Method and apparatus for supporting quality of experience (QoE) prediction

Abstract

A method and apparatus for supporting Quality of Experience (QoE) prediction are disclosed. The method may include transmitting first information indicating at least one or more QoE metrics to a node, wherein the first information is a QoE history information request or QoE prediction configuration information; and receiving second information indicating at least the one or more QoE metrics and a value of the one or more QoE metrics, wherein the second information is a QoE history information report in response to the QoE history information request or a QoE prediction information report in response to the QoE prediction configuration information.

Description

Technical Field

[0001] Embodiments of the present application relate to wireless communication technology, and more particularly, to artificial intelligence (AI) applications in wireless communications, for example, methods and devices supporting quality of experience (QoE) prediction. Background Art

[0002] AI, which includes at least machine learning (ML), is used to learn and perform certain tasks by training neural networks (NN) with large amounts of data, which has been successfully applied in the fields of computer vision (CV) and natural language processing (NLP). Deep learning (DL) is a subconcept of ML that utilizes multiple layers of NN as an "AI model" (or AI algorithm) to learn how to solve problems and / or optimize performance from large amounts of data.

[0003] By leveraging the benefits of AI, the performance of the Radio Access Network (RAN) network can be further optimized in at least the following use cases: energy saving, load balancing, traffic steering, and mobility optimization. Therefore, the Third Generation Partnership Project (3GPP) has been considering the introduction of AI into 3GPP since 2016, including several research projects and work items in SA1, SA2, SA5, and RAN3.

[0004] Taking QoE as an example, in 3GPP Release (Rel)-17, the basic mechanisms for New Radio (NR) QoE have been specified. However, for demanding 5G service-intensive and interactive applications such as Virtual Reality (VR), the current semi-static Quality of Service (QoS) framework cannot meet the diverse QoE requirements because it does not take into account the potential significant fluctuations in radio transmission capacity. It is expected that AI-assisted QoE optimization, for example, AI-assisted QoE prediction (and / or estimation) from the application layer can help handle such uncertainties and improve user experience through QoE-aware optimization, such as QoE-aware scheduling, QoE-aware resource allocation, QoE-aware mobility, and QoE-aware dual connectivity (DC) configuration.

[0005] Therefore, the industry needs a technical solution for QoE optimization based on AI models (or AI algorithms) from the network side or the remote side, which will improve resource optimization and AI applications in RAN. Summary of the invention

[0006] One purpose of an embodiment of the present application is to provide a technical solution for wireless communications, and in particular, a technical solution based on an AI model to support QoE prediction.

[0007] Some embodiments of the present application provide a wireless communication device, such as a RAN node or a core network (CN) node or other network node, comprising: a transceiver; and a processor coupled to the transceiver, wherein the processor is configured to: transmit first information indicating at least one or more QoE metrics to the node, wherein the first information is a QoE history information request or QoE prediction configuration information; and receive second information indicating at least the one or more QoE metrics and the values ​​of the one or more QoE metrics, wherein the second information is a QoE history information report in response to the QoE history information request or a QoE prediction information report in response to the QoE prediction configuration information.

[0008] In some embodiments of the present application, the one or more QoE metrics include at least one of the following metrics: service quality level, which is measured by performing subjective testing between two nodes; application layer buffering level, which indicates the buffering level of the application layer; initial playout delay, which indicates the initial playout delay of the application layer; damage duration, which indicates the time period from the time of the last undamaged frame before the damage to the time of the first subsequent undamaged frame after the damage; presentation viewports, which indicates the list of viewports that have been presented during media presentation; play period, which indicates the time interval between a user action and the earliest occurrence of the next user action, playback end, or failure to stop playback; or comparable quality viewport switching delay, which indicates the delay and quality-related factors when the viewport movement causes quality degradation. An example of a service quality level is a mean opinion score (MOS), which provides a numerical indication of the perceived quality from the user's perspective of the received service.

[0009] In some embodiments of the present application, the QoE history information request further indicates at least one of the following parameters: an indicator indicating whether the QoE history information needs to be reported; a service type indicating the type of the QoE history information to be collected; an area scope indicating at least one object for which the QoE history information is required; a trigger condition indicating criteria for triggering a QoE history information report; or time information indicating the latest time at which the QoE history information is expected to be received.

[0010] In some embodiments of the present application, the QoE history information includes one or more entries of QoE measurement results, and each entry includes, in addition to the one or more QoE metrics and the values ​​of the one or more QoE metrics within the configured valid area of ​​the service type, at least one of the following parameters: the start time and end time of the QoE measurement indicated by the application layer; a list of access cells of a user equipment (UE) and the duration of the UE's stay in each access cell during the performance of the QoE measurement; the radio conditions at the timing of receiving the values ​​of the one or more QoE metrics from an upper layer; or a timestamp of receiving the values ​​of the one or more QoE metrics from an upper layer.

[0011] In some embodiments of the present application, the QoE prediction configuration information further indicates at least one of the following parameters: an indicator indicating whether the QoE prediction information needs to be reported; time information indicating the latest time at which the QoE prediction information is expected to be received; an analysis period indicating a future time interval in which the QoE prediction information is located; a trigger condition indicating a criterion for triggering the reporting of the QoE prediction information; a radio condition indicating an environment in which the QoE prediction information will be applied; or a preferred accuracy level indicating a preferred accuracy level for the QoE prediction information.

[0012] In some embodiments of the present application, the QoE prediction information report further includes at least one of the following: an indicator indicating whether a QoE information report is predicted; a radio condition indicating the environment in which the QoE prediction information will be applied; an analysis-generated timestamp indicating when the QoE prediction information was generated; a validity period indicating a time period during which the QoE assertion information is valid; or a confidence level indicating a probabilistic assertion in the QoE prediction.

[0013] In some embodiments of the present application, the wireless communication device is a RAN node, the node is a UE, the first information is transmitted via a radio resource control (RRC) message, and the second information is received via another RRC message.

[0014] According to some embodiments of the present application, the RAN node is configured to generate QoE prediction information further based on the second information.

[0015] According to some other embodiments of the present application, the RAN node is configured to receive a QoE prediction capability report from the UE. For example, the RAN node is configured to transmit a QoE prediction capability request to the UE, and receive the QoE prediction capability report in response to the QoE prediction capability request. In another example, after receiving a QoE prediction capability report indicating that the UE is capable of predicting QoE, the QoE prediction configuration information is transmitted.

[0016] According to still other embodiments of the present application, the first information is received from another RAN node, a CN node, or an Operation Administration and Maintenance (OAM) system.

[0017] According to some other embodiments of the present application, the second information is transmitted to another RAN node, a CN node or an OAM system. For example, the wireless communication device is further configured to receive a QoE measurement report from the other RAN node, the CN node or the OAM system in response to the second information.

