Communication method, device and system, and storage medium and program product

Abstract

The present disclosure relates to a communication method, device and system, and a storage medium and a program product. The communication method comprises: sending first information to a terminal, wherein the first information indicates that the terminal performs communication sensing based on artificial intelligence (AI) or machine learning (ML). By means of the embodiments of the present disclosure, the communication efficiency can be improved.

Description

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

[0001] This disclosure relates to the field of communication technology, and in particular to communication methods, communication devices, systems, storage media, and program products. Background Technology

[0002] With the rapid development of artificial intelligence (AI) and machine learning (ML) technologies, their applications are becoming increasingly widespread. For example, AI and ML technologies are being applied in integrated sensing and communications (ISAC) communication scenarios. Summary of the Invention

[0003] How network devices can instruct terminals on the relevant configurations for AI-based sensor processing is a problem that needs to be solved.

[0004] This disclosure provides communication methods, communication devices, systems, storage media, and program products.

[0005] According to a first aspect of the present disclosure, a communication method is proposed, executed by a network device, the method comprising: sending first information to a terminal, the first information instructing the terminal to perform communication sensing based on artificial intelligence (AI) or machine learning (ML).

[0006] According to a second aspect of the present disclosure, a communication method is proposed, executed by a terminal, the method comprising: receiving first information sent by a network device, the first information instructing the terminal to perform communication sensing based on artificial intelligence (AI) or machine learning (ML).

[0007] According to a third aspect of the present disclosure, a communication device is provided for performing the communication method of any of the above aspects.

[0008] According to a fourth aspect of the present disclosure, a communication system is proposed, including a network device and a terminal, wherein the network device is configured to implement the communication method of the first aspect, and the terminal is configured to implement the communication method of the second aspect.

[0009] According to a fifth aspect of the present disclosure, a storage medium is provided that stores instructions which, when executed on a communication device, cause the communication device to perform the method of the first aspect or the second aspect.

[0010] According to a sixth aspect of the present disclosure, a program product is provided, including at least one of a program and instructions, wherein when the program or instructions are executed by a communication device, the communication method of the first aspect or the second aspect is implemented.

[0011] Through the embodiments of this disclosure, the network device sends first information to the terminal, the first information instructing the terminal to perform communication sensing based on artificial intelligence (AI) or machine learning (ML), which enables the network device to instruct the terminal on relevant configurations for AI-based sensing processing, thereby improving communication efficiency. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings required for the description of the embodiments are introduced below. The following drawings are only some embodiments of this disclosure and do not impose specific limitations on the protection scope of this disclosure.

[0013] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0014] Figure 1B is a schematic diagram illustrating the perception result based on AI output according to an embodiment of the present disclosure.

[0015] Figure 1C is a schematic diagram illustrating AI-based and non-AI-based output perception results according to an embodiment of the present disclosure.

[0016] Figure 1D is a schematic diagram illustrating the reporting of synesthesia-related capabilities 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 3 is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0019] Figure 4 is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0020] Figure 5A is a schematic diagram of the structure of the network device proposed in an embodiment of this disclosure.

[0021] Figure 5B is a schematic diagram of the structure of the terminal proposed in the embodiment of this disclosure.

[0022] Figure 6A is a schematic diagram of the structure of the communication device proposed in an embodiment of this disclosure.

[0023] Figure 6B is a schematic diagram of the chip structure proposed in an embodiment of this disclosure. Detailed Implementation

[0024] This disclosure provides communication methods, communication devices, systems, storage media, and program products.

[0025] In a first aspect, embodiments of this disclosure propose a communication method executed by a network device, the method comprising: sending first information to a terminal, the first information instructing the terminal to perform communication sensing processing based on artificial intelligence (AI) or machine learning (ML).

[0026] In the above embodiments, the network device sends first information to the terminal, the first information instructing the terminal to perform communication sensing based on artificial intelligence (AI) or machine learning (ML), which enables the network device to instruct the terminal on relevant configurations for AI-based sensing processing, thereby improving communication efficiency.

[0027] In conjunction with some embodiments of the first aspect, in some embodiments, the first information indicates at least one of the following: enabling AI- or ML-based communication awareness processing; disabling AI- or ML-based communication awareness processing.

[0028] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​or ML-based communication sensing processing includes at least one of the following: outputting a sensing result based on AI or ML; outputting a first parameter based on AI or ML, the first parameter being an intermediate parameter used to obtain the sensing result; and processing a first signal based on AI or ML, the first signal being a signal used for sensing measurement.

[0029] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​or ML-based communication sensing processing includes outputting sensing results based on AI or ML; the first information includes the type of sensing result, and the sensing result includes at least one of distance, position, angle, and speed.

[0030] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​or ML-based communication sensing processing includes outputting a first parameter based on AI or ML; the first information includes the first parameter, and the first parameter includes at least one of the number of sensing targets and the transmission path.

[0031] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​or ML-based communication sensing processing includes processing a first signal based on AI or ML; the first information includes the type of processing, and the type of processing includes at least one of denoising processing and feature extraction processing.

[0032] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​or ML-based communication sensing processing includes compressing a first signal based on AI or ML; the first information includes compression parameters, which include at least one of the number of bits in the compression result and the compression ratio.

[0033] In conjunction with some embodiments of the first aspect, in some embodiments, the first information indicates the activation of AI or ML-based communication sensing processing; the first information includes configuration information of the terminal for performance monitoring.

[0034] In conjunction with some embodiments of the first aspect, in some embodiments, the configuration information includes at least one of the following: performance monitoring parameters reported by the terminal, the performance monitoring parameters including performance monitoring results or measurement results used for performance monitoring; a first threshold, the first threshold being used by the terminal to report fourth information to the network device, the fourth information indicating a fallback to communication awareness processing not based on AI or ML, or the first threshold being used by the terminal to fall back to communication awareness processing not based on AI or ML.

[0035] In conjunction with some embodiments of the first aspect, in some embodiments, the first information indicates that AI- or ML-based communication awareness processing is turned off; the method further includes: receiving second information sent by the terminal, the second information being used to request the turning off of AI- or ML-based communication awareness processing, or the second information indicating that the terminal does not apply AI- or ML-based communication awareness processing.

[0036] In some embodiments, in conjunction with the first aspect, the method further includes: receiving third information sent by the terminal, the third information being used to indicate AI- or ML-based communication sensing processing applicable to the terminal.

[0037] Secondly, embodiments of this disclosure propose a communication method executed by a terminal, the method comprising: receiving first information sent by a network device, the first information instructing the terminal to perform communication sensing processing based on AI or ML.

