and where the trained model is later used or delivered for inference. Note: This definition is used as a guide only. There may be cases that may not fit neatly into this definition, but can still be classified as offline training by generally accepted conventions.

[0068] Online field data: Data collected from the field and used for online training of AI / ML models.

[0069] Online Training: The AI / ML training process (where the model is used for inference) is trained in (near) real-time as new training samples arrive (usually continuously). NOTE: The notion of (near) real-time vs. non-real-time is context dependent and is relative to the inference timescale. NOTE: This definition is intended only as a guide. There may be cases that may not fit neatly into this definition, but can still be classified as online training by generally accepted conventions. NOTE: Fine-tuning / re-training can be done via online or offline training. (This note may be removed when we define the term fine-tuning).

[0070] Reinforcement Learning (RL): The process of training an AI / ML model from inputs (also called states) and feedback signals (also called rewards) from the model’s outputs (also called actions) in the environment the model is interacting with.

[0071] Semi-supervised learning: The process of training a model using a mixture of labeled and unlabeled data.

[0072] Supervised learning: The process of training a model from inputs and their corresponding labels.

[0073] Two-Sided (AI / ML) Model: Paired (multiple) AI / ML models on which joint reasoning is performed, where joint reasoning includes AI / ML reasoning whose reasoning is performed jointly across the UE and the network, e.g., the first part of the reasoning is first performed by the UE and then the remaining part is performed by the gNB, or vice versa.

[0074] UE-side (AI / ML) model: an AI / ML model whose reasoning is completely executed at the UE.

[0075] Unsupervised learning: The process of training a model without labeled data.

[0076] Proprietary format models: ML models in vendor / device specific proprietary formats from a 3GPP perspective. They are not mutually recognizable between vendors and hide model design information from other vendors when shared. Note: Example is a device specific binary executable format.

[0077] Open format models: From the 3GPP perspective, ML models in a specified format that are mutually recognizable across vendors and allow interoperability. They are mutually recognizable between vendors and do not hide model design information from other vendors when shared.

[0078] As used herein, the term "network function (NF)" may refer to a single component or service of a control plane (C-plane) or a user plane (U-plane) in a core network (CN). Each network function of a network function may be considered as an independent and autonomous logical network element. Therefore, the addition, update, extension and / or removal of a network function will not affect other network functions in the CN. The network functions may communicate with each other via corresponding air interfaces.

[0079] Figure 1 An example communication environment 100 is illustrated in which example embodiments of the present disclosure may be implemented. The communication environment 100 may be a network framework that supports AI / ML models and features that support various use cases such as UE mobility / trajectory prediction, network energy saving, load balancing, mobility optimization, traffic steering, etc. The AI / ML model may be a data-driven algorithm that applies AI / ML techniques to generate a set of outputs based on a set of inputs. Such an AI / ML model may be deployed at any one or a combination of UE, gNB, OAM, and over-the-top (OTT) servers.

[0080] In the following, some example embodiments will be described in the context of collection and reporting of data related to UE mobility or trajectory, however, other measurements are also applicable to the implementation of the solutions provided in the example embodiments of the present disclosure. Therefore, the present disclosure is not limited in this respect.

[0081] The communication environment 100 may include a first device 110, a second device 120, and a plurality of terminal apparatuses 130-1 to 130-N that may communicate with each other.

[0082] The first device 110 may be a network device for implementing OAM. The second device 120 may be a network device in the RAN. The second device 120 may provide radio coverage for a terminal device (eg, third devices 130-1 to 130-N (hereinafter collectively referred to as third devices 130)).

[0083] In some example embodiments, the second device 120 may be a single network device, such as a gNB. Alternatively, in some example embodiments, the second device 120 may be logically or physically implemented as discrete components. Figure 1 In the example shown, the first device 120 may also include a centralized unit (CU) 120-1 and a distributed unit (DU) 120-2. The CU 120-1 may be divided into a CU-CP and a CU-UP. In particular, the CU-CP may be connected to the DU 120-2 via an F1-C interface, and the CU-UP may be connected to the DU 120-2 via an F1-U interface.

[0084] It should be understood that a single DU is shown for illustration purposes, and in some other embodiments, the second device 120 may include more than one DU. Thus, the present disclosure is not limited in this regard.

[0085] The AI / ML model may be deployed in the communication environment 100. In some example embodiments, AI / ML model training may be performed at the first device 110, while the AI / ML model reasoning function may reside within the second device 120. To this end, the second device 120 may collect data from the third device 130 for LCM purposes, such as model training, monitoring, selection, updating, reasoning, etc. The data to be collected may be UE measurements, performance measurements, radio resource management (RRM) measurements, etc. The communication network 100 may utilize many frameworks for data collection, such as self-organizing networks (SON), minimization of drive tests (MDT), etc.

[0086] In some example embodiments, the link from the second device 120 to the third device 130 is referred to as a downlink (DL), and the link from the third device 130 to the second device 120 is referred to as an uplink (UL). In the DL, the second device 120 is a transmitting (TX) device (or transmitter), and the third device 130 is a receiving (RX) device (or receiver). In the UL, the third device 130 is a TX device (or transmitter), and the second device 120 is an RX device (or receiver).

[0087] In some example embodiments, the operations described in conjunction with a terminal device may be implemented at a network device or other device, and the operations described in conjunction with a network device may be implemented at a terminal device or other device.

[0088] It should be understood that Figure 1The number of devices and their connections shown in the figure are for illustration purposes only and are not intended to be limiting. The communication network 100 may include any suitable number of devices configured to implement the example embodiments of the present disclosure. Although not shown, it should be understood that one or more additional devices and connections may be deployed in the communication network 100.

[0089] The communication in the communication environment 100 can be implemented according to any appropriate communication protocol, including but not limited to cellular communication protocols of the first generation (1G), the second generation (2G), the third generation (3G), the fourth generation (4G), the fifth generation (5G), the sixth generation (6G), etc., wireless local area network communication protocols such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11, and / or any other protocol currently known or developed in the future. In addition, the communication can utilize any appropriate 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 access (OFDM), discrete Fourier transform spread OFDM (DFT-s-OFDM) and / or any other technology currently known or developed in the future.

