Information sending method, information receiving method, communication device, and storage medium

Abstract

The present disclosure relates to the technical field of communications, and specifically relates to an information sending method, an information receiving method, a communication device, and a storage medium. The information sending method comprises: sending capability information to a second device, wherein the capability information is used for indicating a capability of a first device to use an AI model for sensing. According to the present disclosure, a first device can send capability information to a second device, and the second device can determine, on the basis of the capability information, a capability of the first device to use an AI model for sensing, so as to provide an appropriate configuration for an operation that the first device uses the AI model for AI sensing, thereby ensuring that the first device smoothly uses the AI model for AI sensing.

Description

Information sending and receiving method, communication device and storage medium TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of communication, and in particular, to an information sending method, an information receiving method, a communication device, a communication device and a storage medium. BACKGROUND

[0002] Compared with sensing based on a non-AI sensing algorithm, sensing based on an AI (Artificial Intelligence) model requires relatively stronger processing capability. Moreover, since the model is generally trained for a specific scene and a specific mode, the scene and the mode for sensing by the AI sensing model may also be different from the scene and the mode for sensing based on the non-AI sensing algorithm. Moreover, in the two different capability cases, the subsequent steps of the sensing process are also affected. SUMMARY

[0003] Embodiments of the present disclosure provide an information sending and receiving method, a communication device and a storage medium to solve the technical problems in the related art.

[0004] According to a first aspect of embodiments of the present disclosure, an information sending method is provided, executed by a first device, and the method comprises: sending, to a second device, capability information, wherein the capability information is used to indicate a capability of the first device for sensing by using an AI model.

[0005] According to a second aspect of embodiments of the present disclosure, an information receiving method is provided, executed by a second device, and the method comprises: receiving capability information sent by a first device, wherein the capability information is used to indicate a capability of the first device for sensing by using an AI model.

[0006] According to a third aspect of embodiments of the present disclosure, a communication device is provided, which is configured to execute the information sending method of the first aspect and / or the information receiving method of the second aspect.

[0007] According to a fourth aspect of embodiments of the present disclosure, a communication system is provided, comprising a terminal and a network device, wherein the terminal is configured to implement the information sending method of the first aspect, and the network device is configured to implement the information receiving method of the second aspect.

[0008] According to a fifth aspect of embodiments of the present disclosure, a storage medium is provided, which stores instructions, when the instructions run on a communication device, causing the communication device to execute the information sending method of the first aspect and / or the information receiving method of the second aspect.

[0009] According to a sixth aspect of the embodiments of the present disclosure, a program product is provided. The program product, when executed by a communication device, causes the communication device to perform the information sending method of the first aspect and / or the information receiving method of the second aspect.

[0010] According to the embodiments of the present disclosure, the first device can send the capability information to the second device, and the second device can master the capability of the first device for AI perception using the AI model according to the capability information, so as to provide appropriate configuration for the operation of the first device for AI perception using the AI model, and facilitate to ensure that the first device successfully uses the AI model for AI perception. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings. FIG. 1A is a schematic diagram of an architecture of a communication system according to an embodiment of the present disclosure. FIG. 1B is a schematic diagram of a perception mode according to an embodiment of the present disclosure. FIG. 1C is a schematic diagram of a perception process according to an embodiment of the present disclosure. FIG. 1D is a schematic diagram of a sense-integrated simulation channel model according to an embodiment of the present disclosure. FIG. 1E is a schematic diagram of output of a feeling result according to an embodiment of the present disclosure. FIG. 1F is a schematic diagram of output of another feeling result according to an embodiment of the present disclosure. FIG. 1G is a schematic diagram of pre-processing according to an embodiment of the present disclosure. FIG. 2 is an interaction schematic diagram of an information sending method according to an embodiment of the present disclosure. FIG. 3 is a schematic diagram of another perception process according to an embodiment of the present disclosure. FIG. 4 is a schematic block diagram of an information sending apparatus according to an embodiment of the present disclosure. FIG. 5 is a schematic block diagram of an information receiving apparatus according to an embodiment of the present disclosure. FIG. 6A is a structural schematic diagram of a communication device according to an embodiment of the present disclosure. FIG. 6B is a structural schematic diagram of a chip according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] The embodiments of the present disclosure provide information sending and receiving methods, communication devices and storage media.

[0013] In a first aspect, the embodiments of the present disclosure provide an information sending method, executed by a first device, comprising: sending capability information to a second device, wherein the capability information is used to indicate a capability of the first device for perception using an AI model.

[0014] In the above embodiments, the first device can send the capability information to the second device, and the second device can master the capability of the first device for sensing by using the AI model according to the capability information, so as to provide appropriate configuration for the operation of the first device for AI sensing by using the AI model, and facilitate to ensure that the first device successfully uses the AI model for AI sensing.

[0015] In combination with some embodiments of the first aspect. In some embodiments, the capability of the first device for sensing by using the AI model includes at least one of: a processing capability of the first device; a sensing mode supported by using the AI model for sensing; a sensing scene supported by using the AI model for sensing; an accuracy of using the AI model for sensing in the sensing mode; an accuracy of using the AI model for sensing in the sensing scene.

[0016] In combination with some embodiments of the first aspect. In some embodiments, the method further includes: receiving configuration information sent by the second device, wherein the configuration information is used to indicate the configuration of the first device for sensing by using the AI model.

[0017] In combination with some embodiments of the first aspect. In some embodiments, the configuration of the first device for sensing by using the AI model includes at least one of: a quality of service of the first device for sensing by using the AI model; a configuration of a signal required for sensing by the first device by using the AI model.

[0018] In combination with some embodiments of the first aspect. In some embodiments, the quality of service includes at least one of: an accuracy requirement; a latency requirement.

[0019] In combination with some embodiments of the first aspect. In some embodiments, the method further includes: sending sensing information for sensing by using the AI model to the second device, wherein the sensing information includes at least one of: intermediate data obtained by preprocessing a signal required for sensing; a sensing result obtained by sensing the signal required for sensing.

[0020] In combination with some embodiments of the first aspect. In some embodiments, the AI model includes at least one of: a preprocessing model; a sensing model.

[0021] In combination with some embodiments of the first aspect. In some embodiments, in a case where the sensing model is deployed on the second device and the preprocessing model is deployed on the first device, the sensing information includes the intermediate data.

[0022] In combination with some embodiments of the first aspect. In some embodiments, the preprocessing includes at least one of: feature extraction; dimension compression; noise suppression; clutter cancellation.

[0023] In a second aspect, embodiments of the present disclosure provide a method for receiving information, performed by a second device, the method comprising: receiving capability information sent by a first device, wherein the capability information is used to indicate a capability of the first device for sensing using an AI model.

[0024] In some embodiments in combination with the second aspect. In some embodiments, the capability of the first device for sensing using an AI model comprises at least one of: a processing capability of the first device; a sensing mode supported by the first device for sensing using an AI model; a sensing scenario supported by the first device for sensing using an AI model; an accuracy of the first device for sensing using an AI model in the sensing mode; an accuracy of the first device for sensing using an AI model in the sensing scenario.

[0025] In some embodiments in combination with the second aspect. In some embodiments, the method further comprises: sending configuration information to the first device, wherein the configuration information is used to indicate a configuration of the first device for sensing using an AI model.