[0038] In conjunction with some embodiments of the second aspect, in some embodiments, the first information indicates at least one of the following: enabling AI- or ML-based communication awareness processing; disabling AI- or ML-based communication awareness processing.

[0039] In conjunction with some embodiments of the second aspect, in some embodiments, the AI ​​or ML-based communication sensing processing includes at least one of the following: outputting sensing results based on AI or ML; outputting a first parameter based on AI or ML, the first parameter being an intermediate parameter used to obtain the sensing results; and processing a first signal based on AI or ML, the first signal being a signal used for sensing measurement.

[0040] In conjunction with some embodiments of the second aspect, in some embodiments, the AI- or ML-based communication sensing processing includes outputting sensing results based on AI or ML; the first information includes the type of sensing result, and the sensing result includes at least one of distance, position, angle, and speed.

[0041] In conjunction with some embodiments of the second aspect, in some embodiments, the AI ​​or ML-based communication sensing processing includes outputting a first parameter based on AI or ML; the first information includes the first parameter, and the first parameter includes at least one of the number of sensing targets and the transmission path.

[0042] In conjunction with some embodiments of the second aspect, in some embodiments, the AI ​​or ML-based communication sensing processing includes processing a first signal based on AI or ML; the first information includes the type of processing, and the type of processing includes at least one of denoising processing and feature extraction processing.

[0043] In conjunction with some embodiments of the second aspect, in some embodiments, the AI ​​or ML-based communication sensing processing includes compressing a first signal based on AI or ML; the first information includes compression parameters, which include at least one of the number of bits in the compression result and the compression ratio.

[0044] In conjunction with some embodiments of the second aspect, in some embodiments, the first information indicates the activation of AI or ML-based communication sensing processing; the first information includes configuration information for the terminal to perform performance monitoring.

[0045] In conjunction with some embodiments of the second aspect, in some embodiments, the configuration information includes at least one of the following: performance monitoring parameters reported by the terminal, the performance monitoring parameters including performance monitoring results or measurement results used for performance monitoring; a first threshold, the first threshold being used by the terminal to report fourth information to the network device, the fourth information indicating a fallback to communication awareness processing not based on AI or ML, or the first threshold being used by the terminal to fall back to communication awareness processing not based on AI or ML.

[0046] In conjunction with some embodiments of the second aspect, in some embodiments, the first information indicates that AI- or ML-based communication sensing processing is disabled; the method further includes: sending a second information to the network device, the second information being used to request the disabling of AI- or ML-based communication sensing processing, or the second information indicating that AI- or ML-based communication sensing processing is not applicable to the terminal.

[0047] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: sending third information to the network device, the third information being used to indicate AI- or ML-based communication sensing processing applicable to the terminal.

[0048] Thirdly, embodiments of this disclosure provide a communication device for performing the communication method of the first or second aspect.

[0049] Fourthly, embodiments of this disclosure propose a communication system including a network device and a terminal, wherein the network device is configured to implement the communication method of the first aspect, and the terminal is configured to implement the communication method of the second aspect.

[0050] Fifthly, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the method of the first aspect or the second aspect.

[0051] In a sixth aspect, embodiments of this disclosure provide a program product comprising at least one of a program and instructions, wherein when the program or instructions are executed by a communication device, the communication device performs the communication method of the first aspect or the second aspect.

[0052] In a seventh aspect, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the methods described in optional implementations of the first or second aspect.

[0053] It is understood that the aforementioned communication devices, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0054] In some embodiments, the terms communication method, information sending method, information reporting method, and information receiving method can be used interchangeably.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] In the embodiments disclosed herein, "multiple" refers to two or more.

[0060] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0065] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0066] 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”.

[0067] In some embodiments, devices, etc., can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as “device”, “equipment”, “circuit”, “network element”, “node”, “function”, “unit”, “section”, “system”, “network”, “chip”, “chip system”, “entity”, and “subject” can be used interchangeably.

[0068] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0069] In some embodiments, the terms "access network device (AN device)," "radio access network device (RAN device)," "base station (BS)," "radio base station," "fixed station," "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," and "bandwidth part (BWP)" can be used interchangeably.

[0070] In some embodiments, the terms "terminal", "terminal device", "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", and "client" can be used interchangeably.

[0071] In some embodiments, access network devices, core network devices, or network devices can be replaced by terminals. For example, embodiments of this disclosure can also be applied to structures where communication between access network devices, core network devices, or network devices and terminals is replaced by communication between multiple terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the structure can also be configured such that the terminal has all or part of the functions of the access network device. Furthermore, terms such as "uplink" and "downlink" can be replaced with terms corresponding to communication between terminals (e.g., "sidelink"). For example, uplink channel, downlink channel, etc., can be replaced with sidelink channel, and uplink link, downlink, etc., can be replaced with sidelink link.

[0072] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, core network device, or network device may also be configured to have all or some of the functions of the terminal.

[0073] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0074] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0075] 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.

[0076] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0077] As shown in Figure 1A, the communication system 100 includes a network device 101 and a terminal 102.

[0078] In some embodiments, the terminal may be, for example, a user equipment (UE), including, but 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.

[0079] In some embodiments, a network device can be a functional network element within a core network device. The core network device can be a single device, including a first network element, a second network element, etc., or it can be multiple devices or a group of devices, each including all or part of the first network element, the second network element, etc. Network elements can be virtual or physical. The core network includes, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0080] In some embodiments, the network device may include at least one of an access network device and a core network device.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] In some embodiments, a core network device may be a single device comprising one or more network elements, or it may be multiple devices or a group of devices, each comprising all or part of the aforementioned one or more network elements. Network elements may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), or a Next Generation Core (NGC).

[0085] 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.

[0086] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1A, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1A are illustrative. The communication system may include all or some of the main bodies in FIG1A, or it may include other main bodies outside of FIG1A. 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.

[0087] 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), 6th generation mobile communication system (6G), 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).

[0088] The widespread application of 5G technology will bring tremendous changes to all aspects of people's lives. 5G will permeate all areas of future society, building a comprehensive information ecosystem centered on the user. Specifically, 5G user experience speeds can reach 100 Mbit / s to 1 Gbit / s, supporting ultimate service experiences such as mobile virtual reality; 5G peak speeds can reach 10 Gbit / s to 20 Gbit / s, with a traffic density of up to 10 Mbit / s / m. 2 It can support more than a thousand times the growth of mobile traffic in the future; the 5G connection density can reach 1 million / m². 2 5G can effectively support a massive number of IoT devices; its transmission latency is in the millisecond range, meeting the stringent requirements of vehicle-to-everything (V2X) and industrial control; and it can support mobile speeds of 500 km / h, ensuring a good user experience even in high-speed rail environments. Therefore, 5G, as a representative of new infrastructure, will reshape the future information society.

