Information receiving and transmitting method, communication device, and storage medium

Abstract

The present disclosure relates to the technical field of communication, in particular to an information receiving method, an information sending method, a communication device and a storage medium, wherein the information receiving method comprises: receiving first indication information sent by a second device, wherein the first indication information is used to indicate a first AI model used by the first device for sensing service; and performing sensing service according to the first AI model. According to the present disclosure, since the second device is relatively comprehensive for real-time network environment and service needs to understand, the first device adjusts the AI model used for sensing according to the first indication information sent by the second device, which is beneficial to ensure that the sensing result of the AI model used meets the service needs in the real-time network environment.

Description

Information receiving and sending methods, communication equipment and storage media Technical Field

[0001] This disclosure relates to the field of communication technology, and more specifically, to information receiving methods, information sending methods, communication devices, and storage media. Background Technology

[0002] Perception based on AI (Artificial Intelligence) models requires significantly more processing power compared to perception based on non-AI algorithms. Furthermore, since these models are typically trained for specific scenarios and patterns, and the actual network environment can change, potentially altering the perceived scenario and patterns, thus affecting the accuracy of perception results determined by AI models. Summary of the Invention

[0003] The embodiments of this disclosure provide information receiving and sending methods, communication devices, and storage media to solve technical problems in the related art.

[0004] According to a first aspect of the present disclosure, an information receiving method is provided, executed by a first device, the method comprising: receiving first indication information sent by a second device, wherein the first indication information is used to indicate a first AI model for sensing services that the first device needs to use; and performing sensing services according to the first AI model.

[0005] According to a second aspect of the present disclosure, an information sending method is provided, executed by a second device, the method comprising: sending first indication information to a first device, wherein the first indication information is used to indicate a first AI model for sensing services that the first device needs to use.

[0006] According to a third aspect of the present disclosure, a communication device is provided, the communication device being used to perform the information receiving method described in the first aspect, and / or the information sending method described in the second aspect.

[0007] According to a fourth aspect of the present disclosure, a communication system is proposed, including a sensing device and a sensing network element, wherein the sensing device is configured to implement the information receiving method described in the first aspect, and the sensing network element is configured to implement the information sending method described in the second aspect.

[0008] According to a fifth aspect of the present disclosure, a storage medium is provided that stores instructions, which, when executed on a communication device, cause the communication device to perform the information receiving method described in the first aspect and / or the information sending method described in the second aspect.

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

[0010] According to embodiments of this disclosure, since the second device has a relatively comprehensive understanding of the real-time network environment and service needs, the first device adjusts the AI ​​model used for perception based on the first instruction information sent by the second device, which helps to ensure that the perception results of the AI ​​model used meet the service needs in the real-time network environment. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of this disclosure. Figure 1B is a schematic diagram of a sensing mode according to an embodiment of this disclosure. Figure 1C is a schematic diagram of a sensing process according to an embodiment of this disclosure. Figure 1D is a schematic diagram of a sensing-integrated simulation channel model according to an embodiment of this disclosure. Figure 1E is a schematic diagram of an output sensing result according to an embodiment of this disclosure. Figure 1F is a schematic diagram of another output sensing result according to an embodiment of this disclosure. Figure 1G is a schematic diagram of a preprocessing according to an embodiment of this disclosure. Figure 1H is a schematic diagram of a deployment mode according to an embodiment of this disclosure. Figure 2 is an interactive schematic diagram of an information receiving method according to an embodiment of this disclosure. Figure 3 is a schematic flowchart of model management according to an embodiment of this disclosure. Figure 4 is a schematic block diagram of an information receiving device according to an embodiment of this disclosure. Figure 5 is a schematic block diagram of an information transmission device according to an embodiment of the present disclosure. Figure 6A is a structural schematic diagram of a communication device proposed in an embodiment of the present disclosure. Figure 6B is a structural schematic diagram of a chip proposed in an embodiment of the present disclosure. Detailed Implementation

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

[0013] In a first aspect, embodiments of this disclosure propose an information receiving method executed by a first device, the method comprising: receiving first indication information sent by a second device, wherein the first indication information is used to indicate a first AI model for sensing services that the first device needs to use; and performing sensing services according to the first AI model.

[0014] In the above embodiments, since the second device has a relatively comprehensive understanding of the real-time network environment and business needs, the first device adjusts the AI ​​model used for perception according to the first instruction information sent by the second device, which helps to ensure that the perception results of the AI ​​model used meet the business needs in the real-time network environment.

[0015] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: sending sensing information for sensing services based on the first AI model to the second device.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the sensing information includes at least one of the following: intermediate data obtained by preprocessing the signal to be sensed; and a sensing result obtained by sensing prediction of the signal to be sensed.