[0026] In some embodiments in combination with the second aspect. In some embodiments, the configuration of the first device for sensing using an AI model comprises at least one of: a quality of service of the first device for sensing using an AI model; a configuration of a signal required for sensing by the first device using an AI model.

[0027] In some embodiments in combination with the second aspect. In some embodiments, the quality of service comprises at least one of: an accuracy requirement; a latency requirement.

[0028] In some embodiments in combination with the second aspect. In some embodiments, the method further comprises: receiving sensing information for sensing using an AI model sent by the first device, wherein the sensing information comprises at least one of: intermediate data obtained by pre-processing a signal required for sensing; a sensing result obtained by sensing the signal required for sensing.

[0029] In some embodiments in combination with the second aspect. In some embodiments, the AI model comprises at least one of: a pre-processing model; a sensing model.

[0030] In some embodiments in combination with the second aspect. In some embodiments, in a case where the sensing model is deployed on the second device and the pre-processing model is deployed on the first device, the sensing information comprises the intermediate data.

[0031] In some embodiments in combination with the second aspect. In some embodiments, the pre-processing comprises at least one of: feature extraction; dimension compression; noise suppression; clutter cancellation.

[0032] In a third aspect, embodiments of the present disclosure provide an information sending apparatus, the apparatus comprising: a sending module configured to send, to a second device, capability information, wherein the capability information is used to indicate a capability of the first device to use an AI model for sensing.

[0033] In a fourth aspect, embodiments of the present disclosure provide an information receiving apparatus, the apparatus comprising: a receiving module configured to receive capability information sent by a first device, wherein the capability information is used to indicate a capability of the first device to use an AI model for sensing.

[0034] In a fifth aspect, embodiments of the present disclosure provide a communication device configured to perform the information sending method of the first aspect, any one of the optional embodiments of the first aspect, and / or the information receiving method of the second aspect, any one of the optional embodiments of the second aspect.

[0035] In a sixth aspect, embodiments of the present disclosure provide a terminal comprising: one or more processors; wherein the terminal is configured to perform the information sending method of the first aspect, any one of the optional embodiments of the first aspect.

[0036] In a seventh aspect, embodiments of the present disclosure provide a network device comprising: one or more processors; wherein the network device is configured to perform the information receiving method of the second aspect, any one of the optional embodiments of the second aspect.

[0037] In an eighth aspect, embodiments of the present disclosure provide a communication system comprising a terminal and a network device, wherein the terminal is configured to implement the information sending method of the first aspect, any one of the optional embodiments of the first aspect, and the network device is configured to implement the information receiving method of the second aspect, any one of the optional embodiments of the second aspect.

[0038] In a ninth aspect, embodiments of the present disclosure provide a storage medium storing instructions, when the instructions are executed on a communication device, causing the communication device to perform the information sending method of the first aspect, any one of the optional embodiments of the first aspect, and / or the information receiving method of the second aspect, any one of the optional embodiments of the second aspect.

[0039] In a tenth aspect, embodiments of the present disclosure provide a program product, when executed by a communication device, causing the communication device to perform the information sending method of the first aspect, any one of the optional embodiments of the first aspect, and / or the information receiving method of the second aspect, any one of the optional embodiments of the second aspect.

[0040] In the eleventh aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the information sending method described in any one of the first aspect and optional embodiments of the first aspect, and / or the information receiving method described in any one of the second aspect and optional embodiments of the second aspect.

[0041] It is understood that the aforementioned information sending and receiving devices, communication equipment, communication systems, storage media, program products, and computer programs 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.

[0042] This disclosure provides methods for transmitting and receiving information, communication devices, and storage media. In some embodiments, the terms "method for transmitting and receiving information" and "method for processing information" and "method for communication" can be used interchangeably; the terms "device for transmitting and receiving information" and "device for processing information" and "device for communication" can be used interchangeably; and the terms "system for processing information" and "system for communication" can be used interchangeably.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] In the embodiments of this disclosure, unless otherwise stated, elements expressed in the singular, such as “a,” “an,” “the,” “the,” “the,” “the,” “the,” “the,” “this,” etc., may mean “one and only one,” or “one or more,” “at least one,” etc.

[0047] For example, in the case of using articles such as "a", "an", "the" in translation, the noun after the article can be understood as a singular expression or a plural expression.

[0048] In the embodiments of the present disclosure, "plurality" refers to two or more.

[0049] In some embodiments, the terms "at least one of", "one or more", "a plurality of", "multiple", and the like can be replaced with each other.

[0050] In some embodiments, the description manner such as "at least one of A, B", "A and / or B", "A in one case and B in another case", "responding to a case A and responding to another case B", and the like can include the following technical solutions according to the case: A in some embodiments (A is executed regardless of B); B in some embodiments (B is executed regardless of A); A and B are selectively executed in some embodiments (A and B are selected from A and B); A and B are executed in some embodiments (A and B are executed). When there are more branches such as A, B, C, and the like, the above is similar.

[0051] In some embodiments, the description manner such as "A or B" and the like can include the following technical solutions according to the case: A in some embodiments (A is executed regardless of B); B in some embodiments (B is executed regardless of A); A and B are selectively executed in some embodiments (A and B are selected from A and B). When there are more branches such as A, B, C, and the like, the above is similar.

[0052] The prefix words "first", "second" and the like in the embodiments of the present disclosure are only used to distinguish different description objects, and do not constitute a limitation on the position, order, priority, quantity or content of the description objects. The description of the description objects should refer to the description in the context of the claims or embodiments, and should not constitute an unnecessary limitation because of the use of the prefix words.

[0053] For example, if the descriptive object is "field," then 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 "level," then 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; there can be one or more. For example, in "first device," the number of "devices" can be one or more. In addition, objects modified by different prefixes can be the same or different. For example, if the descriptive object 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 descriptive object 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.

[0054] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0055] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0056] 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”.

[0057] 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.

[0058] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0059] 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,” “bandwidth part (BWP),” and the like can be used interchangeably.

[0060] 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," "client," and so on can be replaced with each other.

[0061] In some embodiments, an access network device, a core network device, or a network device can be replaced with a terminal. For example, the embodiments of the present disclosure can also be applied to a structure in which communication between an access network device, a core network device, or a network device and a terminal is replaced with communication between a plurality of terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the structure in which the terminal has all or part of the functions of the access network device can also be provided. In addition, the terms "uplink," "downlink," and the like can be replaced with terms corresponding to the inter-terminal communication (e.g., "side"). For example, an uplink channel, a downlink channel, and the like can be replaced with a side channel, and an uplink, a downlink, and the like can be replaced with a side link.

[0062] In some embodiments, a terminal can be replaced with an access network device, a core network device, or a network device. In this case, the structure in which the access network device, the core network device, or the network device has all or part of the functions of the terminal can also be provided.

[0063] In some embodiments, the data, information, etc. can be obtained in compliance with the laws and regulations of the country in which the location is situated.

[0064] In some embodiments, the data, information, etc. can be obtained after obtaining the consent of the user.

[0065] In addition, each element, each row, or each column in the table of the embodiments of the present 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.

