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

Abstract

The present disclosure relates to a communication method, a communication device, a system, a storage medium, and a program product. The communication method comprises: sending first information to a second device, wherein the first information indicates that a first device has a first capability, and the first capability is a capability of performing sensing and communication processing on the basis of AI or ML. Embodiments of the present disclosure can improve the communication efficiency.

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 to report the ability to perform synesthetic processing based on AI 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 first device, the method comprising: sending first information to a second device, the first information indicating that the first device has a first capability, the first capability being the ability to perform synesthetic processing 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 second device, the method comprising: receiving first information sent by a first device, the first information indicating that the first device has a first capability, the first capability being the ability to perform synesthetic processing 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 provided, including a first device and a second device, wherein the first device is configured to implement the communication method of the first aspect, and the second device 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] In this embodiment of the disclosure, the first device sends first information to the second device. The first information indicates that the first device has the ability to perform synergistic processing based on artificial intelligence (AI) or machine learning (ML), thereby enabling the first device to report the ability to perform synergistic processing based on AI or ML, 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 first device proposed in an embodiment of this disclosure.

[0021] Figure 5B is a schematic diagram of the structure of the second device proposed in an 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 first device, the method comprising: sending first information to a second device, the first information indicating that the first device has a first capability, the first capability being the ability to perform synesthetic processing based on artificial intelligence (AI) or machine learning (ML).

[0026] In the above embodiments, the first device sends first information to the second device. The first information indicates that the first device has the ability to perform synergistic processing based on artificial intelligence (AI) or machine learning (ML), which enables the first device to report the ability to perform synergistic processing based on AI or ML, thereby improving communication efficiency.

[0027] In conjunction with some embodiments of the first aspect, in some embodiments, the first capability includes at least one of the following: the ability to output perception results based on AI or ML; the ability to output a first parameter based on AI or ML, wherein the first parameter is an intermediate parameter used to obtain the perception results; and the ability to process a first signal based on AI or ML.

[0028] In conjunction with some embodiments of the first aspect, in some embodiments, the first capability includes the ability to output perception results based on AI or ML; the first information includes the perception results, which include at least one of distance, position, angle, and speed.

[0029] In conjunction with some embodiments of the first aspect, in some embodiments, the first capability includes the ability to output a first parameter based on AI or ML; the first information includes the first parameter, which includes at least one of the number of sensed targets and the transmission path.

[0030] In conjunction with some embodiments of the first aspect, in some embodiments, the first capability includes the ability to process the first signal based on AI or ML; the processing of the first signal includes at least one of denoising processing and feature extraction processing.

[0031] In conjunction with some embodiments of the first aspect, in some embodiments, the first capability includes the ability to process a first signal based on AI or ML, wherein processing the first signal includes compression processing; the first information includes compression parameters supported by the first device, wherein the compression parameters include at least one of the number of bits in the compression result and the compression ratio.

[0032] In conjunction with some embodiments of the first aspect, in some embodiments, the first information further includes a configuration to which the first capability applies, the configuration including at least one of the following: bandwidth for sensing; antenna port configuration for sensing; reference signal configuration for sensing; and sensing mode.

[0033] In conjunction with some embodiments of the first aspect, in some embodiments, the first information further includes the scope to which the first capability applies, the scope including at least one of the following: applicable cell identifier; applicable network transmit antenna configuration; applicable network scenario, the network scenario including at least one of outdoor scenario and indoor scenario.

[0034] Secondly, embodiments of this disclosure propose a communication method executed by a second device, the method comprising: receiving first information sent by a first device, the first information indicating that the first device has a first capability, the first capability being the ability to perform synesthetic processing based on artificial intelligence (AI) or machine learning (ML).

[0035] In conjunction with some embodiments of the second aspect, in some embodiments, the first capability includes at least one of the following: the ability to output sensing results based on AI or ML; the ability to output a first parameter based on AI or ML, wherein the first parameter is an intermediate parameter used to obtain the sensing results; and the ability to process a first signal based on AI or ML, wherein the first signal is a signal used for sensing measurement.