[0089] AI technology has achieved continuous breakthroughs in multiple fields. The ongoing development of fields such as intelligent voice and computer vision has not only brought a wide variety of applications to smart terminals, but has also found widespread use in education, transportation, home, healthcare, retail, security, and many other sectors, bringing convenience to people's lives while promoting industrial upgrading across various industries. AI technology is also accelerating its cross-disciplinary integration with other disciplines, combining knowledge from different fields while providing new directions and methods for the development of various disciplines.

[0090] During the 3GPP 5GA (5G Advanced) phase, 3GPP has begun exploring the introduction of artificial intelligence (AI) technology into mobile communication systems. Currently, research is underway on how to integrate AI technology into the radio interface, and how AI can assist in improving radio interface transmission technology.

[0091] Integrated Sensing and Communications (ISAC) technology aims to enable sensing services based on existing mobile communication infrastructure. It fully leverages the advantages of mobile communication networks to meet the sensing needs of various services in different scenarios, while simultaneously improving communication performance through sensing capabilities. It offers advantages such as reduced cost and power consumption, and optimized resource utilization, making it a crucial research and development direction in wireless communication. For integrated sensing and communications applications, academia and industry are gradually conducting sensing channel modeling work, providing strong support for the evaluation of sensing technology solutions. Meanwhile, related research has proposed various sensing methods based on channel information to obtain target speed, distance, and angle information for typical sensing scenarios such as highways, aiming to meet the needs of intelligent transportation, smart living, and other application scenarios.

[0092] The application of AI technology in wireless communication is an important research direction in both academia and industry, and also one of the key research topics for 3GPP standardization. The powerful feature extraction and mapping relationship modeling capabilities of neural network models can provide new solutions to key problems in wireless communication systems. They have already been widely applied and researched in specific technical areas such as high-precision positioning, channel state information feedback, and beam management, and will play a crucial role in future 6G communication systems.

[0093] In some embodiments, communication sensing (hereinafter referred to as synesthesia) can be based on AI. AI-based ISAC accuracy enhancement can include two methods: one is to directly output the sensing results based on the AI ​​model, and the other is to output the intermediate parameters of the sensing process based on the AI ​​model.

[0094] Figure 1B is a schematic diagram illustrating the perception result based on AI output according to an embodiment of the present disclosure.

[0095] As shown in Figure 1B, the input data (e.g., the channel matrix) of the perception model can be input into the AI ​​target information perception model. Based on the AI ​​model, the perception results (e.g., target perception information) can be directly output. The output perception results can be multi-dimensional, including distance perception results, velocity perception results, angle perception results, etc. The multi-dimensional perception results can be output by one AI model or by multiple different AI models, and this disclosure does not limit this.

[0096] Figure 1C is a schematic diagram illustrating AI-based and non-AI-based output perception results according to an embodiment of the present disclosure.

[0097] As shown in Figure 1C, intermediate parameters of the perception process can be obtained through the AI ​​component, while the perception results can be output through the non-AI component. For example, the input data of the perception model (e.g., the channel matrix) is input into the AI ​​model (e.g., the AI ​​perception model), and intermediate perception parameter information (e.g., the number of multiple targets, the correlation matrix of the receiving channel) is output. The intermediate perception parameter information is then processed by the non-AI algorithm to obtain the perception results, which include target distance, speed, angle, etc.

[0098] Figure 1D is a schematic diagram illustrating the reporting of synesthesia-related capabilities according to an embodiment of the present disclosure.

[0099] As shown in Figure 1D, the UE or gNB can report its sensing capabilities to the Sensing Function (SF), the SF can configure sensing measurements for the UE or gNB, the UE or gNB can perform sensing measurements, and the UE or gNB can report its sensing measurements to the SF.

[0100] In the sensing mode involving UEs, the SF needs to provide non-resource-related sensing measurement configurations for the participating UEs, such as sensing mode, transmit / receive roles, and reporting modes. Furthermore, in the gNB-transmitting / UE-receiving mode, multiple gNBs may transmit sensing signals to a single UE. In this case, the SF needs to coordinate with multiple gNBs to obtain the configuration for the UE to receive sensing measurement signals and then send this configuration to the UE. In the network-participating UE A-transmitting / UE B-receiving mode, the participating UEs may be served by different gNBs. Therefore, the SF may interact with multiple gNBs to coordinate the UE's resource-related configurations.

[0101] The perception measurement configuration provided by SF to UE mainly includes the following information:

[0102] Perception modes, such as gNB transmitting and UE receiving, UE transmitting and gNB receiving, UE transmitting and receiving independently, and UE A transmitting and UE B receiving.

[0103] Corresponding roles in perception mode: "Receive", "Send", "Receive & Send";

[0104] Perception result reporting modes, such as periodic reporting, event reporting, and event-triggered periodic reporting (for UEs that need to receive perception signals).

[0105] However, SF's current terminal configuration only includes configurations related to traditional sensing processing, and does not include configurations related to AI processing.

[0106] In view of this, the present disclosure provides a communication method in which a network device sends first information to a terminal, the first information instructing the terminal to perform communication sensing based on artificial intelligence (AI) or machine learning (ML), which enables the network device to instruct the terminal on relevant configurations for AI-based sensing processing, thereby improving communication efficiency.

[0107] 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:

[0108] Step S2101: The terminal sends the second information to the network device.

[0109] In some embodiments, the network device receives second information sent by the terminal.

[0110] In some embodiments, the network device may be, for example, a sensing function (SF), which may also be referred to as a first network element, a first function, etc. This disclosure does not limit the name of the sensing function.

[0111] In some embodiments, the second information is used to request the closure of AI- or ML-based communication sensing, or the second information indicates that AI- or ML-based communication sensing processing is not applicable to the terminal.

[0112] In some embodiments, the terminal may send a second message to the network device requesting the network device to disable AI- or ML-based sensing processing; or, the terminal may send a second message to the network device reporting that the currently applicable AI- or ML-based sensing processing is no longer applicable.

[0113] For example, when the terminal detects that the perception accuracy based on AI / ML is relatively low, or when the terminal's battery is low, or when the terminal's computing resources are limited, the terminal can send a second message to the network device.

[0114] In some embodiments, after the terminal sends a second message to the network device, the network device determines to disable the terminal's AI- or ML-based communication sensing processing based on the second message sent by the terminal. The network device then sends a first message to the terminal, indicating that the AI- or ML-based communication sensing processing should be disabled.