[0066] FIG. 1A is a schematic diagram of an architecture of a communication system according to an embodiment of the present disclosure.

[0067] As shown in FIG. 1A, the communication system 100 includes a first device 101 and a second device 102. For example, the first device can include a terminal 101 and / or an access network device, and the second device can include a sensing network element, for example, the sensing network element can belong to a core network device.

[0068] In some embodiments, the terminal 101 includes at least one of a mobile phone, a wearable device, an Internet of Things device, a communication-capable car, a smart car, a Pad, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart grid, a wireless terminal device in transportation safety, a wireless terminal device in smart city, a wireless terminal device in smart home, etc., but is not limited thereto.

[0069] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network, and the access network device can include at least one of an evolved NodeB (eNB) in a 5G communication system, a next generation eNB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved node B (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, an access node in a Wi-Fi system, but is not limited thereto.

[0070] In some embodiments, the core network device can be one device including one or more network elements, or can be multiple devices or device groups including all or part of the one or more network elements described above. The network element 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).

[0071] In some embodiments, the technical solutions of the present disclosure can be applied to an Open RAN architecture, at which time the interfaces between or within the access network devices involved in the embodiments of the present disclosure can become internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be realized by software or programs.

[0072] In some embodiments, the access network device can be composed of a central unit (CU) and a distributed unit (DU), where the CU can also be referred to as a control unit. The CU-DU structure can split the protocol layers of the access network device, and some of the protocol layers are controlled by the CU, and the remaining or all of the protocol layers are distributed in the DU and controlled by the CU, but is not limited thereto.

[0073] It can be understood that the communication system described in the embodiments of the present disclosure is for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and does not constitute a limitation on the technical solutions proposed by the embodiments of the present disclosure. Those skilled in the art can know that, with the evolution of system architecture and the appearance of new business scenarios, the technical solutions proposed by the embodiments of the present disclosure are also applicable to similar technical problems.

[0074] The following embodiments of the present disclosure can be applied to the communication system 100 shown in FIG. 1A or part of the subjects, but are not limited thereto. The subjects shown in FIG. 1A are exemplary, and the communication system can include all or part of the subjects in FIG. 1A, or other subjects other than FIG. 1A. The number and form of each subject is arbitrary, each subject can be physical or virtual, the connection relationship between each subject is exemplary, each subject can not be connected or can be connected, the connection can be in any way, can be direct connection or indirect connection, can be wired connection or wireless connection.

[0075] Embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (Bluetooth (registered trademark)), Public Land Mobile Network (PLMN) network, Device-to-Device (D2D) system, Machine to Machine (M2M) system, Internet of Things (IoT) system, Vehicle-to-Everything (V2X), system using other communication methods, next-generation system expanded based thereon, and the like. In addition, a plurality of systems can be combined (for example, combination of LTE or LTE-A and 5G, and the like).

[0076] In some embodiments, Integrated Sensing and Communication (ISAC) is one of the most important research directions in wireless communication, and is a key enabling technology in 5G-A (5G-Advanced) and future 6G mobile communication.

[0077] The communication-sensing integration technology can enable sensing services based on the existing mobile communication infrastructure, fully utilize the advantages of the mobile communication network to meet the sensing needs in various business service scenarios, and improve the communication performance relying on the sensing capability, thereby helping to realize detection, positioning and tracking, environment reconstruction and target imaging, gesture and posture recognition and other application services.

[0078] In the communication-sensing integration scenario application, the channel modeling of the communication-sensing system based on the 3GPP (3rd Generation Partnership Project) channel modeling related standards is relatively complete, which provides strong support for the evaluation of the communication-sensing technology scheme.

[0079] In the related art, various estimation algorithms for sensing target speed, distance, angle and other parameters are proposed and designed, such as the MUSIC algorithm and the ESPRIT algorithm, which can achieve good target sensing effect in some communication-sensing integration simulation scenarios.

[0080] In some embodiments, the application of artificial intelligence (AI) technology in the field of wireless communication is an important research direction in the current academic and industrial circles, and is also one of the important research topics of 3GPP standardization. Machine learning (ML) and deep learning (DL) technologies have been widely applied in many fields. The powerful feature extraction and mapping relationship modeling capabilities of neural network models can provide new solutions to key problems in wireless communication systems, and have been widely applied and researched in high-precision terminal positioning, channel state information feedback, and beam management.

[0081] Therefore, the communication-sensing integration can be realized based on AI technology, which is a potential application in future 6G wireless communication systems. By using the powerful computing power of special devices such as GPUs (Graphics Processing Units), AI sensing models are trained based on certain form of data sets to optimize the sensing accuracy, computational complexity and application scope, promote the deep application of communication-sensing integration technology, support more intelligent business applications and services, and thus help to promote the development of communication-sensing-computing-intelligence integration.

[0082] FIG. 1B is a schematic diagram of a sensing mode according to an embodiment of the present disclosure.

[0083] In some embodiments, the integrated communication sensing can include six sensing modes, such as Mode 1 to Mode 6 shown in Figure 1A. Mono-static indicates self-transmission and self-reception, where the sending and receiving ends are the same; Bi-static indicates cross-site transmission and reception, where the sending and receiving ends are different.

[0084] Mode 1: Self-transmitting and self-receiving, via a terminal. The first terminal sends a signal from the user's (person's) location and receives the signal reflected by the second terminal (e.g., a car). The first terminal uses the received reflected signal to perceive its own distance, speed, angle, and other information.

[0085] Mode 2: Self-transmission and self-reception, via network devices. The terminal sends a signal from the user's (person's) location and receives the signal reflected by the network device (e.g., gNB). The terminal perceives its own distance, speed, angle, and other information based on the received reflected signal.

[0086] Mode 3: Cross-site transmission and reception, from network device to terminal. The network device (e.g., gNB) sends signals, and the terminal (e.g., a car) receives signals. The terminal (e.g., the car) calculates the channel matrix based on the received signals to sense information such as the distance, speed, and angle of targets in the environment (e.g., people).

[0087] Mode 4: Cross-site transmission and reception, from terminal to network device. The terminal (e.g., a car) sends signals, and the network device (e.g., a gNB) receives the signals. The network device (e.g., the gNB) calculates the channel matrix based on the received signals to sense information such as the distance, speed, and angle of targets in the environment (e.g., people).

[0088] Mode 5: Cross-site transmission and reception, from network device to network device. The first network device (e.g., gNB1) transmits signals, and the second network device (e.g., gNB2) receives signals. The second network device calculates a channel matrix based on the received signals to sense information such as the distance, speed, and angle of targets in the environment (e.g., people).

[0089] Mode 6: Inter-station transceiver, from terminal to terminal. The first terminal (e.g., a car, denoted as UE1) sends signals, and the second terminal (e.g., a car, denoted as UE2) receives signals. The second terminal calculates the channel matrix based on the received signals to sense information such as the distance, speed, and angle of targets in the environment (e.g., people).

[0090] In some embodiments, the sensing target of the synesthetic integration may include at least one of the following:

[0091] Drones, people in indoor and outdoor settings, cars on highways in outdoor settings, automated guided vehicles in factories in indoor settings, and dangerous targets on roads or railways.