[0036] In conjunction with some embodiments of the second aspect, in some embodiments, the first capability includes the ability to output perception results based on AI or ML; the first information includes the perception results, which include at least one of distance, position, angle, and speed.

[0037] In conjunction with some embodiments of the second aspect, in some embodiments, the first capability includes the ability to output a first parameter based on AI or ML; the first information includes the first parameter, which includes at least one of the number of sensed targets and the transmission path.

[0038] In conjunction with some embodiments of the second aspect, in some embodiments, the first capability includes the ability to process 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.

[0039] In conjunction with some embodiments of the second aspect, in some embodiments, the first capability includes the ability to compress a first signal based on AI or ML, wherein processing the first signal includes compression processing; the first information includes compression parameters supported by the first device, wherein the compression parameters include at least one of the number of bits in the compression result and the compression ratio.

[0040] In conjunction with some embodiments of the second aspect, in some embodiments, the first information further includes a configuration to which the first capability applies, the configuration including at least one of the following: bandwidth for sensing; antenna port configuration for sensing; reference signal configuration for sensing; and sensing mode.

[0041] In conjunction with some embodiments of the second aspect, in some embodiments, the first information further includes the scope to which the first capability applies, the scope including at least one of the following: applicable cell identifier; applicable network transmit antenna configuration; applicable network scenario, the network scenario including at least one of outdoor scenario and indoor scenario.

[0042] Thirdly, embodiments of this disclosure provide a communication device for performing the communication method of the first or second aspect.

[0043] Fourthly, embodiments of this disclosure propose a communication system including a first device and a second device, wherein the first device is configured to implement the communication method of the first aspect, and the second device is configured to implement the communication method of the second aspect.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] In some embodiments, the terms communication method, information sending method, information reporting method, and information receiving method can be used interchangeably.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] In the embodiments disclosed herein, "multiple" refers to two or more.

[0054] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0059] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0060] 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”.

[0061] 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.

[0062] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0068] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0069] 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.

[0070] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0071] As shown in Figure 1A, the communication system 100 includes a first device 101 and a second device 102.

[0072] In some embodiments, the first device 101 is a terminal or an access network device.

[0073] In some embodiments, the second device 102 is a network device, such as a Sensing Function (SF).

[0074] 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.

[0075] 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).

[0076] In some embodiments, the network device may include at least one of an access network device and a core network device.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] Figure 1B is a schematic diagram illustrating the perception result based on AI output according to an embodiment of the present disclosure.

[0091] 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.

[0092] Figure 1C is a schematic diagram illustrating AI-based and non-AI-based output perception results according to an embodiment of the present disclosure.

[0093] 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.

[0094] Figure 1D is a schematic diagram illustrating the reporting of synesthesia-related capabilities according to an embodiment of the present disclosure.

[0095] 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.

[0096] In the sensing process, the gNB reports its sensing capabilities to the SF. The main purpose is for the SF to understand the gNB's sensing capabilities so that the SF can determine the gNB's sensing node and related configurations. The gNB's sensing capabilities may include the following:

[0097] Supported sensing modes include gNB self-transmitting and self-receiving mode, gNB A transmitting and gNB B receiving mode, UE transmitting and gNB receiving mode, gNB transmitting and UE receiving mode, UE self-transmitting and self-receiving (within coverage), and UE A transmitting and UE B receiving (within coverage).

[0098] The "send" and "receive" capabilities in gNB's self-transmitting and self-receiving mode;

[0099] The sending and receiving capabilities of gNB A in gNB B send and receive modes;

[0100] The ability of the UE to "receive" in gNB transmit / receive mode;

[0101] The gNB's "transmit" capability in gNB transmit / receive UE mode;

[0102] Resource configuration capabilities in UE self-transmission and self-reception (within coverage) and UE A transmission and UE B reception (within coverage);

[0103] The sensing accuracy for each supported sensing mode, such as sensing distance, distance resolution, sensing speed, speed resolution, sensing angle, angle resolution, and sensing latency.