[0017] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: receiving second indication information sent by the second device, wherein the second indication information is used to instruct the first device to activate the first AI model.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the second indication information is further used to indicate at least one of the following: performing perception services based on non-AI algorithms; updating the first AI model; and performing perception services according to a second AI model.

[0019] In conjunction with some embodiments of the first aspect, in some embodiments, where the second indication information is also used to indicate an update to the first AI model, the method further includes at least one of the following: updating the first AI model based on a sample set consisting of real-time samples; and receiving the updated first AI model sent by the second device.

[0020] Secondly, embodiments of this disclosure propose an information transmission method executed by a second device, the method comprising: sending first indication information to a first device, wherein the first indication information is used to indicate a first AI model for sensing services that the first device needs to use.

[0021] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: receiving perception information from the first device regarding perception services based on the first AI model.

[0022] In conjunction with some embodiments of the second aspect, in some embodiments, the sensing information includes at least one of the following: intermediate data obtained by preprocessing the signal to be sensed; and a sensing result obtained by sensing prediction of the signal to be sensed.

[0023] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: determining whether the perceived information meets the requirements; and if the perceived information does not meet the requirements, sending second indication information to the first device, wherein the second indication information is used to instruct the first device to activate the first AI model.

[0024] In conjunction with some embodiments of the second aspect, in some embodiments, determining whether the perceived information meets the requirements includes: generating a comparison threshold based on real-time network environment information; and determining whether the perceived information meets the requirements based on the relationship between the perceived information and the comparison threshold.

[0025] In conjunction with some embodiments of the second aspect, in some embodiments, the second indication information is further used to indicate at least one of the following: performing perception services based on non-AI algorithms; updating the first AI model; and performing perception services according to a second AI model.

[0026] Thirdly, embodiments of this disclosure provide an information receiving device, executed by a first device, the device comprising: a receiving module configured to receive first indication information sent by a second device, wherein the first indication information is used to indicate a first AI model for sensing services that the first device needs to use; and a processing module configured to perform sensing services according to the first AI model.

[0027] Fourthly, embodiments of this disclosure provide an information sending device executed by a second device, the device comprising: a sending module configured to send first indication information to a first device, wherein the first indication information is used to indicate a first AI model for sensing services that the first device needs to use.

[0028] Fifthly, embodiments of this disclosure provide a communication device for performing the information receiving method according to any one of the first aspect and optional embodiments of the first aspect, and / or the information sending method according to any one of the second aspect and optional embodiments of the second aspect.

[0029] In a sixth aspect, embodiments of this disclosure provide a sensing device comprising: one or more processors; wherein the terminal is configured to perform the information receiving method described in any one of the first aspects and optional embodiments thereof.

[0030] In a seventh aspect, embodiments of this disclosure provide a sensing network element, comprising: one or more processors; wherein the network device is configured to perform the information transmission method described in any one of the second aspect and optional embodiments thereof.

[0031] Eighthly, embodiments of this disclosure provide a communication system including a sensing device and a sensing network element, wherein the sensing device is configured to implement the information receiving method according to any one of the optional embodiments of the first aspect, and the sensing network element is configured to implement the information sending method according to any one of the optional embodiments of the second aspect.

[0032] Ninthly, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the information receiving method of any one of the first aspect and optional embodiments of the first aspect, and / or the information sending method of any one of the second aspect and optional embodiments of the second aspect.

[0033] In a tenth aspect, embodiments of this disclosure provide a program product that, when executed by a communication device, causes the communication device to perform the information receiving method according to any one of the first aspect and optional embodiments of the first aspect, and / or the information sending method according to any one of the second aspect and optional embodiments of the second aspect.

[0034] 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 receiving method according to any one of the first aspect and the optional embodiments of the first aspect, and / or the information sending method according to any one of the second aspect and the optional embodiments of the second aspect.

[0035] It is understood that the aforementioned information receiving and sending 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 that can be achieved can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0036] This disclosure provides information receiving and sending methods, communication devices, and storage media. In some embodiments, the terms "information receiving and sending method" can be used interchangeably with "information processing method" and "communication method," and the terms "information receiving and sending device" can be used interchangeably with "information processing device" and "communication device," and the terms "information processing system" and "communication system" can be used interchangeably.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] For example, when using articles such as "a", "an", and "the" in translation, the noun following the article can be understood as either a singular or a plural form.

[0042] In the embodiments disclosed herein, "multiple" refers to two or more.

