Communication method, communication device, medium and program product

Abstract

The embodiment of the invention provides a communication method, communication equipment, a computer readable storage medium and a computer program product. The communication method includes transmitting a Quality of Experience (QoE) report about a terminal device to a second network device, the QoE report including first information at a first network device, the first information being used to predict a QoE measurement result when the terminal device is under the second network device; and receiving predicted QoE information about the terminal device from the second network device, wherein the predicted QoE information includes the predicted QoE measurement result. In this way, the second network device can predict the local QoE result of the terminal device and then send the result to the first network device, so that the first network device and the second network device can identify the possible QoE problem in advance, thereby being beneficial to improving the overall satisfaction of the user equipment and improving the wireless resource efficiency.

Description

Technical Field

[0001] The present disclosure generally relates to the field of communications, and more particularly to a communication method, a communication device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the development of technology, artificial intelligence (AI) has been increasingly used in communications. In the R17 version of the 3rd Generation Partnership Project (3GPP), it is proposed to apply AI to new radio (NR) to improve network performance and user experience through intelligent data collection and analysis. User experience (quality of experience, QoE) is a performance indicator used to evaluate the user's comprehensive subjective feeling of the quality and performance (including effectiveness and availability) of equipment, networks, systems, applications or services, that is, it is defined from the comfort of business applications. Operators can evaluate the quality of video services through QoE scoring to optimize the network. For some streaming services or voice services, the signal quality alone cannot reflect the user experience when using these services. By collecting measurements from user devices, the network can be better optimized to improve the user experience. This type of measurement collection is called QoE measurement collection, which can also be called application layer measurement collection. In order to ensure the best user experience, the QoE measurement collection mechanism still needs to be further enhanced. Summary of the invention

[0003] The embodiments of the present application provide a communication method, a communication device, and a computer-readable storage medium. Thus, the first network device selects a target cell based on the predicted user experience information, and the target network device optimizes resource allocation based on the predicted user experience information, which is conducive to improving the overall satisfaction of users and improving the efficiency of wireless resources.

[0004] In a first aspect of the present application, a communication method is provided. The method includes: sending a quality of experience (QoE) report about a terminal device to a second network device, wherein the QoE report includes first information of a first network device, and the first information is used to predict a QoE measurement result when the terminal device is under the second network device; and receiving predicted QoE information about the terminal device from the second network device, wherein the predicted QoE information includes the predicted QoE measurement result. In this way, the first network device obtains the predicted QoE information, and can identify possible QoE problems in advance, thereby improving the efficiency of wireless resources.

[0005] In some embodiments, the QoE report includes at least one of the following: time index; QoE measurement result; service type; terminal device identification; first network device identification; second network device identification; cell identification; quality of service QoS parameters; wireless configuration; resource status information; or signal quality of the terminal device in the cell of the second network device. Thus, QoE reports containing different contents can be indicated to the second network device.

[0006] In some embodiments, the predicted QoE information further includes at least one of the following: an identifier of the terminal device; an identifier of the second network device; a service type; an identifier of a cell; or a time index. Thus, the first network device can obtain information related to the QoE result predicted by the UE at the second network device.

[0007] In some embodiments, the method further includes: sending a QoE report to a third network device to which the terminal device can switch. Thus, the first network device can send a QoE report to multiple candidate target network devices. In some embodiments, the method further includes: receiving predicted QoE information about the terminal device from the third network device. Thus, the first network device can obtain predicted QoE information from multiple candidate target network devices. In some embodiments, the method further includes: selecting a target network device to be switched to by the terminal device from the second network device and the third network device based on the predicted QoE information; and sending a first indication to the target network device, wherein the first indication is used to indicate that the terminal device is to switch to the target network device. Thus, the first network device can select an appropriate target network device for the terminal device.

[0008] In some embodiments, selecting a target network device to be switched by the terminal device from the second network device and the third network device includes selecting the second network device as the target network device, and the method further includes: receiving at least one of the following information from the second network device: an identifier of the terminal device; a predicted QoE measurement result; or an accuracy of the predicted QoE measurement result. Thus, the first network device can learn the accuracy of the prediction result of the selected target network device.

[0009] In some embodiments, the accuracy indicator is indicated by the first network device to the second network device. Thus, the second network device can feedback the accuracy of the prediction result based on the indicator. In some embodiments, the accuracy indicator is indicated to the second network device in the handover request of the terminal device. Thus, the accuracy indicator can be flexibly sent to the second network device.

[0010] In some embodiments, the method of the first network device further includes: receiving a second indication from the second network device when the predicted QoE measurement result is lower than a threshold, wherein the second indication is used to instruct the second network device to reject the switching of the terminal device. Thus, providing low-quality service to the user device can be avoided.

[0011] In some embodiments, the prediction of the QoE measurement result of the terminal device under the second network device is performed based on an artificial intelligence / machine learning (AI / ML) model, thereby improving the accuracy and efficiency of the prediction.

[0012] In some embodiments, at least one of the QoE report or the predicted QoE information is transmitted via the Xn interface. Thus, the QoE report or the predicted QoE information can be transmitted between network devices.

[0013] In a second aspect of the present application, a communication method is provided. The method includes: receiving a quality of experience (QoE) report about a terminal device from a first network device, wherein the QoE report includes first information at the first network device, and the first information is used to predict a QoE measurement result when the terminal device is under a second network device; based on the QoE report and auxiliary information at the second network device, predicting the QoE measurement result; and sending predicted QoE information about the terminal device to the first network device, wherein the predicted QoE information includes the predicted QoE measurement result. In this way, the predicted QoE information can be generated for identifying possible QoE problems, thereby improving the efficiency of wireless resources.

[0014] In some embodiments, predicting the QoE measurement result includes: inputting the QoE report and the auxiliary information into an artificial intelligence / machine learning (AI / ML) model to obtain a predicted QoE measurement result, thereby improving the accuracy and efficiency of the prediction.

[0015] In some embodiments, the QoE report includes at least one of the following: time index; QoE measurement result; service type; terminal device identifier; first network device identifier; second network device identifier; cell identifier; quality of service QoS parameter; wireless configuration; resource status information; or signal quality of the terminal device in the cell of the second network device. Thus, the second network device can obtain a QoE report containing different contents.

[0016] In some embodiments, the auxiliary information includes at least one of the following: historical resource status information of the second network device; predicted resource status information of the second network device; an identifier of the second network device; an identifier of a cell; a time index; the number of at least one terminal device; radio resource status information; the number of radio resource control RRC connections; the historical average signal quality of at least one terminal device; the historical average QoE measurement result of at least one terminal device; or the historical average QoS parameter of at least one terminal device. Thus, the second network device can predict the QoE measurement result based on the auxiliary information containing different contents.

[0017] In some embodiments, the auxiliary information further includes at least one of the following: historical resource status information of the second network device; predicted resource status information of the second network device; an identifier of the second network device; an identifier of a cell; a time index; an identifier of a terminal device; a signal quality for the terminal device; a QoE measurement result for the terminal device; or a QoS parameter for the terminal device. Thus, the second network device can predict the QoE measurement result based on the auxiliary information containing different contents.

[0018] In some embodiments, the predicted QoE information further includes at least one of the following: an identifier of the terminal device; an identifier of the second network device; a service type; an identifier of a cell; or a time index. Thus, information related to the QoE result predicted at the second network device can be indicated to the first network device.

[0019] In some embodiments, the method further includes: receiving a first indication from the first network device, wherein the first indication is used to indicate that the terminal device is to switch to the second network device; and performing resource optimization for the terminal device based on the predicted QoE information. Thus, the second network device can optimize resources for the terminal device.

[0020] In some embodiments, resource optimization includes at least one of the following: selection of a switching mode of the terminal device; optimization of random access channel RACH resources; or optimization of scheduled transmission resources. Thus, the second network device can optimize resources in multiple ways.

[0021] In some embodiments, the method further includes: receiving measured QoE information from the terminal device; performing a comparison between the measured QoE information and the predicted QoE information; and based on the comparison, performing at least one of updating, training, or monitoring the execution of the AI / ML model used to obtain the predicted QoE measurement result. Thus, the second network device can optimize the model based on the measured QoE information to improve model performance.

[0022] In some embodiments, the method further comprises: sending at least one of the following information to the first network device: an identification of the terminal device; a predicted QoE measurement result; or an accuracy of the predicted QoE measurement result. Thus, the accuracy of the predicted QoE measurement result can be indicated to the first network device.

[0023] In some embodiments, the accuracy indicator is indicated by the first network device to the second network device. Thus, the second network device can feedback the accuracy of the prediction result based on the indicator. In some embodiments, the accuracy indicator is indicated to the second network device in the handover request of the terminal device. Thus, the accuracy indicator can be flexibly sent to the second network device.

[0024] In some embodiments, the method further includes: based on determining that the predicted QoE measurement result is lower than a threshold, sending a second indication to the first network device, the second indication being used to instruct the second network device to reject the switching of the terminal device, thereby avoiding providing low-quality service to the user device.

[0025] In some embodiments, at least one of the QoE report or the predicted QoE information is transmitted via the Xn interface. Thus, the QoE report or the predicted QoE information can be transmitted between network devices.

[0026] In a third aspect of the present application, a communication method is provided. The method includes: sending a request message for requesting auxiliary information at the second network device to a second network device, wherein the auxiliary information is used to predict the quality of experience (QoE) measurement result when the terminal device is under the second network device; receiving the auxiliary information from the second network device; predicting the QoE measurement result based on the auxiliary information and the QoE report, wherein the QoE report includes information for predicting the QoE measurement result at the first network device; and sending predicted QoE information about the terminal device to the second network device, wherein the predicted QoE information includes the predicted QoE measurement result. In this way, the predicted QoE information can be generated for identifying possible QoE problems, thereby improving the efficiency of wireless resources.

[0027] In some embodiments, predicting the QoE measurement result includes: inputting the QoE report and the auxiliary information into an artificial intelligence / machine learning (AI / ML) model to obtain a predicted QoE measurement result, thereby improving the accuracy and efficiency of the prediction.

[0028] In some embodiments, the request message includes at least one of the following: a measurement identifier that identifies the auxiliary information request process; an identifier of the second network device; an identifier of a cell; a list of at least one terminal device identifier selected by the first network device; a service type; a third indication of the content of the auxiliary information; or time information for the auxiliary information. Thus, the first network device can request the auxiliary information in a variety of ways.

[0029] In some embodiments, the auxiliary information includes at least one of the following: a measurement identifier; an identifier of the second network device; an identifier of a cell; a list of at least one terminal device identifier; a service type; content of the auxiliary information indicated in the third indication; a fourth indication of content of unsupported auxiliary information; or a time index. Thus, the first network device can predict the QoE measurement result based on the auxiliary information containing different contents.

[0030] In some embodiments, the content of the auxiliary information includes at least one of the following: historical QoE information; predicted QoE information; historical resource status information; predicted resource status information; QoS parameters; wireless configuration; historical signal quality; or predicted signal quality. Thus, the first network device can predict the QoE measurement result based on the auxiliary information containing different contents.

[0031] In some embodiments, the QoE report includes at least one of the following: time index; QoE measurement result; service type; terminal device ID; first network device ID; second network device ID; cell ID; quality of service QoS parameter; wireless configuration; resource status information; or signal quality of the terminal device in the cell of the second network device. Thus, the first network device can predict the QoE measurement result based on the QoE report containing different contents.

