Communication method, communication device, communication system, and storage medium

Abstract

The present disclosure provides a communication method, a communication device, a communication system, and a storage medium. In the present disclosure, first information for indicating AI QoS flow-related information is provided to a first communication device, so that the first communication device can ensure, on the basis of the first information, that an AI service meets a QoS requirement, thereby ensuring that the AI service can provide reliable service performance and user experience.

Description

Communication methods, communication equipment, communication systems and storage media Technical Field

[0001] This disclosure relates to the field of communications, and more particularly to a communication method, communication device, communication system, and storage medium. Background Technology

[0002] The sixth-generation mobile communication technology (6G) will provide efficient end-to-end support for artificial intelligence (AI) related services and applications. Through 6G networks, distributed intelligent agents can be intelligently connected, enabling large-scale deployment of AI across various industries. With the widespread application of AI services, ensuring the Quality of Service (QoS) of AI services is becoming increasingly important.

[0003] Summary of the Invention

[0004] To ensure that AI services can provide reliable service performance and user experience under different scenarios and needs, this disclosure provides a communication method, communication device, communication system, and storage medium.

[0005] According to a first aspect of the present disclosure, a communication method is provided, applied to a first communication device, the method comprising: ensuring that an artificial intelligence (AI) service meets Quality of Service (QoS) requirements based on first information, wherein the first information is used to indicate relevant information of the AI ​​QoS flow.

[0006] According to a second aspect of the present disclosure, a communication method is provided, applied to a second communication device, the method comprising: sending first information to a first communication device, the first information being used to indicate relevant information of an AI QoS flow, and the first information being further used by the first communication device to ensure that the AI ​​service meets QoS requirements.

[0007] According to a third aspect of the present disclosure, a first communication device is provided, comprising: a processing module configured to ensure that an artificial intelligence (AI) service meets Quality of Service (QoS) requirements based on first information, wherein the first information is used to indicate relevant information of the AI ​​QoS flow.

[0008] According to a fourth aspect of the present disclosure, a second communication device is provided, comprising: a transceiver module configured to send first information to a first communication device, the first information being used to indicate relevant information of an AI QoS stream, and the first information being further used by the first communication device to ensure that the AI ​​service meets QoS requirements.

[0009] According to a fifth aspect of the present disclosure, a first communication device is provided, comprising: one or more processors; wherein the first communication device is configured to perform the communication method as described in the first aspect above.

[0010] According to a sixth aspect of the present disclosure, a second communication device is provided, comprising: one or more processors; wherein the second communication device is configured to perform the communication method as described in the second aspect above.

[0011] According to a seventh aspect of the present disclosure, a communication system is provided, including a first communication device and a second communication device, wherein the first communication device is configured to implement the communication method as described in the first aspect above, and the second communication device is configured to implement the communication method as described in the second aspect above.

[0012] According to an eighth aspect of the present disclosure, a storage medium is provided that stores instructions that, when executed on a communication device, cause the communication device to perform the communication method described in the first or second aspect above.

[0013] In this embodiment of the disclosure, a second communication device sends first information to a first communication device. The first information is used to indicate relevant information of the AI ​​QoS flow, so that the first communication device can ensure that the AI ​​service meets the QoS requirements based on the first information, thereby ensuring that the AI ​​service can provide reliable service performance and user experience.

[0014] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0016] Figure 1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0017] Figure 2 is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure.

[0018] Figure 3A is a schematic flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0019] Figure 3B is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0020] Figure 4A is a schematic diagram of the structure of the first communication device proposed in an embodiment of this disclosure.

[0021] Figure 4B is a schematic diagram of the structure of the second communication device proposed in an embodiment of this disclosure.

[0022] Figure 5A is a schematic diagram of the structure of the communication device 5100 proposed in an embodiment of this disclosure.

[0023] Figure 5B is a schematic diagram of the structure of the chip 5200 proposed in the embodiments of this disclosure. Detailed Implementation

[0024] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0025] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of at least one associated listed item.

[0026] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various messages, these messages should not be limited to these terms. These terms are used only to distinguish messages of the same type from one another. For example, without departing from the scope of this disclosure, a first message may also be referred to as a second message, and similarly, a second message may also be referred to as a first message. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0027] This disclosure provides a communication method, communication device, communication system, and storage medium.

[0028] In a first aspect, embodiments of this disclosure propose a communication method applied to a first communication device, the method comprising: ensuring that an artificial intelligence (AI) service meets Quality of Service (QoS) requirements based on first information, wherein the first information is used to indicate information related to the AI ​​QoS flow.

[0029] In the above embodiments, by providing first information to the first communication device, the first information is used to indicate relevant information of the AI ​​QoS flow, so that the first communication device can ensure that the AI ​​service meets the QoS requirements based on the first information, thereby ensuring that the AI ​​service can provide reliable service performance and user experience.

[0030] In conjunction with some embodiments of the first aspect, in some embodiments, the first information is used to indicate at least one of the following information related to the AI ​​QoS flow: AI QoS flow type; AI QoS parameters.

[0031] In the above embodiments, multiple optional information that the first information can indicate are provided so that the first information can be used to indicate multiple information related to AI QoS flow, thereby improving the flexibility and diversity of the first information and thus improving the flexibility of the AI ​​service quality assurance process based on the first information.

[0032] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​QoS type includes at least one of the following: a guaranteed AI QoS flow; a non-guaranteed AI QoS flow.

[0033] In the above embodiments, multiple optional AI QoS flow types are provided so that the first information can be used to indicate information related to multiple types of AI QoS flows, thereby improving the flexibility and diversity of the first information and thus improving the flexibility of the AI ​​service quality assurance process based on the first information.

[0034] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​QoS parameter includes at least one of the following: a first AI QoS parameter, which indicates the minimum AI QoS performance metric that the AI ​​QoS flow must achieve; a second AI QoS parameter, which indicates the maximum AI QoS performance metric that the AI ​​QoS flow must achieve; a third AI QoS parameter, which indicates the maximum AI QoS burst performance metric that the AI ​​QoS flow must achieve; and a fourth QoS parameter, which indicates the highest aggregated AI QoS performance metric.

[0035] In the above embodiments, multiple optional AI QoS parameters for the AI ​​QoS stream are provided so that the first information can be used to indicate multiple AI QoS parameters related to the AI ​​QoS stream, thereby improving the flexibility and diversity of the first information and thus improving the flexibility of the AI ​​service quality assurance process based on the first information.

[0036] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI QoS parameter is obtained based on statistics of a first time window, the second AI QoS parameter is obtained based on statistics of a second time window, and the third AI QoS parameter is obtained based on statistics of a third time window.

[0037] In the above embodiments, an optional implementation method for statistically analyzing AI QoS parameters is provided, namely, statistical analysis is performed within a corresponding time window to obtain the corresponding AI QoS parameters, so as to standardize the process of obtaining AI QoS parameters.

[0038] In conjunction with some embodiments of the first aspect, in some embodiments, the time window lengths of the first time window, the second time window, and the third time window are agreed upon by the protocol; or, the time window lengths of the first time window, the second time window, and the third time window are determined based on the first information.

[0039] In the above embodiments, multiple optional implementations are provided for determining the time window lengths of the first time window, the second time window, and the third time window, so that the time window lengths of the first time window, the second time window, and the third time window can be determined in multiple ways, thereby improving the flexibility of the time window length determination process.

[0040] In conjunction with some embodiments of the first aspect, in some embodiments, the fourth AI QoS parameter includes at least one of the following: the highest aggregated AI QoS performance index of multiple AI terminals; the highest aggregated AI QoS performance index of multiple AI services; the highest aggregated AI QoS performance index of multiple AI tasks; the highest aggregated AI QoS performance index of multiple AI resources; and the highest aggregated AI QoS performance index of multiple AI sessions.

[0041] In the above embodiments, a variety of optional fourth AI QoS parameters are provided so that the highest performance indicators of multiple aggregated AI QoS can be indicated through the first information, thereby improving the flexibility and diversity of the first information and thus improving the flexibility of the AI ​​service quality assurance process based on the first information.

[0042] In conjunction with some embodiments of the first aspect, in some embodiments, for a guaranteed AI QoS flow, the AI ​​QoS parameter includes at least one of the first AI QoS parameter, the second AI QoS parameter, and the third AI QoS parameter; for a non-guaranteed AI QoS flow, the AI ​​QoS parameter includes the fourth AI QoS parameter.

[0043] In the above embodiments, AI QoS parameters are provided for guaranteed AI QoS flows and non-guaranteed AI QoS flows respectively, so as to standardize the correspondence between AI QoS flow types and AI QoS parameters, thereby enabling the configuration of corresponding AI QoS parameters for different types of AI QoS flows.

[0044] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​QoS parameter further includes a fifth AI QoS parameter, which is the highest aggregated AI QoS performance indicator of all AI QoS flows associated with the same network slice.

[0045] In the above embodiments, another possible AI QoS parameter for the AI ​​QoS flow is provided so that more AI QoS parameters related to the AI ​​QoS flow can be indicated through the first information, thereby improving the flexibility and diversity of the first information and thus improving the flexibility of the AI ​​service quality assurance process based on the first information.