[0043] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0044] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "in response to one case A, in response to another case B", etc., may include the following technical solutions depending on the situation: in some embodiments, A (execute A regardless of B); in some embodiments, B (execute B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0045] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, C, etc.

[0046] The prefixes such as "first" and "second" in the embodiments of this disclosure are only for distinguishing different descriptive objects and do not constitute restrictions on the position, order, priority, number or content of the descriptive objects. For the description of the descriptive objects, please refer to the description in the claims or the context of the embodiments. The use of prefixes should not constitute unnecessary restrictions.

[0047] 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.

[0048] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0049] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0050] 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”.

[0051] 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.

[0052] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0053] In some embodiments, the terms "access network device (AN device)," "radio access network device (RAN device)," "base station (BS)," "radio base station," "fixed station," "node," "access point," "transmission point (TP)," "reception point (RP)," "transmission / reception point (TRP)," "panel," "antenna panel," "antenna array," "cell," "macro cell," "small cell," "femto cell," "pico cell," "sector," "cell group," "serving cell," "carrier," "component carrier," and "bandwidth part (BWP)" can be used interchangeably.

[0054] In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", "subscriber station", "mobile unit", "subscriber unit", "wireless unit", "remote unit", "mobile device", "wireless device", "wireless communication device", "remote device", "mobile subscriber station", "access terminal", "mobile terminal", "wireless terminal", "remote terminal", "handset", "user agent", "mobile client", and "client" can be used interchangeably.

[0055] In some embodiments, access network devices, core network devices, or network devices can be replaced by terminals. For example, embodiments of this disclosure can also be applied to structures where communication between access network devices, core network devices, or network devices and terminals is replaced by communication between multiple terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the structure can also be configured such that the terminal has all or part of the functions of the access network device. Furthermore, terms such as "uplink" and "downlink" can be replaced with terms corresponding to communication between terminals (e.g., "sidelink"). For example, uplink channel, downlink channel, etc., can be replaced with sidelink channel, and uplink link, downlink, etc., can be replaced with sidelink link.

[0056] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, core network device, or network device may also be configured to have all or some of the functions of the terminal.

[0057] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0058] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0059] Furthermore, each element, each row, or each column in the table of this disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0060] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0061] As shown in Figure 1A, the communication system 100 includes a terminal 101 and a network device 102, wherein the network device includes at least one of the following: an access network device and a core network device.

[0062] In some embodiments, terminal 101 includes, but is not limited to, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet computer, computer with wireless transceiver function, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal device in industrial control, wireless terminal device in self-driving, wireless terminal device in remote medical surgery, wireless terminal device in smart grid, wireless terminal device in transportation safety, wireless terminal device in smart city, and wireless terminal device in smart home.

[0063] In some embodiments, the access network device is, for example, a node or device that connects a terminal to a wireless network. The access network device may include, but is not limited to, at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation eNB (ng-eNB), next-generation Node B (gNB), node B (NB), home node B (HNB), home evolved node B (HeNB), radio backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a Wi-Fi system.

[0064] In some embodiments, a core network device may be a single device comprising one or more network elements, or it may be multiple devices or a group of devices, each comprising all or part of the aforementioned one or more network elements. Network elements may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), or a Next Generation Core (NGC).

[0065] In some embodiments, the technical solutions of this disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within access network devices involved in the embodiments of this disclosure can be transformed into internal interfaces of Open RAN. The processes and information interactions between these internal interfaces can be implemented by software or programs.

[0066] In some embodiments, the access network device may be composed of a central unit (CU) and a distributed unit (DU). The CU may also be called a control unit. The CU-DU structure can separate the protocol layer of the access network device. Some of the protocol layer functions are centrally controlled by the CU, while the remaining part or all of the protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0067] It is understood that the communication system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions proposed in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions proposed in this disclosure are also applicable to similar technical problems.

[0068] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1A, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1A are illustrative. The communication system may include all or some of the main bodies in FIG1A, or it may include other main bodies outside of FIG1A. The number and form of each main body are arbitrary. Each main body may be physical or virtual. The connection relationship between the main bodies is illustrative. The main bodies may not be connected or may be connected. The connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0069] The embodiments disclosed herein can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, systems utilizing other communication methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0070] In some embodiments, Integrated Sensing and Communication (ISAC) is one of the most important research directions in wireless communication and a key enabling technology in 5G-A (5G-Advanced) and future 6G mobile communications.

[0071] The integrated sensing technology enables sensing services based on existing mobile communication infrastructure, fully leverages the advantages of mobile communication networks to meet the sensing needs of various business service scenarios, and improves communication performance by relying on sensing capabilities, thereby helping to realize many application services such as detection, localization and tracking, environmental reconstruction and target imaging, gesture and posture recognition.