[0090] Figure 2 A schematic diagram of a functional framework 200 for RAN intelligence is shown. Figure 2 As shown, data collection function 202 may provide training data and inference data as input data to model training function 204 and model inference function 206, respectively. Examples of input data may include measurements from UEs or other network entities, feedback from performers 208, and outputs from AI / ML models.

[0091] In the functional framework 200, the model training function 204 performs AI / ML model training, validation and testing, which can generate model performance metrics as part of the model testing process. If required, the model training function 204 can also be responsible for data preparation (e.g., data preprocessing and cleaning, formatting and transformation) based on the training data delivered by the data collection function 202.

[0092] The model training function 204 may perform model deployment or updates for the model reasoning function 206. In particular, the model deployment or updates may be used to initially deploy a trained, validated, and tested AI / ML model to the model reasoning function 206 or to deliver an updated model to the model reasoning function 206.

[0093] The model reasoning function 206 can provide AI / ML model reasoning output (e.g., prediction or decision). In addition, the model reasoning function 206 can provide model performance feedback to the model training function 204 when applicable. Model performance feedback can be used to monitor the performance of the AI / ML model. If required, the model reasoning function 206 can also be responsible for data preparation (e.g., data preprocessing and cleaning, formatting and transformation) based on the reasoning data delivered by the data collection function 202.

[0094] The executor 208 may be a function that receives output from the model reasoning function 206 and triggers or performs corresponding actions. The executor 208 may trigger actions directed to other entities or itself. The executor 208 may also provide feedback to the data collection function 202. Feedback refers to information that may be needed to export training data, reasoning data, or monitor the performance of the AI / ML model and its impact on the network through the update of KPIs and performance counters.

[0095] Figure 3 An example process 300 for AI / ML model training in OAM and AI / ML model inference in a RAN node is illustrated. Assuming that an AI / ML model is deployed at RAN node 2 (indicated by "306"), the AI / ML model can generate required inputs such as resource status and utilization predictions or estimates.

[0096] In the process 300, the RAN node 1 (indicated by "304") transmits (310) a configuration message including measurement information to the UE 302. Therefore, the UE 302 collects (315) measurements based on the measurement information. For example, the measurements may involve reference signal received power (RSRP), reference signal received quality (RSRQ), signal to interference plus noise ratio (SINR) of the serving cell and neighboring cells.

[0097] The UE 302 transmits (320) a measurement report message including the collected measurements to the RAN node 1. Upon receiving the measurement report message, the NG-RAN node 1 then transmits (325) input data for AI / ML model training to the OAM 308. The input data for AI / ML model training may include the required input information from the NG-RAN node 1 and the measurements from the UE 302.

[0098] The RAN node 2 may also transmit (330) input data for AI / ML model training to the OAM 308. The input data for AI / ML model training may include required input information from the NG-RAN node 2. If the NG-RAN node 2 executes the AI / ML model, the input data for AI / ML model training may include corresponding inference results from the NG-RAN node 2.

[0099] Accordingly, OAM 308 performs (335) AI / ML model training for UE mobility optimization by making full use of the measurements. OAM 308 then transmits (340) an AI / ML model deployment message to deploy the trained or updated AI / ML model into NG-RAN node 1 and / or NG-RAN node 2. Additionally, the NG-RAN node may continue AI / ML model training based on the AI / ML model received from OAM 308.

[0100] The NG-RAN node 1 receives (345) a measurement report from the UE 302 as inference data for UE mobility optimization. Additionally, the NG-RAN node 1 may receive (350) input data for AI / ML model inference from the NG-RAN node 2 for UE mobility optimization. In particular, the input data for AI / ML model inference may include required input information from the NG-RAN node 2. If the NG-RAN node 2 executes the AI / ML model, the input data for AI / ML model inference may include corresponding inference results from the NG-RAN node 2.

[0101] Accordingly, NG-RAN node 1 performs (355) AI / ML model inference by fully leveraging the measurements and outputs predictions, e.g., UE trajectory prediction, target cell prediction, target NG-RAN node prediction, etc. Additionally, NG-RAN 1 may transmit (360) model performance feedback to OAM 308 where applicable.

[0102] Based on the prediction, recommended action or configuration, the NG-RAN node 1, the target RAN node represented by RAN node 2 in the process 300, and the UE 302 perform a mobility optimization or handover process to handover the UE 302 from the NG-RAN node 1 to the target NG-RAN node.

[0103] Then NG-RAN Node 1 and NG-RAN Node 2 transmit (365, 370) feedback information to OAM 308 respectively.

[0104] In the exit communication system, network-controlled mobility is applied to UE in RRC_CONNECTED mode and is classified into two types of mobility, namely, cell-level mobility and beam-level mobility. Beam-level mobility can include intra-cell beam-level mobility and inter-cell beam-level mobility.

[0105] Cell level mobility may require explicit RRC signaling to be triggered, i.e. a handover procedure. Beam level mobility can be within a cell, or between cells, and the latter case is referred to as inter-cell beam management (ICBM). For ICBM, the UE receives or transmits UE-dedicated channels / signals via a TRP associated with a physical cell identifier (PCI) different from that of the serving cell, while non-UE-dedicated channels / signals can only be received via a transmission and reception point (TRP) associated with the PCI of the serving cell. The gNB provides the UE with a measurement configuration via RRC signaling, which includes the configuration of synchronization signal blocks (SSBs) / channel state information (CSI) resources and resource sets, reports and triggering states for triggering channel and interference measurements and reports. In the case of ICBM, the measurement configuration includes SSB resources associated with a PCI different from that of the serving cell. Beam level mobility is then handled at lower layers by means of physical layer and MAC layer control signaling, and RRC is not required to know which beam is being used at a given point in time.