[0046] In conjunction with some embodiments of the first aspect, in some embodiments, for any one of the following AI QoS performance indicators: minimum AI QoS performance indicator, maximum AI QoS performance indicator, maximum AI QoS burst performance indicator, and aggregated maximum AI QoS performance indicator, the AI ​​QoS performance indicator is an AI QoS performance indicator corresponding to at least one of the following: AI service; AI task; AI resource; AI terminal; AI session; AI data packet.

[0047] In the above embodiments, dimensions that may correspond to the AI ​​QoS performance indicators are provided, such as the minimum AI QoS performance indicator, the maximum AI QoS performance indicator, the maximum AI QoS burst performance indicator, and the aggregated maximum AI QoS performance indicator. This allows AI QoS performance indicators corresponding to multiple dimensions to be used as the minimum AI QoS performance indicator, the maximum AI QoS performance indicator, the maximum AI QoS burst performance indicator, and the aggregated maximum AI QoS performance indicator, thereby improving the flexibility of the AI ​​service quality assurance process based on the first information.

[0048] In conjunction with some embodiments of the first aspect, in some embodiments, the first information is sent by the second communication device to the first communication device, or the first information is pre-configured to the first communication device.

[0049] In the above embodiments, multiple optional configuration methods for the first information are provided so that the first information can be configured for the first communication device in multiple ways, thereby improving the flexibility of the first information configuration process.

[0050] In conjunction with some embodiments of the first aspect, in some embodiments, the second communication device is a core network device; the first communication device includes at least one of a terminal, an access network device, and an independent AI entity node.

[0051] In the above embodiments, optional device types for the first communication device and the second communication device are provided so that AI service quality assurance based on the first information can be achieved in scenarios composed of the first communication device and the second communication device of the corresponding type, thereby improving the generalization of the AI ​​service quality assurance process based on the first information.

[0052] Secondly, embodiments of this disclosure propose a communication method applied to a second communication device. The method includes: sending first information to a first communication device, the first information being used to indicate relevant information of an AI QoS flow, and the first information being used by the first communication device to ensure that the AI ​​service meets QoS requirements.

[0053] In the above embodiments, by sending first information from the second communication device to the first communication device, the first information is used to indicate relevant information of the AI ​​QoS flow, so that the first communication device can ensure that the AI ​​service meets the QoS requirements based on the first information, thereby ensuring that the AI ​​service can provide reliable service performance and user experience.

[0054] In conjunction with some embodiments of the second aspect, in some embodiments, the first information is used to indicate at least one of the following information related to the AI ​​QoS flow: AI QoS flow type; AI QoS parameters.

[0055] In conjunction with some embodiments of the second aspect, in some embodiments, the AI ​​QoS flow type includes at least one of the following: a guaranteed AI QoS flow; a non-guaranteed AI QoS flow.

[0056] In conjunction with some embodiments of the second aspect, in some embodiments, the AI ​​QoS parameter includes at least one of the following: a first AI QoS parameter, which indicates the minimum AI QoS performance metric that the AI ​​QoS flow must achieve; a second AI QoS parameter, which indicates the maximum AI QoS performance metric that the AI ​​QoS flow must achieve; a third AI QoS parameter, which indicates the maximum AI QoS burst performance metric that the AI ​​QoS flow must achieve; and a fourth AI QoS parameter, which indicates the highest aggregated AI QoS performance metric.

[0057] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI QoS parameter is obtained based on statistics of a first time window, the second AI QoS parameter is obtained based on statistics of a second time window, and the third AI QoS parameter is obtained based on statistics of a third time window.

[0058] In conjunction with some embodiments of the second aspect, in some embodiments, the time window lengths of the first time window, the second time window, and the third time window are agreed upon by the protocol; or, the time window lengths of the first time window, the second time window, and the third time window are determined based on the first information.

[0059] In conjunction with some embodiments of the second aspect, in some embodiments, the fourth AI QoS parameter includes at least one of the following: the highest aggregated AI QoS performance index of multiple AI terminals; the highest aggregated AI QoS performance index of multiple AI services; the highest aggregated AI QoS performance index of multiple AI tasks; the highest aggregated AI QoS performance index of multiple AI resources; and the highest aggregated AI QoS performance index of multiple AI sessions.

[0060] In conjunction with some embodiments of the second aspect, in some embodiments, for a guaranteed AI QoS flow, the AI ​​QoS parameter includes at least one of the first AI QoS parameter, the second AI QoS parameter, and the third AI QoS parameter; for a non-guaranteed AI QoS flow, the AI ​​QoS parameter includes the fourth AI QoS parameter.

[0061] In conjunction with some embodiments of the second aspect, in some embodiments, the AI ​​QoS parameter further includes a fifth AI QoS parameter, which is the highest aggregated AI QoS performance indicator of all AI QoS flows associated with the same network slice.

[0062] In conjunction with some embodiments of the second aspect, in some embodiments, for any one of the following AI QoS performance indicators: minimum AI QoS performance indicator, maximum AI QoS performance indicator, maximum AI QoS burst performance indicator, and aggregated maximum AI QoS performance indicator, the AI ​​QoS performance indicator is an AI QoS performance indicator corresponding to at least one of the following: AI service; AI task; AI resource; AI terminal; AI session; AI data packet.

[0063] In conjunction with some embodiments of the second aspect, in some embodiments, the first communication device includes at least one of a terminal, an access network device, and an independent AI entity node; the second communication device is a core network device.

[0064] Thirdly, embodiments of this disclosure provide a first communication device, including: a processing module configured to ensure that an artificial intelligence (AI) service meets Quality of Service (QoS) requirements based on first information, wherein the first information is used to indicate relevant information of the AI ​​QoS flow.

[0065] Fourthly, embodiments of this disclosure propose a second communication device, including: a transceiver module configured to send first information to a first communication device, the first information being used to indicate relevant information of an AI QoS stream, and the first information being used by the first communication device to ensure that the AI ​​service meets QoS requirements.

[0066] Fifthly, embodiments of this disclosure provide a first communication device, comprising: one or more processors; wherein the first communication device is configured to perform the communication method as described in the first aspect above.

[0067] In a sixth aspect, embodiments of this disclosure provide a second communication device, comprising: one or more processors; wherein the second communication device is configured to perform the communication method as described in the second aspect above.

[0068] In a seventh aspect, embodiments of this disclosure provide a communication system including a first communication device and a second communication device, wherein the first communication device is configured to implement the communication method as described in the first aspect above, and the second communication device is configured to implement the communication method as described in the second aspect above.

[0069] Eighthly, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the communication method as described in the first or second aspect above.

[0070] Ninthly, embodiments of this disclosure provide a program product that, when executed by a communication device, causes the communication device to perform the communication method as described in the first or second aspect above.

[0071] In a tenth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the communication method as described in the first or second aspect above.

[0072] Eleventhly, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the communication methods described in the first or second aspect above.

[0073] It is understood that the aforementioned first communication device, second communication device, communication system, storage medium, program product, computer program, chip, or chip system are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0074] This disclosure provides a communication method, a communication device, a communication system, and a storage medium. In some embodiments, the terms "communication method" and "information processing method," "AI service assurance method," etc., can be used interchangeably; the terms "communication device" and "information processing device," "AI service assurance device," etc., can be used interchangeably; and the terms "information processing system" and "communication system," etc., can be used interchangeably.

[0075] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0076] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0077] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0078] In this embodiment of the disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular expression or a plural expression.

[0079] In the embodiments disclosed herein, "multiple" refers to two or more.

[0080] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0081] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "in response to one case A, in response to another case B", etc., may include the following technical solutions depending on the situation: in some embodiments, A (execute A regardless of B); in some embodiments, B (execute B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0082] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, C, etc.

[0083] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects is found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0084] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0085] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0086] In some embodiments, the terms “greater than,” “greater than or equal to,” “not less than,” “more than,” “more than or equal to,” “not less than,” “higher than,” “higher than or equal to,” “not lower than,” and “above” can be used interchangeably, as can the terms “less than,” “less than or equal to,” “not greater than,” “less than,” “less than or equal to,” “not more than,” “lower than,” “lower than or equal to,” “not higher than,” and “below”.

[0087] In some embodiments, the apparatus and device may be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they may also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "body", etc.

[0088] In some embodiments, "network" can be interpreted as devices included in the network, such as access network devices, core network devices, etc.

[0089] In some embodiments, "access network device (AN device)" may also be referred to as "radio access network device (RAN device)," "base station (BS)," "radio base station," or "fixed station." In some embodiments, it may also be understood as "node," "access point," "transmission point (TP)," "reception point (RP)," "transmission / reception point (TRP)," "panel," "antenna panel," "antenna array," "cell," "macro cell," "small cell," "femto cell," "pico cell," "sector," "cell group," "serving cell," "carrier," "component carrier," or "bandwidth part (BWP)."

[0090] In some embodiments, "terminal" or "terminal device" may be referred to as "user equipment (UE)," "user terminal," "mobile station (MS)," "mobile terminal (MT)," "subscriber station," "mobile unit," "subscriber unit," "wireless unit," "remote unit," "mobile device," "wireless device," "wireless communication device," "remote device," "mobile subscriber station," "access terminal," "mobile terminal," "wireless terminal," "remote terminal," "handset," "user agent," "mobile client," "client," etc.