[0018] In some embodiments of the present application, the wireless communication device is a RAN node, a CN node or an OAM system, and the node is another RAN node.

[0019] According to some embodiments of the present application, the wireless communication device is further configured to transmit a QoE measurement report to the other RAN node in response to the second information.

[0020] Some other embodiments of the present application provide a method for supporting QoE prediction, for example, a method for supporting performing QoE prediction in a RAN node or a CN node or an OAM system, comprising: transmitting first information indicating at least one or more QoE metrics to a node, wherein the first information is a QoE history information request or QoE prediction configuration information; and receiving second information indicating at least the one or more QoE metrics and values ​​of the one or more QoE metrics, wherein the second information is a QoE history information report in response to the QoE history information request or a QoE prediction information report in response to the QoE prediction configuration information.

[0021] Still other embodiments of the present application provide a UE comprising: a transceiver; and a processor coupled to the transceiver, wherein the processor is configured to: receive first information indicating at least one or more QoE metrics from a RAN node, wherein the first information is a QoE history information request or QoE prediction configuration information; and transmit second information indicating at least the one or more QoE metrics and values ​​of the one or more QoE metrics, wherein the second information is a QoE history information report in response to the QoE history information request or a QoE prediction information report in response to the QoE prediction configuration information.

[0022] In some embodiments of the present application, the first information is received via an RRC message, and the second information is transmitted via another RRC message.

[0023] In some embodiments of the present application, the processor is configured to transmit a QoE prediction capability report to the RAN node. In an example, the QoE prediction capability report is transmitted in response to receiving a QoE prediction capability request from the RAN node. In another example, the QoE prediction configuration information is received after transmitting the QoE prediction capability report indicating that the UE is capable of predicting QoE.

[0024] In some embodiments of the present application, the processor is configured to generate QoE prediction information based on the QoE prediction configuration information.

[0025] Still other embodiments of the present application provide a method for supporting QoE prediction, for example, a method for supporting performing QoE prediction in a remote side, which includes: receiving first information indicating at least one or more QoE metrics to a node, wherein the first information is a QoE history information request or QoE prediction configuration information; and transmitting second information indicating at least the one or more QoE metrics and values ​​of the one or more QoE metrics, wherein the second information is a QoE history information report in response to the QoE history information request or a QoE prediction information report in response to the QoE prediction configuration information.

[0026] In view of the above, an embodiment of the present application proposes a technical solution to support QoE prediction, wherein the QoE prediction can be based on an AI model in the network side or the remote side, which will optimize the user's QoE and promote the implementation of AI-based RAN. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] To describe the manner in which the advantages and features of the present application can be obtained, the description of the present application is presented by reference to specific embodiments of the application shown in the accompanying drawings. These drawings depict only exemplary embodiments of the present application and are therefore not intended to limit the scope of the present application.

[0028] Figure 1 is a schematic diagram illustrating an exemplary wireless communication system according to some embodiments of the present application.

[0029] Figure 2 A schematic diagram of a wireless communication system 200 according to some other embodiments of the present application is shown.

[0030] Figure 3 is a schematic diagram showing the internal structure of a RAN node (eg, a base station (BS)) according to some embodiments of the present application.

[0031] Figure 4 An exemplary process of a method for supporting QoE prediction according to some embodiments of the present application is shown.

[0032] Figure 5 Another exemplary process of a method for supporting QoE prediction according to some other embodiments of the present application is shown.

[0033] Figure 6 A block diagram showing a device supporting QoE prediction according to some embodiments of the present application is shown.

[0034] Figure 7 A block diagram showing a device supporting QoE prediction according to some other embodiments of the present application. DETAILED DESCRIPTION

[0035] The detailed description of the accompanying drawings is intended as a description of the currently preferred embodiments of the present application, and is not intended to represent the only form in which the present application can be practiced. It should be understood that the same or equivalent functions can be implemented by different embodiments intended to be included in the spirit and scope of the present application.

[0036] Reference will now be made in detail to some embodiments of the present application, examples of which are shown in the accompanying drawings. For ease of understanding, embodiments are provided in specific network architectures and new service scenarios, such as 3GPP 5G, 3GPP Long Term Evolution (LTE), etc. It is considered that all embodiments in the present application are also applicable to similar technical problems as network architectures and new service scenarios develop. In addition, the terms stated in the present application may be changed, which should not affect the principles of the present application.

[0037] Figure 1 A schematic diagram of an exemplary wireless communication system 100 is shown according to some embodiments of the present application.

[0038] like Figure 1 , the wireless communication system 100 includes at least one BS 101 and at least one UE 102. Specifically, for illustrative purposes, the wireless communication system 100 includes one BS 101 and two UEs 102 (e.g., a first UE 102a and a second UE 102b). Figure 1 A specific number of BSs and UEs are shown in FIG. 1 , but it is considered that in some other embodiments of the present application, the wireless communication system 100 may include more or fewer BSs and UEs.

[0039] The wireless communication system 100 is compatible with any type of network capable of sending and receiving wireless communication signals. For example, the wireless communication system 100 is compatible with wireless communication networks, cellular telephone networks, networks based on time division multiple access (TDMA), networks based on code division multiple access (CDMA), networks based on orthogonal frequency division multiple access (OFDMA), LTE networks, networks based on 3GPP, 3GPP 5G networks, satellite communication networks, high altitude platform networks, and / or other communication networks.

[0040] BS101 may communicate with a CN node (not shown) (e.g., a mobility management entity (MME) or a serving gateway (S-GW), a mobility management function (AMF), or a user plane function (UPF), etc.) via an interface. A BS may also be referred to as an access point, an access terminal, a base station, a macro cell, a Node B, an enhanced Node B (eNB), a gNB, a home Node B, a relay node or a device, or described using other terms used in the art. In 5G NR, a BS may also refer to a RAN node or a network device. Each BS may serve several UEs within a service area (e.g., a cell or a cell sector) via a wireless communication link. Neighboring BSs may communicate with each other as needed, for example, during a handover process of a UE.

[0041] UE 102 (e.g., first UE 102a and second UE 102b) should be understood as any type of terminal device, which may include computing devices, such as desktop computers, laptop computers, personal digital assistants (PDAs), tablet computers, smart TVs (e.g., TVs connected to the Internet), set-top boxes, game consoles, security systems (including security cameras), vehicle-mounted computers, network devices (e.g., routers, switches, and modems), etc. According to embodiments of the present application, UE may include portable wireless communication devices, smart phones, cellular phones, flip phones, devices with subscriber identity modules, personal computers, selective call receivers, or any other device capable of sending and receiving communication signals on a wireless network. In some embodiments, UE may include wearable devices, such as smart watches, fitness bands, optical head-mounted displays, etc. In addition, UE may be referred to as a subscriber unit, a mobile device, a mobile station, a user, a terminal, a mobile terminal, a wireless terminal, a fixed terminal, a subscriber station, a user terminal, or a device, or described using other terms used in the art.