[0092] The integration of sensing and sensing mainly involves the receiver calculating and acquiring information such as distance, speed, and angle of the perceived target in the environment based on the received signal. For example, as shown in Figure 1B, the perceived target can be the receiver itself or other objects.

[0093] In a mobile communication network, nodes that implement sensing functions can include sensing network elements (SFs), sensing devices (such as base stations and terminals), and other network elements enhanced to support network sensing capabilities.

[0094] Sensing devices refer to devices that transmit sensing signals or receive sensing signals and perform corresponding measurements. Sensing devices participating in sensing services in mobile communication networks, such as sensing terminals and sensing base stations, need to report their capabilities related to the sensing service to the sensing network elements. This allows subsequent sensing network elements and other relevant network elements to select suitable sensing devices to serve the sensing service based on the type and requirements of the sensing service, thereby transmitting, measuring, and reporting the corresponding sensing signals.

[0095] Figure 1C is a schematic diagram illustrating a sensing process according to an embodiment of the present disclosure.

[0096] In some embodiments, although the network elements and device interactions involved may differ for the six sensing modes, the sensing process is basically the same, generally consisting of three steps, as shown in Figure 1C:

[0097] Sensing capability reporting: Sensing devices report the supported sensing modes and the capabilities related to sensing signal processing, thereby helping sensing network elements determine the appropriate sensing modes and sensing resources to use.

[0098] Sensing measurement configuration: After the sensing network element determines the allocation of sensing resources based on the sensing requirements of the sensing service, it sends the corresponding sensing measurement configuration to the sensing device.

[0099] Sensing measurement reporting: The sensing device reports the collected sensing measurement data to the sensing network element.

[0100] Figure 1D is a schematic diagram of an integrated inductive simulation channel model according to an embodiment of the present disclosure.

[0101] In a typical integrated sensing simulation scenario, there are sensing targets (e.g., which can act as receivers), sensing devices (e.g., which can act as transmitters), and obstructions, which may be other terminals or clutter.

[0102] Therefore, the sensing channel model mainly considers four types of signals: perceived LOS (Line-of-Sight) path, perceived NLOS (Non-Line-of-Sight) path, clutter LOS path, and clutter NLOS path. Additionally, the impact of channel noise is usually also considered.

[0103] LOS path for sensing: The path between the sensing signal transmitter and the sensing target is the LOS path, and the path between the sensing target and the sensing signal receiver is also the LOS path.

[0104] NLOS path for sensing: The path between the sensing signal transmitter and the sensing target is the NLOS path, and the path between the sensing target and the sensing signal receiver is also the NLOS path;

[0105] Clutter LOS path: The LOS path is between the transmitting end of the sensing signal and the scattering clusters in the environment, and the LOS path is also between the scattering clusters in the environment and the receiving end of the sensing signal;

[0106] Clutter NLOS path: The NLOS path is between the transmitting end of the sensing signal and the scattering clusters in the environment, and also between the scattering clusters in the environment and the receiving end of the sensing signal.

[0107] The primary information for target perception is the channel state information matrix H at the receiver. This matrix is ​​a superposition of the target perception channel, clutter channel, and noise, reflecting the channel environment in which the perceived signal resides and the state information of the perceived target within that environment. The dimensions of the channel state information matrix H are related to the specific parameter settings in the actual sensing scenario, and the information in the data on different dimensions can reflect different types of state information of the perceived target.

[0108] In some embodiments, the channel state information matrix H typically includes three dimensions: subcarriers, OFDM (Orthogonal Frequency Division Multiplexing) symbols, and receiver antenna ports.

[0109] Specifically, the channel matrix H represents channel state information. For example, H can be characterized as a complex matrix of dimensions M×N×P, where M is the number of OFDM symbols, N is the number of subcarriers, and P is the number of receive antenna ports. The element at the position of the m-th OFDM symbol, the n-th subcarrier, and the p-th receive antenna port in the channel matrix H is a complex number representing the influence of the signal's amplitude and phase on its propagation from the transmitting antenna to the receiving antenna.

[0110] For example, the element H in matrix H m,n,p It can be represented as a complex number C m,n,p =a m,n,p +i×b m,n,p .

[0111] According to the received signal model, the phase shifts in the three dimensions of the channel matrix do not affect each other, and different types of perception results of the target can be obtained based on the phase changes in the three dimensions. That is, the target range can be estimated based on the phase difference caused by the time delay between subcarriers, the target velocity can be estimated based on the phase difference caused by the Doppler effect between OFDM symbols, and the target angle can be estimated based on the phase change between the received signals of multiple antenna ports.

[0112] Figure 1E is a schematic diagram illustrating an output sensory result according to an embodiment of the present disclosure.

[0113] As shown in Figure 1E, sensing algorithms such as 3D-MUSIC and 3D-ESPRIT can obtain the sensing results of the three parameters of distance, speed and angle of the sensing target based on the channel state information matrix.

[0114] Although the aforementioned sensing algorithms can achieve high accuracy in sensing information such as target distance, speed, and angle in some integrated sensing applications, they still have the following main problems:

[0115] The applicability of perception algorithms is limited. Factors such as clutter and noise in the scene can significantly degrade the performance of some algorithms. In multi-target scenarios, the direct path of non-perceived targets can affect the perception results of perceived targets; when the perceived target is far away, the impact of noise will be significant.

[0116] Perception algorithms have high computational complexity. In some cases, in order to obtain highly accurate perception results, the high complexity of perception algorithms is not conducive to their practical application.

[0117] Accuracy needs to be improved in multi-target sensing scenarios. In practical applications of integrated sensing, existing multi-target sensing algorithms often have certain errors in estimating the number of sensing targets, resulting in large errors in the algorithm estimation results.

[0118] In some embodiments, sensory integration can be achieved based on artificial intelligence (AI).

[0119] Figure 1F is a schematic diagram illustrating another output sensory result according to an embodiment of the present disclosure.

[0120] For example, the strong learning and modeling capabilities of neural network models can be utilized to train an AI perception model based on a certain amount of data. This model can then be used to estimate the number of targets in a multi-target perception scenario and to perceive information such as the distance, speed, and angle of each target. For instance, as shown in Figure 1F, an AI perception model is trained using the channel state information matrix H as input data. This model outputs three types of perception results: distance, speed, and angle of the target.

[0121] AI-based target perception models can be deployed on perception devices (such as user equipment or base stations such as terminals, automobiles, etc.) or on network-side perception network elements. The devices that acquire channel measurement data for AI perception may be different from the devices that complete the AI ​​perception model inference application.

[0122] When the AI ​​perception model is deployed on the perception device, the perception device can acquire channel measurement data (such as the received channel matrix) through a self-transmitting and self-receiving method, and then input it into the AI ​​perception model to obtain the perception results.

[0123] When the AI ​​perception model is deployed on the perception network element, the perception network element first needs to obtain the channel measurement data for perception reported by the perception device, and then input the measurement data into the AI ​​perception model to obtain the perception result.

[0124] Although AI neural network models can effectively overcome the main problems of traditional target perception algorithms and achieve high perception accuracy, the performance of AI perception models can still be affected by non-ideal factors such as noise and non-perceived target clutter when measurement data reporting is required.