[0106] UE trajectory indicates the trajectory information of (multiple) UEs in radio coverage, i.e., the cells and beams visited by the UE (across different RRC states) and the corresponding dwell time. This is the key input data for the UE Trajectory Prediction ML Model. When AIML model training is performed at OAM, this input data needs to be available at OAM. It is expected that UE measurements related to UE trajectories for AI / ML-based network performance optimization will be used in the existing network framework (including MDT and RRM measurements). As mentioned earlier, AI / ML model training is deployed at OAM. However, there is no mechanism for collecting and reporting UE trajectory measurements from RAN to OAM. Therefore, model training at OAM may not be possible for all use cases, even if the RAN nodes can collect such information.

[0107] According to an example embodiment of the present disclosure, a solution is provided to enable NG-RAN nodes to provide UE trajectory information to OAM. The RAN node monitors and collects UE trajectory information based on certain events or conditions and provides it to OAM. Therefore, UE measurements related to UE trajectory can be used as input data samples for offline training of UE trajectory prediction AI / ML models in OAM, or for analyzing performance by using PM counters or KPIs.

[0108] Will combine Figures 4 to 9 Discuss the proposed solution in detail. Figure 4An example mechanism 400 for measurement configuration and reporting between a RAN node and an OAM according to some example embodiments of the present disclosure is illustrated. For example, the OAM and RAN nodes may be Figure 1 For discussion purposes, reference is made to the first device 110 and the second device 120 shown in FIG. Figure 1 The mechanism 400 is described.

[0109] In the example mechanism 400, the first device 110 may activate a new trace for UE measurements (e.g., UE trajectory data). The MDT measurement activation may include a configuration for collecting UE trajectory data at a certain level (e.g., cell level or beam level) based on corresponding conditions or events satisfied at the corresponding UEs collecting these measurements. The second device 120 may collect UE measurements based on the configuration received from the first device, and may report NG-RAN based trace records (e.g., trace session records) to the first device 110. Such UE trajectory data may then be subsequently used as input data samples for AI / ML model training purposes.

[0110] Will combine Figure 5 Describes the signaling and interactions associated with the example mechanism 400, Figure 5 1 shows a signaling diagram for a measurement configuration and reporting process 500 according to some example embodiments of the present disclosure. The measurement configuration and reporting process 500 involves a first device 110 and a second device 120. For discussion purposes, reference is made to Figure 1 The process 500 is described below.

[0111] In process 500, the first device 110 prepares (505) a measurement collection configuration, the measurement collection configuration including a list for MDT measurement collection and a measurement granularity. The list may include at least one network device and a plurality of cells, the at least one network device is associated with the MDT measurement collection, and the plurality of cells include a serving cell. Figure 1 In the example shown in , at least one network device in the RAN is the second apparatus 120, and in particular, it may be the CU 120-1 and / or the DU 120-2. In some example embodiments, the plurality of cells may include a group of cells for MDT measurement collection.

[0112] In some example embodiments, the measurement collection configuration may be associated with a certain UE measurement (e.g., UE trajectory data) to be collected in the RAN. The UE trajectory data may also be referred to as terminal device trajectory measurements, which indicate trajectory information of the UE in radio coverage, such as cells and beams visited by the UE (which may be across different RRC states), and the corresponding dwell time. Therefore, this is considered to be key input data for the UE trajectory prediction ML model. In the case where the AI / ML model training is performed at the OAM, the UE trajectory data needs to be available for input data at the OAM.

[0113] It should be noted that some embodiments are described in the context of mobility / trajectory prediction or optimization, however, this is given as one of the various use cases suitable for implementing the solutions provided by the present disclosure without any limitation. In fact, the proposed solution is also applicable to any other AI / ML model, function or use case. Therefore, the present disclosure is not limited thereto.

[0114] In some example embodiments, the terminal device trace measurements may be collected by the MDT framework, so in this case, the terminal device trace measurements may also be referred to as NG-RAN based MDT measurements. The terminal device trace measurements may also be performance measurement (PM) counter measurements or key performance indicator (KPI) measurements related to the UE trace.

[0115] Table 1 shows a list of RAN-based MDT measurements that may be measured and reported to the first device 110 . Table 1 Example of RAN-based MDT measurements

[0116] The measurement granularity may be associated with the granularity of the UE trajectory ML model and include cell level, beam level, or a combination of both. Table 2 shows the granularity of the UE trajectory ML model

[0117] In the case of UE mobility at the cell level controlled by the gNB-CU-CP (e.g., CU 120-1), the measurement collection configuration may be a configuration of layer 3 measurements. In the case of UE mobility at the beam level controlled by the gNB-DU (e.g., DU 120-1), the measurement collection configuration may be a beam measurement configuration. In the case of UE mobility at both the cell level and the beam level, both the gNB-CU-CP and the gNB-DU are involved. This will be discussed later in conjunction with FIG. 6A to FIG. 6C Discuss these situations in detail.

[0118] The first device 110 transmits 510 a measurement collection configuration to the second device 120. Depending on the configured measurement granularity, the measurement collection configuration may be transmitted only to the CU 120-1, only to the DU 120-2, or both.

[0119] The measurement collection configuration may trigger activation of MDT measurements for the terminal device trace. Accordingly, based on the measurement collection configuration, the second apparatus 120 collects (515) terminal device trace measurements from at least one terminal device in at least a portion of the plurality of cells.

[0120] Then, the second device 120 transmits (520) an MDT measurement report to the first device 110, the MDT measurement report including the terminal device trajectory measurement.

[0121] In some example embodiments, the first apparatus 110 may then apply ( 525 ) ML model training to the at least one ML model by using the end-device trajectory measurements.

[0122] Alternatively or additionally, in some example embodiments, by using the terminal device trajectory measurement as an input data sample, the first device 110 may perform at least one of the following based on the terminal device trajectory measurement: troubleshooting, cell-level or node-level performance analysis, ML training or retraining.

[0123] In some example embodiments, the first device 110 may desire to collect only beam-level UE trajectory information, and thus the configured measurement granularity corresponds to beam-level measurement collection. In this case, measurement configuration and reporting may involve the first device 110 and DU 120-2. In other words, beam-level measurements are exposed from DU 120-2 to the first device 110.