[0042] In the NR-DC scenario, a UE with multiple transceivers may be configured to utilize resources provided by two different nodes connected via a non-ideal backhaul. One of the nodes may provide NR access, and the other node may provide Evolved Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA) (E-UTRA) or NR access. One node may act as a master node (MN), and the other node may act as a secondary node (SN). The MN and the SN are connected via a network interface (e.g., an Xn interface as specified in a 3GPP standard document), and at least the MN is connected to the CN.

[0043] For example, Figure 2 A schematic diagram of a wireless communication system 200 according to some other embodiments of the present application is shown.

[0044] like Figure 2As shown in FIG. 2 , the wireless communication system 200 may be a dual-connectivity system 200 including at least one UE 201, at least one MN 202, and at least one SN 203. Specifically, for illustrative purposes, Figure 2 The dual-connectivity system 200 in FIG. 1 includes a UE 201, a MN 202, and a SN 203. Figure 2 A specific number of UE 201, MN 202, and SN 203 are depicted in FIG. 2 , but it is contemplated that any number of UE 201, MN 202, and SN 203 may be included in the wireless communication system 200.

[0045] refer to Figure 2 UE 201 may be connected to MN 202 and SN 203 via an interface (e.g., a Uu interface as specified in a 3GPP standard document). MN 202 and SN 203 may be connected to each other via a network interface (e.g., an Xn interface as specified in a 3GPP standard document). MN 202 may be connected to SN 203 via a network interface (e.g., an Xn interface as specified in a 3GPP standard document). Figure 2 UE 201 may be configured to utilize resources provided by MN 202 and SN 203 to perform data transmission. Figure 1 In the illustrated embodiment, UE 201 may be any of a variety of remote devices.

[0046] MN 202 refers to a RAN node that provides a control plane connection to a core network. In an embodiment of the present application, in an E-UTRA-NR DC (EN-DC) scenario, MN 202 may be an eNB. In another embodiment of the present application, in a next generation E-UTRA-NR DC (NGEN-DC) scenario, MN 202 may be a next generation (ng)-eNB. In yet another embodiment of the present application, in an NR-DC scenario or an NR-E-UTRA DC (NE-DC) scenario, MN 202 may be a gNB. In some embodiments of the present application, MN 202 may also be referred to as a master NG-RAN (M-NG-RAN) node.

[0047] SN 203 may refer to a RAN node that does not have a control plane connection to the core network but provides additional resources to UE 201. In some embodiments of the present application, in an EN-DC scenario, SN 203 may be an en-gNB. In some other embodiments of the present application, in an NR-DC scenario, SN 203 may be an ng-eNB. In another embodiment of the present application, in an NR-DC scenario or an NGEN-DC scenario, SN 203 may be a gNB. In some embodiments of the present application, SN 203 may also be referred to as a secondary NG-RAN (S-NG-RAN) node.

[0048] A RAN node (eg, BS) may be separated into multiple parts, each of which acts as a RAN node in some scenarios. Figure 3 is a schematic diagram showing the internal structure of a RAN node (eg, BS) according to some embodiments of the present application.

[0049] refer to Figure 3 ,In a split RAN architecture, RAN nodes (e.g. Figure 1 BS101 or Figure 2 The internal structure of the MN 202 or SN 203 in FIG. 200 may be separated into a central unit (CU) 300 and at least one distributed unit (DU) 302 (eg, Figure 3 ). Although Figure 3 A specific number of DUs 302 are depicted in FIG. 3 , but it is contemplated that any number of DUs 302 may be included in a RAN node.

[0050] CU 300 and DU 302 are connected to each other through an interface (referred to as F1) as specified in the 3GPP standard document. RRC layer functions, service data adaptation protocol (SDAP) functions, and packet data convergence protocol (PDCP) layer functions are located in CU 300. Radio link control (RLC) layer functions, medium access control (MAC) layer functions, and physical (PHY) layer functions are located in DU 302.

[0051] According to some embodiments of the present application, the CU 300 may be further separated into a central unit control plane (CU CP or CU-CP) unit and at least one central unit user plane (CU UP or CU-UP) unit. The CU CP unit and each CU UP unit may be connected to each other via an interface (referred to as E1) as specified in the 3GPP standard document. The CU CP unit and the DU are connected via an interface (referred to as F1-C) as specified in the 3GPP document. Each CU UP unit and the DU are connected via an interface (referred to as F1-U) as specified in the 3GPP standard document.

[0052] When AI is introduced into 3GPP, AI-based resource optimization becomes possible. For example, in Rel-18, 3GPP will specify mechanisms to support AI-assisted RAN optimization use cases, such as AI for QoE and slicing, where several issues need to be addressed.

[0053] An exemplary problem to be addressed is: if an AI model (or AI algorithm) for QoE prediction is deployed in the network side (e.g., in a RAN node), what are the required inputs for the AI ​​model and how can the inputs be obtained.

[0054] Another exemplary problem to be addressed is: if the AI ​​model (or AI algorithm) for QoE prediction is deployed in the remote side, how the RAN node obtains the QoE predicted by the UE, for example, how the predicted QoE can be triggered and reported to the RAN node.

[0055] Yet another exemplary problem to be addressed is how the predicted QoE can be communicated between relevant RAN nodes in mobility scenarios (eg handover, SN addition or change).

[0056] Yet another exemplary problem to be addressed is how the predicted QoE can be communicated between the CU and the DU given the split RAN architecture.

[0057] At least in view of the above problems, an embodiment of the present application provides a technical solution for supporting QoE prediction based on an AI model on the network side or the UE side.

[0058] For example, according to some embodiments of the present application, an exemplary method for supporting QoE prediction may include transmitting first information indicating at least one or more QoE metrics to a node (e.g., another RAN node or UE), which may be performed by a RAN node (e.g., a serving gNB in ​​a non-mobility scenario, or an SN in an SN addition or change process, or a target gNB in ​​a handover process) or by a CN node or by an OAM system. The first information is a QoE history information request (or a QoE history information configuration or a historical QoE information request or a historical QoE information configuration, etc.) or QoE prediction configuration information (or QoE configuration information for prediction or predicted QoE configuration information or a QoE prediction information request, etc.). The exemplary method for supporting QoE prediction may further include receiving second information indicating at least one or more QoE metrics and the value of one or more QoE metrics. The second information is a QoE history information report in response to the QoE history information request or a QoE prediction information report in response to the QoE prediction configuration information.