[0032] In some embodiments, the predicted QoE information further includes at least one of the following: an identifier of the terminal device; an identifier of the second network device; a service type; an identifier of a cell; or a time index. Thus, the first network device can obtain information related to the predicted QoE result.

[0033] In some embodiments, the method further includes: sending a request message to a third network device to which the terminal device can switch. Thus, the first network device can send a request message to multiple candidate target network devices. In some embodiments, the method further includes: receiving auxiliary information from the third network device. Thus, the first network device can obtain auxiliary information from multiple candidate target network devices.

[0034] In some embodiments, sending the predicted QoE information to the second network device includes: selecting a target network device to be switched to by the terminal device from the second network device and the third network device based on the predicted QoE information; and sending the predicted QoE information to the second network device. Thus, the first network device can select an appropriate target network device for the terminal device.

[0035] In some embodiments, the method further includes: sending a first indication to the second network device, wherein the first indication is used to indicate that the terminal device is to be switched to the second network device. Thus, the second network device can be notified that the terminal device is to be switched locally. In some embodiments, the method further includes: receiving at least one of the following information from the second network device: an identification of the terminal device; a predicted QoE measurement result; or the accuracy of the predicted QoE measurement result. Based on the information, at least one of updating, training, or monitoring is performed on the AI / ML model used to obtain the predicted QoE measurement result. Thus, the accuracy and efficiency of the prediction can be improved.

[0036] In some embodiments, at least one of the QoE report or the predicted QoE information is transmitted via the Xn interface. Thus, the QoE report or the predicted QoE information can be transmitted between network devices.

[0037] In a fourth aspect of the present application, a communication method is provided. The method includes: receiving a request message for requesting auxiliary information at a second network device from a first network device, wherein the auxiliary information is used to predict a quality of experience (QoE) measurement result when the terminal device is under the second network device; based on receiving the request message, sending the auxiliary information to the first network device; and receiving predicted QoE information about the terminal device from the first network device, wherein the predicted QoE information includes the predicted QoE measurement result. In this way, the predicted QoE information can be assisted in generating for identifying possible QoE problems, thereby improving the efficiency of wireless resources.

[0038] In some embodiments, the request message includes at least one of the following: a measurement identifier that identifies the auxiliary information request process; an identifier of the second network device; an identifier of a cell; a list of at least one terminal device identifier selected by the first network device; a service type; a third indication of the content of the auxiliary information; or time information for the auxiliary information. Thus, the first network device can request the auxiliary information in a variety of ways.

[0039] In some embodiments, the auxiliary information includes at least one of the following: a measurement identifier; an identifier of the second network device; an identifier of a cell; a list of at least one terminal device identifier; a service type; content of the auxiliary information indicated in the third indication; a fourth indication of content of unsupported auxiliary information; or a time index. Thus, the first network device can predict the QoE measurement result based on the auxiliary information containing different contents.

[0040] In some embodiments, the content of the auxiliary information includes at least one of the following: historical QoE information; predicted QoE information; historical resource status information; predicted resource status information; QoS parameters; wireless configuration; historical signal quality; or predicted signal quality. Thus, the first network device can predict the QoE measurement result based on the auxiliary information containing different contents.

[0041] In some embodiments, the predicted QoE information further includes at least one of the following: an identifier of the terminal device; an identifier of the second network device; a service type; an identifier of a cell; or a time index. Thus, the first network device can obtain information related to the predicted QoE result.

[0042] In some embodiments, the method further includes: receiving a first indication from the first network device, wherein the first indication is used to indicate that the terminal device is to switch to the second network device; and performing resource optimization for the terminal device based on the predicted QoE information. Thus, the second network device can optimize resources for the terminal device.

[0043] In some embodiments, the method further comprises: receiving measured QoE information from the terminal device; performing a comparison between the measured QoE information and the predicted QoE information; and evaluating the accuracy of the predicted QoE measurement result based on the comparison. Thus, the performance of the model can be evaluated.

[0044] In some embodiments, the method of the second network device further includes: sending at least one of the following information to the first network device: an identification of the terminal device; a predicted QoE measurement result; or an accuracy of the predicted QoE measurement result. Thus, the accuracy of the predicted QoE measurement result can be indicated to the first network device.

[0045] In some embodiments, the accuracy indicator is indicated by the first network device to the second network device. Thus, the second network device can feedback the accuracy of the prediction result based on the indicator. In some embodiments, the accuracy indicator is indicated to the second network device in the handover request of the terminal device. Thus, the accuracy indicator can be flexibly sent to the second network device.

[0046] In some embodiments, the prediction of the QoE measurement result of the terminal device under the second network device is performed based on an artificial intelligence / machine learning (AI / ML) model, thereby improving the accuracy and efficiency of the prediction.

[0047] In some embodiments, the predicted QoE information is transmitted via an Xn interface. Thus, the predicted QoE information can be transmitted between network devices.

[0048] In a fifth aspect of the present application, a communication device is provided. The communication device includes a processor and a memory storing instructions. When the instructions are executed by the processor, the communication device performs any method according to any aspect of the first to fourth aspects and any implementation thereof. In a sixth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed by an electronic device, the electronic device performs any method according to any aspect of the first to fourth aspects and any implementation thereof.

[0049] In a seventh aspect of the present application, a chip is provided, which includes a processing circuit configured to execute any method of any aspect from the first aspect to the fourth aspect and any implementation thereof.

[0050] In an eighth aspect of the present application, a computer program product is provided, which includes instructions, and when the instructions are executed by an electronic device, the electronic device executes any method of any one of the first to fourth aspects and any implementation thereof.

[0051] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1A A schematic diagram of a communication system in which embodiments of the present disclosure may be implemented is shown.

[0053] Figure 1B A schematic diagram of a centralized unit (CU)-distributed unit (DU) (CU-DU) architecture related to an embodiment of the present disclosure is shown.

[0054] Figure 1C A schematic diagram of an AI application framework in NR related to an embodiment of the present disclosure is shown.

[0055] Figure 1D A schematic diagram showing a QoE measurement process related to an embodiment of the present disclosure is shown.

[0056] Figure 2 A first interactive signaling diagram of a communication process according to some embodiments of the present disclosure is illustrated.

[0057] Figure 3 A schematic diagram showing AI input and output related to an embodiment of the present disclosure is shown.

[0058] Figure 4 A first example process of a communication process according to some embodiments of the present disclosure is shown.

[0059] Figure 5 A second interactive signaling diagram of a communication process according to some embodiments of the present disclosure is illustrated.

[0060] Figure 6 A second example process of a communication process according to some embodiments of the present disclosure is shown.

[0061] Figure 7 A schematic flow chart of a method implemented at a first network device according to an embodiment of the present disclosure is shown.

[0062] Figure 8 A schematic flow chart of a method implemented at a second network device according to an embodiment of the present disclosure is shown.

[0063] Fig. 9 A schematic flow chart of a method implemented at a first network device according to an embodiment of the present disclosure is shown.

[0064] Fig.10 A schematic flow chart of a method implemented at a second network device according to an embodiment of the present disclosure is shown.

[0065] Fig.11 A simplified block diagram of an example device showing a possible implementation of an embodiment of the present application.

[0066] Fig.12 A simplified block diagram of an example device showing a possible implementation of an embodiment of the present application.

[0067] The same or similar reference numerals are used throughout the drawings to designate the same or similar components. DETAILED DESCRIPTION

[0068] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0069] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0070] Embodiments of the present disclosure may be implemented according to any appropriate communication protocol, including but not limited to cellular communication protocols such as third generation (3G), fourth generation (4G), fifth generation (5G) and future communication protocols (e.g., sixth generation (6G)), wireless local area network communication protocols such as Institute of Electrical and Electronics Engineers (IEEE) 802.11, and / or any other protocol currently known or developed in the future.

[0071] The technical solutions of the embodiments of the present disclosure are applied to communication systems that follow any appropriate communication protocols, such as: General Packet Radio Service (GPRS), Global System for Mobile Communications (GSM), Enhanced Datarate for GSM Evolution (EDGE), Universal Mobile Telecommunications Service (UMTS), Long Term Evolution (LTE) system, Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access 2000 (CDMA2000), Time Division-Synchronization Code Division Multiple Access (TD-SCDMA), Frequency Division Duplex (FDD) system, Time Division Duplex (TDD), fifth generation (5G) system (e.g., New Radio (NR)) and future communication systems (e.g., sixth generation (6G) system), etc.

[0072] For the purpose of illustration, the embodiments of the present disclosure are described below with the 5G communication system in 3GPP as the background. However, it should be understood that the embodiments of the present disclosure are not limited to the communication system, but can be applied to any communication system with similar problems, such as wireless local area network (WLAN), wired communication system, or other communication systems developed in the future.

[0073] The term "terminal" or "terminal device" used in this disclosure refers to any terminal device that can communicate with network devices or each other by wire or wirelessly. Terminal devices may sometimes be referred to as user equipment or UE. Terminal devices may be any type of mobile terminal, fixed terminal or portable terminal. Terminal devices may be various wireless communication devices with wireless communication functions. Terminal devices may be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they may also be deployed on the water (such as ships, etc.); they may also be deployed in the air (such as airplanes, balloons, and satellites, etc.). The terminal device may be a mobile phone, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self driving, a wireless terminal device in remote medical, a wireless terminal device in a smart grid, a wireless terminal in transportation safety, a wireless terminal device in a smart city, a wireless terminal in a smart home, and may also include user equipment (UE), etc.

[0074] The term "network node" or "network device" used in the present disclosure is a device in a wireless network, an entity or node that can be used to communicate with a terminal device, for example, an access network device. The access network device can be a device deployed in a wireless access network to provide wireless communication functions for a mobile terminal, such as a radio access network (RAN) node that connects a terminal device to a wireless network. At present, some examples of RAN nodes are: gNB, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), base station controller (BSC), base transceiver station (BTS), home base station (e.g., home evolved NodeB, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wifi) access point (AP), integrated access and backhaul (IAB), etc. In a network structure, the network device may also refer to a centralized unit (central unit, CU) or a distributed unit (distributed unit, DU), or the network device may also be composed of a CU and a DU. CU and DU can be understood as the division of the base station from the perspective of logical functions. Among them, the CU and DU can be physically separated or deployed together, and the embodiment of the present application does not specifically limit this. A CU can be connected to a DU, or multiple DUs can share a CU, which can save costs and facilitate network expansion. The division of CU and DU can be divided according to the protocol stack. One possible way is to deploy the radio resource control (RRC), service data adaptation protocol stack (SDAP) and packet data convergence protocol (PDCP) layer in the CU, and the remaining radio link control (RLC) layer, media access control (MAC) layer and physical layer in the DU.The above-mentioned protocol stack segmentation method is not completely limited in the embodiments of the present application, and other segmentation methods may also be used. The present application will be further described in detail below in conjunction with the accompanying drawings. The specific operation method, function description, etc. in the method embodiment may also be applied to the device embodiment or system embodiment.

[0075] Figure 1A FIG. 1 is a schematic diagram of a communication system 100A in which an embodiment of the present disclosure may be implemented. Figure 1A As shown, the system 100A may include a first network device 110, a second network device 120, a third network device 130 and a terminal device 140. The terminal device 140 may have a wireless transceiver function, which can communicate with one or more network devices of one or more communication systems (such as wireless communication) and accept network services provided by the network devices, where the network devices include but are not limited to the illustrated network devices. For example, the terminal device 140 may be connected to the first network device 110 via an access link. The first network device 110 manages the cell 101, the second network device 120 manages the cell 102, and the third network device 130 manages the cell 103. It should be understood that the cells here include but are not limited to the illustrated cells. The first network device 110 can communicate with the second network device 120 and the third network device 130. For example, the first network device 110 can communicate directly with the second network device 120 or the third network device 130 via the XN port.