[0124] Fig. 6A A schematic diagram of beam-level measurements exposed to OAM from a gNB-DU according to some example embodiments of the present disclosure is shown. In scenario 600, the first device 110 may directly configure DU 120-2 with a beam-level measurement set configuration. Alternatively, the first device 110 may configure DU 120-2 through CU 120-1. In this case, the measurement report may also be provided to the first device 110 through CU 120-1.

[0125] The beam level measurement collection configuration may trigger beam mobility event monitoring at DU 120-2. DU 120-2 may select at least one terminal device for measurement collection. When the selected terminal device is redirected from one beam to another beam, DU 120-2 may start collecting beam level measurements on L2, for example, the visited beams and dwell time for the terminal device in each beam of the beam. The collected measurements may be transmitted from L2 to L3 and then exposed to the first device 110.

[0126] Table 3 shows an example of beam level measurement collection in terms of dwell time of a UE in a beam of a given cell considering two cells C1 and C2, and each cell has two beams B1 and B2. It should be understood that the example in Table 3 is given for illustrative purposes, and other measurements and forms are also applicable to the implementation of the example embodiments. Table 3. Beam level measurement collection C1 Beam ID B1 B2 Dwell time X1 minute X2 minutes C2 Beam ID B1 B2 Dwell time Y1 minute Y2 minutes

[0127] In some example embodiments, the first device 110 may desire to collect only cell-level UE trajectory information, and thus the configured measurement granularity corresponds to a cell-level measurement set. In this case, the measurement configuration and reporting may involve the first device 110 and the CU 120-1. In other words, the cell-level measurements are exposed from the CU 120-1 to the first device 110.

[0128] Figure 6B A schematic diagram of cell-level measurements exposed to OAM from a gNB-CU according to some example embodiments of the present disclosure is shown. In scenario 610, the first device 110 may directly configure the CU 120-1 with a cell-level measurement collection configuration.

[0129] The cell level measurement collection configuration may trigger a cell mobility event at CU 120-1. CU 120-1 may select at least one terminal device for measurement collection. When the selected terminal device switches from one cell to another, CU 120-2 may start collecting cell level measurements, for example, the visited cells and the dwell time of the terminal device in each of the cells. The collected measurements may then be exposed to the first device 110.

[0130] Table 4 shows an example of cell level measurement collection in terms of dwell time of UE in each of two cells C1 and C2. It should be understood that the example in Table 4 is given for illustrative purposes and other measurements, forms and time units are also applicable to the implementation of the example embodiments. Table 4. Cell level measurement collection Cell ID C1 C2 Dwell time X seconds Y seconds

[0131] In some example embodiments, first device 110 may desire to collect both cell-level and beam-level UE trajectory information, and thus the configured measurement granularity corresponds to a beam-level measurement set and a cell-level measurement set. In this case, measurement configuration and reporting may involve first device 110, CU 120-1, and DU 120-2, and cell-level measurements and beam-level measurements are exposed from CU 120-1 to first device 110.

[0132] Figure 6C A schematic diagram of beam-level and cell-level measurements exposed to OAM from a gNB-CU according to some example embodiments of the present disclosure is shown. In scenario 620, the first device 110 may configure CU 120-1 with a cell-level measurement collection configuration and a beam-level measurement collection configuration. CU 120-1 may select at least one terminal device for measurement collection. When the selected terminal device switches from one cell to another, CU 120-2 may start collecting cell-level measurements, such as visited cells and dwell time.

[0133] Additionally, CU 120-1 may configure DU 120-2 with a beam level measurement configuration, which triggers beam mobility event monitoring at DU 120-2. When beam mobility monitoring is enabled in the gNB-DU, the gNB-DU starts monitoring the beam changes of the UE. When the UE camps on a beam, the gNB-DU records the timestamp of the UE camping. When the gNB-DU commands the UE to move to another beam, the gNB-DU calculates the dwell time. The dwell time is calculated as the difference in time units between the timestamp of the UE moving to the new beam and the timestamp of the UE camping on the old beam. This process is repeated for each beam mobility event of the UE. Thus, the gNB-DU may derive a list of {beam id, dwell time} pairs for any UE for which beam mobility monitoring is triggered. An example of beam mobility is triggered by movement of the UE. Each UE is configured with a beam measurement configuration based on the beam for which the UE reports one or more measurements. The gNB-DU selects the best beam and commands the UE to move to the selected beam. Another example is a beam change triggered by a radio condition that fails to trigger the beam for the current beam. Another example is a beam level energy saving action due to the UE having to camp on any other available beam.

[0134] DU 120-2 may start collecting beam level measurements that are transmitted to CU 120-1. CU 120-1 may merge cell level measurements and beam level measurements into a measurement report. When the terminal device moves from RRC connected (RRC_CONENCTED) mode to RRC idle (RRC_IDLE) or RRC inactive (RRC_INACTIVE) mode, CU 120-1 may transmit a measurement report to the first device 110. For example, the merged measurements may include a list of terminal devices for MDT measurement collection, a list of beams accessed by the terminal device, a set of cells accessed by the terminal device, a dwell time of the terminal device in each cell of the cell, and a dwell time associated with each list of beams.

[0135] Table 5 shows an example of a set of cell-level and beam-level measurements in terms of the dwell time of a UE in a beam of each of a given cell considering two cells C1 and C2, cell 1 having four beams B1 to B4 and cell 2 having two beams B1 and B2. It should be understood that the example in Table 5 is given for illustrative purposes and other measurements and forms are also applicable to the implementation of the example embodiments. Table 5. Cell level and beam level measurement collection

[0136] Will combine Figure 7 The details of the measurement configuration and reporting process 700 for cell level measurement and beam level measurement are described. The measurement configuration and reporting process 700 involves the first device 110, the CU 120-1 and the DU 120-2. For the purpose of discussion, reference is made to Figure 1 700 is described below.