[0059] According to some embodiments of the present application, an exemplary method for supporting QoE prediction that can be performed by a UE may include: receiving first information indicating at least one or more QoE metrics from a RAN node (or a CU or CU-CP in a split RAN architecture) (e.g., a serving gNB, or an SN in an SN addition or change process, or a target gNB in ​​a handover process, etc.). The first information is a QoE history information request or QoE prediction configuration information. The exemplary method for supporting QoE prediction may further include transmitting second information indicating at least one or more QoE metrics and values ​​of the one or more QoE metrics, wherein the second information is a QoE history information report in response to a QoE history information request or a QoE prediction information report in response to QoE prediction configuration information.

[0060] Hereinafter, more specific embodiments of the present application will be illustrated in view of various scenarios.

[0061] Figure 4 is a flowchart illustrating an exemplary process of a method for supporting QoE prediction according to some embodiments of the present application. Although the method is illustrated at the system level by a RAN node in the network side and a UE in the remote side, a person skilled in the art should understand that the method implemented in the RAN node and the UE may be implemented separately and / or incorporated by other devices with similar functions. The RAN node may be a gNB, or a CU or CU-CP of a gNB in ​​a separated RAN architecture, or other nodes in the RAN. Depending on different scenarios, the RAN node may be a serving gNB, a target gNB or an SN, etc.; or a CU or CU-CP of a serving gNB, a target gNB or an SN, etc. in a separated RAN architecture. In addition, in Figure 4 In the embodiment shown in , the AI ​​model (or AI algorithm) is deployed at least in the UE side.

[0062] refer to Figure 4 In step 401, the RAN node may send QoE prediction configuration information to the UE, for example, via an RRC message. The QoE prediction configuration information may be initiated by the RAN node itself, or by other network nodes (e.g., another RAN node (a peer RAN node or a DU in a split RAN architecture) or a CN node or an OAM system).

[0063] For example, in some embodiments of the present application, QoE prediction information reporting (or QoE prediction information collection) is activated in the RAN node, and it is initiated by the network node in step 400a. The network node will initiate QoE prediction information collection by sending QoE prediction configuration information to the RAN node, for example, through UE-associated signaling when the network node is another RAN node or a CN node, or through configuration when the network node is an OAM system.

[0064] In the case where the network node is a CN node (e.g., an access and mobility management function (AMF)), the UE associated signaling may be an initial context setup request message during an initial context setup procedure, or a UE context modification request message during a UE context modification procedure, or a handover required message during a handover preparation procedure, or a handover request message during a handover resource allocation procedure.

[0065] In the case where the network node is a peer RAN node, the UE-associated signaling may be a handover request message during a handover preparation procedure (e.g., the RAN node is a target RAN node and the peer RAN node is a source RAN node), or a retrieve UE context response message during a retrieve UE context procedure (e.g., the RAN node is a new RAN node or a receiving RAN node and the peer RAN node is an old RAN node or a last serving RAN node), or an SN add request message during an SN add preparation procedure (e.g., the RAN node is an SN and the peer RAN node is an MN), or an SN modify request message during an SN modification preparation procedure initiated by an MN (e.g., the RAN node is an SN and the peer RAN node is an MN).

[0066] In some other embodiments of the present application, before the RAN node sends the QoE prediction configuration information to the UE, the RAN node may determine whether the UE supports QoE prediction, for example, based on a QoE prediction capability report from the UE. The RAN node transmits the QoE prediction configuration information only after receiving a QoE prediction capability report indicating that the UE is capable of predicting QoE. An exemplary QoE prediction capability report may include an indicator indicating whether the UE supports QoE prediction. For example, the indicator may be expressed by a Boolean (e.g., true or false); or an enumeration (e.g., supported); or by other methods.

[0067] The UE may report its QoE prediction capability on its own initiative or in response to a QoE prediction capability request from a RAN node, for example, through a UE capability information message. For example, in step 400b, when the RAN node requires the QoE prediction capability information of the UE, the RAN node may send a QoE prediction capability request to the UE, for example, through a UE capability query message. After receiving the QoE prediction capability request from the RAN node, the UE will compile a QoE prediction capability report and send the QoE prediction capability report to the RAN node, for example, through a UE capability information message, in step 400c.

[0068] The QoE prediction configuration information at least indicates one or more QoE metrics (or QoE parameters). Each QoE metric and its value may be expressed in a variety of ways.

[0069] An exemplary QoE metric is a service quality level, which is measured by performing a subjective test between two nodes (e.g., between an application server and a UE). In some embodiments of the present application, the service quality level is MOS, which provides a numerical indication of the perceived quality from the user's perspective of the received service. For example, the value of MOS is expressed as a single number in the range of 1 to 5, where 1 is the lowest perceived quality and 5 is the highest perceived quality, or vice versa. In addition to MOS, the service quality level may also be measured by other suitable methods.

[0070] Another exemplary QoE metric is the application layer buffer level, which indicates the buffer level of the application layer. For example, an application layer buffer level value of 1 corresponds to 10ms, and an application layer buffer level value of 2 corresponds to 20ms, etc. The application layer buffer level may have a maximum value, for example, 30000ms.

[0071] Another exemplary QoE metric is initial playout delay, which indicates the initial playout delay of the application layer. Similar to the application layer buffer level, an initial playout delay value of 1 corresponds to 1ms, an initial playout delay value of 2 corresponds to 2ms, and so on. There may also be a maximum value for initial playout delay, for example, 30000ms.

[0072] Yet another exemplary QoE metric is the impairment duration, which indicates the time period from the time of the last undamaged frame (good frame) before the damage (e.g., frame damage) to the time of the first subsequent undamaged frame after the damage. Similarly, a value of 1 for the impairment duration corresponds to 1 ms, a value of 2 for the impairment duration corresponds to 2 ms, and so on. There may also be a maximum value for the impairment duration, for example, 30000 ms.

[0073] Yet another exemplary QoE metric is presentation viewports, which indicates the list of viewports that have been rendered during media presentation.

[0074] Yet another exemplary QoE metric is the play period, which indicates the time interval between a user action and the earliest occurrence of the next user action, the end of playback, or a failure to stop playback. Similarly, a play period value of 1 corresponds to 1 ms, a play period value of 2 corresponds to 2 ms, and so on. There may also be a maximum value for the play period, for example, 30,000 ms.

[0075] Yet another exemplary QoE metric is comparable quality viewport switch latency, which indicates latency and quality-related factors when viewport movement causes quality degradation, such as when low-quality background content is briefly shown before resuming normal, higher quality.

[0076] In some embodiments of the present application, the QoE prediction configuration information further indicates at least one other parameter in addition to the one or more QoE metrics.