[0091] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0092] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0093] Furthermore, each element, each row, or each column in the table of this disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0094] Figure 1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in Figure 1, the communication system 100 includes a first communication device 101 and a second communication device 102.

[0095] In some embodiments, the first communication device 101 includes at least one of a terminal, an access network device, and an independent AI entity node.

[0096] In some embodiments, the terminal includes, but is not limited to, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet computer, computer with wireless transceiver function, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal device in industrial control, wireless terminal device in self-driving, wireless terminal device in remote medical surgery, wireless terminal device in smart grid, wireless terminal device in transportation safety, wireless terminal device in smart city, and wireless terminal device in smart home.

[0097] In some embodiments, the access network device is, for example, a node or device that connects a terminal to a wireless network. The access network device may include, but is not limited to, at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation eNB (ng-eNB), next-generation Node B (gNB), node B (NB), home node B (HNB), home evolved node B (HeNB), radio backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a Wi-Fi system.

[0098] In some embodiments, the technical solutions of this disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within access network devices involved in the embodiments of this disclosure can be transformed into internal interfaces of Open RAN. The processes and information interactions between these internal interfaces can be implemented by software or programs.

[0099] In some embodiments, the access network device may be composed of a central unit (CU) and a distributed unit (DU). The CU may also be called a control unit. The CU-DU structure can separate the protocol layer of the access network device. Some of the protocol layer functions are centrally controlled by the CU, while the remaining part or all of the protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0100] In some embodiments, a standalone AI entity node can be a device embedded with AI technology. Standalone AI entity nodes include, but are not limited to, smartphones, smart speakers, smartwatches, smart home devices, smart TVs, autonomous vehicles, robots, drones, smart glasses, wearable devices, edge computing devices, and VR devices.

[0101] In some embodiments, the second communication device 102 may be a core network device.

[0102] In some embodiments, the core network equipment may be a single device comprising multiple network elements, or it may be multiple devices or a group of devices, each comprising all or part of the multiple network elements. Network elements may be virtual or physical. The core network may include, for example, at least one of the Evolved Packet Core (EPC), 5G Core Network (5GCN), and Next Generation Core (NGC).

[0103] In some embodiments, the core network device may include a first network element, such as a Session Management Function (SMF).

[0104] In some embodiments, the first network element is used for session management of the control plane and user plane, but is not limited thereto.

[0105] In some embodiments, the core network device may include a second network element, such as a Policy Control Function (PCF).

[0106] In some embodiments, the second network element is used to implement user control policy management, including but not limited to QoS control, service access control, etc.

[0107] In some embodiments, the core network equipment may include a third network element, such as an Access and Mobility Management Function (AMF).

[0108] In some embodiments, the third network element is used for user access management and mobility management, but is not limited thereto.

[0109] In some embodiments, the core network device may include a fourth network element, such as a User Plane Function (UPF).

[0110] In some embodiments, the fourth network element is used for user plane data forwarding, traffic statistics, Quality of Service (QoS) management, etc., but is not limited to these.

[0111] In some embodiments, the core network equipment may include a fifth network element, such as a unified data management function (UDM).

[0112] In some embodiments, the fifth network element is used to implement user subscription data management, roaming control, etc., but is not limited to these.

[0113] In some embodiments, the core network device may include a sixth network element, such as an Authentication Server Function (AUSF).

[0114] In some embodiments, the second network element is used to implement user authentication, but is not limited thereto.

[0115] In some embodiments, each of the above network elements can be independent of the core network equipment.

[0116] In some embodiments, each of the above network elements may be part of the core network equipment.

[0117] It is understood that the communication system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions proposed in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions proposed in this disclosure are also applicable to similar technical problems.

[0118] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1 are illustrative. The communication system may include all or some of the main bodies in FIG1, or may include other main bodies outside of FIG1. ​​The number and form of each main body are arbitrary. Each main body may be physical or virtual. The connection relationship between the main bodies is illustrative. The main bodies may not be connected or may be connected. The connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0119] The embodiments disclosed herein can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, systems utilizing other communication methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0120] In some embodiments, Quality of Service (QoS) refers to the performance characteristics of a network or service experienced by the user. In a 5G environment, QoS encompasses a range of factors, including reliability, availability, latency, throughput, and traffic prioritization. These aspects play a crucial role in providing a good user experience and meeting the needs of various applications.

[0121] In some embodiments, QoS in the 5G core network is determined by QoS flows, including Guaranteed Bit Rate (GBR), Non-GBR, and reflection flows for dynamic configuration. Optionally, these QoS flows can be used to define specific quality parameters applicable to user plane traffic.

[0122] In some embodiments, GBR refers to the minimum bit rate that the network must guarantee to provide for a particular service or application at any given point in time. This is a quality of service guarantee that ensures critical applications or services have sufficient bandwidth and transmission rates within the network.

[0123] In some embodiments, GBR can be used in various application scenarios, including real-time services, mission-critical applications, and high-priority data streams. Real-time services include, but are not limited to, applications requiring stable bandwidth and low latency, such as video conferencing, online gaming, and telemedicine; mission-critical applications include, but are not limited to, applications with strict requirements for the continuity and reliability of data transmission, such as industrial automation and intelligent transportation systems; high-priority data streams are data streams that require priority processing, such as emergency communications or the transmission of high-value content.

[0124] In some embodiments, non-GBR refers to data streams or services that do not offer bandwidth guarantees. Such services may be throttled or slowed down during network congestion, but can achieve better service quality when network resources are plentiful.

[0125] In some embodiments, non-GBR can be used for various application scenarios such as non-real-time services, background tasks, and low-priority traffic. Non-real-time services include, but are not limited to, services that do not require real-time transmission, such as email, file downloads, and video-on-demand. Background tasks include, but are not limited to, tasks that can run in the background, such as data backups and software updates. Low-priority traffic refers to data streams that are not critical, for which non-GBR services can be used to save bandwidth resources.

[0126] In some embodiments, each QoS flow in the 5G core network is identified by a Quality of Service Flow ID (QFI). The QFI is a unique identifier for a specific QoS flow, allowing for precise management and control of traffic based on its specific quality requirements.

[0127] In some embodiments, a key aspect of quality of service in a 5G core network is the characterization of QoS parameters (i.e., QoS traffic characteristics). These parameters define the specific requirements and guarantees for a given QoS profile.

[0128] In some embodiments, the Session Management Function (SMF) can provide QoS profiles to the Radio Access Network (RAN) via the Access and Mobility Management Function (AMF) at the N2 reference point, or pre-configure them directly in the RAN.

[0129] In some embodiments, one or more QoS rules, along with options to associate QoS flow-level QoS parameters with these rules, can be defined in the QoS profile. QoS rules are responsible for specifying the specific behavior and processing of user plane traffic based on their respective QoS requirements. By associating QoS flow-level QoS parameters with these rules, further granularity and customization can be achieved to ensure the quality and performance of each flow within the network.

[0130] In some embodiments, to implement and enforce defined QoS rules and parameters, the Session Management Function (SMF) can provide the User Plane Function (UPF) with one or more uplink (UL) and downlink (DL) packet detection rules (PDRs). These PDRs serve as guidelines for the UPF in processing user plane traffic, ensuring that the required QoS guarantees are met. UL and DL PDRs play a crucial role in ensuring efficient traffic management across the entire network.

[0131] In some embodiments, the QoS profile in the 5G core network may include specific QoS parameters for each QoS flow. These parameters can be used to ensure that the network can provide the necessary service levels and meet the QoS requirements of different flows.

[0132] In some embodiments, 5G QoS parameters may include resource type, priority, packet delay budget (PDB), maximum data burst volume (MDBV), packet error rate (PER), average window, etc.

[0133] In some embodiments, the choice of resource type can determine the specific characteristics and requirements associated with a QoS flow. GBR QoS flows can use either the GBR resource type or the delay-critical GBR resource type, which have different definitions for parameters such as PDB and Packet Error Rate (PER). Furthermore, the MDBV parameter is only applicable to the delay-critical GBR resource type.

[0134] In some embodiments, the priority assigned to a QoS flow represents its relative importance in network resource scheduling. Priorities are defined such that the lowest level corresponds to the highest priority. Priority parameters can be used to distinguish between QoS flows from the same UE and QoS flows from different UEs. By assigning priorities, the network can effectively manage and prioritize resource allocation based on the specific needs and requirements of each flow.

[0135] In some embodiments, the packet delay budget can be used to specify the upper limit of delay that a packet can experience between the UE and the UPF endpoint on the N6 interface. The packet delay budget parameter can be used to ensure that packet transmission delay remains within acceptable limits. The same value for the packet delay budget applies to both the UL and DL directions, thereby ensuring consistent delay performance.

[0136] In some embodiments, the MDBV parameter represents the maximum amount of data that the 5G Access Network (5G-AN) must process within a given time period, consistent with the PDB of the 5G-AN. The MDBV parameter represents the data burst size that the 5G-AN can provide without exceeding a specified limit. The MDBV parameter is applicable to resource allocation for QoS flows and ensures effective resource management and allocation based on defined burst size requirements.