[0137] In process 700, ML model training for UE trajectory is performed at the first device 110. The first device 110 can trigger MDT measurement activation for UE trajectory. In some cases, the operator can configure the UE trajectory prediction ML model scope (e.g., cell beam or beam level trajectory) and a set of NG-RAN nodes and cells as candidates for data collection. Therefore, the first device 110 can trigger UE trajectory measurement collection at these NG-RAN nodes.

[0138] To this end, the first device 110 prepares (705) a measurement collection configuration, which includes a list for MDT measurement collection and a measurement granularity. The list may include at least one second device and multiple cells. The measurement granularity may indicate the granularity of the measurement collection, for example, a cell level and / or a beam level.

[0139] exist Figure 7 In the example of , the measurement granularity is a combination of the cell level and the beam level, so the NG-RAN involved is CU 120-1 and DU 120-2. In this case, the measurement collection configuration may include a cell level measurement collection configuration and a beam level measurement collection configuration.

[0140] The first device 110 transmits (710) a measurement collection configuration to CU 120-1, and CU 120-1 can trigger cell mobility-based measurement collection at CU 120-1. In some example embodiments, when terminal device trajectory measurements are collected from all terminal devices, this can be performed for each terminal device residing on an applicable node or cell. Alternatively, the first device 110 can select (715) specific terminal devices for measurement collection. For example, if the NG-RAN node encounters a mobility robustness optimization (MRO) event, such as a handover or ping-pong that is too early or too late, the NG-RAN node can collect terminal device trajectory MDT measurements. OAM can monitor cell level RLF or MRO PM counters. When the failure counter increases above a certain threshold, OAM can select those cells to trigger UE trajectory data collection.

[0141] Alternatively, if the number of MRO events encountered exceeds a certain threshold, the event may be associated with a threshold condition and the NR-RAN may collect terminal device trace MDT measurements. The threshold may be configured by OAM. Alternatively, the threshold may be node dependent. In some examples, the threshold may be given to the NG-RAN node together with the MDT configuration in trace activation.

[0142] Then, CU 120-1 starts cell mobility (eg, L3 handover event) for the terminal device and collects (720) cell mobility based measurements based on the measurement collection configuration. For example, the cell mobility based measurements may be as shown in Table 4.

[0143] Additionally, CU 120-1 may trigger beam mobility based measurement collection at DU 120-2. To this end, CU 120-1 transmits (725) a measurement collection configuration to DU 120-2 via F1 Application Protocol (F1AP). In some example embodiments, CU 120-1 may only provide beam level measurement collection configuration to DU 120-2. As an example, the measurement collection configuration may be carried in a request for activating UE trajectory measurement, which indicates a beam level, a cell list, and a list of beams for each cell.

[0144] In some example embodiments, DU 120-2 may transmit (730) a response for activating UE trajectory measurement as a confirmation indicating whether the request is accepted. If the request is accepted, DU 120-2 activates beam mobility monitoring and collects (735) beam mobility-based measurements based on the measurement collection configuration. For example, the beam mobility-based measurements may be as shown in Table 3.

[0145] DU 120-2 transmits (740) a terminal device trajectory measurement report to CU 120-1 via F1AP, the terminal device trajectory measurement report including the collected beam mobility based measurements. For example, the collected beam mobility based measurements may be carried in the terminal device trajectory measurement report.

[0146] In some example embodiments, the measurement report at CU 120-1 or DU 120-2 may be an MDT trace record based on NG-RAN measurements.

[0147] Alternatively, in some other example embodiments, the measurement report may be based on PM counter measurements. In these embodiments, the measurement may be modeled as a "distribution of terminal device trajectories in the cell". A set of bins may be defined with a specific range of values ​​for the dwell time. The RAN node may compute the terminal device trajectory (e.g., a list of visited cells, or a list of visited beams, and the dwell time) during the measurement period. For each measurement sample, the bin corresponding to the total dwell time experienced by the terminal device is incremented by 1. Figure 8 A schematic diagram of distribution 800 of terminal device trajectories in a cell according to some example embodiments of the present disclosure is illustrated. N1, N2, N3, N4 indicate the number of terminal devices corresponding to the residence time in a sampling period.

[0148] Upon receiving the beam mobility based measurements, CU 120-1 combines (745) the cell mobility measurements and the beam mobility measurements. In particular, CU 120-1 can use the dwell time in each beam to create a detailed view of the dwell time in each cell. Table 6 shows an example of the combined measurements, which includes the beam level measurements shown in Table 3 and the cell level measurements shown in Table 4. It should be understood that the examples in Table 6 are given for illustrative purposes, and other measurements, forms, and time units are also applicable to the implementation of the example embodiment. Table 6. Combined measurements

[0149] CU 120-1 transmits (750) the terminal device trajectory measurement report to the first device 110, the terminal device trajectory measurement report including the merged measurement. Therefore, the first device 110 can then perform (755) ML model training by using the merged measurement as input data samples.

[0150] Fig. 9 900 is a flowchart of an example method 900 implemented at a device according to some example embodiments of the present disclosure. The device may be a network node for implementing OAM. For the purpose of discussion, Figure 1 The perspective description method 900 of the first device 110 in FIG.

[0151] At block 910, a first device prepares a measurement collection configuration including a list and a measurement granularity for minimization of drive test (MDT) measurement collection. The list includes at least one second device and a plurality of cells.

[0152] At block 920 , a measurement collection configuration is transmitted to at least one second apparatus for triggering MDT measurement activation for the terminal device trace.

[0153] At block 930 , the first apparatus receives at least one MDT measurement report from at least one second apparatus, the at least one MDT measurement report comprising terminal device trace measurements collected based on a measurement collection configuration.

[0154] In some example embodiments, the plurality of cells comprises a group of cells for MDT measurement collection.

[0155] In some example embodiments, the measurement granularity corresponds to beam level measurement collection, the terminal device trajectory measurement comprises a list of beams visited by the terminal device, and at least one measurement report comprises a list of beams respectively associated with dwell times.