[0077] For example, the QoE prediction configuration information further indicates an indicator indicating whether the QoE prediction information needs to be reported. For example, the QoE prediction configuration information is an RRC reconfiguration message, which includes an indicator indicating whether the predicted QoE information should be provided by the UE. The indicator can be expressed by an integer (e.g., 1); or a Boolean (e.g., true or false); or an enumeration (e.g., prediction); or by other methods.

[0078] In another example, the QoE prediction configuration information further indicates an analysis period, which indicates a future time interval for which the predicted QoE information is located. An exemplary time interval is expressed, for example, by a start time (e.g., an actual start time) and an end time (e.g., an actual end time) via Coordinated Universal Time (UTC) time. In some embodiments of the present application, the time interval may be a specific point in time, for example, by setting the start time and the end time to the same value.

[0079] In another example, the QoE prediction configuration information further indicates a trigger condition, which indicates a criterion for triggering the QoE prediction information report. An exemplary trigger condition is a threshold, such as a best QoE metric value threshold, a worst QoE metric value threshold, or an average QoE metric value threshold, which triggers the QoE prediction information report. Regarding the best QoE metric value threshold, it indicates the condition of the level to be reached by the QoE prediction information report. For example, if the best MOS value threshold is 3 and the predicted MOS value is lower than 3, the UE will not report the predicted QoE information. Regarding the worst QoE metric value threshold, it indicates the condition below the level for QoE prediction information reporting. For example, if the worst MOS value threshold is 2 and the predicted MOS value is lower than 2, the UE will report the predicted QoE information. Regarding the average QoE metric value threshold, it indicates the condition of the level to be reached by the QoE prediction information report of all predicted QoE information. For example, if the average MOS value threshold is 3 and the average value of the predicted MOS value is lower than 3, the UE will not report the QoE prediction information.

[0080] In yet another example, the QoE prediction configuration information further indicates a radio condition indicating an environment in which the QoE prediction information is to be applied. An exemplary radio condition may be a cell identifier (ID) (e.g., a physical cell identifier or a new radio cell global identifier) ​​or at least one of a reference signal received power (RSRP) or a reference signal received quality (RSRQ).

[0081] In yet another example, the QoE prediction configuration information further indicates a preferred accuracy level, which indicates a preferred accuracy level of the QoE prediction information. For example, the preferred accuracy level may be expressed as: low, medium, high, or highest.

[0082] In yet another example, the QoE prediction configuration information further indicates time information indicating the latest time at which the QoE prediction information is expected to be received.

[0083] After receiving the QoE prediction configuration information, the UE will apply the QoE prediction configuration information and perform QoE prediction, for example, by deploying the AI ​​model (or AI algorithm) in step 403. For example, for the QoE prediction configuration information, the UE will:

[0084] - If the access stratum (AS) layer has received but not yet sent a QoE prediction information report from an upper layer (e.g., higher than the AS layer); and if the QoE prediction information report has not been suspended, then setting the value of the QoE prediction information to the value received from the upper layer;

[0085] - Otherwise, the QoE prediction information report is submitted to a lower layer (eg, a layer lower than the AS layer) for transmission, and the QoE prediction process ends here.

[0086] In step 405, the UE will send a QoE prediction information report to the RAN node, for example, through an RRC message, which at least indicates one or more QoE metrics and the values ​​of the one or more QoE metrics. In the case where there is a trigger condition in the QoE prediction configuration information, the UE will report the predicted QoE information in response to satisfying the trigger condition. An example of such an RRC message is a MeasurementReportAppLayer message. In some embodiments of the present application, the QoE prediction information report may further include at least one other parameter and its value in addition to one or more QoE metrics and the values ​​of the one or more QoE metrics.

[0087] For example, the QoE prediction information report may further include an indicator indicating whether the QoE information report (or the reported QoE information) is predicted. The indicator may be expressed by an integer (e.g., 1), or Boolean (e.g., true or false), or enumeration (e.g., predicted), or any other method.

[0088] In another example, the QoE prediction information report may further include radio conditions indicating the environment in which the QoE prediction information is to be applied. An exemplary radio condition may be a cell ID (eg, a physical cell ID or a new radio cell global ID) or at least one of RSRP or RSRQ.

[0089] In yet another example, the QoE prediction information report may further include an analytically generated timestamp indicating when the QoE prediction information was generated. The analytically generated timestamp allows the RAN node to decide when to use the received QoE prediction information.

[0090] In yet another example, the QoE prediction information report may further include at least one of a validity period indicating a time period for which the QoE assertion information is valid or a confidence level indicating a probability assertion in the QoE prediction.

[0091] After receiving the QoE prediction information report, the RAN node may perform various operations. For example, the RAN node may perform resource optimization (e.g., scheduling, resource allocation) based on the QoE prediction information report in step 407. In another example, the RAN node will generate another QoE prediction information report for the UE based on the QoE prediction information report, for example, using the QoE prediction information report from the UE as input to an AI model (or AI algorithm) deployed in the RAN node in step 407. The format of the QoE prediction information report generated by the RAN node is the same or similar to that received from the UE, and therefore is not shown in detail.

[0092] In some embodiments of the present application, regardless of whether a QoE prediction information request is received from the network node, the RAN node may send a QoE prediction information report received from the UE or generated by itself to the network node in step 409. The network node may use the reported QoE prediction information for resource optimization (e.g., scheduling, resource allocation). Similar to the above, the network node may be another RAN node (a peer node or DU in a split RAN architecture), a CN node, or an OAM system.

[0093] In the case where the network node is a peer RAN node, the QoE prediction information report may be sent from the RAN node to the peer RAN node via an Xn or X2 interface. For example, the QoE prediction information report is included in a handover request message during a handover preparation procedure, or in a retrieve UE context response message during a retrieve UE context procedure, or in an SN add request message during an SN add preparation procedure, or in an SN modify request message during an SN modify preparation procedure initiated by the MN.

[0094] In the case where the RAN node is a CU or CU-CP and the network node is a DU, the QoE prediction information report may be sent from the CU or CU-CP to the DU via the F1 interface. For example, during the QoE information transfer process, the QoE prediction information report is included in a QoE information transfer message.

[0095] In the case where the network node is a CN node (eg, AMF), the QoE prediction information report may be sent from the RAN node to the CN node via a next generation (NG) interface. For example, the QoE prediction information report is included in a handover requirement message during a handover preparation procedure.

[0096] When the network node is an OAM system, a QoE prediction information report may be sent from the RAN node to the OAM system.