[0137] In some embodiments, the packet error rate parameter defines an upper limit on the rate at which Internet Protocol (IP) packets processed by an existing link-layer protocol (e.g., Radio Link Control, RLC) but not successfully transmitted by the corresponding receiver to an upper layer (e.g., Packet Data Convergence Protocol, PDCP) are transmitted. The packet error rate parameter sets a threshold for the acceptable level of packet loss during transmission to ensure reliable data transmission between network entities.

[0138] In some embodiments, each GBR QoS flow is associated with an average window. The average window represents the duration for which GBR and MBR parameters are calculated. This calculation can occur across various network elements, including access networks, UPR (User Plane Functions of the RAN), and UEs. The average window can be used to determine the time period for measuring GBR and MBR values ​​and provides a basis for accurate resource allocation and management.

[0139] In traditional communication networks, QoS assurance is a crucial means of ensuring the stable operation of critical services. In 6G communication networks, with the widespread application of AI technology, QoS assurance for AI services has become equally important.

[0140] In some embodiments, QoS assurance for AI services needs to consider multiple factors such as computing resources, algorithms, connectivity, and data. Therefore, this disclosure aims to provide a QoS assurance mechanism for AI networks, by defining different AI QoS types and resource guarantee requirement parameters, to ensure that AI services can provide reliable service performance and user experience under different scenarios and needs.

[0141] Figure 2 is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in Figure 2, the embodiments of the present disclosure relate to a communication method, which includes:

[0142] Step S2101: The second communication device sends the first information to the first communication device.

[0143] In some embodiments, the first communication device receives first information sent by the second communication device.

[0144] In some embodiments, the first communication device is used to perform QoS assurance for AI services, or in other words, the first communication device is used to implement QoS assurance for AI services; that is, the first communication device is a device node used to perform AI QoS assurance, or in other words, the first communication device is a device node used to implement AI QoS assurance.

[0145] In some embodiments, the first communication device includes at least one of a terminal (i.e., UE), an access network device (i.e., NG-RAN), and an independent AI entity node, but is not limited thereto.

[0146] In some embodiments, the name of the first communication device is not limited, and may be, for example, "first device", "first node", "AI QoS execution device", "AI QoS execution node", etc.

[0147] In some embodiments, the second communication device is used to manage AI QoS, that is, the second communication device is a device node used to manage AI QoS.

[0148] In some embodiments, the second communication device is a core network device (such as SMF, PCF, etc.).

[0149] In some embodiments, the name of the second communication device is not limited, and may be, for example, "second device", "second node", "AI QoS management device", "AI QoS management node", etc.

[0150] In some embodiments, the first information is used to indicate information related to the AI ​​QoS flow. Optionally, the first information is used to indicate information related to the characteristics of the AI ​​QoS flow, but is not limited thereto.

[0151] In some embodiments, the name of the first information is not limited, and it may be, for example, “AI QoS information”, “AI QoS related information (AI QoS Profile)”, etc.

[0152] In some embodiments, the names of information, etc., are not limited to the names described in the embodiments. Terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0153] In some embodiments, the first information is used to indicate the AI ​​QoS flow type and / or AI QoS parameters of the AI ​​QoS flow.

[0154] In some embodiments, the AI ​​QoS flow type includes at least one of a guaranteed AI QoS flow and a non-guaranteed AI QoS flow.

[0155] In some embodiments, guaranteed AI QoS streams can be used in AI services that have high requirements for AI resource protection or real-time performance to ensure the stable operation and rapid response of AI services. For example, guaranteed AI QoS streams can be used in critical services such as autonomous driving and telemedicine, but are not limited to these.

[0156] In some embodiments, non-guaranteed AI QoS streams can be used in AI services that do not have high requirements for real-time performance or AI resource guarantees, in order to save resources and improve the overall efficiency of the system. For example, non-guaranteed AI QoS streams can be used for services such as video analytics and data mining, but are not limited to these.

[0157] In some embodiments, the AI ​​QoS parameters of the AI ​​QoS stream may include at least one of a first AI QoS parameter, a second AI QoS parameter, a third AI QoS parameter, and a fourth AI QoS parameter, but are not limited thereto.

[0158] In some embodiments, the first AI QoS parameter can be used to indicate the minimum AI QoS performance metric that must be guaranteed for the AI ​​QoS flow.

[0159] In some embodiments, the first AI QoS parameter can be obtained based on statistics from a first time window. That is, the first communication device must ensure that the lowest AI QoS performance index obtained within the first time window reaches the lowest AI QoS performance index indicated by the first AI QoS parameter.

[0160] In some embodiments, the name of the first AI QoS parameter is not limited, and it may be, for example, "Guaranteed AI QoS parameter".

[0161] In some embodiments, the second AI QoS parameter is used to indicate the highest AI QoS performance metric that the AI ​​QoS flow must achieve.

[0162] In some embodiments, the second AI QoS parameter can be obtained based on statistics from a second time window. That is, the first communication device must ensure that the highest AI QoS performance index obtained within the second time window reaches the highest AI QoS performance index indicated by the second AI QoS parameter.

[0163] In some embodiments, the name of the second AI QoS parameter is not limited, and it may be, for example, "Maximum AI QoS parameter".

[0164] In some embodiments, the third AI QoS parameter is used to indicate the maximum AI QoS burst performance metric that the AI ​​QoS flow must achieve.

[0165] In some embodiments, the third AI QoS parameter can be obtained based on statistics from a third time window. That is, the first communication device must ensure that the maximum AI QoS burst performance index obtained within the third time window reaches the maximum AI QoS burst performance index indicated by the third AI QoS parameter.

[0166] In some embodiments, the name of the third AI QoS parameter is not limited, and it may be, for example, "Maximum Burst AI QoS parameter".

[0167] In some embodiments, if the actual AI QoS burst of the first communication device within the third time window does not exceed the third AI QoS parameter and does not exceed the first AI QoS, then packets exceeding the third time window can be considered discarded.

[0168] In some embodiments, the time window lengths of the first time window, the second time window, and the third time window may be the same or different.

[0169] In some embodiments, the time window lengths of the first time window, the second time window, and the third time window can be agreed upon by the protocol, or the time window lengths of the first time window, the second time window, and the third time window can be determined based on the first information.

[0170] In some embodiments, the time window lengths of the first time window, the second time window, and the third time window are determined based on the first information. The time window lengths of the first time window, the second time window, and the third time window can be used as an AI QoS parameter included in the first information, so that the first communication device can determine the time window length of at least one time window in the first time window, the second time window, and the third time window based on the AI ​​QoS parameter included in the first information.

[0171] In some embodiments, the name of the AI ​​QoS parameter used to determine the time window length is not limited, and it may be, for example, "time window parameter", "time window information", "time window length information", etc.

[0172] In some embodiments, terms such as "time window," "duration," "segment," "window," and "time" can be used interchangeably, as can terms such as "time," "moment," "point in time," and "time location."

[0173] In some embodiments, the statistical unit for the length of the time window can be absolute time, slot, symbol, etc., but is not limited to these.

[0174] In some embodiments, the terms “slot”, “sub-slot”, “mini-slot”, “symbol”, “frame”, “radio frame”, “subframe”, “symbol”, and “transmission time interval (TTI)” can be used interchangeably.

[0175] In some embodiments, the fourth AI QoS parameter is used to indicate the highest performance metric of the aggregated AI QoS.

[0176] In some embodiments, the name of the fourth AI QoS parameter is not limited, and it may be, for example, “aggregated AI QoS parameter” or “aggregated AI QoS performance metric”.

[0177] In some embodiments, the highest performance metric for aggregated AI QoS can be defined according to at least one dimension of AI terminal, AI service, AI task, AI resource, and AI session.

[0178] In some embodiments, the highest performance metric of aggregated AI QoS can be defined according to the AI ​​terminal dimension. The fourth AI QoS parameter can be the highest performance metric of aggregated AI QoS of multiple AI terminals. That is, the aggregated result of the highest AI QoS performance metrics of multiple AI terminals can be used as the fourth AI QoS parameter.

[0179] In some embodiments, the name of the highest performance metric for aggregated AI QoS defined according to the AI ​​terminal dimension is not limited, and may be, for example, “UE Aggregate Maximum AI QoS”.

[0180] In some embodiments, the highest performance metric of aggregated AI QoS can be defined according to the AI ​​service dimension. The fourth AI QoS parameter can be the highest performance metric of aggregated AI QoS of multiple AI services. That is, the aggregation result of the highest AI QoS performance metrics of multiple AI services can be used as the fourth AI QoS parameter.

[0181] In some embodiments, the name of the highest performance metric for aggregated AI QoS defined according to the AI ​​service dimension is not limited, and may be, for example, "AI Service Aggregate Maximum AI QoS".

[0182] In some embodiments, AI services may include, but are not limited to, AI model training services, AI model inference services, AI model data services, AI model verification services, etc.

[0183] The AI ​​model training service can be used to train AI models using data, enabling them to perform AI processing tasks (including but not limited to recognition, prediction, and classification). The AI ​​model inference service can be used to instruct the process of using the AI ​​model to perform actual AI task processing after the AI ​​model has been trained. The AI ​​model validation service can be used to evaluate the trained AI model to ensure that the performance of the trained AI model meets expectations. The AI ​​model data service can provide data management and processing for training, validation, and inference.