[0156] In some example embodiments, the measurement granularity corresponds to cell level measurement collection, the terminal device trajectory measurement comprises a set of cells visited by the terminal device, and the at least one measurement report comprises a dwell time for the terminal device in each cell of the set of cells.

[0157] In some exemplary embodiments, the measurement granularity corresponds to a combination of beam level measurement collection and cell level measurement collection, and the terminal device trajectory measurement includes a list of beams visited by the terminal device and a set of cells, and at least one measurement report includes a dwell time for the terminal device in each cell in the set of cells, and the list of beams is respectively associated with the dwell time.

[0158] In some exemplary embodiments, method 900 further includes: applying machine learning (ML) model training to at least one ML model by using the terminal device trace measurements collected at the measurement granularity.

[0159] In some exemplary embodiments, the method 900 further includes: based on the terminal device trajectory measurement, performing at least one of the following: troubleshooting, cell-level or node-level performance analysis, or ML training or retraining using the terminal device trajectory measurement as input data samples.

[0160] In some exemplary embodiments, the first device may include a network device for implementing OAM, and the at least one second device includes one of a centralized unit or a distributed unit in a radio access network.

[0161] Fig.10 A flow chart of an example method 1000 implemented at an apparatus according to some example embodiments of the present invention is illustrated. The apparatus may be a network device or node in a NG-RAN, such as a gNB-CU, gNB-DU, etc. For the purpose of discussion, Figure 1 The perspective description method 1000 of the second device 120 in FIG.

[0162] At block 1010, a second device receives a measurement collection configuration from a first device, the measurement collection configuration including a list and a measurement granularity for minimization of drive test (MDT) measurement collection. The list includes at least the second device and a plurality of cells.

[0163] At block 1020 , the second apparatus 120 collects terminal device trace measurements from at least one terminal device in at least a portion of the plurality of cells based on the measurement collection configuration.

[0164] At block 1030 , the second apparatus transmits an MDT measurement report to the first apparatus, the MDT measurement report including the terminal device trace measurement.

[0165] In some example embodiments, the plurality of cells comprises a group of cells for MDT measurement collection.

[0166] In some example embodiments, the second apparatus comprises a centralized unit in the wireless access network, the measurement granularity corresponds to a beam level measurement set, the terminal device trajectory measurement comprises a list of beams accessed by at least one terminal device, and at least one measurement report comprises a list of beams respectively associated with a dwell time.

[0167] In some example embodiments, the second apparatus comprises a centralized unit in the radio access network, and the measurement granularity corresponds to cell level measurement collection, the terminal device trajectory measurement comprises a set of cells visited by at least one terminal device, and at least one measurement report comprises a dwell time in each cell of the set of cells for the at least one terminal device.

[0168] In some example embodiments, the measurement granularity corresponds to a combination of beam level measurement collection and cell level measurement collection, the second apparatus comprises a centralized unit in the radio access network, the centralized unit collecting terminal device trajectory measurements at cell level.

[0169] In some example embodiments, method 1000 may further include: transmitting a measurement collection configuration to at least one distributed unit in a radio access network, the measurement collection configuration being used to trigger activation of MDT measurements for a terminal device trajectory at a beam level; receiving terminal device trajectory measurements collected at a beam level from at least one distributed unit; and generating an MDT measurement report by merging the terminal device trajectory measurements at a cell level and the terminal device trajectory measurements at a beam level.

[0170] In some exemplary embodiments, the first device comprises a network device for implementing Operation Maintenance Management (OAM), and the at least one second device comprises one of a centralized unit or a distributed unit in a radio access network. Example devices, equipment, and media

[0171] In some example embodiments, a first device (eg, Figure 1 The first device 110 in the method 900 may include components for performing the corresponding operations of the method 900. These components may be implemented in any suitable form. For example, the components may be implemented in a circuit system or a software module. The first device may be implemented as or included in Figure 1 In the first device 110.

[0172] In some example embodiments, a first device includes: a component for preparing a measurement collection configuration, the measurement collection configuration including a list and a measurement granularity for minimizing drive test (MDT) measurement collection, the list including at least one second device and a plurality of cells; a component for transmitting the measurement collection configuration to at least one second device for triggering MDT measurement activation for a terminal device trajectory; and a component for receiving at least one MDT measurement report from at least one second device, the at least one MDT measurement report including terminal device trajectory measurements collected based on the measurement collection configuration.

[0173] In some example embodiments, the plurality of cells comprises a group of cells for MDT measurement collection.

[0174] In some example embodiments, the measurement granularity corresponds to beam level measurement collection, the terminal device trajectory measurement comprises a list of beams visited by the terminal device, and at least one measurement report comprises a list of beams respectively associated with dwell times.

[0175] In some example embodiments, the measurement granularity corresponds to cell level measurement collection, the terminal device trajectory measurement comprises a set of cells visited by the terminal device, and the at least one measurement report comprises a dwell time for the terminal device in each cell of the set of cells.

[0176] In some example embodiments, the measurement granularity corresponds to a combination of beam level measurement collection and cell level measurement collection, and the terminal device trajectory measurement includes a list of beams visited by the terminal device and a set of cells, and at least one measurement report includes a dwell time for the terminal device in each cell in the set of cells, and the list of beams is respectively associated with the dwell time.

[0177] In some example embodiments, the first apparatus further comprises: means for applying machine learning (ML) model training to at least one ML model by using the terminal device trace measurements collected at the measurement granularity.

[0178] In some example embodiments, the first apparatus further comprises: means for performing at least one of the following based on the terminal device trajectory measurements: troubleshooting, cell-level or node-level performance analysis, or ML training or retraining using the terminal device trajectory measurements as input data samples.

[0179] In some example embodiments, the first device comprises a network device for implementing Operation Maintenance Management (OAM), and the at least one second device comprises one of a centralized unit or a distributed unit in a radio access network.

[0180] In some example embodiments, the first device further comprises means for performing other operations in some example embodiments of the method 900 or the first device 110. In some example embodiments, these means comprise at least one processor; and at least one memory storing instructions which, when executed by the at least one processor, cause the performance of the first device.