[0076] In some embodiments, the QoE measurement result of the terminal device 140 accessing the cell 101 under the first network device 110 (also referred to as the source station) deteriorates, and the first network device 110 considers that it is necessary to switch the terminal device 140 to another network device or cell. The terminal device 140 can switch to the cell 102 under the second network device 120 (also referred to as the target station) or the cell 103 under the third network device 130.

[0077] It should be understood that in Figure 1A The number of terminals and network nodes shown in the figure is only for example. There may be more or fewer terminals, network devices and cells, and the present disclosure does not impose any limitation on this.

[0078] In addition, it should be understood that the communication system 100A can be applied to various scenarios. For example, the application scenarios of the communication system 100A include but are not limited to the fourth generation system (4G), the fifth generation system (5G), the new radio (NR) communication system, the non-terrestrial network (NTN) system, and other existing communication systems or future evolved communication systems. In addition, it should also be understood that the above communication can follow any appropriate communication technology and corresponding communication standards.

[0079] Figure 1B FIG. 1 shows a schematic diagram of a CU-DU architecture 100B related to an embodiment of the present disclosure. Figure 1B As shown, the CU and DU are connected via the F1 interface. The CU represents the gNB and is connected to the core network via the Ng interface. The network device in the embodiment of the present disclosure may also refer to a centralized unit control plane (CU-CP) node or a centralized unit user plane (CU-UP) node, or the network device may also be a CU-CP and a CU-UP. The CU-CP is responsible for the control plane functions, mainly including RRC and PDCP-C. PDCP-C is mainly responsible for encryption and decryption, integrity protection, data transmission, etc. of the control plane data. The CU-UP is responsible for the user plane functions, mainly including SDAP and PDCP-U. SDAP is mainly responsible for processing the data of the core network and mapping the flow to the bearer. PDCP-U is mainly responsible for encryption and decryption, integrity protection, header compression, sequence number maintenance, data transmission, etc. of the data plane. The CU-CP and CU-UP are connected via the E1 interface. The CU-CP represents the gNB and is connected to the core network via the Ng interface. It is connected to the DU via F1-C (control plane). The CU-UP is connected to the DU via F1-U (user plane). Of course, another possible implementation is that PDCP-C is also in CU-UP.

[0080] The access network equipment mentioned in the embodiments of the present disclosure may be equipment including CU, or DU, or equipment including CU and DU, or equipment including control plane CU node (CU-CP node) and user plane CU node (CU-UP node) and DU node. Wireless access network equipment and terminal equipment can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on the water surface; they can also be deployed on aircraft, balloons and satellites in the air. The embodiments of the present application do not limit the application scenarios of wireless access network equipment and terminal equipment.

[0081] The embodiments of the present disclosure may be applicable to downlink signal transmission, uplink signal transmission, and device-to-device (D2D) signal transmission. For downlink signal transmission, the transmitting device is a wireless access network device, and the corresponding receiving device is a terminal device. For uplink signal transmission, the transmitting device is a terminal device, and the corresponding receiving device is a wireless access network device. For D2D signal transmission, the transmitting device is a terminal device, and the corresponding receiving device is also a terminal device. The embodiments of the present application do not limit the transmission direction of the signal.

[0082] The wireless access network equipment and the terminal equipment, as well as the terminal equipment and the terminal equipment, can communicate through the licensed spectrum (licensed spectrum), can also communicate through the unlicensed spectrum (unlicensed spectrum), or can also communicate through the licensed spectrum and the unlicensed spectrum at the same time. The wireless access network equipment and the terminal equipment, as well as the terminal equipment and the terminal equipment, can communicate through the spectrum below 6 gigahertz (GHz), can also communicate through the spectrum above 6G, and can also communicate using the spectrum below 6G and the spectrum above 6G at the same time. The embodiments of the present disclosure do not limit the spectrum resources used between the wireless access network equipment and the terminal equipment.

[0083] Figure 1C FIG. 1 shows a schematic diagram of an AI application framework 100C in NR according to some embodiments of the present disclosure. Figure 1C As shown, the data source stores data inputs from gNB, gNB-CU, gNB-DU, UE or other management entities as a database for AI model training and data analysis and reasoning. The model training host analyzes the training data provided by the data source to provide the optimal AI model. The model inference host uses the AI ​​model to provide reasonable AI-based predictions for network operation based on the data provided by the data source, or guides the network to make policy adjustments. The relevant policy adjustments are uniformly planned by the execution entity (actor) and sent to multiple network entities for operation. At the same time, after the relevant policies are applied, the specific performance of the network will be entered into the database again for storage.

[0084] QoE is a performance indicator used to evaluate users' comprehensive subjective feelings about the quality and performance (including effectiveness and availability) of equipment, networks, systems, applications or services, that is, it is defined from the comfort of business applications. Operators can use QoE scores to evaluate the quality of video services and optimize the network.

[0085] For some streaming services or voice services, such as dynamic adaptive streaming over HTTP (DASH) services, multimedia telephony services for IMS (MTSI) services, and VR services, the signal quality alone cannot reflect the user experience when using these services. Operators want to know how the user experience is, so as to better optimize the network to improve the user experience. This type of measurement collection is called QoE measurement collection, also known as application layer measurement collection.

[0086] The QoE measurement collection function (QMC) supports collecting application layer measurements from UE. The supported service types include:

[0087] -QoE measurement collection for DASH streaming services;

[0088] -MTSI service QoE measurement and collection;

[0089] -Collection of QoE measurements for VR services.

[0090] QoE measurement collection may be supported only in RRC_CONNECTED state. Signaling-based and management-based QoE measurement collection are supported. QMC is activated in the gNB through direct configuration of the operation administration and maintenance (OAM) system (management-based activation) or by sending signaling from OAM through 5GC (signaling-based activation), which contains the UE-associated QoE configuration. One or more QoE measurement collection jobs can be activated on a UE per service type, and each QoE measurement configuration is uniquely identified by a QoE reference. When the UE is configured with multi-rate dual connectivity (MR-DC), only the MN can configure the QoE configuration.

[0091] For signaling-based QoE measurement, OAM initiates QoE measurement activation for a specific UE through 5GC, and the gNB receives one or more QoE measurement configurations through UE-associated signaling. The QoE measurement configuration activated based on signaling includes an application layer measurement configuration list and corresponding information collected by QoE measurement, such as QoE reference, service type, MCE IP address, slice range, area range, MDT alignment information, and an indication of available RAN-visible QoE metrics.

[0092] For management-based QoE measurement activation, OAM sends one or more QoE measurement configurations directly to the gNB. The management-based activated QoE measurement configuration also includes an application layer measurement configuration list and corresponding QoE measurement collection information. The gNB selects a UE that meets the QoE measurement capability, area range, and slice range.

[0093] The application layer measurement configuration received by the gNB from OAM or CN is encapsulated in a transparent container and forwarded to the UE as the application layer configuration in the RRCReconfiguration message (there can be multiple configurations in the same message). The application layer measurement report received from the UE application layer is encapsulated in a transparent container and sent to the network via the Measurement Report application layer message.

[0094] Figure 1D A schematic diagram of a QoE measurement process related to an embodiment of the present disclosure is shown. The UE may send multiple application layer measurement reports to the gNB in ​​one measurement report application layer message. To allow transmission of application layer measurement reports that exceed the maximum PDCP SDU size, the gNB may enable segmentation of the measurement report application layer message. The RRC identifier passed in the RRC signaling is used to identify the application layer measurement configuration and report between the gNB and the UE. The RRC identifier is mapped to a QoE reference in the gNB, and the gNB forwards the application layer measurement report to the MCE together with the QoE reference. The gNB may release one or more application layer measurement configurations from the UE in one RRCReconfiguration message at any time. The gNB may also configure the UE to report when a QoE measurement session for a certain application layer measurement configuration starts or stops.

[0095] RAN visible QoE measurements are configured by the gNB to the UE, where a subset of the configured QoE metrics are reported from the UE to the gNB as explicit IEs readable by the gNB. The gNB can utilize the RAN visible QoE measurements for network optimization. RAN visible QoE measurements are supported for DASH streaming and VR services. The gNB configures RAN visible QoE measurements to collect all or part of the available RAN visible QoE metrics, where an indication of the availability of the metrics is received from OAM or 5GC. The set of available RAN visible QoE metrics is a subset of the metrics that have been configured as part of the QoE measurement configuration encapsulated in an application layer container. The UE may also report, along with the RAN visible QoE measurement results, the PDU session identifier corresponding to the service subject to the QoE measurement.

[0096] RAN-visible QoE measurements can support multiple simultaneous RAN-visible QoE measurement configurations and reports, each of which is identified by the same RRC identifier as the QoE measurement configuration and measurement report. After the UE RRC layer receives the RAN-visible QoE measurement configuration, it forwards the configuration to the application layer, indicating the service type, RRC identifier and period. The RAN-visible QoE configuration can only be configured when there is a corresponding QoE measurement configuration for the same service type configured by the UE. The application layer sends the RAN-visible QoE measurement report associated with the RRC identifier to the AS layer of the UE. If the reporting period is not defined in the RAN-visible QoE configuration, the UE sends the RAN-visible QoE measurement report and the QoE measurement report to the gNB in ​​the same measurement report application layer message, unless a QoE measurement collection suspension indication is received (for example, in the event of RAN overload). When QoE measurement collection is suspended, if no reporting period is defined in the RAN visible QoE configuration, the encapsulated QoE reports are stored in the RRC layer of the UE, but the RAN visible QoE reports continue to be reported to the gNB with the configured reporting period for the encapsulated QoE reports. When the gNB is configured with a dedicated RAN visible QoE reporting period, RAN visible QoE measurements can be reported with a reporting period different from the corresponding encapsulated QoE measurements. The UE application layer can measure RAN visible QoE indicators based on this reporting period. The gNB can release one or more RAN visible QoE measurement configurations from the UE at any time in an RRCReconfiguration message.

[0097] In the current QoE mechanism, the QoE measurement configuration of the NG port includes: QoE reference, service type, QMC area range, MCE IP address, QoE measurement status, application layer measurement configuration container, measurement configuration application layer identifier, QMC slice support list, MDT alignment information, and indication of available RAN visible QoE metrics.

[0098] Among them, the QoE reference consists of a mobile country code (MCC), a mobile network code (MNC) and a QoE measurement collection function identifier (MCC+MNC+QMC ID), where the MCC and MNC together with the QMC activation request from the management system identify a PLMN containing the management system, and the QMC ID is a 3-byte octet string. The service type indicates the service type of the QoE measurement, which can be DASH streaming, MTSI, VR. The QMC area range is used to indicate the measurement collection range of the QMC, which can be a cell identity list, a tracking area (TA) list, a tracking area identity (TAI) list, or a PLMN list. The MCE IP address is the IP address of the entity that receives the QoE measurement report. The QoE measurement status indicates whether the QoE measurement is started, which exists in the case of NG switching. The application layer measurement configuration container contains the application layer measurement configuration, which is invisible to the RAN side, including the QoE measurement configuration of different services, such as throughput, initial playback delay, time that can be played in the buffer, bandwidth, height, width, bit rate, response time, device information, jitter duration, desynchronization duration, etc. The measurement configuration application layer identifier indicates the identifier of the application layer measurement configuration, corresponding to the MeasConfigAppLayerId information element defined in the Uu port. This information element will only appear when the message containing this information element is related to NG-based switching. The QMC slice support list is used to indicate the slice information supported by QMC. The MDT alignment information is used to indicate the MDT measurements that need to be aligned. The indication of available RAN-visible QoE metrics is used to indicate whether the gNB can perform RAN-visible QoE measurement collection.