[0184] In some embodiments, the highest performance metric of aggregated AI QoS can be defined according to the AI ​​task dimension. The fourth AI QoS parameter can be the highest performance metric of aggregated AI QoS of multiple AI tasks. That is, the aggregated result of the highest AI QoS performance metrics of multiple AI tasks can be used as the fourth AI QoS parameter.

[0185] In some embodiments, the name of the highest performance metric for aggregated AI QoS defined according to the AI ​​task dimension is not limited, and may be, for example, "AI Task Aggregate Maximum AI QoS".

[0186] In some embodiments, AI tasks may include data preprocessing tasks, feature extraction tasks, model training tasks, inference tasks, evaluation tasks, transmission tasks, etc., but are not limited thereto.

[0187] In some embodiments, data preprocessing tasks include, but are not limited to, data cleaning, data formatting, and data normalization.

[0188] In some embodiments, the feature extraction task can be used to extract useful features from raw data for model training, but is not limited thereto.

[0189] In some embodiments, the model training task may be used to train a model using a specific algorithm and dataset, but is not limited thereto.

[0190] In some embodiments, the inference task can be used to predict and classify new data using a trained model, but is not limited thereto.

[0191] In some embodiments, the evaluation task can be used to evaluate and test the performance of the model, but is not limited thereto.

[0192] In some embodiments, the transmission task can be used to transmit AI data or models, but is not limited thereto.

[0193] In some embodiments, the highest performance metric of aggregated AI QoS can be defined according to the AI ​​resource dimension. The fourth AI QoS parameter can be the highest performance metric of aggregated AI QoS of multiple AI resources. That is, the aggregation result of the highest AI QoS performance metrics of multiple AI resources can be used as the fourth AI QoS parameter.

[0194] In some embodiments, the name of the highest performance metric for aggregated AI QoS defined according to the AI ​​resource dimension is not limited, and may be, for example, "AI Resource Aggregate Maximum AI QoS".

[0195] In some embodiments, AI resources may include, but are not limited to, AI computing power resources, AI algorithm resources, AI connectivity resources, AI data resources, etc.

[0196] In some embodiments, AI computing resources are used to ensure that AI services have sufficient computing resources, but are not limited thereto. In some embodiments, AI computing power may be provided by a central processing unit (CPU), a graphics processing unit (GPU), or other dedicated hardware, but is not limited thereto.

[0197] In some embodiments, AI algorithm resources can be used to provide necessary algorithmic and / or model support for AI services to ensure the operation of AI services.

[0198] In some embodiments, AI connectivity resources can be used to ensure the stability and speed of AI models and / or data transmission, so as to ensure that AI services can acquire and process data in a timely manner.

[0199] In some embodiments, AI data resources can be used to provide high-quality data resources to ensure that AI services can perform analysis and decision-making based on accurate data.

[0200] In some embodiments, the terms “resource,” “resource set,” “resource group,” “precoding,” “precoder,” “weight,” “precoding weight,” “quasi-co-location (QCL),” “transmission configuration indication (TCI) status,” “spatial relation,” “spatial domain filter,” “transmission power,” “phase rotation,” “antenna port,” “antenna port group,” “layer,” “the number of layers,” “rank,” “beam,” “beam width,” “beam angular degree,” “antenna,” “antenna element,” and “panel” can be used interchangeably.

[0201] In some embodiments, the highest performance metric of aggregated AI QoS can be defined according to the AI ​​session dimension. The fourth AI QoS parameter can be the highest performance metric of aggregated AI QoS of multiple AI sessions. That is, the aggregated result of the highest AI QoS performance metrics of multiple AI sessions can be used as the fourth AI QoS parameter.

[0202] In some embodiments, the name of the highest performance metric for aggregated AI QoS defined according to the AI ​​session dimension is not limited, and may be, for example, “AI Session Aggregate Maximum AI QoS”.

[0203] In some embodiments, for a guaranteed AI QoS flow, its AI QoS parameters may include at least one of a first AI QoS parameter, a second AI QoS parameter, and a third AI QoS parameter.

[0204] In some embodiments, for non-guaranteed AI QoS flows, the AI ​​QoS parameters may include a fourth AI QoS parameter.

[0205] In some embodiments, the AI ​​QoS parameters of the AI ​​QoS stream may further include a fifth AI QoS parameter.

[0206] In some embodiments, the fifth AI QoS parameter can be the highest aggregated AI QoS performance metric for all AI QoS flows associated with the same network slice. Optionally, the fifth AI QoS parameter can be used to indicate the highest aggregated AI QoS performance metric provided by all guaranteed and non-guaranteed AI QoS flows associated with the AI ​​terminal on the same network slice.

[0207] In some embodiments, the name of the fifth AI QoS parameter is not limited, and it may be, for example, “UE Slice Maximum AI QoS”.

[0208] In some embodiments, AI QoS parameters may be for at least one of AI services, AI tasks, AI resources, AI terminals, AI sessions, and AI data packets.

[0209] That is, for any one of the following AI QoS performance indicators: minimum AI QoS performance indicator, maximum AI QoS burst performance indicator, maximum aggregated AI QoS performance indicator, AI QoS performance indicator can be the AI ​​QoS performance indicator corresponding to at least one of AI services, AI tasks, AI resources, AI terminals, AI sessions, and AI data packets.

[0210] Alternatively, for any of the following AI QoS performance metrics: minimum AI QoS performance metric, maximum AI QoS performance metric, maximum AI QoS burst performance metric, and aggregated AI QoS maximum performance metric, the AI ​​QoS performance metric can be used to measure the AI ​​QoS of at least one of AI services, AI tasks, AI resources, AI terminals, AI sessions, and AI packets.

[0211] It's important to note that the implementation of AI services can involve multiple layers. For example, the implementation of an AI service may include AI services, AI tasks, and AI resources, thus potentially involving multiple layers of QoS mapping. For instance, when a user makes a service request to the network, the request may involve one or more AI services. AI tasks are decomposed and orchestrated from AI services, representing the coordination and allocation of connection, computing, data, and algorithm resources among multiple network nodes; they are a lightweight subset of AI services. AI resources, on the other hand, are the four essential resources (i.e., computing power, algorithm resources, data resources, and connection resources) involved in ensuring the implementation of AI tasks. These include not only physical resources such as CPU computing power or air interface time-frequency domain resources, but also implementation methods. Therefore, ensuring that a user's service request is met may require multi-dimensional cooperation and collaboration among AI services, AI tasks, and AI resources.

[0212] It should be noted that the above embodiment is illustrated by taking the example of the first information being sent from the second communication device to the first communication device. In more possible implementations, the first information may also be pre-configured to the first communication device, but it is not limited to this.

[0213] In some embodiments, the first communication device can obtain pre-configured first information and then perform the following step S2102 based on the pre-configured first information without waiting for the second communication device to send the first information.

[0214] In step S2102, the first communication device ensures that the AI ​​service meets QoS requirements based on the first information.

[0215] Optionally, the first communication device may use various means based on the first information to ensure that the AI ​​service meets QoS requirements.

[0216] In some embodiments, the first communication device may perform resource scheduling based on the first information to ensure that the scheduled resources can ensure that the AI ​​service meets QoS requirements.

[0217] In some embodiments, the first communication device may optimize the data transmission method based on the first information to ensure that the AI ​​service meets QoS requirements.

[0218] In some embodiments, the first communication device may optimize the AI ​​model based on the first information to ensure that the AI ​​service meets QoS requirements.

[0219] In some embodiments, the first communication device may set a priority for AI service traffic based on the first information to ensure that the AI ​​service meets QoS requirements.

[0220] It should be noted that the above are only a few exemplary implementation methods and do not constitute a limitation on the embodiments of this disclosure. In more possible implementation methods, other methods can also be used to ensure that the AI ​​service meets the QoS requirements based on the first information, and the embodiments of this disclosure do not limit this.

[0221] In some embodiments, “get,” “obtain,” “get,” “receive,” “transmit,” “bidirectional transmission,” and “send and / or receive” can be used interchangeably and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining through self-processing, or autonomous implementation, among other meanings.

[0222] In some embodiments, terms such as “send,” “transmit,” “report,” “distribute,” “transfer,” “bidirectional transmission,” “send and / or receive” can be used interchangeably.

[0223] In some embodiments, terms such as "certain," "preset," "default," "set," "indicated," "a certain," "any," and "first" can be used interchangeably. "Certain A," "preset A," "default A," "set A," "indicated A," "a certain A," "any A," and "first A" can be interpreted as A pre-defined in a protocol or the like, or as A obtained through setting, configuration, or instruction, or as specific A, a certain A, any A, or first A, but are not limited thereto.

[0224] In some embodiments, the determination or judgment can be made by a value represented by 1 bit (0 or 1), or by a true or false value (boolean), or by a comparison of numerical values ​​(e.g., a comparison with a predetermined value), but is not limited thereto.

[0225] In some embodiments, "not expecting to receive" can be interpreted as not receiving on time domain resources and / or frequency domain resources, or as not performing subsequent processing on the data after receiving it; "not expecting to send" can be interpreted as not sending, or as sending but not expecting the receiver to respond to the sent content.

[0226] The communication method involved in the embodiments of this disclosure may include at least one of steps S2101 to S2102. For example, step S2101 may be implemented as a standalone embodiment, step S2102 may be implemented as a standalone embodiment, and step S2101+S2102 may be implemented as a standalone embodiment, but is not limited thereto.