[0181] In some example embodiments, a second device (eg, Figure 1 The second device 120 in the method 1000 may include components for performing the corresponding operations of the method 1000. These components may be implemented in any suitable form. For example, the components may be implemented in a circuit system or a software module. The second device may be implemented as or included in Figure 1 In the second device 120.

[0182] In some example embodiments, the second device includes: a component for receiving a measurement collection configuration from the first device, the measurement collection configuration including a list and measurement granularity for minimization of drive test (MDT) measurement collection, the list including at least the second device and multiple cells; a component for collecting terminal device trajectory measurements from at least one terminal device in at least a portion of the multiple cells based on the measurement collection configuration; and a component for transmitting an MDT measurement report to the first device, the MDT measurement report including the terminal device trajectory measurements.

[0183] In some example embodiments, the plurality of cells comprises a group of cells used for the MDT measurement collection.

[0184] In some example embodiments, the second apparatus comprises a centralized unit in a radio access network, the measurement granularity corresponds to beam level measurement collection, the terminal device trajectory measurements comprise a list of beams accessed by the at least one terminal device, and the at least one measurement report comprises the list of beams respectively associated with dwell times.

[0185] In some exemplary embodiments, the second apparatus comprises a centralized unit in a radio access network, and the measurement granularity corresponds to a cell-level measurement collection, the terminal device trajectory measurement comprises a set of cells visited by the at least one terminal device, and the at least one measurement report comprises a residence time for the at least one terminal device in each cell in the set of cells.

[0186] In some example embodiments, the measurement granularity corresponds to a combination of beam level measurement collection and cell level measurement collection, and the second apparatus comprises a centralized unit in a radio access network, the centralized unit collecting the terminal device trajectory measurements at the cell level.

[0187] In some example embodiments, the second apparatus further comprises: a component for transmitting the measurement collection configuration to at least one distributed unit in the radio access network, the measurement collection configuration being used to trigger MDT measurement activation for the terminal device trajectory at the beam level; a component for receiving the terminal device trajectory measurement collected at the beam level from the at least one distributed unit; and a component for generating the MDT measurement report by merging the terminal device trajectory measurement at the cell level and the terminal device trajectory measurement at the beam level.

[0188] In some exemplary embodiments, the first device comprises a network device for implementing Operation Maintenance Management (OAM), and the at least one second device comprises one of a centralized unit or a distributed unit in a radio access network.

[0189] In some example embodiments, the second device further comprises means for performing other operations in some example embodiments of the method 1000 or the second device 120. In some example embodiments, these means comprise at least one processor; and at least one memory storing instructions which, when executed by the at least one processor, cause the performance of the second device.

[0190] Fig.11 1 is a simplified block diagram of a device 1100 suitable for implementing an example embodiment of the present disclosure. The device 1100 may be provided to implement a communication device, such as Figure 1 The first device 110 or the second device 120 is shown. As shown in the figure, the device 1100 includes one or more processors 1110, one or more memories 1120 coupled to the processor 1110, and one or more communication modules 1140 coupled to the processor 1110.

[0191] The communication module 1140 is used for two-way communication. The communication module 1140 has one or more communication interfaces to support communication with one or more other modules or devices. The communication interface may represent any interface necessary for communicating with other network elements. In some example embodiments, the communication module 1140 may include at least one antenna.

[0192] As non-limiting examples, processor 1110 may be of any type suitable for a local technology network, and may include one or more of the following: a general purpose computer, a special purpose computer, a microprocessor, a digital signal processor (DSP), and a processor based on a multi-core processor architecture. Device 1100 may have multiple processors, such as application specific integrated circuit chips, which are time-slaved to a clock of a synchronized master processor.

[0193] The memory 1120 may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM) 1124, electrically programmable read-only memory (EPROM), flash memory, hard disk, compact disk (CD), digital video disk (DVD), optical disk, laser disk, and other magnetic storage and / or optical storage. Examples of volatile memories include, but are not limited to, random access memory (RAM) 1122 and other volatile memories that do not persist over the duration of a power outage.

[0194] The computer program 1130 includes computer executable instructions executed by the associated processor 1110. The instructions of the program 1130 may include instructions for performing the operations / actions of some example embodiments of the present disclosure. The program 1130 may be stored in a memory, such as ROM 1124. The processor 1110 may perform any appropriate actions and processes by loading the program 1130 into the RAM 1122.

[0195] The exemplary embodiments of the present disclosure may be implemented by means of the program 1130 so that the device 1100 may execute the procedures described in the reference Figures 2 to 10 Any process of the present disclosure discussed. The exemplary embodiments of the present disclosure may also be implemented by hardware or a combination of software and hardware.

[0196] In some example embodiments, the program 1130 may be tangibly embodied in a computer-readable medium that may be included in the device 1100 (such as in the memory 1120) or other storage device accessible by the device 1100. The device 1100 may load the program 1130 from the computer-readable medium to the RAM 1122 for execution. In some example embodiments, the computer-readable medium may include any type of non-transitory storage medium, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc. As used herein, the term "non-transitory" is a limitation of the medium itself (i.e., tangible rather than a signal), not a limitation on data storage persistence (e.g., RAM vs. ROM).

[0197] Fig.12 An example of a computer readable medium 1100 is shown which may be in the form of a CD, DVD, or other optical storage disk. The computer readable medium 1100 has a program 1130 stored thereon.

[0198] In general, various embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of the present disclosure are shown and described as block diagrams, flow charts, or using some other graphical representations, it should be understood that, as non-limiting examples, the boxes, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0199] 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 computer-executable instructions, such as those included in a program module executed in a device on a target physical or virtual processor, to perform any method as above. Typically, a program module includes routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of the program modules can be combined or split between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in both local and remote storage media.

[0200] The program code for executing the method of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, as an independent software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0201] In the context of the present disclosure, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform various processes and operations as described above. Examples of carriers include signals, computer readable media, etc.