[0099] The QoE measurement configuration of the Uu port includes: service type, application layer measurement configuration container, measurement configuration application layer identifier, and RAN visible QoE parameter configuration. Among them, the RAN visible QoE parameter configuration includes the number of buffer level entries, the operation visible period, and the report play delay media start. The number of buffer level entries includes the maximum number of buffer level entries that can be reported by the RAN visible application layer measurement. The operation visible period indicates the period of RAN visible application layer measurement reporting. Report play delay media start indicates whether the UE should report the media start play delay measured by the RAN visible application layer.

[0100] The QoE measurement results of the Uu port include: application layer measurement configuration identifier, application layer report container, and RAN visible QoE measurement results. The RAN visible QoE measurement results include application layer buffer level list, media start play delay, and PDU session list. The application layer buffer level list represents a list of application layer buffer levels, and the unit of each application layer buffer level is ms. Value 0 corresponds to 0ms, value 1 corresponds to 10ms, value 2 corresponds to 20ms, and so on. If the cache level is greater than the maximum value of 30000 (5 minutes), the UE reports 30000. The media start play delay is the media start application layer play delay, in ms. Value 0 corresponds to 0ms, value 1 corresponds to 1ms, value 2 corresponds to 2ms, and so on. If the media start play delay is greater than the maximum value of 30000ms, the UE reports 30000. The PDU session list contains the identifier of the PDU session, or the identifier of the PDU session, for the application data flow measured by the RAN visible application layer.

[0101] The QoE measurement results sent by the gNB to the MCE also include the QoE reference, but do not include the RAN visible QoE measurement results mentioned above. The RAN visible QoE measurement results are only used to optimize network resources on the RAN side. In the traditional QoE mechanism, RAN / OAM optimizes network resource allocation based on historical QoE measurement results, and problem identification is delayed, that is, it is impossible to optimize network resource allocation in advance by identifying QoE problems in advance.

[0102] In view of the above research and analysis, an embodiment of the present disclosure provides a communication method. In this method, a quality of experience (QoE) report about a terminal device is sent to a second network device, the QoE report includes first information on a first network device, and the first information is used to predict the QoE measurement result when the terminal device is under the second network device; and the predicted QoE information about the terminal device is received from the second network device, wherein the predicted QoE information includes the predicted QoE measurement result. In this way, the second network device can predict the local QoE result of the terminal device and then send it to the first network device, so that the first network device and the second network device can identify possible QoE problems in advance, which is conducive to improving the overall satisfaction of the user device and improving the efficiency of wireless resources.

[0103] Figure 2 The first interactive signaling diagram of the communication process according to some embodiments of the present disclosure is illustrated. For the sake of clarity of discussion and without any limitation, the process 200 will be combined with Figure 1A to discuss.

[0104] In process 200, the first network device 110 sends 201 a quality of experience (QoE) report 203 about a terminal device to the second network device 120. The QoE report 203 includes first information of the first network device 110, which is used to predict the QoE measurement result when the terminal device is under the second network device 120. The terminal device under the second network device 120 may refer to the terminal device switching to the second network device and being served by the second network device. Accordingly, the second network device 120 receives 205 the QoE report 203 from the first network device 110. Optionally, the first network device 110 also sends the QoE report to the third network device to which the terminal device can switch. It should be understood that the first network device can select multiple candidate network devices including the second network device and the third network device, and send the QoE report to the multiple candidate network devices.

[0105] For example, the first network device 110 receives the QoE measurement result from the terminal device, and the first network device 110 determines whether to trigger the switching process for the terminal device based on the implementation, such as when the first network device 110 detects that the QoE measurement result of the terminal device is lower than a certain threshold, it is considered that the terminal device needs to be switched to another base station or cell. The first network device 110 can generate a QoE report based on the QoE measurement result received from the terminal device, and then send the QoE report of the terminal device to the candidate network device for the candidate network device to predict the QoE result of the UE in the candidate network device or the cell of the candidate network device, that is, the QoE report can be used as input data of the AI ​​model used to predict QoE in the candidate network device.

[0106] In some embodiments, the QoE report may include a time index, a QoE measurement result, a service type, an identifier (ID) of a terminal device, an identifier of the first network device 110, an identifier of the second network device 120, an identifier of a cell, a quality of service (QoS) parameter, a wireless configuration, resource status information, a signal quality of the terminal device in the cell of the second network device 120, or one or more of the above. For example, the QoE measurement result may be a RAN visible QoE measurement result. The wireless configuration may include dual connectivity (DC), or carrier aggregation (CA), etc. The signal quality may include reference signal receiving power (RSRP), reference signal receiving quality (RSRQ), signal to interference plus noise ratio (SINR), bit error rate, throughput, etc. The content of the above QoE report may have a specific association relationship. A possible implementation method is as follows, namely, the RAN visible QoE measurement result, QoS parameters, wireless configuration of each cell of each service type in the historical time, and the signal quality of the UE in the candidate target cell:

[0107] -gNB logo

[0108] -UE identification

[0109] -Business Type

[0110] -Time Index

[0111] -Source station cell ID

[0112] -RAN visible QoE measurement results

[0113] -QoS parameters

[0114] -Wireless Configuration

[0115] - Resource status information

[0116] -Radio resource status information

[0117] - Number of activated UEs

[0118] - Number of RRC connections

[0119] -Signal quality of candidate target cells

[0120] -Target candidate cell identifier

[0121] -Signal quality measurement results

[0122] -Time Index

[0123] -RSRP

[0124] -RSRQ

[0125] -SINR

[0126] -Bit Error Rate

[0127] -Throughput

[0128] -Packet loss rate

[0129] Continue to refer Figure 2 , based on the QoE report and the auxiliary information at the second network device 120, the second network device 120 predicts 207QoE measurement results. Additionally or alternatively, the prediction of the QoE measurement results of the terminal device under the second network device is performed based on an artificial intelligence / machine learning (AI / ML) model. Optionally, the second network device 120 can input the QoE report and the auxiliary information into the artificial intelligence / machine learning (AI / ML) model to obtain the predicted QoE measurement results. For example, after receiving the QoE report, the second network device 120 triggers the local AI model to predict the QoE parameters of the UE. The predicted QoE parameters in the embodiments of the present application are only for the RAN visible QoE parameters, and do not involve the prediction of the QoE parameters in the application layer report container, because this part is transparently transmitted on the RAN side in the form of a container, that is, it is not visible on the RAN side.

[0130] Figure 3 The diagram illustrates a schematic diagram of AI input and output related to an embodiment of the present disclosure. A candidate network device (e.g., the second network device 120 or the third network device) can predict the QoE result of the UE in the candidate target cell, i.e., the pQoE metric, through an AI model based on pQoE auxiliary information (e.g., auxiliary information at the second network device 120 or the third network device) and historical QoE reports. In the figure, the historical QoE report is the received QoE report 203, i.e., information related to QoE prediction sent by the first network device 110.

[0131] Alternatively, the auxiliary information may include: historical resource status information of the second network device 120, predicted resource status information of the second network device 120, an identifier of the second network device 120, an identifier of a cell, a time index, the number of at least one terminal device, wireless resource status information, the number of wireless resource control RRC connections, the historical average signal quality of at least one terminal device, the historical average QoE measurement result of at least one terminal device, the historical average QoS parameter of at least one terminal device, or one or more of the above.

[0132] Since the terminal device has not established a connection with the candidate network device, the candidate network device can associate the possible performance of the terminal device in the candidate network device based on the historical UE average signal quality and the historical UE average RAN visible QoE results. A possible implementation of the auxiliary information is as follows:

[0133] -gNB resource status

[0134] -Cell ID

[0135] -Time Index

[0136] - Resource status

[0137] -Radio resource status information

[0138] - Number of activated UEs

[0139] - Number of RRC connections

[0140] -Business Type

[0141] -Cell ID

[0142] -Time Index

[0143] -UE average signal quality

[0144] -RSRP

[0145] -RSRQ

[0146] -SINR

[0147] -Bit Error Rate

[0148] -Throughput

[0149] -Packet loss rate

[0150] -UE average RAN visible QoE results

[0151] -UE average QoS parameters

[0152] Alternatively, the auxiliary information may also include: historical resource status information of the second network device 120, predicted resource status information of the second network device 120, an identifier of the second network device 120, an identifier of a cell, a time index, an identifier of a terminal device, a signal quality for the terminal device, a QoE measurement result for the terminal device, a QoS parameter for the terminal device, or one or more of the above. Another possible implementation of the auxiliary information may involve detailed terminal device information, i.e., information of the terminal device in the second network device 120 that may be associated with QoE, as follows:

[0153] -gNB resource status

[0154] -Cell ID

[0155] -Time Index

[0156] - Resource status

[0157] -Radio resource status information

[0158] - Number of activated UEs

[0159] - Number of RRC connections

[0160] -Business Type

[0161] -UE identification

[0162] -Cell ID

[0163] -Time Index

[0164] -UE signal quality

[0165] -RSRP

[0166] -RSRQ

[0167] -SINR

[0168] -Bit Error Rate

[0169] -Throughput

[0170] -Packet loss rate

[0171] -RAN visible QoE results

[0172] -QoS parameters

[0173] See also Figure 2 , the second network device 120 sends 210 predicted QoE information 213 about the terminal device to the first network device 110, wherein the predicted QoE information 213 includes the predicted QoE measurement result. Accordingly, the first network device 110 receives 215 predicted QoE information 213 about the terminal device from the second network device 120. Optionally, the first network device 110 receives the predicted QoE information about the terminal device from the third network device. Additionally, the predicted QoE information may also include an identifier of the terminal device, an identifier of the second network device, a service type, an identifier of a cell, a time index, or one or more of the above.

[0174] The predicted QoE information 213 is Figure 3 The pQoE metirc in the example contains the QoE result predicted by the candidate network device at the local UE. A possible implementation is as follows:

[0175] -UE identification

[0176] -gNB logo

[0177] -Business Type

[0178] -Cell ID

[0179] -Time Index

[0180] -RAN visible QoE prediction results

[0181] In some embodiments, at least one of the QoE report or predicted QoE information is transmitted via an Xn interface. The Xn interface refers to the network interface between NG-RAN nodes (gNB or eNB).

[0182] Additionally or alternatively, based on the predicted QoE information, the first network device 110 may select a target network device to which the terminal device is to switch from the second network device 120 and the third network device, and send a first indication to the target network device, wherein the first indication is used to indicate that the terminal device is to switch to the target network device. For example, after the first network device 110 receives the pQoE measurements of multiple candidate network devices, it selects one of the cells of the multiple candidate network devices as the target cell based on the implementation selection. For example, the first network device 110 may select the candidate cell with the smallest predicted playback delay value as the best target cell, and notify the target network device to prepare for switching.