[0097] In some scenarios, the network node may send a QoE measurement report of the UE to the RAN node in response to receiving the QoE prediction information report, and the RAN node will receive the QoE measurement report in step 411. The QoE measurement report contains at least a QoE metric and a value of the QoE metric measured by the UE. The RAN node may use the QoE measurement report to evaluate the accuracy of the AI ​​model (or AI algorithm) used for QoE prediction, and / or trigger further training of the AI ​​model or AI algorithm when necessary.

[0098] Similarly, in the case where the network node is a peer RAN node, the QoE measurement report may be sent from the peer RAN node to the RAN node via an Xn or X2 interface. In the case where the RAN node is a CU or CU-CP and the network node is a DU, the QoE measurement report may be sent from the DU to the CU or CU-CP via an F1 interface. In the case where the network node is a CN node (e.g., AMF), the QoE measurement report may be sent from the CN node to the RAN node via an NG interface. In the case where the network node is an OAM system, the QoE measurement report may be provided by the OAM system, for example, via configuration.

[0099] According to some other embodiments of the present application, the QoE prediction capability of the UE is not required, but the QoE historical information from the UE is required. For example, Figure 5 is a flowchart showing another exemplary process of a method for supporting QoE prediction according to some other embodiments of the present application. Figure 4 , although the method is illustrated at the system level by a RAN node in the network side and a UE in the remote side, it should be understood by those skilled in the art that the methods implemented in the RAN node and the UE may be implemented separately and / or incorporated by other devices with similar functions. The RAN node may be a gNB, or a CU or CU-CP of a gNB in ​​a split RAN architecture, or other nodes in the RAN. Depending on different scenarios, the RAN node may be a serving gNB, a target gNB, or a SN, etc.; or a CU or CU-CP of a serving gNB, a target gNB, or a SN, etc. in a split RAN architecture. In addition, Figure 5 In the embodiments shown in , the AI ​​model (or AI algorithm) is deployed at least in the network side (e.g., a RAN node or other network node).

[0100] refer to Figure 5 In step 501, the RAN node may send a QoE history information request to the UE, for example, via an RRC message. The QoE history information request may be initiated by the RAN node itself, or by other network nodes (e.g., another RAN node (a peer RAN node or a DU in a split RAN architecture) or a CN node or an OAM system).

[0101] For example, in some embodiments of the present application, the QoE history information request is activated in the RAN node, and it is initiated by the network node in step 500. The network node will send the QoE history information request to the RAN node, for example, through UE-associated signaling when the network node is another RAN node or a CN node, or through configuration when the network node is an OAM system. In some other embodiments of the present application, the RAN node may initiate the QoE history information request in response to the QoE prediction configuration information from the network node. The QoE history information request or the QoE prediction configuration information is transmitted between the RAN node and the network node through messages and interfaces similar to those shown above, and therefore will not be shown in further detail.

[0102] The QoE history information request at least indicates one or more QoE metrics (or QoE parameters), which will not be repeated herein. In some embodiments of the present application, the QoE history information request may further indicate at least one other parameter in addition to the one or more QoE metrics.

[0103] For example, the QoE history information request further indicates an indicator indicating whether the QoE history information needs to be reported. For example, the QoE history information request is a UE information request message, which includes an indicator indicating whether the UE should report the QoE history information. The indicator can be expressed by an integer (e.g., 1); or a Boolean (e.g., true or false); or an enumeration (e.g., history); or by other methods.

[0104] In another example, the QoE history information request further indicates a service type, which indicates the type of QoE history information to be collected. For example, a value of "streaming" indicates QoE history information collection for streaming services, a value of "mtsi" indicates QoE history information collection for multimedia telephony services of IP multimedia subsystem (MTSI), and a value of "vr" indicates QoE history information collection for VR services.

[0105] In another example, the QoE history information request further indicates an area scope, which indicates at least one object for which QoE history information is required. The area scope may be a cell ID (eg, a physical cell ID or a new radio cell global ID), or a tracking area identity (TAI).

[0106] In another example, the QoE history information request further indicates a trigger condition, which indicates a criterion for triggering the QoE history information report. An exemplary trigger condition is a threshold, such as a best QoE metric value threshold, a worst QoE metric value threshold, or an average QoE metric value threshold, which triggers the QoE history information report. Regarding the best QoE metric value threshold, it indicates the condition of the level to be reached by the QoE history information report. For example, if the best MOS value threshold is 3 and the best MOS value is lower than 3, the UE will not report the QoE history information. Regarding the worst QoE metric value threshold, it indicates the condition below the level for the QoE history information report. For example, if the worst MOS value threshold is 2 and the worst MOS value is lower than 2, the UE will report the QoE history information. Regarding the average QoE metric value threshold, it indicates the condition of the level to be reached by the QoE history information report of all recorded QoE measurements. For example, if the average MOS value threshold is 3 and the average value of the MOS value is lower than 3, the UE will not report the QoE history information.

[0107] In yet another example, the QoE history information request further indicates time information indicating the latest time at which the QoE history information is expected to be received.

[0108] After receiving the QoE history information request, the UE will collect QoE history information in step 503 and store relevant information (assuming that the UE supports such information storage). In some embodiments of the present application, the QoE history information may be stored as one or more entries (e.g., a QoE measurement result list) of QoE measurement results that the UE has measured in a previous period. For example, the stored QoE history information may include up to 16 (or other number) entries of the most recently collected QoE measurement results, for example, in chronological order. The most recently collected QoE measurement information is first stored in the list (or the first entry).

[0109] Each entry of the QoE history information includes at least a QoE metric and its measured value within a configured effective area of ​​a service type. In some embodiments of the present application, each entry further includes at least one other parameter in addition to one or more QoE metrics and the values ​​of the one or more QoE metrics.

[0110] For example, each entry may further include a start time and an end time of the QoE measurement indicated by the application layer. An exemplary time interval is expressed by the start time and the end time, for example, via the UTC time during which the QoE measurement is performed by the application layer. In some embodiments of the present application, the time interval may be a specific point in time, for example, by setting the start time and the end time to the same value.

[0111] In another example, each entry may further include a list of visited cells for the UE, and optionally may also include the duration that the UE stayed in each visited cell during the performance of the QoE measurement.

[0112] In yet another example, each entry may further include radio conditions at the timing of receiving values ​​of one or more QoE metrics from an upper layer (e.g., a layer higher than the application layer). The radio conditions indicate the environment in which the QoE metric values ​​are collected or measured, for example, by RSRP or RSRQ.

[0113] In yet another example, each entry may further include a timestamp of when the value of the one or more QoE metrics was received from an upper layer. The timestamp indicates when the QoE metric value was generated.