[0202] The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or apparatus, or any suitable combination of the foregoing. More specific examples of computer readable storage media would include an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0203] In addition, although operations are depicted in a particular order, this should not be understood as requiring such operations to be performed in the particular order shown or in sequence, or performing all of the operations shown, to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these details should not be interpreted as limitations on the scope of the present disclosure, but as descriptions of features that may be specific to a particular embodiment. Unless explicitly stated, certain features described in the context of a separate embodiment may also be implemented in combination in a single embodiment. On the contrary, unless explicitly stated, the various features described in the context of a single embodiment may also be implemented in multiple embodiments individually or in any suitable sub-combination.

[0204] Although the disclosure has been described in language specific to structural features and / or methodological acts, it should be understood that the 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

1. A first device for communication, comprising: at least one processor; as well as at least one memory storing instructions, which, when executed by the at least one processor, cause the first device to at least: preparing a measurement collection configuration, the measurement collection configuration comprising a list and a measurement granularity for minimizing drive tests (MDT) measurement collection, the list comprising at least one second device and a plurality of cells; transmitting the measurement collection configuration to the at least one second apparatus for triggering MDT measurement activation for a terminal device trajectory; as well as At least one MDT measurement report is received from the at least one second apparatus, the at least one MDT measurement report comprising terminal device trace measurements collected based on the measurement collection configuration. 2 . The first apparatus of claim 1 , wherein the plurality of cells comprises a group of cells used for the MDT measurement collection.

3. A first device according to claim 1, wherein the measurement granularity corresponds to beam-level measurement collection, the terminal device trajectory measurement includes a list of beams accessed by the terminal device, and the at least one measurement report includes the list of beams respectively associated with the dwell time.

4. The first apparatus of claim 1 , wherein the measurement granularity corresponds to a cell-level measurement collection, the terminal device trajectory measurement comprises a set of cells visited by the terminal device, and the at least one measurement report comprises a dwell time for the terminal device in each cell in the set of cells.

5. A first device according to claim 1, wherein the measurement granularity corresponds to a combination of beam level measurement collection and cell level measurement collection, and the terminal device trajectory measurement includes a list of beams accessed by the terminal device and a set of cells, and the at least one measurement report includes a residence time for the terminal device in each cell in the set of cells, and the list of beams is associated with the residence time, respectively.

6. The first device of claim 1, wherein the first device is further caused to: Applying machine learning (ML) model training to at least one ML model by using the end device trace measurements collected at the measurement granularity.

7. The first device of claim 1, wherein the first device is further caused to: Based on the terminal device trajectory measurement, perform at least one of the following: troubleshooting, Cell-level or node-level performance analysis, or The end device trajectory measurements are used as input data samples for ML training or retraining.

8. The first device according to claim 1, wherein the first device comprises a network device for implementing Operation Maintenance Management (OAM), and the at least one second device comprises one of a centralized unit or a distributed unit in a radio access network.

9. A second device for communication, comprising: at least one processor; as well as at least one memory storing instructions, which, when executed by the at least one processor, cause the second device to at least: receiving a measurement collection configuration from a first device, the measurement collection configuration comprising a list and a measurement granularity for minimization of drive test (MDT) measurement collection, the list comprising at least the second device and a plurality of cells; collecting terminal device trajectory measurements from at least one terminal device in at least a portion of the plurality of cells based on the measurement collection configuration; as well as Transmitting an MDT measurement report to the first apparatus, the MDT measurement report including the terminal device trajectory measurement.

10. The second apparatus of claim 9, wherein the plurality of cells comprises a group of cells used for the MDT measurement collection.

11. A second device according to claim 9, wherein the second device comprises a centralized unit in a radio access network, the measurement granularity corresponds to beam level measurement collection, the terminal device trajectory measurement comprises a list of beams accessed by the at least one terminal device, and the at least one measurement report comprises the list of beams respectively associated with a dwell time.

12. A second device according to claim 9, wherein the second device comprises a centralized unit in a radio access network, and the measurement granularity corresponds to cell-level measurement collection, the terminal device trajectory measurement comprises a set of cells visited by the at least one terminal device, and the at least one measurement report comprises a residence time for the at least one terminal device in each cell in the set of cells.

13. The second device according to claim 9, wherein the measurement granularity corresponds to a combination of beam level measurement collection and cell level measurement collection, and the second device comprises a centralized unit in a radio access network, and the centralized unit collects the terminal device trajectory measurements at the cell level.

14. The second device of claim 13, wherein the second device is further caused to: transmitting the measurement collection configuration to at least one distributed unit in the radio access network, the measurement collection configuration being used to trigger activation of MDT measurements for the terminal device trace at the beam level; receiving, from said at least one distributed unit, terminal device trajectory measurements collected at said beam level; as well as The MDT measurement report is generated by combining the terminal device trajectory measurement at the cell level and the terminal device trajectory measurement at the beam level.

15. The second device according to claim 9, wherein the first device comprises a network device for implementing Operation Maintenance Management (OAM), and the at least one second device comprises one of a centralized unit or a distributed unit in a radio access network.

16. A method for communication, comprising: Prepare a measurement collection configuration at a first device, the measurement collection configuration comprising a list and a measurement granularity for minimization of drive tests (MDT) measurement collection, the list comprising at least one second device and a plurality of cells; transmitting the measurement collection configuration to the at least one second apparatus for triggering MDT measurement activation for a terminal device trajectory; as well as At least one MDT measurement report is received from the at least one second apparatus, the at least one MDT measurement report comprising terminal device trace measurements collected based on the measurement collection configuration.

17. A method for communication, comprising: receiving, at a second device and from a first device, a measurement collection configuration, the measurement collection configuration comprising a list for minimization of drive tests (MDT) measurement collection and a measurement granularity, the list comprising at least the second device and a plurality of cells; collecting terminal device trajectory measurements from at least one terminal device in at least a portion of the plurality of cells based on the measurement collection configuration; as well as Transmitting an MDT measurement report to the first apparatus, the MDT measurement report including the terminal device trajectory measurement.

18. A computer-readable medium comprising instructions stored thereon, the instructions being configured to cause an apparatus to at least perform the method of claim 16 or 17.

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