[0183] Accordingly, the second network device 120 may receive a first indication from the first network device 110. Based on the predicted QoE information, the second network device 120 may perform resource optimization for the terminal device. Additionally, resource optimization may include selection of a switching mode of the terminal device, optimization of RACH resources of a random access channel, optimization of scheduled transmission resources, or one or more of the above. For example, after the source station notifies the target station that it is ready to switch, the target station may optimize resources for the terminal based on the pQoE metric. For example, selection of a switching mode (DAPS, CHO, legacy), RACH resource optimization, scheduling transmission resource optimization, etc.

[0184] In some embodiments, the second network device 120 may also receive measured QoE information from the terminal device, and compare the measured QoE information with the predicted QoE information. Based on the comparison, the second network device 120 performs at least one of updating, training, or monitoring the AI / ML model used to obtain the predicted QoE measurement result. Alternatively or additionally, the second network device 120 may also send an identification of the terminal device, a predicted QoE measurement result, an accuracy of the predicted QoE measurement result, or one or more of the above to the first network device 110. Optionally, an indicator of accuracy may be indicated to the second network device 120 by the first network device 110. Additionally, an indicator of accuracy may be indicated to the second network device 120 in a handover request regarding the terminal device.

[0185] For example, after receiving the QoE measurement results, the target station can compare the QoE measurement results with the predicted pQoE metric to perform AI model optimization / monitoring. At the same time, since the target station has sent the pQoE metric to the source station for selecting the target cell, the target station can also indicate the true value or accuracy of pQoE to the source station, that is, pQoE feedback.

[0186] In one example, the pQoE feedback may be a UE identifier and a pQoE metric. In another example, the pQoE feedback may be pQoE accuracy. In this manner, the accuracy index may be specified in advance by the source station, for example, indicating in the handover request that the accuracy index is an AI model index such as macro-averaging, micro-averaging, mean absolute error (MAE, Mean Absolute Error), and mean square error (MSE, Mean Squared Error). The mean absolute error is the expected value of the absolute error loss and the expectation of the mean square error. Macro-averaging refers to first calculating each evaluation index of each category and then calculating the arithmetic mean of the index. Micro-averaging refers to averaging the elements of the confusion matrix and then calculating each evaluation index. The above-mentioned evaluation indicators generally include: accuracy, precision, recall, and comprehensive evaluation index (F1). Among them, the accuracy is the number of correctly classified samples divided by the total number of samples. The precision is the number of samples predicted as positive and correct divided by the number of samples predicted as positive. The recall rate is the number of samples predicted as positive and correct divided by the number of samples labeled as positive. The comprehensive evaluation index is the weighted harmonic average of precision and recall, that is, F1 = 2 (Precision + Recall) / (Precision * Recall).

[0187] Correspondingly, the first network device 110 selects the target network device to which the terminal device is to switch from the second network device 120 and the third network device, which may include selecting the second network device 120 as the target network device. The first network device 110 may receive the identifier of the terminal device, the predicted QoE measurement result, the accuracy of the predicted QoE measurement result, or one or more of the above from the second network device 120.

[0188] In some embodiments, based on determining that the predicted QoE measurement result is lower than a threshold, the second network device 120 may also send a second indication to the first network device 110, where the second indication is used to instruct the second network device 120 to reject the switching of the terminal device. For example, if the candidate target station evaluates that the pQoE measurement result is too poor, that is, the candidate target station does not receive the switching of the UE, then the candidate target station may indicate to the source station that the switching is rejected. Optionally, the reason for rejecting the switching may be carried, such as pQoE being lower than a threshold. Additionally, in the case where the predicted QoE measurement result is lower than a threshold, the first network device 110 may receive a second indication from the second network device 120, where the second indication is used to instruct the second network device 120 to reject the switching of the terminal device.

[0189] In process 200, the specific contents of the QoE report and the predicted QoE information are clarified, as well as the transmission of the QoE report and the predicted QoE information at the XN port. The candidate target station predicts the local QoE result of the UE, the source station selects the target station based on the predicted QoE information, and the target station optimizes resources based on the predicted QoE information. In this way, the candidate target station can predict the local QoE result of the target UE and send it to the source station, so that the source station and the target station can identify possible QoE problems in advance. The source station selects the target cell based on the predicted QoE information, and the target station optimizes resource allocation based on the predicted QoE information, which is conducive to improving the overall satisfaction of the UE and improving the efficiency of wireless resources.

[0190] Figure 4 A first example process of a communication process according to some embodiments of the present disclosure is shown, which can be considered as Figure 2 A specific implementation of the communication process 200. The first example process involves UE 401, S-gNB 402 (i.e., source station 402), T-gNB1 403 (i.e., candidate target station 403) and T-gNB2 404 (i.e., candidate target station 404).

[0191] At 411, UE 401 sends the QoE measurement result to S-gNB 402. The content of the QoE measurement result can refer to the specific content of the QoE measurement result of the aforementioned Uu port, which will not be repeated here.

[0192] At 413, S-gNB 402 triggers the process of selecting candidate target stations and target cells.

[0193] Subsequently, S-gNB 402 sends QoE reports to the selected candidate target stations respectively.

[0194] At 415, S-gNB 402 sends a QoE report of UE 401 to T-gNB1 403. The content of the QoE report can be found in the embodiment of process 200.

[0195] At 417, S-gNB 402 sends a QoE report of UE 401 to T-gNB2 404. The QoE report can be used by the candidate target station to predict the QoE result of UE 401 at the candidate target station, that is, the QoE report can be used as input data for the AI ​​model used to predict QoE in the candidate target station.

[0196] The candidate target station receives the QoE report and predicts the QoE parameters of UE 401 in the candidate target cell.

[0197] At 419, T-gNB1 403 uses the local AI model to infer the QoE parameters of UE 401 in the candidate target cell, namely pQoE metric 1. The content of the QoE parameters can refer to the embodiment of the predicted QoE information in process 200.

[0198] At 421, T-gNB2 404 uses the local AI model to infer the QoE parameters of UE 401 in the candidate target cell, namely pQoE metric 2.

[0199] At 423, T-gNB1 403 sends the predicted QoE result, i.e., pQoE metric 1, to S-gNB 402. The content of the predicted QoE result can refer to the embodiment of the predicted QoE information in process 200.

[0200] At 425, T-gNB2 404 sends the predicted QoE result, i.e., pQoE metric 2, to S-gNB 402.

[0201] If T-gNB1 403 and T-gNB2 404 evaluate that the pQoE measurement result is too poor, T-gNB1 403 and T-gNB2 404 may not receive the UE's handover, and the candidate target station may indicate to the source station that the handover is rejected. Optionally, the reason for rejecting the handover may be carried in the indication, such as the pQoE measurement being lower than a threshold.

[0202] At 427, after receiving the pQoE measurement, S-gNB 402 selects a target cell and a target gNB based on the implementation.

[0203] At 429, S-gNB 402 notifies the selected target station to prepare for switching. The target station can optimize resources for UE 401 based on the pQoE metric. The specific method of optimizing resources can refer to the embodiment in process 200.

[0204] At 431, UE 401 may send actual QoE measurements to T-gNB1 403. The content of the actual QoE measurements may refer to the specific content of the QoE measurement results of the aforementioned Uu port.

[0205] At 433, after T-gNB1 403 receives the actual QoE measurement, it compares it with the predicted pQoE measurement and can send pQoE feedback to S-gNB402. The content of the pQoE feedback can refer to the embodiment in process 200.

[0206] In the first example process, the candidate target station predicts the local QoE parameters of the UE and sends them to the source station. Based on the pQoE metric, the source station selects the target cell and the target station performs network optimization.

[0207] Figure 5 A second interactive signaling diagram of a communication process according to some embodiments of the present disclosure is illustrated. For the sake of clarity of discussion and without any limitation, process 500 will be combined with Figure 1A to discuss.

[0208] In the process 500, the first network device 110 sends 501 to the second network device 120 a request message 503 for requesting auxiliary information at the second network device 120, wherein the auxiliary information is used to predict a quality of experience (QoE) measurement result when the terminal device is under the second network device 120. The request message is used for the source station to request information related to the predicted QoE from the candidate target station.

[0209] Additionally or alternatively, the request message may include: a measurement identifier that identifies the auxiliary information request process, an identifier of the second network device; an identifier of the cell; a list of at least one terminal device identifier selected by the first network device, a service type, a third indication of the content of the auxiliary information, time information for the auxiliary information, or one or more of the above.

[0210] For example, a possible request message implementation is as follows:

[0211] -Measurement ID

[0212] -gNB logo

[0213] -Cell ID

[0214] -UE identity list

[0215] -Business Type

[0216] -Request time indication

[0217] - Time point

[0218] -Time period

[0219] -pQoE auxiliary information request indication

[0220] -RAN visible QoE measurement / prediction results

[0221] -QoS parameters

[0222] -Wireless Configuration

[0223] -Resource status measurement / prediction results

[0224] -Radio resource status information

[0225] - Number of activated UEs

[0226] - Number of RRC connections

[0227] -Signal quality measurement / prediction results

[0228] -RSRP

[0229] -RSRQ

[0230] -SINR

[0231] -Bit Error Rate

[0232] -Throughput

[0233] -Packet loss rate

[0234] -Reporting cycle

[0235] Among them, the measurement ID is used to identify which pQoE auxiliary information request process it is. The UE identification list is optional and is used to indicate the candidate target station to select UE, such as all UEs whose buffer level is lower than threshold 1 in service type 1 in cell 1. If the UE identification list is not carried, the information of the average UE requested is the default. The request time indication is used to indicate the period of time for which the candidate target station provides pQoE auxiliary information. The specific implementation form is not limited and can be a time point or a time period. The service type is used to indicate which service or services pQoE auxiliary information is requested. The pQoE auxiliary information request indication information is used to indicate which auxiliary information the candidate target station provides, and may include at least one of the following: RAN visible QoE measurement / prediction results, wireless resource status measurement / prediction results, QoS parameters, wireless configuration, signal quality measurement / prediction results. The implementation form of the pQoE auxiliary information request indication information can be 16 bits, for example, the second bit is 1, indicating a request for QoS parameters. The reporting period is used to indicate the time interval for each candidate target station to report pQoE auxiliary information. If it is carried, it defaults to periodic reporting. If it is not carried, it defaults to a one-time report, that is, the candidate target station reports all pQoE auxiliary information at one time according to the request time indication information.

[0236] Additionally, the first network device 110 may also send a request message to a third network device to which the terminal device can switch. Optionally, the first network device 110 may receive auxiliary information from the third network device. It should be understood that the first network device (i.e., the source station) may send a request message to one or more candidate target stations and receive auxiliary information from one or more candidate stations.

[0237] Based on receiving 505 the request message 503, the second network device 120 sends 510 assistance information 513 to the first network device 110. Accordingly, the first network device 110 receives 515 assistance information 513 from the second network device 120. For example, the pQoE assistance information message may be pQoE assistance information that the candidate target station may provide after receiving the pQoE assistance information request message.

[0238] Additionally or alternatively, the auxiliary information may include: a measurement identifier, an identifier of a second network device, an identifier of a cell, a list of at least one terminal device identifier, a service type, the content of the auxiliary information indicated in a third indication, a fourth indication of the content of unsupported auxiliary information, a time index, or one or more of the above.

[0239] Additionally, the content of the auxiliary information may include: historical QoE information, predicted QoE information, historical resource status information, predicted resource status information, QoS parameters, wireless configuration, historical signal quality, predicted signal quality, or one or more of the above.