[0227] In some embodiments, step S2101 is optional and may be omitted or replaced in different embodiments.

[0228] In some embodiments, step S2102 is optional and may be omitted or replaced in different embodiments.

[0229] In some embodiments, other optional implementations described before or after the specification corresponding to FIG2 may be referred to.

[0230] According to the solution provided in the embodiments of this disclosure, the first communication device ensures that the AI ​​service meets specific quality of service requirements based on AI QoS related information (i.e., first information). The AI ​​QoS related information includes AI QoS flow type and its related QoS parameters.

[0231] In some embodiments, AI QoS-related information may be sent from the second communication device to the first communication device, or it may be pre-configured to the first communication device.

[0232] In some embodiments, the AI ​​QoS flow type includes at least one of Guaranteed AI QoS Flow and Non-Guaranteed AI QoS Flow.

[0233] It should be noted that Guaranteed AI QoS Flow can be used in AI services that require high AI resource protection or real-time performance, and need to ensure the stable operation and rapid response of the AI ​​system, such as critical services like autonomous driving and telemedicine. Non-Guaranteed AI QoS Flow can be used in AI services that do not have high real-time or AI resource protection requirements, in order to save resources and improve the overall efficiency of the system, such as video analytics or data mining.

[0234] In some embodiments, for a Guaranteed AI QoS flow, the QoS parameters include, but are not limited to, at least one of the following:

[0235] (1) Guaranteed AI QoS is used to indicate the minimum performance index that the first communication device guarantees the AI ​​QoS flow to reach. The minimum performance index can be based on the statistics of the first time window.

[0236] (2) Maximum AI QoS is used to indicate the highest performance index that the first communication device can guarantee the AI ​​QoS flow to reach. The highest performance index can be based on statistics of the second time window.

[0237] (3) Maximum Burst AI QoS is used to indicate the maximum burst performance index of the AI ​​QoS flow guaranteed by the first communication device. The maximum burst performance index can be statistically analyzed based on the third time window. It should be noted that if the actual burst volume of the system does not exceed the Maximum burst AI QoS and does not exceed the Guaranteed AI QoS within the third time window, then the packets exceeding the third time window will be considered lost.

[0238] In some embodiments, the first time window, the second time window, and the third time window may be the same or different.

[0239] In some embodiments, the time window length may be agreed upon by the protocol, or the time window length information may be used as a QoS parameter. The first communication device determines at least one of the first time window, the second time window, and the third time window based on the time window information contained in the QoS parameter.

[0240] In some embodiments, for a Non-Guaranteed AI QoS flow, the QoS parameters include the aggregated AI QoS highest performance metric parameter, which can be defined according to at least one of per UE, per AI service, per AI task, per AI resource, and per session.

[0241] In some embodiments, the aggregated AI QoS performance metric can be at least one of the following:

[0242] (1)UE Aggregate Maximum AI QoS: This indicates the highest performance metric for aggregated AI QoS for each terminal.

[0243] (2) AI Service Aggregate Maximum AI QoS: This indicates the highest performance metric for the aggregated AI QoS of each AI service.

[0244] (3) AI task Aggregate Maximum AI QoS: This indicates the highest performance metric for the aggregated AI QoS of each AI task.

[0245] (4) AI resource Aggregate Maximum AI QoS: This indicates the highest performance metric for the aggregated AI QoS of each AI resource.

[0246] (5) AI Session Aggregate Maximum AI QoS: This indicates the highest performance metric for aggregated AI QoS for each AI session.

[0247] In some embodiments, the QoS parameter also includes UE Slice Maximum AI QoS, which indicates the maximum aggregated AI QoS performance metric provided by all UE-related Guaranteed AI QoS flows and Non-Guaranteed AI QoS flows on the same slice.

[0248] In some embodiments, AI QoS may be applied to at least one of the following: a specific AI service, a specific AI task, a specific AI resource, a specific AI terminal, a specific AI session, or a specific AI packet.

[0249] It should be noted that the implementation of a service can involve multiple layers, such as services, tasks, and resources, thus potentially involving multi-layered QoS mapping. When a user makes a service request to the network, the request may involve one or more AI services. AI tasks are decomposed and orchestrated from AI services, representing the coordination and allocation of connectivity, computation, data, and algorithm resources among multiple network nodes; they are a lightweight subset of AI services. AI resources, on the other hand, are the four essential resources (i.e., computation, algorithm, data, and connectivity) required to ensure the implementation of AI tasks. These resources include not only physical resources such as CPU computing power or air interface time-frequency domain resources, but also the implementation methods.

[0250] In some embodiments, AI resources may include at least one of the following:

[0251] (1) Computing resources: The computing resources guarantee service can obtain sufficient computing resources, which are provided by CPU, GPU and other dedicated hardware.

[0252] (2) Algorithm resources: Algorithm resources provide the necessary algorithm or model support for AI services to ensure the operation of AI services.

[0253] (3) Connectivity resources: Connectivity resources ensure the stability and speed of AI model or data transmission, and ensure that AI services can acquire and process data in a timely manner.

[0254] (4) Data resources: Computing resources provide high-quality data resources to ensure that AI services can make decisions and analyses based on accurate data.

[0255] In some embodiments, AI services include, but are not limited to, at least one of AI training, inference, data, and verification services.

[0256] In some embodiments, the AI ​​task includes, but is not limited to, at least one of the following:

[0257] (1) Data preprocessing tasks, including data cleaning, formatting, normalization, etc.

[0258] (2) Feature extraction task, which includes extracting useful features from raw data for model training.

[0259] (3) Model training tasks, including training models using specific algorithms and datasets.

[0260] (4) Reasoning tasks, including using trained models to predict or classify new data.

[0261] (5) Evaluation tasks, including evaluating and testing the performance of the model.

[0262] (6) Transmission tasks, including the transmission of AI data or models.

[0263] In some embodiments, the first communication device is an AI QoS execution node, such as NG-RAN, UE, or other independent AI entity.

[0264] In some embodiments, the second communication device is an AI QoS management node, such as an SMF or PCF.

[0265] Figure 3A is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3A, the present disclosure relates to a communication method, which includes:

[0266] Step S3101: Obtain the first information.

[0267] The optional implementation of step S3101 can be found in the optional implementation of step S2101 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0268] In some embodiments, the first communication device receives first information sent by the second communication device, but is not limited thereto; it may also receive first information sent by other entities.

[0269] In some embodiments, the first communication device acquires pre-configured first information.

[0270] In some embodiments, the first communication device acquires first information as defined by a protocol.

[0271] In some embodiments, the first communication device obtains first information from the upper layer(s).

[0272] In some embodiments, the first communication device processes information to obtain the first information.

[0273] In some embodiments, step S3101 is omitted, and the first communication device autonomously implements the function indicated by the first information, or the above function is a default or default setting.

[0274] In some embodiments, the first information is used to indicate relevant information about the AI ​​QoS flow.

[0275] In some embodiments, the first information is used to indicate at least one of the following information related to the AI ​​QoS flow: AI QoS flow type; AI QoS parameters.

[0276] In some embodiments, the AI ​​QoS flow type includes at least one of the following: a guaranteed AI QoS flow; or a non-guaranteed AI QoS flow.

[0277] In some embodiments, the AI ​​QoS parameters include at least one of the following: a first AI QoS parameter, which indicates the minimum AI QoS performance metric that the AI ​​QoS flow must achieve; a second AI QoS parameter, which indicates the maximum AI QoS performance metric that the AI ​​QoS flow must achieve; a third AI QoS parameter, which indicates the maximum AI QoS burst performance metric that the AI ​​QoS flow must achieve; and a fourth AI QoS parameter, which indicates the highest aggregated AI QoS performance metric.

[0278] In some embodiments, the first AI QoS parameter is obtained based on statistics from a first time window, the second AI QoS parameter is obtained based on statistics from a second time window, and the third AI QoS parameter is obtained based on statistics from a third time window.

[0279] In some embodiments, the time window lengths of the first time window, the second time window, and the third time window are agreed upon by the protocol; or, the time window lengths of the first time window, the second time window, and the third time window are determined based on the first information.

[0280] In some embodiments, the fourth AI QoS parameter includes at least one of the following: the highest aggregated AI QoS performance index of multiple AI terminals; the highest aggregated AI QoS performance index of multiple AI services; the highest aggregated AI QoS performance index of multiple AI tasks; the highest aggregated AI QoS performance index of multiple AI resources; and the highest aggregated AI QoS performance index of multiple AI sessions.

[0281] In some embodiments, for a guaranteed AI QoS flow, the AI ​​QoS parameters include at least one of a first AI QoS parameter, a second AI QoS parameter, and a third AI QoS parameter.

[0282] In some embodiments, for non-guaranteed AI QoS flows, the AI ​​QoS parameters include a fourth AI QoS parameter.

[0283] In some embodiments, the AI ​​QoS parameter further includes a fifth AI QoS parameter, which is the highest aggregated AI QoS performance metric for all AI QoS flows associated with the same network slice.

[0284] In some embodiments, for any one of the following AI QoS performance metrics: minimum AI QoS performance metric, maximum AI QoS performance metric, maximum AI QoS burst performance metric, and aggregated AI QoS maximum performance metric, the AI ​​QoS performance metric is an AI QoS performance metric corresponding to at least one of the following: AI service; AI task; AI resource; AI terminal; AI session; AI data packet.