[0125] In practical wireless communication systems, when data is transmitted through a wireless communication link, the data received by the receiver deviates from the original transmitted data due to channel noise. Furthermore, in target information perception tasks, AI perception algorithms and models primarily rely on measurement data such as the channel state information matrix corresponding to the target to complete target information perception. Measurement data of non-targets can be considered interference factors in the target perception process, affecting the accuracy of target information perception.

[0126] In practice, the receiving end of the sensing signal, such as a terminal or base station, can measure and acquire a channel matrix that is a superposition of the sensing target channel matrix, the non-sensing target channel matrix, and noise. Furthermore, after the sensing signal receiving end reports the sensing measurement data to the sensing network element, noise is added to the model input data. Therefore, if the channel measurement data reported by the sensing signal measurement node is directly used as the input data for the AI ​​sensing model, the interference from non-sensing targets and noise will have a certain impact on the sensing accuracy.

[0127] Figure 1G is a schematic diagram illustrating a preprocessing step according to an embodiment of the present disclosure.

[0128] In addition to using AI models to directly obtain sensing results based on sensing measurement data, sensing measurement data processing can also be achieved based on AI models. As shown in Figure 1G, the key feature extraction, data dimensionality reduction, and noise reduction capabilities of AI neural network models (such as preprocessing models) can be used to complete the preprocessing of sensing measurement signals. Then, the preprocessed intermediate data (such as signal data and channel data) can be input into AI sensing models or non-AI sensing algorithms. In this way, the reporting overhead of channel measurement data used for sensing can be effectively reduced, while improving the accuracy of sensing.

[0129] As shown in Figure 1C, although perception capability reporting is possible in some embodiments, the perception capability reported in Figure 1C is based on non-AI perception algorithms, rather than on AI models.

[0130] The ability to perceive based on non-AI perception algorithms is generally not the same as the ability to perceive based on AI perception models.

[0131] For example, perception based on AI perception models requires significantly more processing power compared to perception based on non-AI perception algorithms. Furthermore, since these models are typically trained for specific scenarios and patterns, the scenarios and patterns perceived by AI perception models may differ from those perceived by non-AI perception algorithms. Moreover, these differences in capabilities can impact subsequent steps in the perception process. Therefore, reporting perception capabilities based solely on the scenario shown in Figure 1C is insufficient for situations involving perception based on AI models.

[0132] Figure 2 is an interactive schematic diagram of an information sending method according to an embodiment of the present disclosure.

[0133] In some embodiments, the information transmission method may be performed by a first device, such as a sensing device, which may be a terminal (e.g., a mobile phone, a car, etc.) or a network device (e.g., a base station).

[0134] As shown in Figure 2, the information sending method may include the following steps:

[0135] In step S201, the first device sends capability information to the second device.

[0136] For example, capability information is used to indicate the ability of the first device to perceive using an AI model (e.g., also referred to as an AI perception model or perception model).

[0137] For example, the second device can be a sensing network element, which can be a network element in the core network used for sensing services (also known as a function).

[0138] In step S201, the second device determines the first device's ability to perceive using the AI ​​model based on the capability information.

[0139] According to embodiments of this disclosure, the first device can send capability information to the second device. Based on the capability information, the second device can understand the first device's ability to use an AI model for perception, so as to provide appropriate configuration for the first device's operation of using the AI ​​model for AI perception, which helps to ensure that the first device can smoothly use the AI ​​model for AI perception.

[0140] In some embodiments, the first device's ability to perceive using an AI model includes at least one of the following:

[0141] The processing capacity of the first device;

[0142] Using AI models to perceive the supported perception patterns;

[0143] Using AI models to perceive the supported perception scenarios;

[0144] The accuracy of perception is achieved using an AI model in the aforementioned perception mode;

[0145] The accuracy of perception using AI models in the perceived scenario.

[0146] In some embodiments, capability information may indicate the processing capabilities of the first device. For example, processing capabilities may include general processing capabilities or capabilities specifically designed for running AI models. The content of processing capabilities may include information such as the memory size and computing resources of the first device.

[0147] In some embodiments, capability information may instruct the first device to use an AI model to perceive supported perception modes, such as at least one of the six perception modes shown in Figure 1B.

[0148] Based on this, when the second device instructs the first device to use the AI ​​model for perception, it can instruct the first device to use the AI ​​model for perception in its supported perception modes; when the second device provides the first device with a configuration for using the AI ​​model for perception, it can provide a configuration applicable to its supported perception modes.

[0149] For example, capability information can also instruct the first device to use AI models to achieve a certain level of accuracy in perception (e.g., distance accuracy, speed accuracy, angle accuracy, etc.) within its supported perception modes.

[0150] Based on this, the second device can determine the accuracy of the first device's perception using the AI ​​model in each supported perception mode. For example, the supported perception modes include mode1 and mode3, where mode1 corresponds to accuracy 1 and mode3 corresponds to accuracy 3. Furthermore, it can determine the perception mode that meets the accuracy requirements, thereby instructing the first device to use the AI ​​model for perception in that mode. For instance, if accuracy 3 meets the accuracy requirements, the second device can be instructed to use the AI ​​model for perception in mode3, and to provide the first device with configurations suitable for mode3 (e.g., resources for communication between network devices and terminals).

[0151] In some embodiments, capability information may instruct the first device to use an AI model to perceive supported perception scenarios, such as highways, high-speed railways, indoor environments, etc.

[0152] Based on this, when the second device instructs the first device to use the AI ​​model for perception, it can instruct the first device to use the AI ​​model for perception in the perception scenarios it supports; when the second device provides the first device with a configuration for using the AI ​​model for perception, it can provide a configuration suitable for the perception scenarios it supports.

[0153] For example, capability information can also instruct the first device to use AI models to achieve a certain level of accuracy in perception (e.g., distance accuracy, speed accuracy, angle accuracy, etc.) within its supported perception scenarios.

[0154] Based on this, the second device can determine the accuracy of the first device's perception using the AI ​​model in each supported perception scenario. For example, the supported perception scenarios include highways and indoors, with accuracy 'a' corresponding to the highway scenario and accuracy 'b' corresponding to the indoor scenario. Furthermore, it can determine the perception scenario corresponding to the accuracy requirement, thereby instructing the first device to use the AI ​​model for perception in that scenario. For example, if accuracy 'b' meets the accuracy requirement, then the second device can be instructed to use the AI ​​model for perception in the indoor scenario, and the second device can be provided with configurations suitable for indoor scenarios (e.g., resources for receiving reference signals emitted by indoor access points).

[0155] It should be noted that the ability of the first device to use the AI ​​model for perception is not limited to the situations described in the above embodiments, but may also include other capabilities, such as the first association between the perception mode and the AI ​​model, the first association between the perception scene and the AI ​​model, etc. For example, when the AI ​​model is trained by the first device, it is unknown to the second device which perception mode the AI ​​model is applicable to. Therefore, the terminal can indicate the first association to the second device so that the second device can determine the perception mode applicable to the AI ​​model. Accordingly, the configuration provided by the second device to the terminal is applicable to both the perception mode and the AI ​​model associated with the perception mode.