[0240] In some embodiments, the auxiliary information may be implemented as follows:

[0241] -Measurement ID

[0242] -gNB logo

[0243] -Cell ID

[0244] -UE identity list

[0245] -Business Type

[0246] -Time Index

[0247] -pQoE auxiliary information content

[0248] -RAN visible QoE measurement / prediction results

[0249] -Resource status measurement / prediction results

[0250] -Radio resource status information

[0251] - Number of activated UEs

[0252] - Number of RRC connections

[0253] -QoS parameters

[0254] -Wireless Configuration

[0255] -Signal quality measurement / prediction results

[0256] -RSRP

[0257] -RSRQ

[0258] -SINR

[0259] -Bit Error Rate

[0260] -Throughput

[0261] -Packet loss rate

[0262] - Unsupported pQoE auxiliary information indication

[0263] - List of unsupported pQoE auxiliary information

[0264] -reason

[0265] The unsupported pQoE auxiliary information indication is used to indicate which pQoE auxiliary information the source station does not support reporting and the reasons thereof, for example, the candidate target station does not support AI prediction.

[0266] Continue to refer Figure 5 , the first network device 110 predicts 520 a QoE measurement result based on the auxiliary information and the QoE report, wherein the QoE report includes information at the first network device 110 for predicting the QoE measurement result.

[0267] In some embodiments, the first network device 110 may input the QoE report and auxiliary information into an artificial intelligence / machine learning (AI / ML) model to obtain a predicted QoE measurement result. For example, after the source station receives the pQoE auxiliary information, it predicts the QoE parameters of the terminal device at the candidate target station based on the local AI model. It should be understood that the QoE parameters predicted in the present disclosure are only for the RAN visible QoE parameters, and do not involve the prediction of the QoE parameters in the application layer report container, because this part is transparently transmitted on the RAN side in the form of a container, that is, it is not visible on the RAN side.

[0268] The input and output of AI / ML models can be referenced Figure 3. The source station can predict the QoE results of the UE in the candidate target cell, that is, the pQoE metric, through the AI ​​model based on the pQoE auxiliary information and historical QoE reports. The pQoE auxiliary information is the auxiliary information related to the QoE prediction sent by the candidate target station. The historical QoE report is the information related to the QoE prediction collected by the source station based on the implementation, which may include: time index, RAN visible QoE measurement results, service type, cell identification, QoS parameters, wireless configuration, resource status information, UE signal quality in the candidate target cell, or one or more of the above. The wireless configuration may include dual link (DC), or carrier aggregation (CA), etc. Signal quality may include RSRP, RSRQ, SINR, bit error rate, throughput, etc. A possible implementation of pQoEmetric is as follows:

[0269] -UE identification

[0270] - Candidate target gNB identity

[0271] -Business Type

[0272] -Cell ID

[0273] -Time Index

[0274] -RAN visible QoE prediction results

[0275] After the source station predicts the pQoE metric of the UE at the candidate target station, it selects one of them as the target cell based on the implementation. For example, the source station may select the candidate target cell with the smallest predicted playback delay value as the best target cell.

[0276] Continue to refer Figure 5 , the first network device 110 sends 525 predicted QoE information 527 about the terminal device to the second network device 120, wherein the predicted QoE information includes the predicted QoE measurement result. Additionally, the predicted QoE information may also include: an identifier of the terminal device, an identifier of the second network device, a service type, an identifier of the cell, a time index, or one or more of the above. Additionally or alternatively, the predicted QoE information may be transmitted via an Xn interface.

[0277] Optionally, the first network device 110 may switch to a target network device from the second network device 120 and the third network device terminal device based on the predicted QoE information. The second network device 120 is determined as the target network device, and the first network device 110 may send the predicted QoE information to the second network device 120. Accordingly, the second network device 120 receives 530 the predicted QoE information 527 from the first network device 110.

[0278] In some embodiments, the first network device 110 may send a first indication to the second network device 120, and the first indication may be used to indicate that the terminal device is to switch to the second network device 120. Accordingly, the second network device 120 may receive the first indication from the first network device 110. Based on the predicted QoE information, the second network device 120 may perform resource optimization for the terminal device. Additionally, resource optimization may include selection of a switching mode for the terminal device, optimization of random access channel RACH resources, optimization of scheduled transmission resources, or one or more of the above. For example, after the source station notifies the target station that it is ready to switch, the target station may optimize resources for the terminal based on the pQoE metric. For example, selection of switching mode (DAPS, CHO, legacy), RACH resource optimization, scheduling transmission resource optimization, etc.

[0279] Optionally, the second network device 120 may receive measured QoE information from the terminal device, and compare the measured QoE information with the predicted QoE information. Based on the above comparison, the second network device 120 may evaluate the accuracy of the predicted QoE measurement result. For example, after the target station receives the measured QoE measurement result, it can evaluate the accuracy of pQoE by comparing the pQoE metric received from the source station. Additionally, the second network device 120 may send the identifier of the terminal device, the predicted QoE measurement result, the accuracy of the predicted QoE measurement result, or one or more of the above to the first network device 110. For example, the target station may also indicate the true value or accuracy of pQoE to the source station, i.e., pQoE feedback, to assist the source station in training / monitoring the AI ​​model. Correspondingly, the first network device 110 may also receive the identifier of the terminal device, the predicted QoE measurement result, the accuracy of the predicted QoE measurement result, or one or more of the above from the second network device 120. Based on the received information, the first network device 110 may update, train, or monitor the AI / ML model used to obtain the predicted QoE measurement result.

[0280] In some embodiments, the accuracy indicator may be indicated by the first network device 110 to the second network device 120. Alternatively, the accuracy indicator may be indicated to the second network device 120 in a handover request regarding the terminal device.

[0281] In one example, the pQoE feedback may be a UE identifier and a pQoE metric. In another example, the pQoE feedback may be pQoE accuracy. In this manner, the accuracy index may be specified in advance by the source station, for example, indicating in the handover request that the accuracy index is an AI model index such as macro-averaging, micro-averaging, mean absolute error (MAE, Mean Absolute Error), and mean square error (MSE, Mean Squared Error). The mean absolute error is the expected value of the absolute error loss and the expectation of the mean square error. Macro-averaging refers to first calculating each evaluation index of each category and then calculating the arithmetic mean of the index. Micro-averaging refers to averaging the elements of the confusion matrix and then calculating each evaluation index. The above-mentioned evaluation indicators generally include: accuracy, precision, recall, and comprehensive evaluation index (F1). Among them, the accuracy is the number of correctly classified samples divided by the total number of samples. The precision is the number of samples predicted as positive and correct divided by the number of samples predicted as positive. The recall rate is the number of samples predicted as positive and correct divided by the number of samples labeled as positive. The comprehensive evaluation index is the weighted harmonic average of precision and recall, that is, F1 = 2 (Precision + Recall) / (Precision * Recall).

[0282] In process 500, the source station requests the target station for pQoE auxiliary information, and the source station predicts the QoE result of the UE at the candidate target station. In this way, the source station can predict the QoE result of the UE at the candidate target station, so that the source station and the candidate target station can identify possible QoE problems in advance. The source station selects the target cell based on the predicted QoE information, and the target station optimizes resource allocation based on the predicted QoE information, which is conducive to improving the overall satisfaction of the UE and improving the efficiency of wireless resources.

[0283] Figure 6 A second example process of the communication process according to some embodiments of the present disclosure is shown. It can be considered as Figure 5 A specific implementation of the communication process 500. The second example process involves UE 601, S-gNB 602 (i.e., source station 602), T-gNB1 603 (i.e., candidate target station 603) and T-gNB2 604 (i.e., candidate target station 604).

[0284] At 611, UE 601 sends the QoE measurement result to S-gNB 602. The content of the QoE measurement result can refer to the specific content of the QoE measurement result of the aforementioned Uu port, which will not be repeated here.

[0285] At 613, S-gNB 602 triggers the process of selecting candidate target stations and target cells.

[0286] Then S-gNB 602 sends a pQoE assistance information request message to the selected candidate target stations respectively.

[0287] At 615, S-gNB 602 sends a pQoE assistance information request message to T-gNB1 603. The specific content of the pQoE assistance information request message can refer to the embodiment of the request message in process 500.

[0288] At 619, S-gNB 602 sends a pQoE assistance information request message to T-gNB2 604.

[0289] Accordingly, at 617, T-gNB1 603 sends pQoE auxiliary information to S-gNB 602. For the specific content of the pQoE auxiliary information, refer to the embodiment of the auxiliary information in process 500.

[0290] At 621, T-gNB2 604 sends pQoE assistance information to S-gNB 602.

[0291] At 623, after receiving the pQoE auxiliary information, S-gNB 602 predicts the QoE parameters of UE 601 in the candidate gNB based on the local AI model, i.e., the pQoE report in the candidate gNB. The specific content of the QoE parameters can refer to the embodiment of the predicted QoE information in process 500.

[0292] At 625, S-gNB 602 sends the predicted QoE result, i.e., pQoE metric, to T-gNB1 603. The specific content of the pQoE metric can refer to the embodiment of the predicted QoE information in process 500.

[0293] At 627, T-gNB1 603 optimizes resource allocation based on pQoE, and S-gNB 602 notifies the target station to prepare for switching. The target station can optimize resources for UE 601 based on pQoE measurement. For specific resource optimization methods, please refer to the embodiment in process 500.

[0294] At 629, UE 601 also sends the QoE measurement result to T-gNB1 603. The content of the QoE measurement result can refer to the specific content of the QoE measurement result of the aforementioned Uu port. After receiving the QoE measurement result, T-gNB1 603 compares the received pQoE metric to evaluate the pQoE accuracy.

[0295] At 631, T-gNB1 603 may indicate the true value or accuracy of pQoE, i.e., pQoE feedback, to S-gNB 602 to assist the source station in training / monitoring the AI ​​model. The content of pQoE feedback may refer to the embodiment in process 500.

[0296] In the second example process, the source station predicts the QoE parameters of the UE at the candidate target station. Based on the pQoE metric, the source station selects the target cell, and the target station performs network optimization.

[0297] Figure 7 FIG. 7 is a schematic flow chart of a method 700 implemented at a first network device according to an embodiment of the present disclosure. Figure 1A In one possible implementation, the method 700 may be implemented by the first network device 110 in the communication system 100A. In other possible implementations, the method 700 may also be implemented by other communication devices independent of the communication system 100A. As an example, the method 700 will be described below by taking the implementation by the network device 110 in the communication system 100A as an example.

[0298] In block 710, the first network device 110 sends a QoE report about the terminal device to the second network device 120, wherein the QoE report includes first information at the first network device, and the first information is used to predict a QoE measurement result when the terminal device is under the second network device. In block 720, the first network device 110 receives predicted QoE information about the terminal device from the second network device 120, wherein the predicted QoE information includes the predicted QoE measurement result.

[0299] In some example embodiments, the QoE report may include: a time index; a QoE measurement result; a service type; an identification of a terminal device; an identification of a first network device; an identification of a second network device; an identification of a cell; quality of service QoS parameters; a wireless configuration; resource status information; a signal quality of a terminal device in a cell of a second network device; or one or more of the above.

[0300] In some example embodiments, the predicted QoE information may further include: an identifier of the terminal device; an identifier of the second network device; a service type; an identifier of a cell; a time index; or one or more of the above.