[0285] In some embodiments, the first communication device includes at least one of a terminal, an access network device, and an independent AI entity node.

[0286] In some embodiments, the second communication device is a core network device.

[0287] Step S3102: Based on the first information, ensure that the AI ​​service meets the QoS requirements.

[0288] The optional implementation of step S3102 can be found in the optional implementation of step S2102 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0289] The communication method involved in the embodiments of this disclosure may include at least one of steps S3101 to S3102. For example, step S3101 may be implemented as a standalone embodiment, step S3102 may be implemented as a standalone embodiment, and step S3101+S3102 may be implemented as a standalone embodiment, but is not limited thereto.

[0290] In some embodiments, step S3101 is optional and may be omitted or replaced in different embodiments.

[0291] In some embodiments, step S3102 is optional and may be omitted or replaced in different embodiments.

[0292] Figure 3B is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3B, the present disclosure relates to a communication method, which includes:

[0293] Step S3201: Send the first message.

[0294] The optional implementation of step S3201 can be found in the optional implementation of step S2101 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0295] In some embodiments, the second communication device sends the first information to the first communication device, but is not limited thereto; it may also send the first information to other entities.

[0296] In some embodiments, the first information is used to indicate relevant information about the AI ​​QoS flow.

[0297] In some embodiments, the first information is used to indicate at least one of the following information related to the AI ​​QoS flow: AI QoS flow type; AI QoS parameters.

[0298] In some embodiments, the AI ​​QoS flow type includes at least one of the following: a guaranteed AI QoS flow; or a non-guaranteed AI QoS flow.

[0299] In some embodiments, the AI ​​QoS parameters include at least one of the following: a first AI QoS parameter, which indicates the minimum AI QoS performance metric that the AI ​​QoS flow must achieve; a second AI QoS parameter, which indicates the maximum AI QoS performance metric that the AI ​​QoS flow must achieve; a third AI QoS parameter, which indicates the maximum AI QoS burst performance metric that the AI ​​QoS flow must achieve; and a fourth AI QoS parameter, which indicates the highest aggregated AI QoS performance metric.

[0300] In some embodiments, the first AI QoS parameter is obtained based on statistics from a first time window, the second AI QoS parameter is obtained based on statistics from a second time window, and the third AI QoS parameter is obtained based on statistics from a third time window.

[0301] In some embodiments, the time window lengths of the first time window, the second time window, and the third time window are agreed upon by the protocol; or, the time window lengths of the first time window, the second time window, and the third time window are determined based on the first information.

[0302] In some embodiments, the fourth AI QoS parameter includes at least one of the following: the highest aggregated AI QoS performance index of multiple AI terminals; the highest aggregated AI QoS performance index of multiple AI services; the highest aggregated AI QoS performance index of multiple AI tasks; the highest aggregated AI QoS performance index of multiple AI resources; and the highest aggregated AI QoS performance index of multiple AI sessions.

[0303] In some embodiments, for a guaranteed AI QoS flow, the AI ​​QoS parameters include at least one of a first AI QoS parameter, a second AI QoS parameter, and a third AI QoS parameter.

[0304] In some embodiments, for non-guaranteed AI QoS flows, the AI ​​QoS parameters include a fourth AI QoS parameter.

[0305] In some embodiments, the AI ​​QoS parameter further includes a fifth AI QoS parameter, which is the highest aggregated AI QoS performance metric for all AI QoS flows associated with the same network slice.

[0306] In some embodiments, for any one of the following AI QoS performance metrics: minimum AI QoS performance metric, maximum AI QoS performance metric, maximum AI QoS burst performance metric, and aggregated AI QoS maximum performance metric, the AI ​​QoS performance metric is an AI QoS performance metric corresponding to at least one of the following: AI service; AI task; AI resource; AI terminal; AI session; AI data packet.

[0307] In some embodiments, the first information is also used by the first communication device to ensure that the AI ​​service meets QoS requirements.

[0308] In some embodiments, the first communication device includes at least one of a terminal, an access network device, and an independent AI entity node.

[0309] In some embodiments, the second communication device is a core network device.

[0310] The communication method involved in the embodiments of this disclosure may include at least step S3201, and step S3201 may be implemented as an independent embodiment, but is not limited thereto.

[0311] In this embodiment of the disclosure, step S3201 can be combined with step S3101 of FIG3A.

[0312] In the embodiments disclosed herein, some or all of the steps and their optional implementations may be arbitrarily combined with some or all of the steps in other embodiments, or may be arbitrarily combined with the optional implementations in other embodiments.

[0313] This disclosure also provides an apparatus for implementing any of the above methods. For example, an apparatus is provided that includes units or modules for implementing the steps performed by the first communication device (e.g., a terminal, access network device) in any of the above methods. Alternatively, another apparatus is provided that includes units or modules for implementing the steps performed by the second communication device (e.g., a core network functional node, core network device, etc.) in any of the above methods.

[0314] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of the units or modules in the above device. The processor can be, for example, a general-purpose processor, such as a Central Processing Unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits. The functionality of some or all of the units or modules can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC). The functionality of some or all of the units or modules is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a programmable logic device (PLD). Taking a field-programmable gate array (FPGA) as an example, it can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the units or modules. All units or modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0315] In this embodiment, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute capabilities, such as a Central Processing Unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. The logical relationships of the aforementioned hardware circuits are fixed or reconfigurable. For example, the processor is a hardware circuit implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. Furthermore, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a Neural Network Processing Unit (NPU), a Tensor Processing Unit (TPU), or a Deep Learning Processing Unit (DPU).

[0316] Figure 4A is a schematic diagram of the structure of the first communication device proposed in an embodiment of this disclosure. As shown in Figure 4A, the first communication device 4100 may include at least a processing module 4101. In some embodiments, the processing module 4101 is configured to ensure that the artificial intelligence (AI) service meets the Quality of Service (QoS) requirements based on first information, wherein the first information is used to indicate relevant information of the AI ​​QoS flow. Optionally, the processing module 4101 is used to perform at least one of the other steps (e.g., step S2102, but not limited thereto) performed by the first communication device in any of the above methods, which will not be elaborated here. In some embodiments, the first communication device 4100 may also include a transceiver module. Optionally, the transceiver module is used to perform at least one of the communication steps (e.g., step S2101, but not limited thereto) performed by the first communication device in any of the above methods, such as sending and / or receiving, which will not be elaborated here.

[0317] Figure 4B is a schematic diagram of the structure of the second communication device proposed in an embodiment of this disclosure. As shown in Figure 4B, the second communication device 4200 may include at least a transceiver module 4201. In some embodiments, the transceiver module 4201 is configured to send first information to a first communication device, the first information being used to indicate relevant information of an AI QoS flow, and the first information being used by the first communication device to ensure that the AI ​​service meets QoS requirements. Optionally, the transceiver module is used to perform at least one of the communication steps (e.g., step S2101, but not limited thereto) performed by the second communication device in any of the above methods, which will not be elaborated here. In some embodiments, the second communication device 4200 may also include a processing module. Optionally, the processing module is used to perform at least one of the other steps performed by the terminal 101 in any of the above methods, which will not be elaborated here.

[0318] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, which may be separate or integrated. Optionally, the transceiver module may be interchangeable with a transceiver.

[0319] In some embodiments, the processing module may be a single module or may include multiple sub-modules. Optionally, the multiple sub-modules may each perform all or part of the steps required by the processing module. Optionally, the processing module may be interchangeable with a processor.

[0320] Figure 5A is a schematic diagram of the structure of the communication device 5100 proposed in an embodiment of this disclosure. The communication device 5100 can be a first communication device (e.g., a terminal, access network device), a second communication device (e.g., a core network functional node, core network device, etc.), a chip, chip system, or processor that supports the first communication device in implementing any of the above methods, or a chip, chip system, or processor that supports the second communication device in implementing any of the above methods. The communication device 5100 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0321] As shown in Figure 5A, the communication device 5100 includes one or more processors 5101. The processor 5101 can be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. The communication device 5100 is used to execute any of the above methods.

[0322] In some embodiments, the communication device 5100 further includes one or more memories 5102 for storing instructions. Optionally, all or part of the memories 5102 may also be located outside the communication device 5100.

[0323] In some embodiments, the communication device 5100 further includes one or more transceivers 5103. When the communication device 5100 includes one or more transceivers 5103, the transceivers 5103 perform at least one of the communication steps such as sending and / or receiving in the above method (e.g., step S2101, but not limited thereto), and the processor 5101 performs at least one of other steps (e.g., step S2102, but not limited thereto).

[0324] In some embodiments, a transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, etc., may be used interchangeably; the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc., may be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., may be used interchangeably.

[0325] In some embodiments, the communication device 5100 may include one or more interface circuits 5104. Optionally, the interface circuit 5104 is connected to the memory 5102, and the interface circuit 5104 can be used to receive signals from the memory 5102 or other devices, and can be used to send signals to the memory 5102 or other devices. For example, the interface circuit 5104 can read instructions stored in the memory 5102 and send the instructions to the processor 5101.