[0115] In some embodiments, the terminal disables AI- or ML-based synergistic processing based on first information sent by the network device. The terminal can then revert to non-AI-based synergistic processing.

[0116] In some embodiments, step S2101 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0117] In step S2102, the terminal sends third information to the network device.

[0118] In some embodiments, the network device receives third information sent by the terminal.

[0119] In some embodiments, the third information is used to indicate the AI- or ML-based communication sensing processing applicable to the terminal.

[0120] In some embodiments, the network device determines whether to enable or disable AI- or ML-based communication sensing processing based on third-party information.

[0121] In some embodiments, the terminal may report the AI / ML communication sensing processing applicable to the terminal to the network device, and the network device determines whether to enable or disable the AI / ML communication sensing processing based on the AI / ML communication sensing processing reported by the terminal.

[0122] For example, if the AI / ML communication sensing processing applicable to the terminal is based on AI or ML output sensing results, the network device can determine to enable AI or ML output sensing results. The network device sends first information to the terminal, and the first information indicates that AI or ML output sensing results should be enabled.

[0123] For example, if the AI / ML communication sensing processing applicable to the terminal is based on AI or ML output sensing results, it can be assumed that the terminal is not applicable to other AI or ML-based communication sensing processing (e.g., processing the first signal based on AI or ML). The network device can determine to disable processing of the first signal based on AI or ML, and the network device sends first information to the terminal, the first information indicating to disable processing of the first signal based on AI or ML.

[0124] In some embodiments, step S2102 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0125] In step S2103, the network device sends the first information to the terminal.

[0126] In some embodiments, the terminal receives first information sent by the network device.

[0127] In some embodiments, the first information instructs the terminal to perform communication sensing based on artificial intelligence (AI) or machine learning (ML).

[0128] In some embodiments, the first information may be configuration information for communication sensing. The first information instructs the terminal to perform AI / ML-based communication sensing related processing.

[0129] In some embodiments, the first information indicates at least one of the following: enabling AI- or ML-based communication awareness processing; disabling AI- or ML-based communication awareness processing.

[0130] In some embodiments, the first information may indicate whether to enable or disable AI / ML-based communication awareness processing.

[0131] In some embodiments, the network device may determine whether to enable or disable AI- or ML-based communication sensing processing based on information sent by the terminal. For example, the network device receives second or third information sent by the terminal, and determines to disable AI- or ML-based communication sensing processing based on the second or third information.

[0132] For example, if a network device receives information from a terminal instructing the terminal to apply AI- or ML-based communication sensing processing, the network device will determine to enable AI- or ML-based communication sensing processing.

[0133] In some embodiments, AI or ML-based communication sensing processing includes at least one of the following: outputting sensing results based on AI or ML; outputting a first parameter based on AI or ML, the first parameter being an intermediate parameter used to obtain the sensing results; and processing a first signal based on AI or ML, the first signal being a signal used for sensing measurement.

[0134] In some embodiments, AI or ML-based communication sensing processing includes outputting sensing results based on AI or ML, meaning the network device can instruct the terminal to enable or disable the function of outputting sensing results based on AI or ML. For example, if the network device instructs the terminal to enable outputting sensing results based on AI or ML, the terminal can input the channel matrix into the AI ​​model or ML model and output the sensing results.

[0135] In some embodiments, AI- or ML-based communication sensing processing includes outputting a first parameter based on AI or ML. This first parameter is an intermediate parameter used to obtain the sensing result; it can also be referred to as the intermediate sensing parameter. For example, a network device instructs a terminal to enable outputting the first parameter based on AI or ML. The terminal can input the channel matrix into the AI ​​model or ML model and output the intermediate sensing parameter. After obtaining the intermediate sensing parameter, a non-AI algorithm can be used to process it to obtain the sensing result.

[0136] In some embodiments, AI or ML-based communication sensing processing includes processing a first signal based on AI or ML. The first signal is a signal used for sensing measurement, and can also be referred to as a sensing measurement signal. By measuring the first signal, a sensing result can be obtained. For example, a network device instructs a terminal to enable denoising processing of the first signal based on an AI model or ML model, and the terminal can perform denoising processing of the first signal based on the AI ​​model or ML model.

[0137] In some embodiments, AI or ML-based communication sensing processing includes compressing the first signal based on AI or ML. For example, a network device instructs a terminal to enable compression of the first signal based on an AI model or ML model. The terminal can input the first signal into the AI ​​model or ML model and output the compressed first signal.

[0138] In some embodiments, the first information may include a configuration for AI / ML-based communication awareness, which may include at least one of the following: the type of the perception result; a first parameter; the type of processing, which includes at least one of denoising processing, feature extraction processing, and compression processing; and compression parameters.

[0139] In some embodiments, AI or ML-based communication sensing processing includes outputting sensing results based on AI or ML; the first information includes the type of sensing result, and the sensing result includes at least one of distance, position, angle, and speed.

[0140] In some embodiments, the perception results are output based on AI or ML, which means that the perception results can be output directly based on AI or ML without the need for other non-AI algorithms.

[0141] In some embodiments, where AI- or ML-based communication sensing processing includes outputting sensing results based on AI or ML, the first information sent by the network device to the terminal includes the type of the sensing result. That is, the network device indicates the type of the sensing result to the terminal, the terminal performs sensing according to the network device's indication, and reports the corresponding type of sensing result to the network device.

[0142] In some embodiments, the sensing result may include at least one of distance, position, angle, and speed. Distance may be the distance between the sensing target and the sensing transmitter or the distance between the sensing target and the sensing receiver; position may be the location of the sensing target; angle may be the angle between the sensing target and the sensing transmitter or the angle between the sensing target and the sensing receiver; and speed may be the moving speed of the sensing target.

[0143] In some embodiments, AI or ML-based communication sensing processing includes outputting a first parameter based on AI or ML; the first information includes the first parameter, which includes at least one of the number of sensing targets and the transmission path.

[0144] In some embodiments, the first parameter (which is an intermediate parameter used to obtain the perception result) is output based on AI or ML. The first parameter is then processed by other non-AI algorithms to obtain the perception result.

[0145] In some embodiments, where the AI- or ML-based communication sensing processing includes outputting a first parameter based on AI or ML, the first information sent by the network device to the terminal includes the first parameter. That is, the network device indicates the first parameter to the terminal, the terminal senses according to the network device's indication, and reports the corresponding type of first parameter to the network device.

[0146] In some embodiments, the first parameter includes at least one of the number of sensed targets and the transmission path. The number of sensed targets can be a positive integer; the transmission path, also known as the transmission route, is used to determine the distance to the targets.