[0114] In view of the different parameters and values ​​to be collected and stored, in an exemplary QoE history information storage process, the UE will store the entries of the QoE history information (possibly after removing the oldest entry if necessary) and set the QoE metric value in the entry as follows, where the UE will:

[0115] – If service type is available, then set service type to indicate the service type for the QoE metric;

[0116] – if the QoE measurement interval is available, then set the QoE measurement start time and end time during which the QoE measurement is performed; and

[0117] If a visited cell list is available, then an entry is included in the visited cell list (possibly after removing the oldest entry if necessary) according to the following:

[0118] If a new air interface cell global identifier of the cell is available, then including the new air interface cell global identifier of the cell;

[0119] otherwise, a physical cell identifier of the cell;

[0120] comprising the duration of stay in the cell, during which QoE measurements are performed;

[0121] – if radio conditions are available, setting the radio conditions to the environment at the timing of receiving the QoE metric value from the upper layer;

[0122] – If a timestamp is available, then set the timestamp to indicate the time when the QoE metric value was received;

[0123] – Set the QoE metric value to the value received from the upper layer.

[0124] In step 505, the UE will send a QoE history information report to the RAN node based on the collected QoE history information, for example, through an RRC message. In the event that a trigger condition is present in the QoE history information request, the UE will report the QoE history information in response to the trigger condition being met. An example of such an RRC message is a UE Information Response message. The QoE history information report may be the same or similar to the stored QoE history information as shown above, for example, including several entries of QoE history information, and therefore will not be repeated herein.

[0125] After receiving the QoE history information report, the RAN node may perform various operations in step 507. For example, the RAN node will generate a QoE prediction information report of the UE based on the QoE history information report, for example, using the QoE history information from the UE as an input to an AI model (or AI algorithm) deployed in the RAN node in step 507.

[0126] In some embodiments of the present application, regardless of whether a QoE prediction information request is received from the network node, the RAN node may send a QoE prediction information report generated by itself to the network node in step 509 as shown above. Similarly, the network node may send a QoE measurement report of the UE to the RAN node in response to receiving the QoE prediction information report, and the RAN node will receive the QoE measurement report in step 511.

[0127] In some other embodiments of the present application, regardless of whether a QoE history information request is received from the network node, the RAN node may send the received QoE history information report to the network node in step 513, which is similar to the transmission of the QoE prediction information report and will not be shown in further detail. In some embodiments of the present application, the network node may use the received QoE history information report to perform QoE prediction on the UE by itself.

[0128] In addition to the method for supporting QoE prediction, some embodiments of the present application also provide a device for supporting QoE prediction. For example, Figure 6 A block diagram of a device 600 supporting QoE prediction according to some embodiments of the present application is shown.

[0129] like Figure 6As shown in , the apparatus 600 may include at least one non-transitory computer-readable medium 601, at least one receiving circuit system 602, at least one transmitting circuit system 604, and at least one processor 606, which is coupled to the non-transitory computer-readable medium 601, the receiving circuit system 602, and the transmitting circuit system 604. The at least one processor 606 may be a CPU, a DSP, a microprocessor, etc. The apparatus 600 may be a network node, such as a RAN node, a CN node, or an OAM system, or a UE configured to perform the methods shown above, etc.

[0130] Although in this figure, elements such as at least one processor 606, transmit circuit system 604, and receive circuit system 602 are described in the singular, the plural form is contemplated unless limitation to the singular form is explicitly stated. In some embodiments of the present application, receive circuit system 602 and transmit circuit system 604 may be combined into a single device, such as a transceiver. In certain embodiments of the present application, apparatus 600 may further include an input device, a memory, and / or other components.

[0131] In some embodiments of the present application, the non-transitory computer-readable medium 601 may store thereon computer-executable instructions to enable the processor to implement the method described above with respect to the remote device (e.g., UE). For example, when the computer-executable instructions are executed, the processor 606 interacts with the receiving circuit system 602 and the transmitting circuit system 604 to perform the method described above (e.g., Figure 4 and 5 ) for the remote device.

[0132] In some embodiments of the present application, the non-transitory computer-readable medium 601 may store thereon computer-executable instructions to enable the processor to implement the method described above with respect to the RAN node, CN node, or OAM system. For example, when the computer-executable instructions are executed, the processor 606 interacts with the receiving circuit system 602 and the transmitting circuit system 604 to perform the method described above (e.g., Figure 4 and 5 Steps related to a wireless communication device or network node) as shown in FIG.

[0133] Figure 7 is a block diagram of a device 700 supporting QoE prediction according to some other embodiments of the present application.

[0134] refer to Figure 7, the device 700 (e.g., a UE or a network node, such as a RAN node, a CN node, or an OAM system) may include at least one processor 702 and at least one transceiver 704 coupled to the at least one processor 702. The transceiver 704 may include at least one separate receiving circuit system 706 and transmitting circuit system 708, or at least one integrated receiving circuit system 706 and transmitting circuit system 708. The at least one processor 702 may be a CPU, a DSP, a microprocessor, etc.

[0135] According to some embodiments of the present application, when device 700 is a remote device (e.g., UE), the UE is configured to: receive first information indicating at least one or more QoE metrics from a RAN node, wherein the first information is a QoE history information request or QoE prediction configuration information; and transmit second information indicating at least one or more QoE metrics and values ​​of the one or more QoE metrics, wherein the second information is a QoE history information report in response to the QoE history information request or a QoE prediction information report in response to the QoE prediction configuration information.

[0136] According to some other embodiments of the present application, when the device 700 is a network node (for example, a RAN node or a CN or an OAM system), the device is configured to: transmit first information indicating at least one or more QoE metrics to the node, wherein the first information is a QoE history information request or QoE prediction configuration information; and receive second information indicating at least one or more QoE metrics and values ​​of the one or more QoE metrics, wherein the second information is a QoE history information report in response to the QoE history information request or a QoE prediction information report in response to the QoE prediction configuration information.

[0137] The method according to the embodiment of the present application can also be implemented on a programmed processor. However, the controller, flow chart and module can also be implemented on a general or special-purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit elements, an integrated circuit, a hardware electronic or logic circuit (such as a discrete element circuit), a programmable logic device, etc. In general, any device capable of implementing the flow chart shown in the figure can be used to implement the processor function of the present application. For example, an embodiment of the present application provides an apparatus comprising a processor and a memory. Computer programmable instructions for implementing the method are stored in a memory, and the processor is configured to execute the computer programmable instructions to implement the method. The method may be a method as stated above or other methods according to an embodiment of the present application.

[0138] Alternative embodiments preferably implement the method according to the embodiments of the present application in a non-transitory computer-readable storage medium storing computer programmable instructions. The instructions are preferably executed by a computer executable component that is preferably integrated with a network security system. The non-transitory computer-readable storage medium may be stored on any suitable computer-readable medium, such as RAM, ROM, flash memory, EEPROM, optical storage device (CD or DVD), hard drive, floppy disk drive, or any suitable device. The computer executable component is preferably a processor, but the instructions may be executed alternatively or additionally by any suitable dedicated hardware device. For example, an embodiment of the present application provides a non-transitory computer-readable storage medium having computer programmable instructions stored therein. The computer programmable instructions are configured to implement the method as stated above or other methods according to the embodiments of the present application.