[0156] In addition, the capability information sent by the first device to the second device can also instruct the first device to use non-AI perception algorithms (such as 3D-MUSIC, 3D-ESPRIT, etc.) for perception, including but not limited to: supported perception modes; the ability to send and receive perception signals under each supported perception mode; the perception accuracy under each supported perception mode, such as perception distance, distance resolution, perception speed, speed resolution, perception angle, angle resolution, perception latency, etc.; and supported perception functions, such as perception control functions, data preprocessing functions, and perception result calculation, etc.

[0157] In some embodiments, the second device may send configuration information to the first device.

[0158] For example, the second device can determine the first device's ability to perceive using an AI model based on capability information, and send configuration information to the first device based on the first device's ability to perceive using an AI model.

[0159] For example, the configuration information is used to instruct the first device to configure its perception using an AI model.

[0160] As can be seen from the preceding examples, the ability to perceive based on non-AI perception algorithms is generally not the same as the ability to perceive based on AI perception models. For example, perception based on AI perception models can yield more accurate perception results compared to perception based on non-AI perception algorithms. Therefore, the second device can be configured with relatively higher accuracy for services perceived by the first device based on AI perception models compared to services perceived based on non-AI perception algorithms.

[0161] In some embodiments, the first device uses an AI model for perception configuration, including at least one of the following:

[0162] The first device uses an AI model to perceive the Quality of Service (QoS).

[0163] The first device uses an AI model to configure the signal to be sensed.

[0164] For example, based on different sensing modes, the first device can act as either a signal transmitter or a signal receiver.

[0165] When the first device can act as a signal transmitter, the first device uses an AI model to perceive the signals that need to be perceived, including the configuration of the signals sent by the first device during the perception service process.

[0166] When the first device can act as a signal receiver, the first device uses an AI model to sense the signals that need to be sensed, including the configuration of signals received by the first device during the sensing process.

[0167] The configuration of signals includes, but is not limited to, signal resources and signal type (periodic, aperiodic).

[0168] For example, the service quality includes at least one of the following: accuracy requirements; latency requirements.

[0169] In the previous embodiment, perception based on an AI perception model can achieve more accurate perception results compared to perception based on a non-AI perception algorithm. However, this is generally achieved at the cost of greater computational overhead, which in turn leads to higher latency.

[0170] Therefore, the accuracy and latency requirements in QoS for perception services based on AI perception models may differ from those based on non-AI perception algorithms. Thus, the second device can configure the corresponding accuracy and / or latency requirements for perception using the AI ​​model of the first device in order to distinguish it from the QoS of perception services using non-AI perception algorithms of the first device.

[0171] It should be noted that the configuration information sent by the second device to the first device can also instruct the first device to use non-AI perception algorithms (such as 3D-MUSIC, 3D-ESPRIT, etc.) for perception, including but not limited to: perception mode; perception signal-related operations (such as sending, receiving, and transmitting / receiving) that the first device needs to perform in the corresponding perception mode; QoS requirements; measurement result reporting mode, such as periodic reporting, event-triggered reporting, event-triggered periodic reporting, etc.; and the configuration for the first device to send and / or receive measurement signals, recommending the configuration for the first device to send and / or receive measurement signals based on the perception mode.

[0172] When the first device uses an AI model for perception, if the configuration for perception using the AI ​​model is different from the configuration for perception using a non-AI perception algorithm, the terminal can perform perception services based on the configuration for perception using the AI ​​model. However, if the configuration for perception using the AI ​​model does not include the required configuration, the terminal can perform perception services based on the configuration for perception using a non-AI perception algorithm, or it can request the corresponding configuration from the second device.

[0173] In some embodiments, the first device may send perception information, which is perceived using an AI model, to the second device.

[0174] For example, the perception result includes at least one of the following:

[0175] Intermediate data obtained by preprocessing the signals to be sensed;

[0176] The perception result obtained by sensing the signal to be perceived.

[0177] In some embodiments, the AI ​​model includes at least one of the following:

[0178] Preprocessing model;

[0179] Perception model.

[0180] For example, a preprocessing model can be used to preprocess the signal to be sensed, and the preprocessing may include at least one of the following:

[0181] Feature extraction; dimensionality compression; noise suppression; clutter elimination.

[0182] As shown in Figure 1G, the AI ​​models involved in perception can include either preprocessing models or perception models. When using a perception model for perception, preprocessed data (also known as intermediate data) can be input into the perception model, which then outputs the perception result.

[0183] Since preprocessed data is smaller than unprocessed data, transmitting preprocessed data requires less communication resources compared to transmitting unprocessed data. Furthermore, because the preprocessing model is a trained model specifically designed for signal preprocessing, it can effectively perform preprocessing operations such as feature extraction, dimensionality compression, noise suppression, and clutter cancellation. This helps minimize noise and clutter interference in the input sensing model, thereby improving sensing accuracy.

[0184] In some embodiments, the preprocessing model and the perception model may include two deployment methods:

[0185] One deployment method is to deploy both the preprocessing model and the perception model on the first device, so that the first device can obtain the perception results and send the perception results to the second device;

[0186] Another deployment method is to deploy the preprocessing model on the first device and the perception model on the second device. In this way, the first device can obtain the output of the preprocessing model, that is, the intermediate data, and then send the intermediate data to the second device.

[0187] In some implementations, the perceived result may include at least one of the following:

[0188] Results information: target distance, speed, etc., and even vehicle inspection information, smart intersections, and dynamic maps, etc.

[0189] Process information includes point cloud information, time delay spread spectrum, Doppler spectrum, micro-Doppler spectrum, angle spectrum, signal intensity spectrum, etc. generated by sensing and measurement. Among them, the spectral information contains information on multiple paths or multiple motion modes, and each path or each motion mode can be reflected by independent spectral lines or parameters.

[0190] Raw information: Received signal or raw channel information, such as the complex result of the received signal or channel response, amplitude, phase, I-channel, Q-channel and related calculation results.

[0191] When both the preprocessing model and the perception model are deployed on the first device, the perception results sent by the first device to the second device are similar in type to the perception results obtained based on non-AI perception algorithms, which can include the aforementioned result information, process information, raw information, etc.

[0192] However, when the preprocessing model is deployed on the first device and the perception model is deployed on the second device, the first device needs to send intermediate data to the second device. This intermediate data is the data obtained after the preprocessing model processes the signal and channel. This data is abstract data obtained through feature extraction, rather than the specific data in the result information, process information, or original information.

[0193] Therefore, to support AI model-based perception, the reporting process of the first device needs to be extended to enable it to send intermediate data to the second device. Correspondingly, the second device can configure specific resources for sending intermediate data.

[0194] In solutions that directly acquire perception results based on AI models, the reported perception measurement data is the same as existing data types; however, in solutions that process perception measurement data using AI models, the model output data does not belong to any defined data type. Therefore, to support AI-based perception solutions, the following addition is made to the existing perception measurement reporting:

[0195]

[0196] The communication method involved in the embodiments of this disclosure may include at least one of steps S201 to S202. For example, step S201 may be implemented as a standalone embodiment, step S202 may be implemented as a standalone embodiment, and step S201+S202 may be implemented as a standalone embodiment, but is not limited thereto.

[0197] In some embodiments, steps S201 and S202 may be performed in an alternate order or simultaneously.