[0147] For example, the network device indicates that the first parameter is the number of sensing targets, and the network device sends the number of sensing targets to the terminal, which then performs sensing based on the number of sensing targets indicated by the network device.

[0148] In some embodiments, AI or ML-based communication sensing processing includes processing a first signal based on AI or ML; processing the first signal includes at least one of denoising processing, feature extraction processing, and compression processing.

[0149] In some embodiments, where the AI- or ML-based communication sensing processing includes processing a first signal (the first signal being a signal used for sensing measurement) based on AI or ML, the first information sent by the network device to the terminal includes the type of processing. That is, the network device indicates the type of processing to the terminal, the terminal performs the processing according to the network device's instructions, and reports the result of the corresponding processing to the network device.

[0150] For example, the network device instructs the first signal to be denoised based on AI or ML, the terminal performs denoising based on AI or ML, and sends the denoised result to the network device.

[0151] In some embodiments, AI or ML-based communication sensing processing includes compressing a first signal based on AI or ML; the first information includes compression parameters, which include at least one of the number of bits in the compression result and the compression ratio.

[0152] In some embodiments, where the AI- or ML-based communication sensing processing includes compressing a first signal (the first signal being a signal used for sensing measurements) based on AI or ML, the first information sent by the network device to the terminal includes compression parameters. That is, the network device instructs the terminal on the compression parameters, the terminal performs compression according to the network device's instructions, and reports the compression result to the network device.

[0153] For example, the network device indicates a compression ratio, the terminal compresses the first signal according to the compression ratio indicated by the network, and reports the compressed first signal to the network device.

[0154] In some embodiments, the first information indicates the activation of AI or ML-based communication sensing processing; the first information includes configuration information for terminal performance monitoring.

[0155] In some embodiments, when the first information instructs the terminal to enable AI- or ML-based communication sensing processing, the first information may include configuration information for the terminal to perform performance monitoring for AI- or ML-based communication sensing.

[0156] In some embodiments, the network device may instruct the terminal to enable AI or ML-based communication sensing and instruct the terminal to perform performance monitoring on the AI ​​or ML-based communication sensing.

[0157] In some embodiments, the configuration information includes at least one of the following: performance monitoring parameters reported by the terminal, the performance monitoring parameters including performance monitoring results or measurement results used for performance monitoring; a first threshold, the first threshold being used by the terminal to report fourth information to the network device, the fourth information indicating a fallback to communication awareness processing not based on AI or ML, or the first threshold being used by the terminal to fall back to communication awareness processing not based on AI or ML.

[0158] In some embodiments, communication sensing processing not based on AI or ML refers to communication sensing without using AI and ML, for example, communication sensing can be performed using non-AI algorithms.

[0159] In some embodiments, the aforementioned performance monitoring parameters may be performance monitoring parameters that the terminal needs to report, as indicated by the network device. For example, reporting measurement results for performance monitoring, or the terminal directly reporting the performance monitoring results. The measurement results for performance monitoring may be numerical values, and the performance monitoring results may be related operations determined by the terminal based on these values. These related operations may, for example, be enabling or disabling AI- or ML-based communication sensing.

[0160] In some embodiments, the first threshold may be, for example, a perceived error threshold value.

[0161] In some embodiments, the network device may indicate a first threshold to the terminal. When the value monitored by the terminal performance exceeds the first threshold, the terminal reports to the network to fall back to conventional sensing processing, or the terminal automatically falls back to conventional sensing processing.

[0162] The communication method provided in this embodiment involves a network device sending first information to a terminal. The first information instructs the terminal to perform communication sensing based on artificial intelligence (AI) or machine learning (ML). This enables the network device to instruct the terminal on relevant configurations for AI-based sensing processing, thereby improving communication efficiency.

[0163] The communication method involved in the embodiments of this disclosure may include at least one of steps S2101 to S2103. For example, step S2103 may be implemented as a standalone embodiment, step S2101+S2013 may be implemented as a standalone embodiment, and step S2102+S2013 may be implemented as a standalone embodiment, but is not limited thereto.

[0164] In some embodiments, step S2101 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0165] In some embodiments, step S2102 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0166] In some embodiments, other optional implementations described before or after the specification corresponding to FIG2 may be referred to.

[0167] 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.

[0168] In some embodiments, the terms “downlink control information (DCI),” “downlink (DL) assignment,” “DL DCI,” “uplink (UL) grant,” and “UL DCI” can be used interchangeably.

[0169] In some embodiments, terms such as "physical downlink shared channel (PDSCH)" and "DL data" can be used interchangeably, as can terms such as "physical uplink shared channel (PUSCH)" and "UL data".

[0170] In some embodiments, terms such as “moment,” “point in time,” “time,” and “time location” can be used interchangeably, as can terms such as “duration,” “segment,” “time window,” “window,” and “time.”

[0171] In some embodiments, the terms "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", "resource", "resource set", "resource group", "beam", "beam width", "beam angular degree", "antenna", "antenna element", and "panel" can be used interchangeably.

[0172] In some embodiments, “get,” “obtain,” “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.

[0173] In some embodiments, terms such as “send,” “transmit,” “report,” “distribute,” “transfer,” “bidirectional transmission,” “send and / or receive” can be used interchangeably.

[0174] 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.

[0175] 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 value (bool)) represented by true or false, or by a numerical comparison (e.g., a comparison with a predetermined value), but is not limited thereto.

[0176] 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.

[0177] Figure 3 is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3, the embodiments of the present disclosure relate to a communication method executed by a network device, the method including:

[0178] Step S3101: Receive the second information sent by the terminal.

[0179] 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.

[0180] In some embodiments, the network device receives second information sent by a terminal, but is not limited thereto; it may also receive second information sent by other entities.

[0181] In some embodiments, the network device obtains second information as defined by the protocol.

[0182] In some embodiments, the network device obtains second information from the upper layer(s).

[0183] In some embodiments, the network device processes the information to obtain the second information.

[0184] Step S3102: Receive the third information sent by the terminal.

[0185] 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.

[0186] In some embodiments, the network device receives third information sent by a terminal, but is not limited thereto; it may also receive third information sent by other entities.

[0187] In some embodiments, the network device acquires third information as defined by the protocol.

[0188] In some embodiments, network devices obtain third information from upper layer(s).

[0189] In some embodiments, the network device processes information to obtain third-party information.

[0190] Step S3103: Send the first information to the terminal.

[0191] The optional implementation of step S3103 can be found in the optional implementation of step S2103 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0192] The communication method involved in the embodiments of this disclosure may include at least one of steps S3101 to S3103. For example, step S3103 may be implemented as a standalone embodiment, step S3101+S3103 may be implemented as a standalone embodiment, and step S3102+S3103 may be implemented as a standalone embodiment, but is not limited thereto.