[0104] In the sensing process, the UE reports its sensing capabilities to the SF (Sensing Controller). The main purpose is to allow the SF to perceive the UE's sensing capabilities, so that the SF can determine the sensing node UE and related configurations. The UE's sensing capabilities may include the following:

[0105] Supported perception modes:

[0106] The UE's "transmit" and "receive" capabilities in the UE's self-transmitting and self-receiving mode;

[0107] The transmit and receive capabilities of the UE in UE A transmit UE B receive mode;

[0108] The UE's "receive" capability in gNB transmit-UE receive mode;

[0109] The UE's "transmit" capability in gNB receive mode;

[0110] Supported terminal roles: sensing transmitting terminal, sensing receiving terminal, sensing management terminal;

[0111] Perception accuracy for each supported perception mode: perception distance, distance resolution, perception speed, speed resolution, perception angle, angle resolution, perception latency, etc.

[0112] However, the terminal capabilities currently reported by terminals or base stations to SF only include those related to traditional sensing processing, and do not include those related to AI processing.

[0113] In view of this, the present disclosure provides a communication method in which a first device sends first information to a second device, the first information indicating that the first device has the ability to perform sensing processing based on AI or ML, thereby enabling the first device to report the ability to perform sensing processing based on AI or ML, thereby improving communication efficiency.

[0114] 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:

[0115] In step S2101, the first device 101 sends the first information to the second device 102.

[0116] In some embodiments, the second device 102 receives first information sent by the first device 101.

[0117] In some embodiments, the first device 101 is a terminal.

[0118] In other embodiments, the first device 101 is an access network device, such as a gNB.

[0119] In some embodiments, the second device 102 is a network device, such as a sensing function (SF). The sensing function 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. The second device 102 may also be other network elements or functions, and this disclosure does not limit them.

[0120] In some embodiments, the first information indicates that the first device has a first capability, which is the ability to perform synesthetic processing based on AI or ML.

[0121] In some embodiments, the first information may include indication information, which indicates that the first device has a first capability, i.e., the first device directly indicates to the second device that the first device has the first capability. In addition to including the aforementioned indication information, the first information may further include relevant information representing the first capability (e.g., at least one of the following: sensing result, type of processing of the first signal, first parameter, compression parameter).

[0122] In other embodiments, the first information includes relevant information representing the first capability, without needing to include indication information. That is, the first device indirectly indicates to the second device that the first device has the first capability. In other words, when the first information includes relevant information representing the first capability, it indicates that the first device has the first capability.

[0123] In some embodiments, the first device may report to the second device that the first device has the ability to perform sensing processing based on AI or ML.

[0124] For example, the first device is a terminal, which can report to the second device that the terminal has a first capability, namely, the terminal has the ability to perform synergistic processing based on AI, or the terminal has the ability to perform synergistic processing based on ML.

[0125] For example, the first device is an access network device, which can report to the second device that the access network device has a first capability, namely, the access network device has the capability to perform sensing processing based on AI, or the access network device has the capability to perform sensing processing based on ML.

[0126] In some embodiments, the first device has the ability to perform sensing processing based on AI or ML, which means that the first device can process sensing-related information based on AI or ML to obtain sensing results or intermediate parameters of the sensing process.

[0127] In some embodiments, the first capability includes at least one of the following: the ability to output a perception result based on AI or ML; the ability to output a first parameter based on AI or ML, wherein the first parameter is an intermediate parameter used to obtain the perception result; and the ability to process a first signal based on AI or ML, wherein the first signal is a signal used for perception measurement.