[0072] In applications oriented towards integrated communication and sensing scenarios, relatively complete channel modeling work for sensing systems has been carried out based on 3GPP (3rd Generation Partnership Project) channel modeling standards, providing strong support for the evaluation of sensing technology solutions.

[0073] In related technologies, various estimation algorithms for parameters such as speed, distance, and angle of perceived targets have been proposed and designed, such as the MUSIC algorithm and the ESPRIT algorithm. These algorithms can achieve good target perception effects in some integrated sensory simulation scenarios.

[0074] In some embodiments, the application of Artificial Intelligence (AI) technology in the field of wireless communication is an important research direction in both academia and industry, and also one of the key research topics in 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 areas such as high-precision terminal positioning, channel state information feedback, and beam management.

[0075] Therefore, communication and sensing integration can be achieved based on AI technology, which is a potential application in future 6G wireless communication systems. By utilizing the powerful computing capabilities of dedicated devices such as GPUs (Graphics Processing Units), AI sensing models can be trained based on a certain type of dataset to optimize sensing accuracy, computational complexity, and applicability. This will promote the deep application of communication and sensing integration technology, support more intelligent business applications and services, and thus help promote the development of communication, sensing, computing, and intelligence integration.

[0076] Figure 1B is a schematic diagram illustrating a sensing mode according to an embodiment of the present disclosure.

[0077] In some embodiments, the integrated communication sensing can include six sensing modes, such as Mode 1 to Mode 6 shown in Figure 1A. Among them, Mono-static indicates self-transmission and self-reception, where the sending end and the receiving end are the same; Bi-static indicates cross-station transmission and reception, where the sending end and the receiving end are different.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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).

[0082] 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).

[0083] 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).

[0084] In some embodiments, the sensing target of the synesthetic integration may include at least one of the following:

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] Figure 1C is a schematic diagram illustrating a sensing process according to an embodiment of the present disclosure.

[0090] 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:

[0091] Sensing capability reporting: Sensing devices report the supported sensing modes and capabilities related to sensing signal processing, thereby helping sensing network elements determine the appropriate sensing modes and sensing resources to use.

[0092] 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.

[0093] Sensing measurement reporting: The sensing device reports the collected sensing measurement data to the sensing network element.

[0094] Figure 1D is a schematic diagram of an integrated inductive simulation channel model according to an embodiment of the present disclosure.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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;

[0099] 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;

[0100] 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.

[0101] 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.

[0102] In some embodiments, the channel state information matrix H typically includes three dimensions: subcarriers, OFDM (Orthogonal Frequency Division Multiplexing) symbols, and receiver antenna ports.

[0103] 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.

[0104] 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 .

[0105] 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.

[0106] Figure 1E is a schematic diagram illustrating an output sensory result according to an embodiment of the present disclosure.

[0107] 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.

[0108] 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:

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In some embodiments, sensory integration can be achieved based on artificial intelligence (AI).

[0113] Figure 1F is a schematic diagram illustrating another output sensory result according to an embodiment of the present disclosure.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Figure 1G is a schematic diagram illustrating a preprocessing step according to an embodiment of the present disclosure.

[0122] 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.

[0123] Based on Figures 1F and 1G, the sensing process can be divided into two methods. One method involves directly inputting the sensing data (such as the channel matrix H, raw channel data, signal data, etc.) into the sensing model, which then outputs the sensing results. The other method involves first inputting the sensing data into a preprocessing model, which can output intermediate data (such as preprocessed channel data), and then inputting this intermediate data into the sensing model, which then outputs the sensing results.

[0124] In other words, the perception process can be implemented based on a perception model, or it can be implemented based on a preprocessing model and a perception model.

[0125] Figure 1H is a schematic diagram illustrating a deployment mode according to an embodiment of the present disclosure.

[0126] As shown in deployment mode 1 of Figure 1H, when the sensing process is based on a sensing model, the deployment device can deploy the sensing model on sensing devices such as terminals and base stations. The sensing devices can send the sensing results output by the sensing model to the SF. The SF can receive the reporting capabilities of the sensing devices and manage the model in the process of sensing.

[0127] As shown in deployment mode 2 of Figure 1H, when the sensing process is based on a sensing model and a preprocessing model, the deployment device can deploy the preprocessing model on sensing devices such as terminals and base stations, while the sensing model is deployed on the SF. The sensing devices can send intermediate data output from the preprocessing model to the SF, which can then input this intermediate data into the sensing model to calculate the sensing result. Additionally, the SF can receive reports from the sensing devices and manage the models in the sensing process.