[0193] In some embodiments, step S3101 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0194] In some embodiments, step S3102 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0195] Figure 4 is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 4, the embodiments of the present disclosure relate to a communication method executed by a terminal, the method including:

[0196] Step S4101: Send the second information to the network device.

[0197] The optional implementation of step S4101 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.

[0198] Step S4102: Send third information to the network device.

[0199] The optional implementation of step S4102 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.

[0200] Step S4103: Receive the first information sent by the network device.

[0201] The optional implementation of step S4103 can be found in the optional implementation of step S2103 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0202] In some embodiments, the terminal receives first information sent by a network device, but is not limited thereto; it may also receive first information sent by other entities.

[0203] In some embodiments, the terminal obtains first information as defined by the protocol.

[0204] In some embodiments, the terminal obtains first information from the upper layer(s).

[0205] In some embodiments, the terminal processes the information to obtain the first information.

[0206] The communication method involved in the embodiments of this disclosure may include at least one of steps S4101 to S4103. For example, step S4101 may be implemented as a standalone embodiment, step S4101+S4103 may be implemented as a standalone embodiment, and step S4102+S4103 may be implemented as a standalone embodiment, but is not limited thereto.

[0207] In some embodiments, step S4101 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0208] In some embodiments, step S4102 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0209] In some embodiments, the above methods may include the methods of the embodiments described above on the communication system side, network device side, terminal side, etc., which will not be repeated here.

[0210] This disclosure presents an embodiment of an SF configuration method for AI-based sensor processing of a terminal.

[0211] (1) The first network element (Sensing Function) sends first information to the terminal, and the first information instructs the terminal to perform related processing of communication sensing based on AI / ML.

[0212] (2) The related processing of communication perception based on AI / ML includes the following aspects:

[0213] Enable AI / ML-based communication-aware processing;

[0214] Disable AI / ML-based communication-aware processing.

[0215] (3) Based on (2), the AI / ML related processing initiated by the notification terminal includes any one of the following aspects:

[0216] Based on AI / ML models, the perception results are directly output, such as the perceived distance, angle, speed, etc.

[0217] Intermediate parameters for AI / ML-based perception output, such as the number of perceived objects.

[0218] AI / ML is used to process measurement signals for perception, such as denoising and feature extraction.

[0219] AI / ML is used to compress the sensed measurement signals.

[0220] (4) In response to the first information configuration terminal directly outputting the perception result through the AI / ML model, the first information also includes the configuration for performing the AI ​​processing, such as: the direct result type based on the AI / ML output, the result type including one or more of the perceived distance, position, angle, and speed.

[0221] (5) In response to the first information configuration terminal, intermediate parameters for perception are output based on the AI / ML model. The first information also includes the configuration for performing the AI ​​processing, such as the type of the intermediate parameters (number of perceived targets, Path).

[0222] (6) Based on (2) in response to AI / ML-based compression of the perceived signal, the first information also includes the configuration for performing the AI ​​processing, such as the number of bits of the supported output compression, or the compression ratio.

[0223] (7) Based on (2) in response to the first message notification terminal to start AI / ML related operations, the message also includes the terminal’s relevant configuration for performance monitoring of AI / ML processing.

[0224] (8) The relevant configuration for performance monitoring based on the AI / ML described in (7) includes at least one of the following:

[0225] The types of performance monitoring parameters that the terminal needs to report: for example, reporting measurement results for performance monitoring, or the terminal directly reporting the performance monitoring results;

[0226] The first threshold is set so that when the terminal performance monitoring result exceeds the threshold, the terminal reports to the network or the terminal reverts to traditional sensing processing.

[0227] (9) Based on (1), in response to the network instructing the terminal to turn off AI / ML-based sensing operations, the method further includes, before sending the first message, the network receiving a second message sent by the terminal, the second message being either a request from the terminal to the network to turn off AI / ML-based sensing processing, or a report from the terminal that the currently used AI / ML-based sensing processing is no longer applicable.

[0228] (10) Based on (1), in response to the network instructing the terminal to turn off AI / ML-based sensing operations, the method further includes, before sending the first message, the network receiving a third message sent by the terminal, the third message being used to report to the network the AI / ML sensing processing currently applicable to the terminal.

[0229] 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.

[0230] 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 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 network device (e.g., access network device, core network functional node, core network device, etc.) in any of the above methods.

[0231] 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.

[0232] 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).

[0233] Figure 5A is a schematic diagram of the structure of a network device according to an embodiment of this disclosure. As shown in Figure 5A, the network device 5100 may include a transceiver module 5101. In some embodiments, the transceiver module 5101 sends first information to a terminal. Optionally, the transceiver module is used to perform at least one of the transceiver steps (e.g., step S2101, but not limited thereto) performed by the network device in any of the above methods, which will not be described in detail here.

[0234] In some embodiments, the network device may further include a processing module.

[0235] In some embodiments, the first information indicates at least one of the following: enabling AI- or ML-based communication awareness processing; disabling AI- or ML-based communication awareness processing.

[0236] In some embodiments, the AI ​​or ML-based communication sensing processing includes at least one of the following: outputting a sensing result based on AI or ML; outputting a first parameter based on AI or ML, wherein the first parameter is an intermediate parameter used to obtain the sensing result; and processing a first signal based on AI or ML, wherein the first signal is a signal used for sensing measurement.

[0237] In some embodiments, the AI ​​or ML-based communication sensing processing includes outputting sensing results based on AI or ML; the first information includes the type of sensing result, and the sensing result includes at least one of distance, position, angle, and speed.

[0238] In some embodiments, the AI ​​or ML-based communication sensing processing includes outputting a first parameter based on AI or ML; the first information includes the first parameter, which includes at least one of the number of sensing targets and the transmission path.

[0239] In some embodiments, the AI ​​or ML-based communication sensing processing includes processing a first signal based on AI or ML; the first information includes the type of processing, which includes at least one of denoising processing and feature extraction processing.

[0240] In some embodiments, the AI ​​or ML-based communication sensing processing includes compressing a first signal based on AI or ML; the first information includes compression parameters, which include at least one of the number of bits in the compression result and the compression ratio.

[0241] In some embodiments, the first information indicates the activation of AI or ML-based communication awareness processing; the first information includes configuration information for the terminal to perform performance monitoring.