[0128] In some embodiments, the first device has the ability to output perception results based on AI or ML. For example, the first device can input the channel matrix into an AI model or an ML model and output perception results.

[0129] In some embodiments, the first device has the capability to output 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, the first device can input a channel matrix into an AI model or an 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.

[0130] In some embodiments, the first device has the capability to process a first signal based on AI or ML. The first signal is a signal used for sensing and measurement, and can also be referred to as a sensing and measurement signal. By measuring the first signal, a sensing result can be obtained. For example, the first device can perform at least one of the following processing on the first signal: denoising, feature extraction, and compression based on an AI model or ML model.

[0131] In some embodiments, the first device has the ability to compress the first signal based on AI or ML. For example, the first device can input the first signal into an AI model or an ML model and output a compressed first signal.

[0132] In some embodiments, the first capability includes the ability to output perception results based on AI or ML; the first information includes the perception results, which include at least one of distance, position, angle, and speed.

[0133] In some embodiments, the first information includes indication information that the first device has a first capability, that is, the first device directly indicates to the second device that the first device has the first capability. In addition to including the aforementioned indication information, the first information may further include a sensing result.

[0134] In other embodiments, the first information includes the perception result and does not need to include the indication information. That is, the first device indirectly indicates to the second device that the first device has the first capability. In other words, when the first information includes the perception result, it indicates that the first device has the first capability.

[0135] 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.

[0136] In some embodiments, where the first capability of the first device includes the ability to output perception results based on AI or ML, the first information sent by the first device to the second device includes the perception results that the first device can obtain. That is, the first device can output perception results based on AI or ML, and when the first device sends the perception results to the second device, the second device can know that the first device has the ability to output perception results based on AI or ML, and the second device can know which perception results the first device can output, which facilitates the second device in determining the relevant configuration of the first device.

[0137] 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.

[0138] In some embodiments, the first capability includes the ability to output a first parameter based on AI or ML; the first information includes the first parameter, which includes at least one of the number of sensed targets and the transmission path.

[0139] 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.

[0140] In some embodiments, where the first device's first capability includes the ability to output a first parameter based on AI or ML, the first information sent by the first device to the second device includes the first parameter that the first device can obtain. That is, the first device can output the first parameter based on AI or ML, and the first device sends the first parameter to the second device. The second device can know that the first device has the ability to output the first parameter based on AI or ML, and the second device can know what type of first parameter the first device can output, which facilitates the second device in determining the relevant configuration of the first device.

[0141] 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.

[0142] For example, the first device can output the number of perceived targets based on AI or ML. The first device sends the number of perceived targets that it can obtain to the second device. The second device can know the number of perceived targets that the first device can obtain, which makes it easier for the second device to determine the relevant configuration of the first device in the future.

[0143] In some embodiments, the first capability includes the ability to process the first signal based on AI or ML; processing the first signal may include at least one of denoising, feature extraction, and compression.

[0144] In some embodiments, where the first device has a first capability including the ability to process a first signal (the first signal being a signal used for sensing and measurement) based on AI or ML, the first information sent by the first device to the second device includes the types of processing the first device can perform. That is, the first device can process the first signal based on AI or ML, and the first device sends the processing type to the second device. The second device can know that the first device has the capability to process the first signal based on AI or ML, and the second device can know which types of processing the first device can perform, facilitating the second device's subsequent determination of the relevant configuration of the first device.

[0145] For example, the first device can perform noise reduction processing on the first signal based on AI or ML. The first device sends information indicating that noise reduction processing can be performed to the second device. The second device can know that the first device can perform noise reduction processing, which facilitates the second device to determine the relevant configuration of the first device in the future.

[0146] In some embodiments, the first capability includes the ability to process the first signal based on AI or ML, and processing the first signal includes compression processing; the first information includes compression parameters supported by the first device, and the compression parameters include at least one of the number of bits in the compression result and the compression ratio.