[0128] As shown in deployment mode 3 of Figure 1H, when the sensing process is based on a sensing model, the deployment device can deploy the sensing model to the SF. The sensing device can send sensing data to the SF, and the SF inputs the sensing data into the sensing model to calculate the sensing result.

[0129] As can be seen, in deployment modes 1 and 2, the AI ​​model (e.g., perception model, preprocessing model) is deployed in the perception device. Since AI models are generally not universally applicable across all scenarios and modes, but rather trained for specific scenarios and modes—for example, an AI model suitable for mode 1 in Figure 1B might not achieve the required accuracy in perception results when applied to mode 3—it is necessary to properly manage the lifecycle of the AI ​​model to ensure that the perception results obtained by the perception device based on the AI ​​model meet the requirements.

[0130] Figure 2 is an interactive schematic diagram of an information receiving method according to an embodiment of the present disclosure.

[0131] In some embodiments, the information receiving method may be performed by the first device.

[0132] For example, the first device can be a terminal (such as a mobile phone, vehicle, etc.) or a network device (such as an access network device, base station).

[0133] As shown in Figure 2, the information receiving method may include the following steps:

[0134] In step S201, the first device receives the first instruction information sent by the second device.

[0135] For example, the first instruction information is used to indicate that the first device needs a first AI model applicable to the perception service. For example, if the first device stores the first AI model, the first instruction information may carry relevant information about the first AI model (e.g., an identifier), and the terminal can determine that the first AI model needs to be activated based on the relevant information of the first AI model. Alternatively, if the first device does not store the first AI model, the first instruction information may carry the first AI model and send it to the terminal.

[0136] For example, the second device is a sensing network element (SF).

[0137] In step S202, the second device determines the first AI model and performs perception services based on the first AI model.

[0138] For example, if the first device has not yet activated the AI ​​model for perception, it can activate the first AI model after receiving the first instruction information.

[0139] For example, when the first device has activated the AI ​​model used for perception (e.g., referred to as model #a), after receiving the first instruction information, the first device can activate the first AI model, while for model #a, it can keep it in the activated state or deactivate it.

[0140] According to embodiments of this disclosure, since the second device has a relatively comprehensive understanding of the real-time network environment and service needs, the first device adjusts the AI ​​model used for perception based on the first instruction information sent by the second device, which helps to ensure that the perception results of the AI ​​model used meet the service needs in the real-time network environment.

[0141] In some embodiments, the first device can perform perception services based on a first AI model and send perception information based on the first AI model to the second device.

[0142] For example, the perceived information includes at least one of the following:

[0143] Intermediate data obtained by preprocessing the signals to be sensed;

[0144] The perception result obtained by sensing the signal to be perceived.

[0145] In some embodiments, the AI ​​model includes at least one of the following:

[0146] Preprocessing model;

[0147] Perception model.

[0148] 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:

[0149] Feature extraction; dimensionality compression; noise suppression; clutter elimination.

[0150] 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.

[0151] Because preprocessed data is smaller than unprocessed data, transmitting preprocessed data requires fewer communication resources compared to transmitting unprocessed data. Furthermore, since 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.

[0152] In some embodiments, the first device may receive second instruction information sent by the second device.

[0153] For example, the second instruction information is used to instruct the first device to activate the first AI model.

[0154] As the network environment and business needs change, the perception information output by the first AI model already activated in the first device may no longer meet the requirements. Therefore, the second device can instruct the first device to activate the first AI model through the second instruction information.

[0155] In some embodiments, the second device can determine whether the sensing information reported by the first device meets the requirements. If the sensing information does not meet the requirements, the second device can send second indication information to the first device.

[0156] For example, the second device can generate a comparison threshold based on real-time network environment information; then, it can determine whether the perceived information meets the requirements based on the relationship between the perceived information and the comparison threshold.

[0157] Because the network environment can change—for example, changes in the network environment can lead to changes in perception patterns and perception environment—the applicability of the first AI model may decrease.

[0158] The second device can generate a comparison threshold based on real-time network environment information, and then compare the perceived information (e.g., the perceived information most recently obtained by the first device based on the first AI model) with the comparison threshold. For example, if the comparison threshold is an accuracy threshold, and the accuracy of the perceived information is lower than the accuracy threshold, the second device can determine that the currently activated first AI model in the first device can no longer meet the requirements. Therefore, it can send a second instruction to the first device to deactivate the first AI model.