[0242] In some embodiments, the configuration information includes at least one of the following: performance monitoring parameters reported by the terminal, the performance monitoring parameters including performance monitoring results or measurement results for performance monitoring; a first threshold, the first threshold being used by the terminal to report fourth information to the network device, the fourth information indicating a fallback to communication awareness processing not based on AI or ML, or the first threshold being used by the terminal to fall back to communication awareness processing not based on AI or ML.

[0243] In some embodiments, the first information indicates that AI- or ML-based communication awareness processing is turned off; the transceiver module is further configured to: receive second information sent by the terminal, the second information being used to request the turning off of AI- or ML-based communication awareness processing, or the second information indicating that the terminal does not apply AI- or ML-based communication awareness processing.

[0244] In some embodiments, the transceiver module is further configured to: receive third information sent by the terminal, the third information being used to indicate the AI- or ML-based communication sensing processing applicable to the terminal.

[0245] Figure 5B is a schematic diagram of the terminal structure proposed in an embodiment of this disclosure. As shown in Figure 5B, the terminal 5200 may include a transceiver module 5201. In some embodiments, the transceiver module 5201 is used to receive first information sent by a network device. Optionally, the transceiver module is used to perform at least one of the sending and receiving steps performed by the terminal in any of the above methods, which will not be described in detail here.

[0246] In some embodiments, the terminal may further include a processing module.

[0247] In some embodiments, the first information indicates at least one of the following: enabling AI- or ML-based communication awareness processing; disabling AI- or ML-based communication awareness processing.

[0248] In some embodiments, the AI ​​or ML-based communication sensing processing includes at least one of the following: outputting a sensing result based on AI or ML; outputting a first parameter based on AI or ML, wherein the first parameter is an intermediate parameter used to obtain the sensing result; and processing a first signal based on AI or ML, wherein the first signal is a signal used for sensing measurement.

[0249] In some embodiments, the AI ​​or ML-based communication sensing processing includes outputting sensing results based on AI or ML; the first information includes the type of sensing result, and the sensing result includes at least one of distance, position, angle, and speed.

[0250] In some embodiments, the AI ​​or ML-based communication sensing processing includes outputting a first parameter based on AI or ML; the first information includes the first parameter, which includes at least one of the number of sensing targets and the transmission path.

[0251] In some embodiments, the AI ​​or ML-based communication sensing processing includes processing a first signal based on AI or ML; the first information includes the type of processing, which includes at least one of denoising processing and feature extraction processing.

[0252] In some embodiments, the AI ​​or ML-based communication sensing processing includes compressing a first signal based on AI or ML; the first information includes compression parameters, which include at least one of the number of bits in the compression result and the compression ratio.

[0253] In some embodiments, the first information indicates the activation of AI or ML-based communication awareness processing; the first information includes configuration information for the terminal to perform performance monitoring.

[0254] In some embodiments, the configuration information includes at least one of the following: performance monitoring parameters reported by the terminal, the performance monitoring parameters including performance monitoring results or measurement results for performance monitoring; a first threshold, the first threshold being used by the terminal to report fallback to a fourth information to the network device, the fourth information indicating fallback to communication awareness processing not based on AI or ML, or the first threshold being used by the terminal to fallback to communication awareness processing not based on AI or ML.

[0255] In some embodiments, the first information indicates that AI- or ML-based communication sensing processing is turned off; the transceiver module is further configured to: send a second information to the network device, the second information being used to request the turning off of AI- or ML-based communication sensing processing, or the second information indicating that AI- or ML-based communication sensing processing is not applicable to the terminal.

[0256] In some embodiments, the transceiver module is further configured to: send third information to the network device, the third information being used to indicate AI- or ML-based communication sensing processing applicable to the terminal.

[0257] Figure 6A is a schematic diagram of the structure of the communication device 6100 proposed in an embodiment of this disclosure. The communication device 6100 can be a network device (e.g., access network device, core network device, etc.), a terminal (e.g., user equipment, etc.), a chip, chip system, or processor that supports the network device in implementing any of the above methods, or a chip, chip system, or processor that supports the terminal in implementing any of the above methods. The communication device 6100 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.

[0258] As shown in Figure 6A, the communication device 6100 includes one or more processors 6101. The processor 6101 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. Optionally, the communication device 6100 can be used to execute any of the above methods. Optionally, one or more processors 6101 can be used to invoke instructions to cause the communication device 6100 to execute any of the above methods.

[0259] In some embodiments, the communication device 6100 further includes one or more transceivers 6102. When the communication device 6100 includes one or more transceivers 6102, the transceiver 6102 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 6101 performs at least one of the other steps. In optional embodiments, the transceiver may include a receiver and / or a transmitter, which may be separate or integrated together. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, interface, etc., can be used interchangeably; the terms transmitter, sending unit, transmitter, sending circuit, etc., can be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., can be used interchangeably.

[0260] In some embodiments, the communication device 6100 further includes one or more memories 6103 for storing data. Optionally, all or part of the memories 6103 may be located outside the communication device 6100. In optional embodiments, the communication device 6100 may include one or more interface circuits 6104. Optionally, the interface circuits 6104 are connected to the memories 6103 and can be used to receive data from the memories 6103 or other devices, and to send data to the memories 6103 or other devices. For example, the interface circuits 6104 can read data stored in the memories 6103 and send that data to the processor 6101.

[0261] The communication device 6100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 6100 described in this disclosure is not limited thereto, and the structure of the communication device 6100 may not be limited by FIG. 6A. 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.

[0262] Figure 6B is a schematic diagram of the structure of chip 6200 according to an embodiment of this disclosure. For cases where the communication device 6100 can be a chip or a chip system, please refer to the schematic diagram of chip 6200 shown in Figure 6B, but it is not limited thereto.

[0263] Chip 6200 includes one or more processors 6201. Chip 6200 is used to perform any of the methods described above.

[0264] In some embodiments, chip 6200 further includes one or more interface circuits 6202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 6200 further includes one or more memories 6203 for storing data. Optionally, all or part of the memories 6203 may be located outside chip 6200. Optionally, interface circuit 6202 is connected to memory 6203, and interface circuit 6202 can be used to receive data from memory 6203 or other devices, and interface circuit 6202 can be used to send data to memory 6203 or other devices. For example, interface circuit 6202 can read data stored in memory 6203 and send the data to processor 6201.

[0265] In some embodiments, the interface circuit 6202 performs at least one of the communication steps such as sending and / or receiving in the above-described method (e.g., step S2101, but not limited thereto). For example, the interface circuit 6202 performing the communication steps such as sending and / or receiving in the above-described method means that the interface circuit 6202 performs data interaction between the processor 6201, the chip 6200, the memory 6203, or the transceiver device. In some embodiments, the processor 6201 performs at least one of the other steps.