[0147] In some embodiments, where the first device has a first capability including the ability to compress a first signal (a signal used for sensing and measurement) based on AI or ML, the first information sent by the first device to the second device includes compression parameters supported by the first device. That is, the first device can compress the first signal based on AI or ML, and the first device sends the supported compression parameters to the second device. The second device can know that the first device has the capability to compress the first signal based on AI or ML, and the second device can know the compression parameters that the first device can support, facilitating the second device's subsequent determination of the relevant configuration of the first device.

[0148] For example, the first device can compress the first signal based on AI or ML, and the compression rate that the first device can support is sent to the second device. The second device can know that the first device can perform compression and the compression rate it can support, which makes it easier for the second device to determine the relevant configuration of the first device.

[0149] In some embodiments, the first information further includes a configuration applicable to the first capability, the configuration applicable to the first capability including at least one of the following: bandwidth for sensing; antenna port configuration for sensing; reference signal configuration for sensing; sensing mode.

[0150] In some embodiments, the configuration applicable to the first capability refers to the configuration corresponding to the first capability being able to function normally or achieve relatively good performance. The first device sends the configuration applicable to the first capability to the second device, so that the second device can subsequently determine the relevant configuration of the first device.

[0151] In some embodiments, the sensing mode may include, but is not limited to, self-sending and self-receiving, A sending and B receiving, etc.

[0152] In some embodiments, the first information further includes the scope of application of the first capability, which includes at least one of the following: applicable cell identifier; applicable network transmit antenna configuration; applicable network scenario, which includes at least one of outdoor scenario and indoor scenario.

[0153] In some embodiments, the scope of application of the first capability refers to the network configuration under which the first capability achieves better performance, and / or the network configuration suitable for the terminal to use the first capability. The first device sends the scope of application of the first capability to the second device, so that the second device can subsequently determine the relevant configuration of the first device.

[0154] In step S2102, the second device 102 sends the second information to the first device 101.

[0155] In some embodiments, the first device 101 receives second information sent by the second device 102.

[0156] In some embodiments, the second information may be configuration information, such as configuration information for sensing measurements.

[0157] In some embodiments, the second device 102 may determine the second information based on the first information sent by the first device 101.

[0158] The communication method provided in this embodiment involves a first device sending first information to a second device. The first information indicates that the first device has the capability to perform sensing processing based on AI or ML, thereby enabling the first device to report its ability to perform sensing processing based on AI or ML, thus improving communication efficiency.

[0159] The communication method involved in the embodiments of this disclosure may include at least one of steps S2101 to S2102. For example, step S2101 may be implemented as a separate embodiment, and step S2102 may be implemented as a separate embodiment, but are not limited thereto.

[0160] In some embodiments, step S2102 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0161] In some embodiments, other optional implementations described before or after the specification corresponding to FIG2 may be referred to.

[0162] 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.

[0163] In some embodiments, the terms “downlink control information (DCI),” “downlink (DL) assignment,” “DL DCI,” “uplink (UL) grant,” and “UL DCI” can be used interchangeably.

[0164] 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".

[0165] 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.”

[0166] 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.

[0167] 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.

[0168] In some embodiments, terms such as “send,” “transmit,” “report,” “distribute,” “transfer,” “bidirectional transmission,” “send and / or receive” can be used interchangeably.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Figure 3 is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3, the present disclosure relates to a communication method executed by a first device, the method comprising:

[0173] Step S3101: Send the first information to the second device.

[0174] 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.

[0175] Step S3102: Receive the second information sent by the second device.

[0176] 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.

[0177] In some embodiments, the first device receives second information sent by the second device, but is not limited thereto; it may also receive second information sent by other entities.

[0178] In some embodiments, the first device acquires second information as defined by the protocol.

[0179] In some embodiments, the first device obtains the second information from the upper layer(s).

[0180] In some embodiments, the first device processes information to obtain the second information.