[0139] In addition, in the present disclosure, the term "includes / including" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment comprising a series of elements not only comprises the elements, but also may comprise other elements that are not explicitly listed or inherent to such processes, methods, articles or equipment. In the absence of more constraints, an element beginning with "a / an" or the like does not exclude the presence of additional identical elements in the process, method, article or equipment comprising the elements. In addition, the term "another" is defined as at least a second or more. The term "having" and the like as used herein are defined as "comprising".

Claims

1. A wireless communication device, comprising: Transceiver; and a processor coupled to the transceiver, wherein the processor is configured to: Transmitting first information indicating at least one or more quality of experience (QoE) metrics to a node, wherein the first information is a QoE history information request or QoE prediction configuration information; and Second information indicating at least the one or more QoE metrics and values ​​of the one or more QoE metrics is received, wherein the second information is a QoE history information report in response to the QoE history information request or a QoE prediction information report in response to the QoE prediction configuration information.

2. The wireless communication device according to claim 1, wherein: The QoE history information request further indicates at least one of the following parameters: an indicator indicating whether the QoE history information needs to be reported; A service type, which indicates the type of the QoE history information to be collected; an area scope indicating at least one object for which the QoE history information is required; A trigger condition indicating a criterion for triggering a QoE history information report; or Time information indicating the latest time when the QoE history information is expected to be received.

3. The wireless communication device according to claim 1, wherein: The QoE history information includes one or more entries of QoE measurement results, and each entry includes at least one of the following parameters in addition to the one or more QoE metrics and the values ​​of the one or more QoE metrics within the configured validity area of ​​the service type: The start and end time of the QoE measurement indicated by the application layer; a list of visited cells of the user equipment UE and the duration that the UE stays in each visited cell during performing said QoE measurement; radio conditions at the timing of receiving said values ​​of said one or more QoE metrics from an upper layer; or A timestamp of the value of the one or more QoE metrics is received from an upper layer.

4. The wireless communication device according to claim 1, wherein: The QoE prediction configuration information further indicates at least one of the following parameters: An indicator indicating whether QoE prediction information needs to be reported; time information indicating the latest time at which the QoE prediction information is expected to be received; Analysis period, which indicates the future time interval for which QoE prediction information is located; A trigger condition indicating a criterion for triggering a QoE prediction information report; Radio conditions, which indicate the environment in which the QoE prediction information is to be applied; or Preferred accuracy level, which indicates the preferred accuracy level of the QoE prediction information.

5. The wireless communication device according to claim 1, wherein: The QoE prediction information report further includes at least one of the following: An indicator indicating whether to predict QoE information reporting; Radio conditions, which indicate the environment in which the QoE prediction information will be applied; analyzing a generated timestamp indicating when the QoE prediction information was generated; A validity period indicating a time period during which the QoE assertion information is valid; or Confidence level, which indicates the probability assertion in the QoE prediction.

6. The wireless communication device according to claim 1, wherein: The wireless communication apparatus is a Radio Access Network (RAN) node, and the RAN node is configured to generate QoE prediction information further based on the second information.

7. The wireless communication device according to claim 1, wherein: The wireless communication apparatus is a Radio Access Network (RAN) node, the node is a User Equipment (UE), and the RAN node is configured to receive a QoE prediction capability report from the UE.

8. The wireless communication device according to claim 1, wherein: The wireless communication device is a Radio Access Network RAN ​​node, and the first information is received from another RAN node, a Core Network CN node, or an Operation Administration and Maintenance OAM system.

9. A user equipment UE, comprising: Transceiver; and a processor coupled to the transceiver, wherein the processor is configured to: receiving first information at least indicating one or more quality of experience (QoE) metrics from a radio access network (RAN) node, wherein the first information is a QoE history information request or QoE prediction configuration information; and Second information indicating at least the one or more QoE metrics and values ​​of the one or more QoE metrics is transmitted, wherein the second information is a QoE history information report in response to the QoE history information request or a QoE prediction information report in response to the QoE prediction configuration information.

10. The UE according to claim 9, wherein: The QoE history information request further indicates at least one of the following parameters: an indicator indicating whether the QoE history information needs to be reported; A service type, which indicates the type of the QoE history information to be collected; an area scope indicating at least one object for which the QoE history information is required; A trigger condition indicating a criterion for triggering a QoE history information report; or Time information indicating the latest time when the QoE history information is expected to be received.

11. The UE according to claim 9, wherein: The QoE history information includes one or more entries of QoE measurement results, and each entry includes at least one of the following parameters in addition to the one or more QoE metrics and the values ​​of the one or more QoE metrics within the configured validity area of ​​the service type: The start and end time of the QoE measurement indicated by the application layer; a list of visited cells of the UE and the duration that the UE stayed in each visited cell during the performance of the QoE measurement; radio conditions at the timing of receiving said values ​​of said one or more QoE metrics from an upper layer; or A timestamp of the value of the one or more QoE metrics is received from an upper layer.

12. The UE according to claim 9, wherein: The QoE prediction configuration information further indicates at least one of the following parameters: An indicator indicating whether QoE prediction information needs to be reported; time information indicating the latest time at which the QoE prediction information is expected to be received; An analysis period, which indicates a future time interval in which the QoE prediction information is located; A trigger condition indicating a criterion for triggering a QoE prediction information report; Radio conditions, which indicate the environment in which the QoE prediction information is to be applied; or Preferred accuracy level, which indicates the preferred accuracy level of the QoE prediction information.

13. The UE according to claim 9, wherein: The QoE prediction information report further includes at least one of the following: An indicator indicating whether to predict QoE information reporting; Radio conditions, which indicate the environment in which the QoE prediction information will be applied; analyzing a generated timestamp indicating when the QoE prediction information was generated; A validity period indicating a time period during which the QoE assertion information is valid; or Confidence level, which indicates the probability assertion in the QoE prediction.

14. The UE according to claim 9, wherein: The processor is configured to transmit a QoE prediction capability report to the RAN node.

15. A method for supporting quality of experience (QoE) prediction, comprising: receiving, at a node, first information indicating at least one or more QoE metrics, wherein the first information is a QoE history information request or QoE prediction configuration information; and Second information indicating at least the one or more QoE metrics and values ​​of the one or more QoE metrics is transmitted, wherein the second information is a QoE history information report in response to the QoE history information request or a QoE prediction information report in response to the QoE prediction configuration information.

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