[0266] The modules and / or devices described in the various embodiments, such as virtual devices, physical devices, and chips, can be combined or separated arbitrarily as needed. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0267] This disclosure also proposes a storage medium storing instructions that, when executed on the communication device 6100, cause the communication device 6100 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.

[0268] This disclosure also provides a program product that, when executed by the communication device 6100, causes the communication device 6100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0269] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

Claims

1. A communication method, characterized in that, Performed by a network device, the method includes: Send a first message to the terminal, the first message instructing the terminal to perform communication sensing processing based on artificial intelligence (AI) or machine learning (ML).

2. The method according to claim 1, characterized in that, The first information indicates at least one of the following: Enable communication awareness based on AI or ML; Turn off AI or ML-based communication awareness.

3. The method according to claim 1 or 2, characterized in that, The AI ​​or ML-based communication sensing processing includes at least one of the following: Based on AI or ML to output perception results; The first parameter is output based on AI or ML, and the first parameter is an intermediate parameter used to obtain the perception result; The first signal, which is a signal used for sensing and measurement, is processed based on AI or ML.

4. The method according to any one of claims 1 to 3, characterized in that, The AI ​​or ML-based communication sensing processing includes outputting sensing results based on AI or ML; the first information includes the type of sensing result, and the sensing result includes at least one of distance, position, angle, and speed.

5. The method according to any one of claims 1 to 4, characterized in that, The AI ​​or ML-based communication sensing processing includes outputting a first parameter based on AI or ML; the first information includes the first parameter, which includes at least one of the following: the number of sensing targets and the transmission path.

6. The method according to any one of claims 1 to 5, characterized in that, The AI ​​or ML-based communication sensing processing includes processing the first signal based on AI or ML; the processing of the first signal includes at least one of denoising processing, feature extraction processing, and compression processing.

7. The method according to any one of claims 1 to 6, characterized in that, The AI ​​or ML-based communication sensing processing includes compressing a first signal based on AI or ML; the first information includes compression parameters, which include at least one of the number of bits in the compression result and the compression ratio.

8. The method according to any one of claims 1 to 7, characterized in that, The first information indicates that AI or ML-based communication sensing processing is enabled; the first information includes configuration information for the terminal to perform performance monitoring.

9. The method according to claim 8, characterized in that, The configuration information includes at least one of the following: The performance monitoring parameters reported by the terminal include performance monitoring results or measurement results used for performance monitoring. A first threshold is used for the terminal to report fourth information to the network device, the fourth information indicating a fallback to communication awareness processing not based on AI or ML, or the first threshold is used for the terminal to fall back to communication awareness processing not based on AI or ML.

10. The method according to any one of claims 1 to 9, characterized in that, The first information indicates that AI- or ML-based communication awareness processing should be disabled; The method further includes: The terminal receives a second message, which requests the closure of AI- or ML-based communication awareness processing, or indicates that the terminal does not apply AI- or ML-based communication awareness processing.

11. The method according to any one of claims 1 to 10, characterized in that, The method further includes: The terminal receives third information, which is used to indicate the appropriate AI or ML-based communication sensing processing for the terminal.

12. A communication method, characterized in that, The method, executed by a terminal, includes: The terminal receives first information sent by a network device, the first information instructing the terminal to perform communication sensing processing based on artificial intelligence (AI) or machine learning (ML).

13. The method according to claim 12, characterized in that, The first information indicates at least one of the following: Enable AI or ML-based communication-aware processing; Disable AI or ML-based communication-aware processing.

14. The method according to claim 12 or 13, characterized in that, The AI ​​or ML-based communication sensing processing includes at least one of the following: Based on AI or ML to output perception results; The first parameter is output based on AI or ML, and the first parameter is an intermediate parameter used to obtain the perception result; The first signal, which is a signal used for sensing and measurement, is processed based on AI or ML.

15. The method according to any one of claims 12 to 14, characterized in that, The AI ​​or ML-based communication sensing processing includes outputting sensing results based on AI or ML; the first information includes the type of sensing result, and the sensing result includes at least one of distance, position, angle, and speed.

16. The method according to any one of claims 12 to 15, characterized in that, The AI ​​or ML-based communication sensing processing includes outputting a first parameter based on AI or ML; the first information includes the first parameter, which includes at least one of the following: the number of sensing targets and the transmission path.

17. The method according to any one of claims 12 to 16, characterized in that, The AI ​​or ML-based communication sensing processing includes processing the first signal based on AI or ML; the processing of the first signal includes at least one of denoising processing, feature extraction processing, and compression processing.

18. The method according to any one of claims 12 to 17, characterized in that, The AI ​​or ML-based communication sensing processing includes compressing a first signal based on AI or ML; the first information includes compression parameters, which include at least one of the number of bits in the compression result and the compression ratio.

19. The method according to any one of claims 12 to 18, characterized in that, The first information indicates that AI or ML-based communication sensing processing is enabled; the first information includes configuration information for the terminal to perform performance monitoring.

20. The method according to claim 19, characterized in that, The configuration information includes at least one of the following: The performance monitoring parameters reported by the terminal include performance monitoring results or measurement results used for performance monitoring. A first threshold is used for the terminal to report fourth information to the network device, the fourth information indicating a fallback to communication awareness processing not based on AI or ML, or the first threshold is used for the terminal to fall back to communication awareness processing not based on AI or ML.

21. The method according to any one of claims 12 to 20, characterized in that, The first information indicates that AI- or ML-based communication awareness processing should be disabled; The method further includes: Send a second message to the network device, the second message being used to request the shutdown of AI- or ML-based communication sensing processing, or the second message indicating that the terminal does not apply AI- or ML-based communication sensing processing.

22. The method according to any one of claims 12 to 21, characterized in that, The method further includes: Send a third message to the network device, the third message being used to indicate the appropriate AI- or ML-based communication sensing processing for the terminal.

23. A communication device, characterized in that, The communication device is used to perform the communication method according to any one of claims 1 to 11 or the communication method according to any one of claims 12 to 22.

24. A communication system, characterized in that, The device includes a network device and a terminal, wherein the network device is configured to implement the communication method of any one of claims 1 to 11, and the terminal is configured to implement the communication method of any one of claims 12 to 22.

25. A storage medium, characterized in that, The storage medium stores instructions that, when executed on a communication device, cause the communication device to perform the communication method as described in any one of claims 1 to 11 or the communication method as described in any one of claims 12 to 22.

26. A program product, characterized in that, It includes at least one of a program and instructions, wherein when the program or instructions are executed by a communication device, they implement the communication method of any one of claims 1 to 11 or perform the communication method of any one of claims 12 to 22.

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