[0181] The communication method involved in the embodiments of this disclosure may include at least one of steps S3101 to S3102. For example, step S3101 may be implemented as a separate embodiment, and step S3102 may be implemented as a separate embodiment, but are not limited thereto.

[0182] In some embodiments, step S3102 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0183] Figure 4 is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 4, the embodiment of the present disclosure relates to a communication method executed by a second device, the method comprising:

[0184] Step S4101: Receive the first information sent by the first device.

[0185] 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.

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

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

[0188] In some embodiments, the second device obtains the first information from the upper layer(s).

[0189] In some embodiments, the second device processes the information to obtain the first information.

[0190] Step S4102: Send the second information to the first device.

[0191] 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.

[0192] The communication method involved in the embodiments of this disclosure may include at least one of steps S4101 to S4102. For example, step S4101 may be implemented as a separate embodiment, and step S4102 may be implemented as a separate embodiment, but are not limited thereto.

[0193] In some embodiments, step S4102 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0194] In some embodiments, the above methods may include the methods of the embodiments of the communication system side, the first device side, the second device side, etc., which will not be described again here.

[0195] This disclosure proposes a method for a base station or terminal to report its AI-based sensing capabilities to a SF (Sensing Network Array), which may include at least one of the following.

[0196] (1) The terminal sends first information to the first network element, the first information being used to indicate that the terminal has a first capability, the first capability being that the terminal has the capability to perform sensor-related processing based on AI / ML.

[0197] (2) Based on (1), the AI / ML-based synesthetic processing includes the following aspects:

[0198] Based on AI / ML models, the perception results are directly output, such as the perceived distance, angle, speed, etc.

[0199] Intermediate parameters for AI / ML-based perception output, such as the number of perceived objects.

[0200] AI / ML is used to process measurement signals for perception, such as denoising and feature extraction.

[0201] AI / ML is used to compress the sensed measurement signals.

[0202] (3) Based on (2), in response to the first capability being that the terminal has the ability to output perception results through AI / ML models, the first information further includes a direct result type based on the AI / ML output, the result type including one or more of the perceived distance, position, angle, and speed.

[0203] (4) Based on (2), in response to the first capability being that the terminal has intermediate parameters for perception output by an AI / ML model, the first information also includes the type of the intermediate parameters, such as the number of perceived targets, Path, where Path refers to the detected transmission path, based on which the distance of the target can be determined.

[0204] (5) Based on (2), in response to AI / ML processing of the perceived signal, the first information also includes the type of processing.

[0205] (6) Based on (2) in response to AI / ML-based compression of the perceived signal, the first information also includes the number of compressed bits of the supported output, or the compression ratio.

[0206] (7) Based on (1) or (2), the first capability further includes the configuration or scope to which the first capability is applicable. Wherein, the configuration to which the first capability is applicable is the configuration corresponding to which the first capability can work normally or can achieve relatively good performance; the scope to which the first capability is applicable refers to the network configuration under which the first capability can achieve relatively good performance and is suitable for the terminal to use the first capability.

[0207] (8) Based on (7), the configuration to which the first capability applies includes at least one of the following aspects:

[0208] Bandwidth used for sensing;

[0209] Antenna port configuration for sensing;

[0210] Reference signal configuration for sensing;

[0211] The sensing modes include spontaneous transmission and reception, A transmitting and B receiving, etc.

[0212] (9) Based on (7), the scope to which the first capability applies includes:

[0213] Applicable cell ID;

[0214] Applicable network transmit antenna configuration;

[0215] Network scenarios: such as outdoor scenarios and indoor scenarios.

[0216] In this embodiment of the disclosure, the base station can also report the aforementioned capabilities to the SF.

[0217] 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.

[0218] This disclosure also provides an apparatus for implementing any of the above methods. For example, an apparatus is provided that includes units or modules for implementing the steps performed by the first device in any of the above methods. Alternatively, another apparatus is provided that includes units or modules for implementing the steps performed by a network device (e.g., an access network device, a core network functional node, a core network device, etc.) in any of the above methods.