[0301] In some example embodiments, the first network device 110 may also send a QoE report to a third network device to which the terminal device can switch. In some example embodiments, the first network device 110 may also receive predicted QoE information about the terminal device from the third network device. In some example embodiments, the first network device 110 may also select a target network device to which the terminal device is to switch from the second network device and the third network device based on the predicted QoE information, and send a first indication to the target network device, wherein the first indication is used to indicate that the terminal device is to switch to the target network device.

[0302] In some example embodiments, the first network device 110 selects a target network device to which the terminal device is to switch from the second network device and the third network device, including selecting the second network device as the target network device, and the first network device 110 may also receive the following information from the second network device: an identification of the terminal device; a predicted QoE measurement result; an accuracy of the predicted QoE measurement result; or one or more of the above.

[0303] In some example embodiments, the indicator of accuracy may be indicated by the first network device 110 to the second network device, and the indicator of accuracy may be indicated to the second network device in a handover request regarding the terminal device.

[0304] In some example embodiments, when the predicted QoE measurement result is lower than a threshold, the first network device 110 may receive a second indication from the second network device, wherein the second indication is used to instruct the second network device to reject the switching of the terminal device.

[0305] In some example embodiments, the prediction of the QoE measurement result of the terminal device under the second network device may be performed based on an artificial intelligence / machine learning (AI / ML) model.

[0306] In some example embodiments, at least one of the QoE report or the predicted QoE information may be transmitted via an Xn interface.

[0307] Figure 8 FIG. 8 is a schematic flow chart of a method 800 implemented at a second network device according to an embodiment of the present disclosure. Figure 1A In one possible implementation, the method 800 may be implemented by the second network device 120 in the communication system 100A. In other possible implementations, the method 800 may also be implemented by other communication devices independent of the communication system 100A. As an example, the method 700 will be described below by taking the implementation by the second network device 120 in the communication system 100A as an example.

[0308] In block 810, the second network device 120 receives a QoE report about a terminal device from the first network device, wherein the QoE report includes first information at the first network device, and the first information is used to predict a QoE measurement result when the terminal device is under the second network device. In block 820, based on the QoE report and the auxiliary information at the second network device, a QoE measurement result is predicted. In block 830, the second network device 120 sends predicted QoE information about the terminal device to the first network device 110, wherein the predicted QoE information includes the predicted QoE measurement result.

[0309] In some example embodiments, predicting the QoE measurement results may include: inputting the QoE report and the auxiliary information into an artificial intelligence / machine learning (AI / ML) model to obtain the predicted QoE measurement results.

[0310] In some example embodiments, the QoE report may include: a time index; a QoE measurement result; a service type; an identification of a terminal device; an identification of a first network device; an identification of a second network device; an identification of a cell; quality of service QoS parameters; a wireless configuration; resource status information; a signal quality of a terminal device in a cell of a second network device; or one or more of the above.

[0311] In some example embodiments, the auxiliary information may include: historical resource status information of the second network device; predicted resource status information of the second network device; an identifier of the second network device; an identifier of a cell; a time index; the number of at least one terminal device; wireless resource status information; the number of wireless resource control RRC connections; the historical average signal quality of at least one terminal device; the historical average QoE measurement result of at least one terminal device; the historical average QoS parameter of at least one terminal device; or one or more of the above.

[0312] In some example embodiments, the auxiliary information may also include: historical resource status information of the second network device; predicted resource status information of the second network device; an identifier of the second network device; an identifier of a cell; a time index; an identifier of a terminal device; a signal quality for the terminal device; a QoE measurement result for the terminal device; a QoS parameter for the terminal device; or one or more of the above. In some example embodiments, the predicted QoE information may also include: an identifier of the terminal device; an identifier of the second network device; a service type; an identifier of a cell; a time index; or one or more of the above.

[0313] In some example embodiments, the second network device 120 may also receive a first indication from the first network device, wherein the first indication is used to indicate that the terminal device is to switch to the second network device; and perform resource optimization for the terminal device based on the predicted QoE information. In some example embodiments, the resource optimization may include: selection of a switching mode for the terminal device; optimization of random access channel RACH resources; optimization of scheduled transmission resources; or one or more of the above.

[0314] In some example embodiments, the second network device 120 may also receive measured QoE information from the terminal device; perform a comparison of the measured QoE information with the predicted QoE information; and based on the comparison, at least one of updating, training, or monitoring the execution of an AI / ML model used to obtain the predicted QoE measurement result.

[0315] In some example embodiments, the second network device 120 may also send the following information to the first network device: an identification of the terminal device; a predicted QoE measurement result; an accuracy of the predicted QoE measurement result; or one or more of the above. In some example embodiments, the accuracy indicator may be indicated by the first network device to the second network device 120. In some example embodiments, the accuracy indicator may be indicated to the second network device 120 in a handover request regarding the terminal device.

[0316] In some example embodiments, the second network device 120 may also send a second indication to the first network device based on determining that the predicted QoE measurement result is lower than the threshold, the second indication being used to instruct the second network device to reject the switching of the terminal device. In some example embodiments, at least one of the QoE report or the predicted QoE information may be transmitted via an Xn interface.

[0317] Fig. 9 FIG. 1 is a schematic flow chart of a method 900 implemented at the first network device 110 according to an embodiment of the present disclosure. For the sake of clarity of discussion and without any limitation, the process 900 will be combined with Figure 1A In one possible implementation, the method 900 may be implemented by the first network device 110 in the communication system 100A. In other possible implementations, the method 900 may also be implemented by other communication devices independent of the communication system 100A. As an example, the method 900 will be described below by taking the implementation by the first network device 110 in the communication system 100A as an example.

[0318] In box 910, the first network device 110 sends a request message for requesting auxiliary information at the second network device to the second network device, wherein the auxiliary information is used to predict the QoE measurement result when the terminal device is under the second network device. In box 920, the first network device 110 receives the auxiliary information from the second network device. In box 930, the first network device 110 predicts the QoE measurement result based on the auxiliary information and the QoE report, wherein the QoE report includes information for predicting the QoE measurement result at the first network device. In box 940, the first network device 110 sends the predicted QoE information about the terminal device to the second network device, wherein the predicted QoE information includes the predicted QoE measurement result.

[0319] In some example embodiments, predicting the QoE measurement result may include: inputting the QoE report and the auxiliary information into an artificial intelligence / machine learning (AI / ML) model to obtain the predicted QoE measurement result. In some example embodiments, the request message may include: a measurement identifier that identifies the auxiliary information request process; an identifier of the second network device; an identifier of the cell; a list of at least one terminal device identifier selected by the first network device; a service type; a third indication of the content of the auxiliary information; time information for the auxiliary information; or one or more of the above.

[0320] In some example embodiments, the auxiliary information may include: a measurement identifier; an identifier of a second network device; an identifier of a cell; a list of at least one terminal device identifier; a service type; the content of the auxiliary information indicated in the third indication; a fourth indication of the content of the unsupported auxiliary information; a time index; or one or more of the above. In some example embodiments, the content of the auxiliary information may include: historical QoE information; predicted QoE information; historical resource status information; predicted resource status information; QoS parameters; wireless configuration; historical signal quality; or predicted signal quality; or one or more of the above. In some example embodiments, the predicted QoE information may also include: an identifier of the terminal device; an identifier of the second network device; a service type; an identifier of a cell; a time index; or one or more of the above.

[0321] In some embodiments, the QoE report includes at least one of the following: time index; QoE measurement results; service type; identification ID of the terminal device; identification of the first network device; identification of the second network device; identification of the cell; quality of service QoS parameters; wireless configuration; resource status information; or signal quality of the terminal device in the cell of the second network device.

[0322] In some example embodiments, the first network device 110 may send a request message to a third network device to which the terminal device can switch, and receive auxiliary information from the third network device. In some example embodiments, sending the predicted QoE information to the second network device may include: selecting a target network device to which the terminal device is to switch from the second network device and the third network device based on the predicted QoE information; and sending the predicted QoE information to the second network device.

[0323] In some example embodiments, the first network device 110 may send a first indication to the second network device, wherein the first indication is used to indicate that the terminal device is to switch to the second network device. In some example embodiments, the first network device 110 may receive the following information from the second network device: an identification of the terminal device; a predicted QoE measurement result; an accuracy of the predicted QoE measurement result; or one or more of the above. In some example embodiments, the first network device 110 may perform at least one of updating, training, or monitoring on the AI / ML model used to obtain the predicted QoE measurement result based on the above information. In some example embodiments, at least one of the QoE report or the predicted QoE information may be transmitted via an Xn interface.

[0324] Fig.10 FIG. 1 is a schematic flow chart of a method 1000 implemented at a second network device according to an embodiment of the present disclosure. For the sake of clarity of discussion and without any limitation, the process 1000 will be combined with Figure 1A In one possible implementation, the method 1000 may be implemented by the network device 1420 in the communication system 100A. In other possible implementations, the method 1000 may also be implemented by other communication devices independent of the communication system 100A. As an example, the method 1000 will be described below by taking the implementation by the network device 120 in the communication system 100A as an example.

[0325] In block 1010, the second network device 120 receives a request message for requesting auxiliary information at the second network device 120 from the first network device, wherein the auxiliary information is used to predict a QoE measurement result when the terminal device is under the second network device 120. In block 1020, the second network device 120 sends the auxiliary information to the first network device based on receiving the request message. In block 1030, the second network device 120 receives predicted QoE information about the terminal device from the first network device, wherein the predicted QoE information includes the predicted QoE measurement result.

[0326] In some example embodiments, the request message may include: a measurement identifier that identifies the auxiliary information request process; an identifier of the second network device 120; an identifier of the cell; a list of at least one terminal device identifier selected by the first network device; a service type; a third indication of the content of the auxiliary information; time information for the auxiliary information; or one or more of the above.

[0327] In some example embodiments, the auxiliary information may include: a measurement identifier; an identifier of the second network device 120; an identifier of a cell; a list of at least one terminal device identifier; a service type; the content of the auxiliary information indicated in the third indication; a fourth indication of the content of the unsupported auxiliary information; a time index; or one or more of the above. In some example embodiments, the content of the auxiliary information may include: historical QoE information; predicted QoE information; historical resource status information; predicted resource status information; QoS parameters; wireless configuration; historical signal quality; predicted signal quality; or one or more of the above.

[0328] In some example embodiments, the predicted QoE information may further include: an identifier of the terminal device; an identifier of the second network device 120; a service type; an identifier of a cell; a time index; or one or more of the above. In some example embodiments, the second network device 120 may receive a first indication from the first network device, wherein the first indication is used to indicate that the terminal device is to switch to the second network device; and perform resource optimization for the terminal device based on the predicted QoE information.

[0329] In some example embodiments, second network device 120 may receive measured QoE information from a terminal device; perform a comparison between the measured QoE information and predicted QoE information; and based on the comparison, evaluate the accuracy of the predicted QoE measurement result.

[0330] In some example embodiments, the second network device 120 may send the following information to the first network device: an identification of the terminal device; a predicted QoE measurement result; an accuracy of the predicted QoE measurement result; or one or more of the above. In some example embodiments, an accuracy indicator may be indicated by the first network device to the second network device 120, and the accuracy indicator may be indicated to the second network device 120 in a handover request regarding the terminal device.

[0331] In some example embodiments, the second network device 120 may send a second indication to the second network device 120 based on determining that the predicted QoE measurement result is lower than the threshold, the second indication being used to instruct the second network device 120 to reject the switching of the terminal device. In some example embodiments, the prediction of the QoE measurement result of the terminal device under the second network device 120 may be performed based on an artificial intelligence / machine learning (AI / ML) model. In some example embodiments, the predicted QoE information may be transmitted via an Xn interface.