[0159] It should be noted that the second device's determination of whether to send the second instruction information to the first device is not limited to the determination method in the above embodiments, but can also be determined by other methods, and this disclosure does not limit this. For example, the second device can determine the perception mode (e.g., denoted as perception mode #a) and the perception mode applicable to the first AI model (perception mode #b) based on the real-time network environment. If perception mode #a and perception mode #b are the same, the second instruction information may not be sent; if perception mode #a and perception mode #b are different, the second instruction information may be sent.

[0160] In some embodiments, the second indication information is also used to indicate at least one of the following:

[0161] Sensing services based on non-AI algorithms (such as 3D-MUSIC, 3D-ESPRIT, etc.);

[0162] Update the first AI model;

[0163] Perception operations are performed based on the second AI model.

[0164] When the second device sends the first instruction information to the first device, the first AI model will be deactivated. In this case, if the first device still needs to perform perception services, the second device can also instruct other content through the second instruction information.

[0165] For example, the second instruction information could instruct the first device to perform sensing services based on non-AI algorithms. In this case, the first device could revert (or switch) to performing sensing services based on non-AI algorithms. Since non-AI algorithms have relatively high versatility, this helps ensure that the sensing results meet the requirements.

[0166] For example, the second instruction information can instruct the first device to update the first AI model. For instance, the update method can include two approaches: one is that the first device constructs a sample set (e.g., denoted as the updated sample set) based on real-time data in the current network environment, and then trains the first AI model based on the updated sample set to obtain an updated AI model; the other is that the second device constructs an updated sample set based on real-time data in the current network environment, and then trains the first AI model based on the updated sample set to obtain an updated AI model, which is then sent to the first device. Because the updated AI model is trained based on real-time data in the current network environment, it is more accurate in calculating perceived information in the current network environment, thus helping to ensure that the perception results meet the requirements.

[0167] For example, the second instruction information can instruct the first device's second AI model to perform sensing services. The second AI model can be a different AI model from the first AI model; for example, it can be an AI model suitable for the current network environment. For instance, the second AI model can be pre-stored in the first device, allowing the first device to switch from the first AI model to the second AI model. In this case, the second instruction information can indicate the identifier of the second AI model so that the terminal can determine whether to switch to the second AI model. Alternatively, the second device can send the second AI model (e.g., by including it in the second instruction information) to the first device. Because the second AI model is suitable for the current network environment, it is more accurate in calculating sensing information for that environment, thus helping to ensure that the sensing results meet the requirements.

[0168] Figure 3 is a schematic flowchart illustrating a model management according to an embodiment of the present disclosure.

[0169] As shown in Figure 3, when a sensing network element determines that there is a sensing service (also known as a business) requirement (e.g., requirement #1), it can determine the first AI model that meets the sensing service requirement, and then instruct the sensing device to activate the first AI model (e.g., the sensing device has stored the first AI model), or instruct the sensing device to activate the first AI model (e.g., the sensing device does not have stored the first AI model).

[0170] After determining the first AI model to be used, the sensing device can use the first AI model to perform model inference. For example, it can input the channel matrix H, raw channel data, signal data, etc. into the first AI model to obtain intermediate information, sensing results, and other sensing information. Then, the sensing information can be sent (for example, periodically, conditionally triggered, or sent once) to the sensing network element.

[0171] Subsequently, the sensing network element can receive new sensing service requests (e.g., request #2). Since request #2 can be different from request #1 (e.g., different sensing scenarios, different sensing modes, different accuracy requirements, different latency requirements, etc.), the sensing network element needs to perform performance monitoring on the first AI model.

[0172] For example, the sensing network element first generates a comparison threshold based on real-time network environment information, and then compares the sensing information (e.g., the sensing information most recently obtained by the sensing device based on the first AI model) with the comparison threshold. For example, if the comparison threshold is an accuracy threshold, the sensing network element can determine that the currently activated first AI model in the sensing device can no longer meet the performance requirements if the accuracy of the sensing information is lower than the accuracy threshold.

[0173] In this scenario, the sensing network element can send a second instruction to the sensing device to deactivate the first AI model. It can also instruct the sensing device to perform sensing operations based on a non-AI algorithm, update the first AI model, or perform sensing operations based on the second AI model. This helps ensure that the sensing results meet the requirements.

[0174] 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.

[0175] In some embodiments, steps S201 and S202 may be performed in an alternate order or simultaneously.

[0176] In some embodiments, step S201 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0177] In some embodiments, step S202 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0178] In some embodiments, other optional implementations described before or after the specification corresponding to FIG2 may be referred to.

[0179] The technical solutions of this disclosure will be further illustrated by several embodiments below.