[0326] The communication device 5100 described in the above embodiments may be a first communication device or a second communication device, but the scope of the communication device 5100 described in this disclosure is not limited thereto, and the structure of the communication device 5100 may not be limited by FIG. 5A. The communication device may be a standalone device or a part of a larger device. For example, the communication device may be: (1) a standalone integrated circuit IC, or chip, or chip system or subsystem; (2) a collection of one or more ICs, optionally, the IC collection may also include storage components for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, terminal device, smart terminal device, cellular phone, wireless device, handheld device, mobile unit, vehicle device, network device, cloud device, artificial intelligence device, etc.; (6) others, etc.

[0327] Figure 5B is a schematic diagram of the structure of chip 5200 according to an embodiment of this disclosure. For cases where the communication device 5100 can be a chip or a chip system, please refer to the schematic diagram of chip 5200 shown in Figure 5B, but it is not limited thereto.

[0328] Chip 5200 includes one or more processors 5201, which are used to perform any of the above methods.

[0329] In some embodiments, chip 5200 further includes one or more interface circuits 5202. Optionally, the interface circuit 5202 is connected to memory 5203, and the interface circuit 5202 can be used to receive signals from memory 5203 or other devices, and the interface circuit 5202 can be used to send signals to memory 5203 or other devices. For example, the interface circuit 5202 can read instructions stored in memory 5203 and send the instructions to processor 5201.

[0330] In some embodiments, the interface circuit 5202 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., step S2101, but not limited thereto), and the processor 5201 performs at least one of the other steps (e.g., step S2102, but not limited thereto).

[0331] In some embodiments, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0332] In some embodiments, chip 5200 further includes one or more memories 5203 for storing instructions. Optionally, all or part of the memories 5203 may be located outside of chip 5200.

[0333] This disclosure also proposes a storage medium storing instructions that, when executed on the communication device 5100, cause the communication device 5100 to perform any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but not limited thereto; it may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but not limited thereto; it may also be a temporary storage medium.

[0334] This disclosure also provides a program product that, when executed by the communication device 5100, causes the communication device 5100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0335] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

[0336] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0337] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A communication method characterized by comprising: Applied to a first communication device, the method includes: Based on the first information, ensure that the artificial intelligence (AI) service meets the Quality of Service (QoS) requirements. The first information is used to indicate relevant information of the AI ​​QoS flow.

2. The method of claim 1, wherein, The first information is used to indicate at least one of the following information related to the AI ​​QoS flow: AI QoS flow type; AI QoS parameters.

3. The method of claim 2, wherein, AI QoS flow types include at least one of the following: Guaranteed AI QoS flow; Unguaranteed AI QoS flow.

4. The method according to claim 2 or 3, characterized in that, AI QoS parameters include at least one of the following: The first AI QoS parameter indicates the minimum AI QoS performance metric that must be guaranteed for the AI ​​QoS flow. The second AI QoS parameter indicates the highest AI QoS performance metric that the AI ​​QoS flow must achieve. The third AI QoS parameter is used to indicate the maximum AI QoS burst performance index that the AI ​​QoS flow must reach. The fourth AI QoS parameter is used to indicate the highest performance metric of the aggregated AI QoS.

5. The method of claim 4, wherein, The first AI QoS parameter is obtained based on statistics from a first time window, the second AI QoS parameter is obtained based on statistics from a second time window, and the third AI QoS parameter is obtained based on statistics from a third time window.

6. The method of claim 5, wherein, The time window lengths of the first time window, the second time window, and the third time window are agreed upon by the protocol; or, the time window lengths of the first time window, the second time window, and the third time window are determined based on the first information.

7. The method according to any one of claims 4 to 6, characterized in that, The fourth AI QoS parameter includes at least one of the following: The highest performance index of aggregated AI QoS from multiple AI terminals; The highest performance QoS metric for aggregated AI services; The highest performance QoS metric for aggregated AI tasks; The highest performance QoS metric for the aggregation of multiple AI resources; The highest performance QoS metric for aggregated AI sessions.

8. The method according to any one of claims 4 to 7, characterized in that, For a guaranteed AI QoS flow, the AI ​​QoS parameters include at least one of the first AI QoS parameter, the second AI QoS parameter, and the third AI QoS parameter; For non-guaranteed AI QoS flows, the AI ​​QoS parameters include the fourth AI QoS parameter.

9. The method according to any one of claims 2 to 8, characterized in that, The AI ​​QoS parameters also include a fifth AI QoS parameter, which is the highest performance metric of the aggregated AI QoS for all AI QoS flows associated with the same network slice.

10. The method according to any one of claims 2 to 9, characterized in that, For any one of the following AI QoS performance metrics: minimum AI QoS performance metric, maximum AI QoS performance metric, maximum AI QoS burst performance metric, and aggregated AI QoS maximum performance metric, the AI ​​QoS performance metric is an AI QoS performance metric corresponding to at least one of the following: AI services; AI task; AI resources; AI terminals; AI conversation; AI data packets.

11. The method according to any one of claims 1 to 10, characterized in that, The first information is sent from the second communication device to the first communication device, or the first information is pre-configured for the first communication device.

12. The method of claim 11, wherein, The second communication device is a core network device; The first communication device includes at least one of a terminal, an access network device, and an independent AI entity node.

13. A method of communication, comprising: Applied to a second communication device, the method includes: Send first information to a first communication device, the first information being used to indicate relevant information of the AI ​​QoS flow, and the first information also being used by the first communication device to ensure that the AI ​​service meets QoS requirements.

14. The method of claim 13, wherein, The first information is used to indicate at least one of the following information related to the AI ​​QoS flow: AI QoS flow type; AI QoS parameters.

15. The method of claim 14, wherein, AI QoS flow types include at least one of the following: Guaranteed AI QoS flow; Unguaranteed AI QoS flow.

16. The method according to claim 14 or 15, characterized in that AI QoS parameters include at least one of the following: The first AI QoS parameter indicates the minimum AI QoS performance metric that must be guaranteed for the AI ​​QoS flow. The second AI QoS parameter indicates the highest AI QoS performance metric that the AI ​​QoS flow must achieve. The third AI QoS parameter is used to indicate the maximum AI QoS burst performance index that the AI ​​QoS flow must reach. The fourth AI QoS parameter is used to indicate the highest performance metric of the aggregated AI QoS.

17. The method of claim 16, wherein, The first AI QoS parameter is obtained based on statistics from a first time window, the second AI QoS parameter is obtained based on statistics from a second time window, and the third AI QoS parameter is obtained based on statistics from a third time window.

18. The method of claim 17, wherein, The time window lengths of the first time window, the second time window, and the third time window are agreed upon by the protocol; or, the time window lengths of the first time window, the second time window, and the third time window are determined based on the first information.

19. The method of any one of claims 16-18, wherein, The fourth AI QoS parameter includes at least one of the following: The highest performance index of aggregated AI QoS from multiple AI terminals; The highest performance QoS metric for aggregated AI services; The highest performance QoS metric for aggregated AI tasks; The highest performance QoS metric for the aggregation of multiple AI resources; The highest performance QoS metric for aggregated AI sessions.

20. The method of any one of claims 16-19, wherein, For a guaranteed AI QoS flow, the AI ​​QoS parameters include at least one of the first AI QoS parameter, the second AI QoS parameter, and the third AI QoS parameter; For non-guaranteed AI QoS flows, the AI ​​QoS parameters include the fourth AI QoS parameter.

21. The method according to any one of claims 14 to 20, characterized in that, The AI ​​QoS parameters also include a fifth AI QoS parameter, which is the highest performance metric of the aggregated AI QoS for all AI QoS flows associated with the same network slice.

22. The method according to any one of claims 14 to 21, characterized in that, For any one of the following AI QoS performance metrics: minimum AI QoS performance metric, maximum AI QoS performance metric, maximum AI QoS burst performance metric, and aggregated AI QoS maximum performance metric, the AI ​​QoS performance metric is an AI QoS performance metric corresponding to at least one of the following: AI services; AI task; AI resources; AI terminals; AI conversation; AI data packets.

23. The method according to any one of claims 13 to 22, characterized in that, The first communication device includes at least one of a terminal, an access network device, and an independent AI entity node; The second communication device is a core network device.

24. A first communication device, characterized by include: The processing module is configured to ensure that the artificial intelligence (AI) service meets the Quality of Service (QoS) requirements based on first information, whereby the first information is used to indicate relevant information about the AI ​​QoS flow.

25. A second communication device, characterized by include: The transceiver module is configured to send first information to a first communication device, the first information being used to indicate relevant information of the AI ​​QoS stream, and the first information being used by the first communication device to ensure that the AI ​​service meets QoS requirements.

26. A first communication device, characterized by include: One or more processors; The first communication device is used to execute the communication method according to any one of claims 1-12.

27. A second communication device, characterized by include: One or more processors; The second communication device is used to perform the communication method according to any one of claims 13-23.

28. A communication system, characterized by The device includes a first communication device and a second communication device, wherein the first communication device is configured to implement the communication method according to any one of claims 1-12, and the second communication device is configured to implement the communication method according to any one of claims 13-23.

29. A storage medium, the storage medium storing instructions, wherein, When the instruction is executed on the communication device, the communication device performs the communication method as described in any one of claims 1-12 or 13-23.

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