[0198] In some embodiments, step S201 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0199] In some embodiments, step S202 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0200] In some embodiments, other optional implementations described before or after the specification corresponding to FIG2 may be referred to.

[0201] The technical solutions of this disclosure will be illustrated by several further embodiments below.

[0202] Figure 3 is a schematic diagram illustrating another sensing process according to an embodiment of the present disclosure.

[0203] As shown in Figure 3, this disclosure, based on perception using non-AI perception algorithms, considers the main needs and steps of AI perception solutions in the actual application process of communication systems. It adds the necessary functions for implementing AI perception in the three main steps of perception capability reporting, perception measurement configuration, and perception measurement reporting to enhance the basic perception process and thus support the application of AI perception solutions.

[0204] Example 1: Sensing ability reporting:

[0205] The main purpose of the perception capability reporting process is to enable the second device (e.g., the perception network element shown in Figure 3) to understand the perception capabilities of the first device (e.g., the terminal shown in Figure 3, which can communicate with the perception network element through a base station), so that the second device can determine the perception nodes and related configurations. Perception capabilities may include the following:

[0206] Supported perception modes;

[0207] The ability to send and receive sensing signals in each supported sensing mode;

[0208] 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;

[0209] Supported sensing functions, such as sensing control functions, data preprocessing functions, and sensing result calculation.

[0210] To support the AI ​​perception solution, the second device not only needs to understand the perception capabilities of the first device, but also its AI model application capabilities to execute the AI ​​perception solution. Therefore, in addition to the above-mentioned perception capability reporting, the following can be added:

[0211] AI model-related capabilities, such as sensing device memory size and computing resources;

[0212] Supports the use of AI models for perception in various scenarios and perception modes, i.e., which scenarios and perception modes have AI perception models configured.

[0213] Perception accuracy in each supported AI perception scenario and mode.

[0214] Example 2: Sensing and Measurement Configuration:

[0215] The purpose of the sensing measurement configuration process is that, after determining the sensing capabilities of the first device, the second device can select an appropriate sensing method and approach to perform sensing services. In other words, the second device sends the relevant sensing measurement configuration for the selected sensing method to the first device. In this measurement configuration, the second device can provide the following information:

[0216] Perception patterns;

[0217] In the corresponding sensing mode, what operations does the first device need to perform for sending, receiving, and transmitting sensing signals?

[0218] Sensed QoS requirements, such as sensing accuracy requirements and latency requirements;

[0219] Sensing measurement data reporting modes, such as periodic reporting, event reporting, and event-triggered periodic reporting;

[0220] The configuration for the first device to send / receive measurement signals is recommended based on the sensing mode;

[0221] The measurement configuration of AI-based sensing solutions may differ from that of non-AI-based sensing solutions. For example, using AI models for sensing can achieve higher sensing accuracy based on the same data, but the higher computational cost may also lead to higher latency, resulting in different QoS requirements for AI sensing. Simultaneously, using AI models can reduce resource consumption and data transmission overhead for sensing while maintaining accuracy, impacting the configuration of measurement signals used for sensing. Therefore, the following can be added to the above-mentioned sensing measurement configuration:

[0222] AI-sensing QoS requirements, such as AI sensing accuracy requirements and latency requirements;

[0223] The configuration for the first device to send / receive measurement signals for AI perception is recommended based on the perception mode.

[0224] Example 3: Sensing Measurement Reporting:

[0225] The purpose of the sensing measurement reporting process is to perform corresponding measurements and report the sensing measurement data after the first device completes the sensing method configuration. The required sensing measurement data may differ depending on the sensing mode; therefore, the sensing measurement data may include multiple levels, including but not limited to:

[0226] Perception results: the distance and speed of the target, and even vehicle inspection information, smart intersections and dynamic maps, etc.

[0227] Sensing intermediate data: point cloud information generated by sensing measurements, etc.;

[0228] Preliminary sensing data includes: time-delay spread spectrum, Doppler spectrum, micro-Doppler spectrum, angular spectrum, signal intensity spectrum, etc. This spectral information contains information about multiple paths or motion modes, each of which can be reflected by independent spectral lines or parameters.

[0229] Sensing raw data: received signals or raw channel information (e.g., complex results of received signals or channel responses, amplitude and / or phase, I-channel / Q-channel and their related calculation results).

[0230] In schemes that directly acquire perception results based on AI models, the reported perception measurement data is the same as existing data types; however, in schemes that process perception measurement data based on AI models, the data output by the model does not belong to a defined data type. Therefore, to support AI-based perception schemes, the following can be added to the above-mentioned perception measurement reporting:

[0231] AI perception intermediate data: Data output by the AI ​​perception data processing model deployed on the first device.

[0232] In the context of integrated communication and sensing applications, this disclosure addresses the issue that existing sensing processes cannot adequately support AI-based target sensing schemes. Considering the practical application needs of sensing schemes based on AI models, it proposes a basic process enhancement method for AI-based sensing. Building upon the basic sensing process, AI sensing-related functions are added to the three main steps of sensing capability reporting, sensing measurement configuration, and sensing measurement reporting to support AI sensing schemes. This facilitates the effective deployment and application of AI sensing schemes in actual communication systems.

[0233] 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.

[0234] 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.

[0235] In some embodiments, terms such as “send,” “transmit,” “report,” “distribute,” “transmit,” “bidirectional transmission,” “send and / or receive” can be used interchangeably.

[0236] 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.

[0237] Corresponding to the aforementioned embodiments of the information sending and receiving methods, this disclosure also provides embodiments of the information sending apparatus and the information receiving apparatus.

[0238] Figure 4 is a schematic block diagram illustrating an information transmitting device according to an embodiment of the present disclosure. For example, the information transmitting device can be disposed on and / or applied to a first device (e.g., a sensing device, which can be a terminal, base station, etc.). As shown in Figure 4, the information transmitting device includes: a transmitting module 401 and a receiving module 402.

[0239] In some embodiments, the sending module is configured to send capability information to a second device, wherein the capability information is used to indicate the first device's ability to perceive using an AI model.

[0240] In some embodiments, the ability of the first device to perceive using an AI model includes at least one of the following: the processing power of the first device; the perception modes supported by the AI ​​model; the perception scenarios supported by the AI ​​model; the accuracy of the AI ​​model in the perception modes; and the accuracy of the AI ​​model in the perception scenarios.

[0241] In some embodiments, the receiving module is configured to receive configuration information sent by the second device, wherein the configuration information is used to instruct the first device to use an AI model for perception configuration.

[0242] In some embodiments, the first device uses an AI model to configure perception, including at least one of the following: the first device uses an AI model to perceive service quality; the first device uses an AI model to configure the signals to be perceived.

[0243] In some embodiments, the service quality includes at least one of the following: accuracy requirements; latency requirements.

[0244] In some embodiments, the sending module is further configured to send sensing information obtained by using an AI model to the second device, wherein the sensing information includes at least one of the following: intermediate data obtained by preprocessing the signal to be sensed; and sensing results obtained by sensing the signal to be sensed.

[0245] In some embodiments, the AI ​​model includes at least one of the following: a preprocessing model; a perception model.