[0180] This disclosure focuses on AI-based perception enhancement technologies and proposes an AI perception model lifecycle management method for application modes where AI perception models are deployed and applied to perception devices. The main process is shown in Figure 3. Perception model lifecycle management is completed by the perception network element. The specific steps differ depending on the perception mode and the application mode of the AI ​​perception model, and may include steps such as model activation / deactivation, indication, performance detection, switching, updating, and rollback to non-AI algorithms.

[0181] Example 1: Model Activation / Model Indication:

[0182] When a sensing device deploys an AI sensing model and possesses model inference capabilities, the sensing network element can determine the AI ​​sensing model identifier (e.g., model ID) to be activated based on the sensing scene and sensing mode, and then send a model activation command to the sensing device. The sensing network element can include both the model activation command and the AI ​​sensing model identifier in a single instruction message, or it can send the model activation command first, followed by the AI ​​sensing model identifier. The model activation process signifies that the device loads and subsequently uses an AI model for model inference to complete a specific task.

[0183] Example 2: Model Reasoning:

[0184] The sensing device receives the instruction information from the sensing network element, activates the AI ​​sensing model corresponding to the AI ​​sensing model identifier, inputs the measurement data used for sensing into the AI ​​sensing model, and then obtains the sensing result or the processed measurement data.

[0185] Example 3: Sensing Results / Data Reporting:

[0186] The sensing device reports the output of the AI ​​model, including the sensing results or intermediate sensing data processed by the model, to the sensing network element.

[0187] Example 4: Model Performance Monitoring

[0188] The sensing network element directly obtains the sensing results, or calculates the sensing results based on the data reported by the sensing device and then performs model performance monitoring to determine whether the sensing accuracy meets the performance requirements. If the requirements are met, the currently activated AI model continues to be used to complete the sensing task.

[0189] Example 5: Model Deactivation / Rollback / Update / Switch:

[0190] If the model performance monitoring results do not meet the performance requirements, model deactivation is performed first. The sensing network element sends a model deactivation command to the sensing device, and the sensing device stops using the current AI sensing model. At this point, subsequent steps can be completed as needed, such as at least one of the following:

[0191] Fallback to non-AI solution: The sensing network element sends a command to the sensing device to fall back to the non-AI solution. The sensing device then uses the non-AI sensing algorithm in the current sensing scene and sensing mode to complete the sensing task.

[0192] Model Update: The sensing network element sends a model update command to the sensing device. The sensing device updates the AI ​​model parameters based on real-time data of the current scene and uses the updated AI model to complete the sensing task. The model update process can be completed by the sensing network element, which then sends the updated model to the sensing device, resulting in significant model transmission overhead. Alternatively, the sensing device can complete the update directly after receiving the model update command, which places higher demands on the hardware computing power of the sensing device.

[0193] Model switching: The sensing network element first selects a model based on the information of the AI ​​sensing model deployed on the sensing device, as well as the current sensing scene and sensing mode. It selects the AI ​​model that best suits the current sensing scene and sensing mode, and then sends a model switching command to the sensing device, specifying the identifier (model ID) of the AI ​​sensing model to be switched to. The sensing network element can include both the model switching command and the identifier of the AI ​​sensing model to be switched to in a single instruction message, or it can send the model switching command first, followed by the identifier of the AI ​​sensing model to be switched to.

[0194] 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.

[0195] 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.

[0196] In some embodiments, terms such as “send,” “transmit,” “report,” “distribute,” “transmit,” “bidirectional transmission,” “send and / or receive” can be used interchangeably.

[0197] 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.

[0198] Corresponding to the aforementioned information receiving method and information sending embodiments, this disclosure also provides embodiments of an information receiving device and an information sending device.

[0199] Figure 4 is a schematic block diagram illustrating an information receiving device according to an embodiment of the present disclosure. For example, the information receiving device can be disposed in and / or applied to a sensing device. As shown in Figure 4, the information receiving device includes: a receiving module 401, a processing module 402, and a transmitting module 403.

[0200] In some embodiments, the receiving module is configured to receive first indication information sent by the second device, wherein the first indication information is used to indicate a first AI model for sensing services that the first device needs to use; the processing module is configured to perform sensing services according to the first AI model.

[0201] In some embodiments, the sending module is configured to send perception information for perception services based on the first AI model to the second device.

[0202] In some embodiments, 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 prediction of the signal to be sensed.

[0203] In some embodiments, the receiving module is further configured to receive second indication information sent by the second device, wherein the second indication information is used to instruct the first device to activate the first AI model.

[0204] In some embodiments, the second indication information is further used to indicate at least one of the following: performing perception services based on non-AI algorithms; updating the first AI model; according to a second... In some embodiments...