[0219] 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.

[0220] 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).

[0221] Figure 5A is a schematic diagram of the structure of the first device proposed in an embodiment of this disclosure. As shown in Figure 5A, the first device 5100 may include a transceiver module 5101. In some embodiments, the transceiver module 5101 sends first information to a second device. Optionally, the transceiver module is used to perform at least one of the transceiver steps (such as step S2101, but not limited thereto) performed by the first device in any of the above methods, which will not be described in detail here.

[0222] In some embodiments, the first device may further include a processing module.

[0223] In some embodiments, the first capability includes at least one of the following: the ability to output sensing results based on AI or ML; the ability to output a first parameter based on AI or ML, wherein the first parameter is an intermediate parameter used to obtain the sensing results; and the ability to process a first signal based on AI or ML, wherein the first signal is a signal used for sensing measurement.

[0224] In some embodiments, the first capability includes the ability to output perception results based on AI or ML; the first information includes the perception results, which include at least one of distance, position, angle, and speed.

[0225] In some embodiments, the first capability includes the ability to output a first parameter based on AI or ML; the first information includes the first parameter, which includes at least one of the number of perceived targets and the transmission path.

[0226] In some embodiments, the first capability includes the ability to process 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.

[0227] In some embodiments, the first capability includes the ability to process the first signal based on AI or ML, wherein processing the first signal includes compression processing; the first information includes compression parameters supported by the first device, wherein the compression parameters include at least one of the number of bits in the compression result and the compression ratio.

[0228] In some embodiments, the first information further includes a configuration to which the first capability applies, the configuration including at least one of the following: bandwidth for sensing; antenna port configuration for sensing; reference signal configuration for sensing; and sensing mode.

[0229] In some embodiments, the scope of application of the first capability includes at least one of the following: applicable cell identifier; applicable network transmit antenna configuration; applicable network scenario, wherein the network scenario includes at least one of outdoor scenario and indoor scenario.

[0230] Figure 5B is a schematic diagram of the structure of the second device proposed in an embodiment of this disclosure. As shown in Figure 5B, the second device 5200 may include a transceiver module 5201. In some embodiments, the transceiver module 5201 is used to receive first information sent by the first device. Optionally, the transceiver module is used to perform at least one of the sending and receiving steps performed by the second device in any of the above methods, which will not be described in detail here.

[0231] In some embodiments, the second device may further include a processing module.

[0232] In some embodiments, the first capability includes at least one of the following: the ability to output sensing results based on AI or ML; the ability to output a first parameter based on AI or ML, wherein the first parameter is an intermediate parameter used to obtain the sensing results; and the ability to process a first signal based on AI or ML, wherein the first signal is a signal used for sensing measurement.

[0233] In some embodiments, the first capability includes the ability to output perception results based on AI or ML; the first information includes the perception results, which include at least one of distance, position, angle, and speed.

[0234] In some embodiments, the first capability includes the ability to output a first parameter based on AI or ML; the first information includes the first parameter, which includes at least one of the number of perceived targets and the transmission path.

[0235] In some embodiments, the first capability includes the ability to process 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.

[0236] In some embodiments, the first capability includes the ability to process the first signal based on AI or ML, wherein processing the first signal includes compression processing; the first information includes compression parameters supported by the first device, wherein the compression parameters include at least one of the number of bits in the compression result and the compression ratio.

[0237] In some embodiments, the first information further includes a configuration to which the first capability applies, the configuration including at least one of the following: bandwidth for sensing; antenna port configuration for sensing; reference signal configuration for sensing; and sensing mode.