[0246] In some embodiments, where the perception model is deployed on the second device and the preprocessing model is deployed on the first device, the perception information includes the intermediate data.

[0247] In some embodiments, the preprocessing includes at least one of the following: feature extraction; dimensionality compression; noise suppression; and clutter removal.

[0248] Figure 5 is a schematic block diagram illustrating an information receiving device according to an embodiment of the present disclosure. For example, the information receiving device may be disposed in and / or applied to a second device (e.g., a sensing network element). As shown in Figure 5, the information receiving device includes: a receiving module 501 and a transmitting module 502.

[0249] In some embodiments, the receiving module is configured to receive capability information sent by the first device, wherein the capability information is used to indicate the first device's ability to perceive using an AI model.

[0250] In some embodiments, the ability of the first device to perceive using an AI model includes at least one of the following: the processing power of the first device; the perception modes supported by the AI ​​model; the perception scenarios supported by the AI ​​model; the accuracy of the AI ​​model in the perception modes; and the accuracy of the AI ​​model in the perception scenarios.

[0251] In some embodiments, the sending module is configured to send configuration information to the first device, wherein the configuration information is used to instruct the first device to configure the perception using an AI model.

[0252] In some embodiments, the first device uses an AI model to configure perception, including at least one of the following: the first device uses an AI model to perceive service quality; the first device uses an AI model to configure the signals to be perceived.

[0253] In some embodiments, the service quality includes at least one of the following: accuracy requirements; latency requirements.

[0254] In some embodiments, the receiving module is further configured to receive sensing information sent by the first device using an AI model, wherein the sensing information includes at least one of the following: intermediate data obtained by preprocessing the signal to be sensed; and sensing results obtained by sensing the signal to be sensed.

[0255] In some embodiments, the AI ​​model includes at least one of the following: a preprocessing model; a perception model.

[0256] In some embodiments, where the perception model is deployed on the second device and the preprocessing model is deployed on the first device, the perception information includes the intermediate data.

[0257] In some embodiments, the preprocessing includes at least one of the following: feature extraction; dimensionality compression; noise suppression; and clutter removal.

[0258] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0259] 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 terminal 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.

[0260] 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.

[0261] 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).

[0262] 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.

[0263] 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.

[0264] 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 (e.g., steps S201, S202, but not limited thereto) in the above method, such as sending and / or receiving, while the processor 6101 performs at least one of other steps (e.g., steps S201, S202, but not limited thereto). In optional embodiments, the transceiver may include a receiver and / or a transmitter, which may be separate or integrated. 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.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] Chip 6200 includes one or more processors 6201. Chip 6200 is used to perform any of the methods described above.

[0269] 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.

[0270] In some embodiments, the interface circuit 6202 performs at least one of the communication steps (e.g., steps S201, S202, but not limited thereto) in the above-described method, such as sending and / or receiving. For example, the interface circuit 6202 performing the communication steps (e.g., 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 other steps (e.g., steps S201, S202, but not limited thereto).

[0271] 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.

[0272] 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.

[0273] 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.

[0274] 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 method for sending information, characterized in that, Performed by a first device, the method includes: The first device sends capability information to the second device, wherein the capability information is used to indicate the first device's ability to use an AI model for perception.

2. The method according to claim 1, characterized in that, The first device's ability to perceive using an AI model includes at least one of the following: The processing capacity of the first device; Using AI models to perceive the supported perception patterns; Using AI models to perceive the supported perception scenarios; The accuracy of perception is achieved using an AI model in the aforementioned perception mode; The accuracy of perception using AI models in the perceived scenario.

3. The method according to claim 1 or 2, characterized in that, The method further includes: The first device receives configuration information sent by the second device, wherein the configuration information is used to instruct the first device to configure the perception using an AI model.

4. The method according to claim 3, characterized in that, The first device uses an AI model for perception configuration, including at least one of the following: The first device uses an AI model to perceive service quality. The first device uses an AI model to configure the signals to be sensed.

5. The method according to claim 4, characterized in that, The service quality includes at least one of the following: Accuracy requirements; Latency requirements.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Sending perception information obtained using an AI model to the second device, wherein the perception information includes at least one of the following: Intermediate data obtained by preprocessing the signals to be sensed; The perception result obtained by sensing the signal to be perceived.

7. The method according to claim 6, characterized in that, The AI ​​model includes at least one of the following: Preprocessing model; Perception model.

8. The method according to claim 7, characterized in that, When the perception model is deployed on the second device and the preprocessing model is deployed on the first device, the perception information includes the intermediate data.

9. The method according to any one of claims 6 to 8, characterized in that, The preprocessing includes at least one of the following: Feature extraction; Dimensional compression; Noise suppression; Clutter cancellation.

10. An information receiving method, characterized in that, Performed by a second device, the method includes: Receive capability information sent by a first device, wherein the capability information is used to indicate the first device's ability to perceive using an AI model.

11. The method according to claim 10, characterized in that, The first device's ability to perceive using an AI model includes at least one of the following: The processing capacity of the first device; Using AI models to perceive the supported perception patterns; Using AI models to perceive the supported perception scenarios; The accuracy of perception is achieved using an AI model in the aforementioned perception mode; The accuracy of perception using AI models in the perceived scenario.

12. The method according to claim 10 or 11, characterized in that, The method further includes: Send configuration information to the first device, wherein the configuration information is used to instruct the first device to configure the perception using an AI model.

13. The method according to claim 12, characterized in that, The first device uses an AI model for perception configuration, including at least one of the following: The first device uses an AI model to perceive service quality. The first device uses an AI model to configure the signals to be sensed.

14. The method according to claim 13, characterized in that, The service quality includes at least one of the following: Accuracy requirements; Latency requirements.

15. The method according to any one of claims 10 to 14, characterized in that, The method further includes: The device receives perception information sent by the first device, which is obtained using an AI model, wherein the perception information includes at least one of the following: Intermediate data obtained by preprocessing the signals to be sensed; The perception result obtained by sensing the signal to be perceived.

16. The method according to claim 15, characterized in that, The AI ​​model includes at least one of the following: Preprocessing model; Perception model.

17. The method according to claim 16, characterized in that, When the perception model is deployed on the second device and the preprocessing model is deployed on the first device, the perception information includes the intermediate data.

18. The method according to any one of claims 15 to 17, characterized in that, The preprocessing includes at least one of the following: Feature extraction; Dimensional compression; Noise suppression; Clutter cancellation.

19. A communication device, characterized in that, The communication device is used to perform the information transmission method according to any one of claims 1 to 9, and / or the information reception method according to any one of claims 10 to 18.

20. A communication system, characterized in that, The device includes a terminal and a network device, wherein the terminal is configured to implement the information sending method according to any one of claims 1 to 9, and the network device is configured to implement the information receiving method according to any one of claims 10 to 18.

21. A storage medium storing instructions, characterized in that, When the instruction is executed on the communication device, the communication device performs the information transmission method of any one of claims 1 to 9, and / or the information reception method of any one of claims 10 to 18.

22. A program product, characterized in that, When the above-mentioned program product is executed by a communication device, the communication device performs the information transmission method according to any one of claims 1 to 9, and / or the information reception method according to any one of claims 10 to 18.

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