[0205] In some embodiments, where the second indication information is also used to indicate an update to the first AI model, the processing module is further configured to update the first AI model based on a sample set consisting of real-time samples; and / or, the receiving module is further configured to receive the updated first AI model sent by the second device.

[0206] Figure 5 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 in and / or applied to a sensing network element. As shown in Figure 5, the information transmitting device includes: a transmitting module 501, a receiving module 502, and a processing module 503.

[0207] In some embodiments, the sending module is configured to send first indication information to a first device, wherein the first indication information is used to indicate a first AI model for sensing services that the first device needs to use.

[0208] In some embodiments, the receiving module is configured to receive perception information sent by the first device for perception services based on the first AI model.

[0209] In some embodiments, 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 prediction of the signal to be sensed.

[0210] In some embodiments, the processing module is configured to determine whether the perceived information meets the requirements; wherein, the sending module is configured to send second indication information to the first device when the perceived information does not meet the requirements, the second indication information being used to instruct the first device to activate the first AI model.

[0211] In some embodiments, the processing module is configured to generate a comparison threshold based on real-time network environment information; and to determine whether the perceived information meets the requirements based on the relationship between the perceived information and the comparison threshold.

[0212] In some embodiments, the second indication information is further used to indicate at least one of the following: performing perception services based on non-AI algorithms; updating the first AI model; and performing perception services according to a second AI model.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] Chip 6200 includes one or more processors 6201. Chip 6200 is used to perform any of the methods described above.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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. An information receiving method, characterized in that, Performed by a first device, the method includes: Receive first indication information sent by the second device, wherein the first indication information is used to indicate the first AI model for sensing services that the first device needs to use; The perception service is performed based on the first AI model.

2. The method according to claim 1, characterized in that, The method further includes: Send perception information for perception services based on the first AI model to the second device.

3. The method according to claim 2, characterized in that, The perceived information includes at least one of the following: Intermediate data obtained by preprocessing the signals to be sensed; The perception result obtained by performing perception prediction on the signal to be perceived.

4. The method according to claim 2 or 3, characterized in that, The method further includes: The first device receives a second instruction message sent by the second device, wherein the second instruction message is used to instruct the first device to activate the first AI model.

5. The method according to claim 4, characterized in that, The second indication information is also used to indicate at least one of the following: Sensing services based on non-AI algorithms; Update the first AI model; Perception operations are performed based on the second AI model.

6. The method according to claim 5, characterized in that, When the second indication information is also used to indicate an update to the first AI model, the method further includes at least one of the following: The first AI model is updated based on a sample set consisting of real-time samples; Receive the updated first AI model sent by the second device.

7. A method for sending information, characterized in that, Performed by a second device, the method includes: Send a first instruction message to a first device, wherein the first instruction message is used to indicate the first AI model that the first device needs to use for sensing services.

8. The method according to claim 7, characterized in that, The method further includes: Receive perception information sent by the first device based on the first AI model for perception services.

9. The method according to claim 8, characterized in that, The perceived information includes at least one of the following: Intermediate data obtained by preprocessing the signals to be sensed; The perception result obtained by performing perception prediction on the signal to be perceived.

10. The method according to claim 8 or 9, characterized in that, The method further includes: Determine whether the perceived information meets the requirements; If the perceived information does not meet the requirements, a second instruction message is sent to the first device, wherein the second instruction message is used to instruct the first device to activate the first AI model.

11. The method according to claim 10, characterized in that, The step of determining whether the perceived information meets the requirements includes: A comparison threshold is generated based on real-time network environment information; The relationship between the perceived information and the comparison threshold is used to determine whether the perceived information meets the requirements.

12. The method according to claim 10 or 11, characterized in that, The second indication information is also used to indicate at least one of the following: Sensing services based on non-AI algorithms; Update the first AI model; Perception operations are performed based on the second AI model.

13. A communication device, characterized in that, The communication device is used to perform the information receiving method according to any one of claims 1 to 6, and / or the information sending method according to any one of claims 7 to 12.

14. A communication system, characterized in that, The system includes a sensing device and a sensing network element, wherein the sensing device is configured to implement the information receiving method according to any one of claims 1 to 6, and the sensing network element is configured to implement the information sending method according to any one of claims 7 to 12.

15. A storage medium storing instructions, characterized in that, When the instruction is executed on the communication device, the communication device performs the information receiving method according to any one of claims 1 to 6, and / or the information sending method according to any one of claims 7 to 12.

16. A program product, characterized in that, When the above-mentioned program product is executed by a communication device, the communication device performs the information receiving method according to any one of claims 1 to 6, and / or the information sending method according to any one of claims 7 to 12.

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