[0238] In some embodiments, the first information further includes the scope to which the first capability applies, the scope including at least one of the following: applicable cell identifier; applicable network transmit antenna configuration; applicable network scenario, the network scenario including at least one of outdoor scenario and indoor scenario.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] Chip 6200 includes one or more processors 6201. Chip 6200 is used to perform any of the methods described above.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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 first device, the method includes: Send a first message to the second device, the first message indicating that the first device has a first capability, the first capability being the ability to perform synesthetic processing based on artificial intelligence (AI) or machine learning (ML).

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

3. The method according to claim 1 or 2, characterized in that, The first capability includes the ability to output perception results based on AI or ML; the first information includes the perception results, which include at least one of distance, position, angle, and speed.

4. The method according to any one of claims 1 to 3, characterized in that, The first capability includes the ability to output 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 perceived targets and the transmission path.

5. The method according to any one of claims 1 to 4, characterized in that, The first capability includes the ability to process 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.

6. The method according to any one of claims 1 to 5, characterized in that, The first capability includes the ability to process the first signal based on AI or ML, wherein the processing of the first signal includes compression processing; the first information includes compression parameters supported by the first device, wherein the compression parameters include at least one of the number of bits in the compression result and the compression ratio.

7. The method according to any one of claims 1 to 6, characterized in that, The first information also includes a configuration to which the first capability applies, the configuration including at least one of the following: Bandwidth used for sensing; Antenna port configuration for sensing; Reference signal configuration for sensing; Perception mode.

8. The method according to any one of claims 1 to 7, characterized in that, The first information also includes the scope to which the first capability applies, the scope including at least one of the following: Applicable community signage; Applicable network transmit antenna configuration; Applicable network scenarios, including at least one of outdoor scenarios and indoor scenarios.

9. A communication method, characterized in that, Performed by a second device, the method includes: The device receives first information sent by a first device, the first information indicating that the first device has a first capability, the first capability being the ability to perform synesthetic processing based on artificial intelligence (AI) or machine learning (ML).

10. The method according to claim 9, characterized in that, The first capability includes at least one of the following: The ability to output perception results based on AI or ML; Based on the ability of AI or ML to output a first parameter, which is an intermediate parameter used to obtain the perception result; The ability to process the first signal, which is a signal used for sensing and measurement, is based on AI or ML.

11. The method according to claim 9 or 10, characterized in that, The first capability includes the ability to output perception results based on AI or ML; the first information includes the perception results, which include at least one of distance, position, angle, and speed.

12. The method according to any one of claims 9 to 11, characterized in that, The first capability includes the ability to output 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 perceived targets and the transmission path.

13. The method according to any one of claims 9 to 12, characterized in that, The first capability includes the ability to process 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.

14. The method according to any one of claims 9 to 13, characterized in that, The first capability includes the ability to process the first signal based on AI or ML, wherein the processing of the first signal includes compression processing; the first information includes compression parameters supported by the first device, wherein the compression parameters include at least one of the number of bits in the compression result and the compression ratio.

15. The method according to any one of claims 9 to 14, characterized in that, The first information also includes a configuration to which the first capability applies, the configuration including at least one of the following: Bandwidth used for sensing; Antenna port configuration for sensing; Reference signal configuration for sensing; Perception mode.

16. The method according to any one of claims 9 to 15, characterized in that, The first information also includes the scope to which the first capability applies, the scope including at least one of the following: Applicable community signage; Applicable network transmit antenna configuration; Applicable network scenarios, including at least one of outdoor scenarios and indoor scenarios.

17. A communication device, characterized in that, The communication device is used to perform the communication method according to any one of claims 1 to 8 or the communication method according to any one of claims 9 to 16.

18. A communication system, characterized in that, The device includes a first device and a second device, wherein the first device is configured to implement the communication method of any one of claims 1 to 8, and the second device is configured to implement the communication method of any one of claims 9 to 16.

19. 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 8 or the communication method as described in any one of claims 9 to 16.

20. 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 8 or perform the communication method of any one of claims 9 to 16.

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