[0332] Fig.11 The following is a schematic diagram of the structure of possible communication devices provided in the embodiments of the present application. These communication devices can implement the functions of the terminal device or network device in the above method embodiments, and thus can also achieve the beneficial effects of the above method embodiments. In the embodiments of the present application, the communication device can be as follows: Figure 1A The first network device 110 , the second network device 120 , the third network device 130 or the terminal device 140 shown may also be a module (such as a chip) applied to the first network device 110 , the second network device 120 , the third network device 130 or the terminal device 140 .

[0333] like Fig.11 As shown, the communication device 1100 includes a processing unit 1110, a receiving unit 1120, and a sending unit 1130. The communication device can be used to implement the above Figure 2 , Figure 5 , Figures 7 to 10 The function of the network device in any of the method embodiments shown in . In some embodiments, the processing unit may be a processor, the sending unit may be a sender, and the receiving unit may be a receiver.

[0334] like Fig.12 As shown, the communication device 1200 includes a processor 1210 and an interface circuit 1220. The processor 1210 and the interface circuit 1220 are coupled to each other. It is understood that the interface circuit 1220 can be a transceiver or an input-output interface. Optionally, the communication device 1200 may also include a memory 1230 for storing instructions executed by the processor 1210 or storing input data required by the processor 1210 to execute instructions or storing data generated after the processor 1210 executes instructions.

[0335] When the communication device 1200 is used to implement the method in the above method embodiment, the processor 1210 is used to execute the function of the above processing unit 1110, and the interface circuit 1220 is used to execute the functions of the above receiving unit 1120 and the sending unit 1130.

[0336] When the communication device is a chip applied to the first network device 110, the second network device 120, the third network device 130 or the terminal device 140, the device chip implements the functions of the first network device 110, the second network device 120, the third network device 130 or the terminal device 140 in the above method embodiment. The device chip receives information from other modules (such as radio frequency modules or antennas) in the first network device 110, the second network device 120, the third network device 130 or the terminal device 140, and the information may be sent by other first network devices 110, the second network device 120, the third network device 130 or the terminal device 140; or, the terminal device chip sends information to other modules (such as radio frequency modules or antennas) in the first network device 110, the second network device 120, the third network device 130 or the terminal device 140, and the information is sent to other first network devices 110, the second network device 120, the third network device 130 or the terminal device 140.

[0337] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0338] The present application embodiment provides a communication system. The communication system may include the above Fig.11 The communication device involved in the embodiment shown is a first network device 110, a second network device 120, a third network device 130 or a terminal device 140. Optionally, the first network device 110, the second network device 120, the third network device 130 or the terminal device 140 in the communication system may execute Figure 2 , Figure 5 , Figures 7 to 10 The communication method shown in any one of the items.

[0339] The present application also provides a circuit, which can be coupled to a memory and can be used to execute the process related to the first network device 110, the second network device 120, the third network device 130 or the terminal device 140 in any of the above method embodiments. The chip system may include the chip and other components such as a memory or a transceiver.

[0340] It should be understood that the processor mentioned in the embodiments of the present application may be a CPU, or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0341] It should also be understood that the memory mentioned in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (doubledatarate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (synchlink DRAM, SLDRAM), and direct rambus RAM (DR RAM).

[0342] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, the memory (storage module) is integrated in the processor.

[0343] It should be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0344] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0345] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0346] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0347] In the several embodiments provided in the present application, it should be understood that the disclosed communication methods and devices can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the module is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0348] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0349] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0350] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or the part that makes the contribution or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of each embodiment of the present application. The aforementioned computer-readable storage medium can be any available medium that can be accessed by a computer. By way of example but not limitation, computer-readable media may include random access memory (RAM), read-only memory (ROM), electrically erasable programmable read only memory (EEPROM), compact disc read-only memory (CD-ROM), universal serial bus flash disk, mobile hard disk, or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer.

[0351] As used herein, the term "including" and similar terms should be understood as open inclusion, i.e., "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. can refer to different or identical objects, and are only used to distinguish the objects referred to, without implying a specific spatial order, temporal order, order of importance, etc. of the objects referred to. In some embodiments, values, processes, selected items, determined items, equipment, devices, means, components, assemblies, etc. are referred to as "best", "lowest", "highest", "minimum", "maximum", etc. It should be understood that such descriptions are intended to indicate that a selection can be made among many available functional options, and such selections do not need to be better, lower, higher, smaller, larger or otherwise preferred than other options in other aspects or all aspects. As used herein, the term "determine" can cover a variety of actions. For example, "determine" can include calculation, calculation, processing, export, investigation, search (e.g., search in a table, database or another data structure), ascertainment, etc. Additionally, "determining" may include receiving (eg, receiving information), accessing (eg, accessing data in a memory), etc. Furthermore, "determining" may include resolving, selecting, choosing, establishing, etc.

[0352] The above is only a specific implementation of the present application, but the protection scope of the embodiments of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed in the embodiments of the present application, which should be included in the protection scope of the embodiments of the present application. Therefore, the protection scope of the embodiments of the present application shall be based on the protection scope of the claims.

Claims

1. A method applied to a first network device, comprising: Sending a quality of experience (QoE) report about a terminal device to a second network device, wherein the QoE report includes first information on the first network device, the first information being used to predict a QoE measurement result when the terminal device is under the second network device; and The predicted QoE information about the terminal device is received from the second network device, wherein the predicted QoE information includes the predicted QoE measurement result.

2. The method of claim 1, wherein the QoE report comprises at least one of the following: Time index; QoE measurement result; service type; identification ID of the terminal device; identification of the first network device; identification of the second network device; identification of the cell; quality of service QoS parameters; wireless configuration; resource status information; or signal quality of the terminal device in the cell of the second network device.

3. The method according to claim 1 or 2, wherein the predicted QoE information further comprises at least one of the following: The identifier of the terminal device; the identifier of the second network device; the service type; the identifier of the cell; or a time index.

4. The method according to any one of claims 1 to 3, further comprising: The QoE report is sent to a third network device to which the terminal device can switch.

5. The method according to any one of claims 1 to 4, further comprising: The predicted QoE information about the terminal device is received from the third network device.

6. The method according to any one of claims 1 to 5, further comprising: Based on the predicted QoE information, selecting a target network device to be switched by the terminal device from the second network device and the third network device; as well as A first indication is sent to the target network device, wherein the first indication is used to indicate that the terminal device is to switch to the target network device.

7. The method according to claim 6, wherein selecting the target network device to be switched by the terminal device from the second network device and the third network device comprises selecting the second network device as the target network device, and the method further comprises: Receive at least one of the following information from the second network device: The identification of the terminal device; The predicted QoE measurement result; or the accuracy of the predicted QoE measurement result. The method according to claim 7 , wherein the indicator of the accuracy is indicated by the first network device to the second network device.

9. The method according to claim 7, wherein the indicator of the accuracy is indicated to the second network device in a handover request regarding the terminal device.

10. The method according to any one of claims 1 to 9, further comprising: In a case where the predicted QoE measurement result is lower than a threshold, a second indication is received from the second network device, wherein the second indication is used to instruct the second network device to reject the switching of the terminal device.

11. The method according to any one of claims 1 to 10, wherein the prediction of the QoE measurement result of the terminal device under the second network device is performed based on an artificial intelligence / machine learning (AI / ML) model.

12. The method according to any one of claims 1 to 11, wherein at least one of the QoE report or the predicted QoE information is transmitted via an Xn interface.

13. A method applied to a second network device, comprising: receiving a quality of experience (QoE) report about a terminal device from a first network device, wherein the QoE report includes first information at the first network device, the first information being used to predict a QoE measurement result when the terminal device is under the second network device; predicting the QoE measurement result based on the QoE report and the auxiliary information at the second network device; as well as Sending predicted QoE information about the terminal device to the first network device, wherein the predicted QoE information includes the predicted QoE measurement result.

14. The method of claim 13, wherein predicting the QoE measurement result comprises: The QoE report and the auxiliary information are input into an artificial intelligence / machine learning (AI / ML) model to obtain the predicted QoE measurement result.

15. The method of claim 13, wherein the QoE report comprises at least one of the following: Time index; QoE measurement result; service type; identification ID of the terminal device; identification of the first network device; identification of the second network device; identification of the cell; quality of service QoS parameters; wireless configuration; resource status information; or signal quality of the terminal device in the cell of the second network device.

16. The method according to any one of claims 13 to 15, wherein the auxiliary information comprises at least one of the following: Historical resource status information of the second network device; predicted resource status information of the second network device; identification of the second network device; identification of the cell; time index; number of at least one terminal device; wireless resource status information; number of wireless resource control RRC connections; historical average signal quality of at least one terminal device; historical average QoE measurement result of at least one terminal device; historical average QoS parameter of at least one terminal device.

17. The method according to any one of claims 13 to 15, wherein the auxiliary information further comprises at least one of the following: Historical resource status information of the second network device; predicted resource status information of the second network device; identification of the second network device; identification of the cell; time index; identification of the terminal device; signal quality for the terminal device; QoE measurement results for the terminal device; or QoS parameters for the terminal device.

18. The method according to any one of claims 13 to 17, wherein the predicted QoE information further comprises at least one of the following: The identifier of the terminal device; the identifier of the second network device; the service type; the identifier of the cell; or the time index.

19. The method according to any one of claims 13 to 18, further comprising: receiving a first indication from the first network device, wherein the first indication is used to indicate that the terminal device is to switch to the second network device; as well as Resource optimization is performed for the terminal device based on the predicted QoE information.

20. The method of claim 19, wherein the resource optimization comprises at least one of the following: Selection of a switching mode of the terminal device; Optimization of random access channel RACH resources; or Optimization of scheduling transmission resources.

21. The method according to any one of claims 13 to 20, further comprising: receiving measured QoE information from the terminal device; performing a comparison between the measured QoE information and the predicted QoE information; as well as Based on the comparison, at least one of updating, training, or monitoring execution of an AI / ML model used to obtain the predicted QoE measurement result.

22. The method according to claim 21, further comprising: Send at least one of the following information to the first network device: The identifier of the terminal device; the predicted QoE measurement result; or the accuracy of the predicted QoE measurement result.

23. The method of claim 22, wherein the indicator of accuracy is indicated by the first network device to the second network device.

24. The method according to claim 23, wherein the indicator of the accuracy is indicated to the second network device in a handover request regarding the terminal device.

25. The method according to any one of claims 13 to 24, further comprising: Based on determining that the predicted QoE measurement result is lower than a threshold, a second indication is sent to the first network device, where the second indication is used to instruct the second network device to reject the switching of the terminal device.

26. The method of any one of claims 13 to 25, wherein at least one of the QoE report or the predicted QoE information is transmitted via an Xn interface.

27. A communication device, comprising: A processor, and a memory storing instructions, wherein when the instructions are executed by the processor, the communication device executes the method according to any one of claims 1 to 12 and any one of claims 13 to 26.

28. A chip comprising a processing circuit, wherein the processing circuit is configured to perform the method according to any one of claims 1 to 12 and any one of claims 13 to 26.

29. A computer-readable storage medium storing instructions, wherein when the instructions are executed by a communication device, the communication device executes the method according to any one of claims 1 to 12 and any one of claims 13 to 26.

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